Charging pile number prediction method and device, medium and product

Abstract

The embodiment of the invention relates to the technical field of vehicle and road cloud, and discloses a charging pile number prediction method and device, a medium and a product. Calculating the number of available charging piles of the charging station in each time period according to the total number of the charging piles and the number of the occupied charging piles of the charging station in the preset time period; calculating statistical indexes based on the number of the available charging piles, wherein the statistical indexes comprise a probability density function, an expected value and a variance of the number of the available charging piles; a target interval is set, the available charging pile interval probability meeting the target interval is calculated based on the statistical index, and the charging station with the maximum reliability is determined according to the available charging pile interval probability; and a probability condition is set, the number of available charging piles meeting the probability condition is calculated based on the statistical index, and the charging station with the most available charging piles is determined according to the number of the available charging piles. The technical problem that charging pile resources are not fully utilized can be at least solved.

Description

Technical Field

[0001] This application relates to the field of vehicle-road-cloud technology, and in particular to a method, device, medium and product for predicting the number of charging piles. Background Technology

[0002] With the rapid development of Intelligent Connected Vehicles (ICVs) technology, electric vehicles (EVs), as an important component of future transportation systems, are experiencing a gradual increase in charging demand. The construction of charging infrastructure has become a key factor driving the widespread adoption of electric vehicles. Especially against the backdrop of accelerated urbanization, how to efficiently utilize existing charging pile resources and solve problems such as insufficient number of charging piles, uneven distribution, and charging queues has become an urgent technical challenge.

[0003] Currently, the construction and layout of charging stations often rely on empirical planning and pre-set rules, lacking effective prediction and optimization mechanisms. This leads to situations where, during peak demand periods, some charging stations may experience insufficient charging capacity, while others may waste resources. This negatively impacts the user's charging experience, resulting in excessively long waiting times and potentially hindering the travel efficiency of electric vehicles due to long queues.

[0004] Furthermore, with the emergence and application of the Vehicle-Road-Cloud Integration (VCR) concept, the deep integration of intelligent transportation systems and charging infrastructure has become a future development direction. Against this backdrop, how to dynamically assess the charging station resources through accurate charging pile availability prediction models, and provide electric vehicle users with more scientific charging decisions, has become a key issue in the current construction and management of charging infrastructure. Summary of the Invention

[0005] One objective of this application is to provide a method, device, medium, and product for predicting the number of charging piles, at least to address the technical problem of insufficient utilization of charging pile resources.

[0006] To achieve the above objectives, some embodiments of this application provide the following aspects:

[0007] In a first aspect, some embodiments of this application also provide a method for predicting the number of charging piles, including calculating the number of available charging piles at a charging station in each time period based on the total number of charging piles and the number of occupied charging piles at the charging station in a predetermined time period; calculating statistical indicators based on the number of available charging piles, including the probability density function, expected value, and variance of the number of available charging piles; setting a target interval, calculating the probability of available charging pile intervals that meet the target interval based on the statistical indicators, and determining the charging station with the highest reliability based on the probability of available charging pile intervals; setting probability conditions, calculating the number of available charging piles that meet the probability conditions based on the statistical indicators, and determining the charging station with the most available charging piles based on the number of available charging piles.

[0008] Secondly, some embodiments of this application also provide an electronic device, the electronic device comprising: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method described above.

[0009] Thirdly, some embodiments of this application also provide a computer-readable medium having computer program instructions stored thereon, which can be executed by a processor to implement the method described above.

[0010] Fourthly, some embodiments of this application also provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method described above.

[0011] Compared with related technologies, the solution provided in this application proposes a method for predicting the number of available charging piles for the construction of a vehicle-road-cloud integrated system, solving for the most reliable charging station and the charging station with the most available charging piles. Specifically, it includes a method for calculating statistical indicators of the number of available charging piles at a charging station, a probability calculation method for setting a range of available charging pile numbers, a method for calculating the number of available charging piles with set probability conditions, a method for finding the most reliable charging station, and a method for finding the charging station with the most available charging piles. This provides a scientific basis for electric vehicles to select appropriate charging stations at different times, achieving balanced utilization of charging infrastructure resources and reducing charging queue times for electric vehicles. Attached Figure Description

[0012] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0013] Figure 1 This is a flowchart illustrating a method for predicting the number of charging piles according to an embodiment of this application.

[0014] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] First Embodiment

[0017] The first embodiment of this application relates to a method for predicting the number of charging piles. For example... Figure 1 As shown, the method may include the following steps:

[0018] S101, calculate the number of available charging piles at the charging station in each time period based on the total number of charging piles and the number of occupied charging piles at the charging station during the predetermined time period.

[0019] S102, Calculate statistical indicators based on the number of available charging piles, including the probability density function, expected value and variance of the number of available charging piles;

[0020] S103, Set a target range, calculate the probability of available charging pile ranges that meet the target range based on the statistical indicators, and determine the charging station with the highest reliability based on the probability of available charging pile ranges.

[0021] S104, Set probability conditions, calculate the number of available charging piles that meet the probability conditions based on the statistical indicators, and determine the charging station with the most available charging piles based on the number of available charging piles.

[0022] The following sections will provide a detailed explanation of each of the above steps.

[0023] For step S101, the total number of charging piles at the charging station is obtained in real time through the vehicle-road-cloud integrated system. The real-time monitoring system monitors the usage status of the charging piles and obtains the number of charging piles that are occupied in each time period. This data can usually be obtained by connecting with the user interface within the charging station or by updating it in real time through the cloud platform. The user interface can display the usage status of the charging piles (e.g., charging, idle, faulty, etc.).

[0024] During the data collection phase, not only can the current status of charging stations be obtained through real-time monitoring systems, but historical data can also be used to assess trends in charging demand. Historical data helps to understand patterns in charging station usage, especially for predicting peak or special periods.

[0025] The total number of charging piles and the number of occupied charging piles at the charging station were obtained, and the number of available charging piles was calculated. All calculated available charging pile numbers need to be stored in a database in a timely manner for subsequent use and analysis. The stored data includes not only the number of available charging piles for each time period, but also relevant information such as charging pile usage time and charging pile fault records. This stored data will constitute a dynamic dataset, supporting long-term trend analysis, real-time data updates, and subsequent predictive modeling. Regularly updated datasets can be used to calculate charging pile load trends and provide a basis for predicting future charging demand.

[0026] By dynamically acquiring the charging pile status for each time period through a real-time monitoring system and combining it with historical data, the accuracy and comprehensiveness of the data are ensured. The system can not only perform calculations for single time periods but also conduct periodic or periodic calculations for multiple time periods (e.g., all-day, weekly, monthly, etc.), forming dynamic datasets. This data can be used to analyze charging pile usage patterns and trends in charging demand, providing data support for further optimization, prediction, and resource scheduling.

[0027] In step S102, the number of available charging piles in each time period is analyzed in depth using statistical analysis methods, and a series of statistical indicators are calculated. These indicators provide an important basis for subsequent prediction models. Specifically, these statistical indicators include probability density function, expected value, and variance, which help to evaluate the usage patterns and stability of charging piles, thereby optimizing the scheduling and prediction accuracy of charging pile resources.

[0028] Based on the obtained data on the number of available charging stations, select an appropriate time range as the analysis sample. For example, you can choose hourly data on the number of available charging stations over the past 7 days (or you can choose different time ranges, such as daily data, weekly data, etc., depending on data availability and analysis needs). After selecting the time period, the data needs to be cleaned to remove outliers (such as data entry errors, sensor malfunctions, etc.) to ensure the accuracy of the analysis data.

[0029] Based on the collected data on the number of available charging stations, an appropriate probability distribution model is selected. By fitting the data, a probability density function for the number of charging stations is obtained, which can describe the distribution of the number of available charging stations in each time period, i.e., the probability of different numbers of charging stations appearing.

[0030] Based on historical data, the distribution pattern of the number of charging piles can be estimated, and its probability density function curve can be plotted. By calculating the probability density function, the changing patterns of the number of charging piles can be scientifically described, and their usage patterns in different time periods can be identified, providing a basis for predicting future changes in charging pile resources.

[0031] The expected value is a statistic describing the central tendency of the number of available charging stations. It represents the expected number of available charging stations in each time period within a given time frame. By calculating the expected value, we can understand the overall utilization of charging station resources.

[0032] Variance is a statistical indicator that describes the degree of fluctuation in data, representing the dispersion or range of change in the number of charging piles within a given time period. A larger variance indicates greater fluctuation in the number of charging piles and unstable usage; a smaller variance indicates greater stability in the number of charging piles.

[0033] By statistically analyzing data on the number of available charging piles, key statistical indicators (such as expected value, variance, and probability density function) are calculated, providing accurate data support for the predictive model of charging piles. The calculation of expected value and variance not only helps to understand the overall utilization level of charging piles but also assesses the volatility and stability of charging pile resources, thereby optimizing the management and scheduling strategies for charging piles. Through these statistical indicators, the dynamic management and predictive model of charging pile resources become more accurate, improving the resource utilization efficiency and reliability of the entire charging infrastructure.

[0034] In step S103, by setting a target range and calculating the probability of the number of charging piles within that range, the system helps users select charging stations with sufficient resources that meet their needs. This provides a more accurate basis for selecting charging stations, optimizes the user charging experience, and provides data support for charging station management.

[0035] Set a target range. For example, suppose a user wants to select a charging station with more than 15 but less than 20 charging piles; the target range would be [15, 20]. This target range should be adjusted based on the user's specific needs (e.g., the number of charging piles, the charging station's load status, etc.). If the user requires more than a certain threshold (e.g., more than 20), or needs additional charging resources during a specific time period, the target range can be changed accordingly.

[0036] The probability of a charging station falling within a set range of available charging piles is calculated based on statistical indicators, and the charging station with the highest reliability is determined according to this probability. This probability calculation allows for recommendations of charging stations with sufficient charging pile resources that meet user needs, improving the accuracy of user selection. Users can better choose charging stations with a sufficient number of charging piles to meet their needs, avoiding the selection of charging stations with limited resources.

[0037] With users' charging needs met, charging queue times will be significantly reduced, improving the user charging experience. Users can choose charging stations with a limited number of available charging piles within their target charging time slot. This avoids long queues due to insufficient charging piles during peak demand periods, greatly enhancing the charging experience. Recommending more reliable charging stations also prevents overcrowding and queues at charging stations with insufficient piles.

[0038] Charging station operators can optimize charging pile configuration by calculating the interval probability of the number of charging piles within each time period, ensuring sufficient charging piles are available during peak demand periods. Based on fluctuations in charging pile demand across different time periods, charging stations can flexibly adjust the number and layout of charging piles to provide more efficient service. This method can also help operators assess the distribution of charging pile resources during specific time periods, adjust resources in real time, and avoid overcrowding.

[0039] For step S104, by setting a probability condition, the number of available charging piles at the charging station under this condition is calculated based on previously calculated statistical indicators, and the charging station with the most available charging piles under the set condition is identified. The charging station with the most abundant charging pile resources is recommended to users to reduce charging queuing time and optimize the resource allocation of charging infrastructure.

[0040] Based on changes in charging pile demand or user charging needs, a probability condition can be set. For example, users may want to select a charging pile with an 80% availability probability, meaning the probability of the number of charging piles exceeding a certain threshold is 80%. This probability condition can be adjusted according to specific application scenarios. For instance, during periods of high demand, users may require a higher probability of charging pile availability, while during periods of low demand, users may accept a lower probability.

[0041] For the case of a normal distribution, the required number of available charging stations can be determined using an inverse function. That is, based on a set probability condition, the minimum number of available charging stations that meets that probability condition is calculated. For example, if the set condition is that there is an 80% probability that the number of available charging stations is greater than a certain threshold, for each charging station, the number of available charging stations under the set probability condition is calculated, and combined with the set probability condition, the number of available charging stations that meet the condition is obtained.

[0042] Based on the calculation results, the charging station with the most available charging piles under the set probability conditions is selected as the charging station with the most available charging piles. If multiple charging stations have the same number of available charging piles, further filtering can be performed based on other factors (such as geographical location, user reviews, etc.).

[0043] Different charging station availability probability conditions are set according to user needs, allowing users to choose the most suitable charging station under different circumstances. Using the expected value (mean) and standard deviation (variance) of the number of charging stations, along with the set probability conditions, the number of available charging stations that meet the conditions is accurately calculated using a normal distribution or other statistical distribution model. Based on the calculation results, the charging station with the largest number of charging stations meeting the conditions is selected, providing the best choice for users.

[0044] By setting probabilistic conditions, the system can recommend charging stations that best meet users' needs. Users can select charging stations with appropriate probabilistic conditions based on their charging requirements (such as the number of charging piles and waiting time), thereby optimizing their charging experience. By accurately calculating the number of charging piles at each charging station under the set probabilistic conditions, users can choose charging stations with the most abundant charging pile resources, thus avoiding situations where there are not enough charging piles, reducing waiting time, and improving charging efficiency. Users can obtain highly reliable charging station recommendations, reducing anxiety and wasted time caused by insufficient charging piles.

[0045] Charging station operators can optimize the distribution and configuration of charging piles based on predefined probabilistic conditions. By accurately predicting charging pile demand at different times, operators can ensure sufficient charging piles are available during peak demand periods, avoiding queuing caused by resource shortages. This method helps operators flexibly adjust charging pile usage strategies, ensuring efficient utilization of charging infrastructure.

[0046] It is easy to see that, compared with related technologies, the charging pile quantity prediction method based on statistical analysis provided in this application's embodiments accurately predicts the availability of charging piles by combining real-time monitoring data and historical data, helping electric vehicle users choose charging stations with sufficient charging pile resources. This not only improves the utilization efficiency of charging piles but also significantly reduces users' charging waiting time, optimizes the allocation of charging station resources, and enhances the overall utilization rate of charging infrastructure.

[0047] Second Embodiment

[0048] The second embodiment of this application relates to a method for predicting the number of charging piles. The second embodiment is an improvement upon the first embodiment, specifically in that:

[0049] Furthermore, the statistical indicators calculated based on the number of available charging stations include:

[0050] Indicates charging station Total number of charging stations Indicates charging station In the The number of charging stations occupied during a given time period Indicates charging station In the The number of available charging stations during a given time period;

[0051] ;

[0052] Number of available charging stations The probability density function is:

[0053] ;

[0054] In the formula, Indicates the number of available charging stations The average value, Indicates the number of available charging stations Standard deviation;

[0055] Number of available charging stations The formula for calculating the expected value is as follows:

[0056] ;

[0057] Number of available charging stations The formula for calculating the variance is as follows:

[0058] .

[0059] Furthermore, the statistical indicators calculated based on the number of available charging stations include:

[0060] When given the expected value and variance hour, and The formula for calculation is as follows:

[0061] ;

[0062] .

[0063] Furthermore, calculating the probability of an available charging pile interval satisfying the target interval based on the statistical indicators includes:

[0064] charging station In the Number of available charging stations during the time period In the interval probability The calculation formula is as follows:

[0065] ;

[0066] Will , , Substituting the calculation formula into the above formula and converting it, we obtain the charging station. In the Number of charging stations available during a given time period Greater than and less than or equal to The probability calculation formula is as follows:

[0067]

[0068] In the formula, The cumulative probability distribution function representing the normal distribution;

[0069] Expected threshold for a given number of available charging stations At that time, charging station In the The number of available charging stations during the time period is greater than the expected threshold. probability The calculation formula is as follows:

[0070] .

[0071] Furthermore, the charging station with the highest reliability is determined based on the probability of the available charging pile intervals, including:

[0072] Find the most reliable charging station by setting a range of available charging piles and finding the charging station with the highest reliability.

[0073] Number of available charging stations In the interval At that time, the first The charging station with the highest reliability during the time period The solution formula is as follows:

[0074] ;

[0075] In the formula, Indicates the first The most reliable charging station during peak hours Indicates a collection of charging stations. ;

[0076] Will Substituting the calculation formula into the above equation, we obtain the most reliable charging station. The solution formula is shown below:

[0077] .

[0078] Furthermore, calculating the number of available charging piles that meet the probability conditions based on the statistical indicators includes:

[0079] In setting the probability condition In this case, the number of available charging stations The calculation formula is as follows:

[0080] ;

[0081] In the formula, It represents the inverse function of the cumulative probability density function of the normal distribution;

[0082] Will and Substituting the calculation formula into the above equation, we obtain the given probability condition as follows: charging station In the Number of available charging stations during the time period The calculation formula is as follows:

[0083] .

[0084] Furthermore, based on the number of available charging piles, the charging station with the largest number of available charging piles includes:

[0085] Find the most reliable charging station by setting a range of available charging piles and finding the charging station with the highest reliability.

[0086] Under the given probability conditions In the case of the first The charging station with the most available charging piles during the specified time period The solution formula is as follows:

[0087] ;

[0088] In the formula, Indicates the first The charging station with the most available charging piles during the specified time period. Indicates a collection of charging stations. ;

[0089] Will and Substituting the calculation formula into the above formula, we get the first... The formula for determining the charging station with the most available charging piles during a given time period is:

[0090] .

[0091] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0092] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0093] The electronic device includes: one or more processors; and a memory storing computer program instructions that, when executed, cause the processor to perform the steps of the methods provided in any one or more of the above embodiments. Figure 2 An exemplary structural diagram of the electronic device is disclosed. For example... Figure 2 As shown, the electronic device includes one or more processors 1101, a memory 1102, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0094] The electronic device may further include an input device 1103 and an output device 1104. The processor 1101, memory 1102, input device 1103, and output device 1104 may be connected via a bus or other means. Figure 2 Taking the example of a connection between China and Israel via a bus.

[0095] Input device 1103 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 1104 may include a display device, auxiliary lighting device (e.g., LED), and haptic feedback device (e.g., vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0096] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0097] In this embodiment, a computer-readable medium stores a computer program / instructions that, when executed by a processor, implement the steps of the methods provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The aforementioned computer-readable medium carries one or more computer-readable instructions.

[0098] The memory 1102 can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 1102, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0099] The memory 1102 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 1102 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1102 may optionally include memory remotely located relative to the processor 1101, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0100] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0101] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0102] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "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 standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via 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., via the Internet using an Internet service provider).

[0103] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, or similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0104] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0105] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0106] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection described in the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method for predicting the number of charging piles, characterized in that, The method includes: The number of available charging piles at the charging station in each time period is calculated based on the total number of charging piles and the number of occupied charging piles within the predetermined time period. Statistical indicators are calculated based on the number of available charging piles, including the probability density function, expected value, and variance of the number of available charging piles. Set a target range, calculate the probability of available charging pile ranges that meet the target range based on the statistical indicators, and determine the charging station with the highest reliability based on the probability of available charging pile ranges. Set probability conditions, calculate the number of available charging piles that meet the probability conditions based on the statistical indicators, and determine the charging station with the most available charging piles based on the number of available charging piles.

2. The method according to claim 1, characterized in that, The statistical indicators calculated based on the number of available charging piles include: Indicates charging station Total number of charging stations Indicates charging station In the The number of charging stations occupied during a given time period Indicates charging station In the The number of available charging stations during a given time period; ； Number of available charging stations The probability density function is: ； In the formula, Indicates the number of available charging stations The average value, Indicates the number of available charging stations Standard deviation; Number of available charging stations The formula for calculating the expected value is as follows: ； Number of available charging stations The formula for calculating the variance is as follows: 。 3. The method according to claim 2, characterized in that, The statistical indicators calculated based on the number of available charging piles include: When given the expected value and variance hour, and The formula for calculation is as follows: ； 。 4. The method according to claim 3, characterized in that, The calculation of the probability of available charging pile intervals satisfying the target interval based on the statistical indicators includes: charging station In the Number of available charging stations during the time period In the interval probability The calculation formula is as follows: ； Will , , Substituting the calculation formula into the above formula and converting it, we obtain the charging station. In the Number of charging stations available during a given time period Greater than and less than or equal to The probability calculation formula is as follows: In the formula, The cumulative probability distribution function representing the normal distribution; Expected threshold for a given number of available charging stations At that time, charging station In the The number of available charging stations during the time period is greater than the expected threshold. probability The calculation formula is as follows: 。 5. The method according to claim 4, characterized in that, The step of determining the charging station with the highest reliability based on the probability of available charging pile intervals includes: Number of available charging stations In the interval At that time, the first Charging stations with the highest reliability during the time period The solution formula is as follows: ； In the formula, Indicates the first The most reliable charging station during peak hours Indicates a collection of charging stations. .

6. The method according to claim 3, characterized in that, The calculation of the number of available charging piles that meet the probability conditions based on the statistical indicators includes: In setting the probability condition In the case of [missing information], the number of available charging stations The calculation formula is as follows: ； In the formula, It represents the inverse function of the cumulative probability density function of the normal distribution; Will and Substituting the calculation formula into the above equation, we obtain the given probability condition as follows: charging station In the Number of available charging stations during the time period The calculation formula is as follows: 。 7. The method according to claim 6, characterized in that, The charging station with the largest number of available charging piles based on the number of available charging piles includes: Under the given probability conditions In the case of the first The charging station with the most available charging piles during the specified time period The solution formula is as follows: ； In the formula, Indicates the first The charging station with the most available charging piles during the specified time period. Indicates a collection of charging stations. ; Will and Substituting the calculation formula into the above formula, we get the first... The formula for determining the charging station with the most available charging piles during a given time period is: 。 8. An electronic device, characterized in that, The electronic device includes: One or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as described in any one of claims 1 to 7.

9. A computer-readable medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

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