An electric bicycle station supply-demand prediction method and system based on big data analysis

Abstract

The application provides an electric bicycle station supply and demand prediction method and system based on big data analysis, relates to the technical field of data processing, determines the distribution total amount of electric bicycles in a target area at a moment and the number of electric bicycles that pass through a secondary station and enter the target area from a moment to a moment, and finally, based on the distribution total amount, the distribution inventory and the number of electric bicycles that pass through the secondary station and enter the target area from the moment to the moment, the distribution inventory at the moment is predicted. The electric bicycle station supply and demand prediction method and system based on big data analysis provided in the application divides a relatively accurate station service area by combining an actual road network and station distribution, dynamically adjusts the station distribution inventory by combining the number of vehicles in the service area, and avoids the redundancy or deficiency of inventory vehicles caused by uneven distribution of stations.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method for predicting the supply and demand of electric bicycle stations based on big data analysis. Background Technology

[0002] With the acceleration of urbanization and the enhancement of environmental awareness, electric bicycles, as a green and convenient mode of transportation, are gradually becoming an important choice for short-distance urban transportation. The popularization of shared electric bicycle systems has further promoted this trend.

[0003] However, the imbalance between supply and demand at electric bicycle stations is becoming increasingly prominent. During peak hours, some stations may experience a shortage of bicycles, while others may have an oversupply. This mismatch not only affects users' travel experience but also reduces the utilization rate of electric bicycles. Furthermore, the inaccurate service areas of each station make it difficult to accurately count the number of bicycles within a given area.

[0004] Generally, inventory is allocated based on historical inventory data from electric bicycle stations. This method is not very timely and cannot dynamically adjust the allocation of stations based on real-time conditions. Summary of the Invention

[0005] This application provides a method and system for predicting the supply and demand of electric bicycle stations based on big data analysis, in order to improve the above-mentioned problems.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] In a first aspect, embodiments of this application propose a method for predicting the supply and demand of electric bicycle stations based on big data analysis. The method includes:

[0008] Obtain the operating area of ​​the electric bicycles. Within this area, multiple stations are distributed. Identify one station as the target station. The target station is located in... The inventory quantity is allocated at any time. ;

[0009] Obtain a road network traffic map within the operating area, and generate a first connected graph based on the road network traffic map, wherein the paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, respectively.

[0010] Determine the n stations surrounding the target station as secondary stations;

[0011] Define the target area, which is the area formed by multiple secondary stations surrounding it;

[0012] Determining the target area based on big data Total number of electric bicycles distributed at any time ,as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time, respectively , ... ;

[0013] Based on total distribution The allocated inventory is as well as Time to The number of electric bicycles that pass through secondary stations and enter the target area at any given time is determined. The amount of inventory allocated at any given time.

[0014] In conjunction with the first aspect, optionally, based on the total distribution The allocated inventory is as well as Time to The number of electric bicycles that pass through secondary stations and enter the target area at any given time is determined. Allocate inventory at any given time, and satisfy the following conditions:

[0015]

[0016] in, for The amount of inventory allocated at any given time.

[0017] In conjunction with the first aspect, optionally, a road network traffic map of the operating area is obtained, and a first connected graph is generated based on the road network traffic map, wherein the paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, respectively, including:

[0018] Using the road network traffic map as paths, and road intersections and dead-end points as nodes, an initial connected graph is generated.

[0019] Using any node in the initial connected graph as the start and end point, obtain multiple Eulerian circuits;

[0020] Multiple Euler circuits are superimposed to form the first connected graph.

[0021] In conjunction with the first aspect, optionally, multiple stations surrounding the target station may be identified as secondary stations, including:

[0022] A second connected graph is obtained by using multiple stations as nodes and connecting lines between adjacent stations as paths. Based on the second connected graph, n secondary stations are determined, wherein the connecting path between any two secondary stations is parallel to the path in the first connected graph.

[0023] In conjunction with the first aspect, optionally, a target area is defined, which is an area formed by multiple secondary stations surrounding it, including:

[0024] The target station and secondary stations are projected onto the first connected graph, and the target region is determined based on the target station and secondary stations in the first connected graph. The target region is the area formed by connecting multiple secondary stations and surrounding the target station.

[0025] In conjunction with the first aspect, optionally, the target area can be determined based on big data. Total number of electric bicycles distributed at any time ,as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time, respectively , ... ,include:

[0026] Determine the target boundary based on the target region;

[0027] Based on the number of electric bicycles located within the target boundary and equipped with signal sensors. Determine the number of electric bicycles in the target area. Among them, the ratio of the number of electric bicycles with signal sensors to the number of electric bicycles without signal sensors is k, and / - =k.

[0028] In conjunction with the first aspect, the method may optionally also include:

[0029] The average speed of electric bicycles in the target area is obtained based on big data. ;

[0030] In the first connected graph, starting from each second-level station, connect the target stations and obtain multiple path lengths. , ... ;

[0031] Based on multiple path lengths , ... Determine the average path length And based on average path length and average speed Determine the average commute time and obtain Time to Interval time between ;

[0032] Based on average commute time With interval time The ratio of the target site to the target site is used to determine the target site's location. The inventory quantity is allocated at any time. and Inventory allocation at any time The ratio of .

[0033] Secondly, this application proposes a supply and demand forecasting system for electric bicycle stations based on big data analysis. The system includes:

[0034] The first submodule is used to obtain the operating area of ​​the electric bicycles. Multiple stations are distributed within the operating area. One station is identified as the target station. The inventory quantity is allocated at any time. ;

[0035] The second submodule is used to obtain the road network traffic map within the operating area and generate the first connected graph based on the road network traffic map. The paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, respectively.

[0036] The third submodule is used to determine the n stations surrounding the target station as secondary stations;

[0037] The fourth submodule is used to determine the target area, which is the area formed by multiple secondary stations surrounding it.

[0038] The fifth submodule is used to determine the target area based on big data. Total number of electric bicycles distributed at any time ,as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time, respectively , ... ;

[0039] The sixth submodule is used for distribution based on total amount. The allocated inventory is as well as Time to The number of electric bicycles that pass through secondary stations and enter the target area at any given time is determined. The amount of inventory allocated at any given time.

[0040] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0041] Based on total distribution The allocated inventory is as well as Time to The number of electric bicycles that pass through secondary stations and enter the target area at any given time is determined. Allocate inventory at any given time, and satisfy the following conditions:

[0042]

[0043] in, for The amount of inventory allocated at any given time.

[0044] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0045] Obtain a road network traffic map within the operating area, and generate a first connected graph based on the road network traffic map. The paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, respectively, including:

[0046] Using the road network traffic map as paths, and road intersections and dead-end points as nodes, an initial connected graph is generated.

[0047] Using any node in the initial connected graph as the start and end point, obtain multiple Eulerian circuits;

[0048] Multiple Euler circuits are superimposed to form the first connected graph.

[0049] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0050] The multiple sites surrounding the target site are identified as secondary sites, including:

[0051] A second connected graph is obtained by using multiple stations as nodes and connecting lines between adjacent stations as paths. Based on the second connected graph, n secondary stations are determined, wherein the connecting path between any two secondary stations is parallel to the path in the first connected graph.

[0052] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0053] The target area is defined as the region formed by multiple secondary stations surrounding it, including:

[0054] The target station and secondary stations are projected onto the first connected graph, and the target region is determined based on the target station and secondary stations in the first connected graph. The target region is the area formed by connecting multiple secondary stations and surrounding the target station.

[0055] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0056] Determining the target area based on big data Total number of electric bicycles distributed at any time ,as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time, respectively , ... ,include:

[0057] Determine the target boundary based on the target region;

[0058] Based on the number of electric bicycles located within the target boundary and equipped with signal sensors. Determine the number of electric bicycles in the target area. Among them, the ratio of the number of electric bicycles with signal sensors to the number of electric bicycles without signal sensors is k, and / - =k.

[0059] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0060] The method also includes:

[0061] The average speed of electric bicycles in the target area is obtained based on big data. ;

[0062] In the first connected graph, starting from each second-level station, connect the target stations and obtain multiple path lengths. , ... ;

[0063] Based on multiple path lengths , ... Determine the average path length And based on average path length and average speed Determine the average commute time and obtain Time to Interval time between ;

[0064] Based on average commute time With interval time The ratio of the target site to the target site is used to determine the target site's location. The inventory quantity is allocated at any time. and Time-based allocation of inventory The ratio of .

[0065] A third aspect of this invention provides an electronic device, which includes:

[0066] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method proposed in the first aspect of the present invention.

[0067] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the present invention.

[0068] In summary, the above methods and systems have the following technical effects:

[0069] This application proposes a method and system for predicting the supply and demand of electric bicycle stations based on big data analysis. First, it obtains the operating area of ​​the electric bicycles. Then, it obtains a road network traffic map within the operating area and generates a first connectivity graph based on the road network traffic map. Next, it identifies n stations surrounding a target station as secondary stations. Then, it determines the target area, which is the area formed by multiple secondary stations surrounding the target station. Finally, it determines the area within the target region based on big data analysis. The total number of electric bicycles distributed at any given time, and Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time; finally, based on the total distribution and allocated inventory. For and Time to The number of electric bicycles passing through secondary stations and entering the target area at any time is predicted. The allocation of inventory at different times. This application proposes a method and system for predicting the supply and demand of electric bicycle stations based on big data analysis. By combining the actual road network and station distribution, it divides the station service areas more accurately, and dynamically adjusts the allocated inventory at each station based on changes in the number of vehicles within the service area, thus avoiding redundancy or shortage of inventory vehicles caused by uneven station allocation. Attached Figure Description

[0070] Figure 1 This is a flowchart illustrating a method for predicting the supply and demand of electric bicycle stations based on big data analysis, as proposed in an embodiment of this application. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] This application proposes a method for predicting the supply and demand of electric bicycle stations based on big data analysis. Please participate. Figure 1 The method includes the following steps:

[0073] S101: Obtain the operating area of ​​the electric bicycles. Multiple stations are distributed within the operating area. Identify one station as the target station. The target station is located in... The inventory quantity is allocated at any time. .

[0074] Understandably, electric bicycles operate within specific areas, and within these areas, designated stations are responsible for allocating and recharging them. Therefore, these stations typically maintain a certain inventory of electric bicycles. In this application, the target station is the one that needs to regulate the allocated inventory, for any given time point... At any given moment, the current inventory at the target site is .

[0075] S102: Obtain the road network traffic map within the operating area, and generate a first connected graph based on the road network traffic map, wherein the paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, respectively.

[0076] Understandably, while the stations are evenly distributed spatially, the distances between stations vary due to different actual road conditions. Therefore, a road network traffic map of the operating area can be obtained, and a first connectivity graph can be generated based on this map.

[0077] Specifically, to exclude independent points in a connected graph, such as dead-end roads in a city, we can use the road network graph as paths, and the intersections of roads and the endpoints of dead-end roads as nodes to generate an initial connected graph. Then, using any node in the initial connected graph as the start and end point, we can obtain multiple Eulerian circuits. An Eulerian circuit is a path in a graph that traverses every edge exactly once and eventually returns to the starting point. The existence of such a path requires certain conditions: the graph must be connected, and the degree of each vertex must be even. Therefore, when a node is a dead-end road, an Eulerian circuit cannot be formed.

[0078] Based on this, by superimposing multiple Eulerian circuits to form the first connected graph, it can be ensured that dead-end or non-connected paths are excluded.

[0079] S103: Determine the n stations surrounding the target station as secondary stations.

[0080] The main travel route of an electric bicycle is from one station to another. Therefore, n stations surrounding the target station can be defined as secondary stations. As one implementation, a second connected graph can be obtained by using multiple stations as nodes and the connecting lines between adjacent stations as paths. Based on the second connected graph, n secondary stations are determined, wherein the connecting path between any two secondary stations is parallel to the path in the first connected graph.

[0081] S104: Determine the target area, which is the area formed by multiple secondary stations surrounding it.

[0082] Understandably, the target area is the area primarily served by the target station. However, due to factors such as road planning, the actual boundaries of the target area are not clearly defined. Therefore, in this embodiment, the target station and secondary stations are projected onto a first connected graph, and the target area is determined based on these two locations within the first connected graph. The connection path between any two secondary stations runs parallel to the paths in the first connected graph; that is, the boundary of the area is defined by routes in the road network, rather than by direct spatial connections. This ensures that the defined area approximates the actual area.

[0083] S105: Determining the target area based on big data Total number of electric bicycles distributed at any time ,as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time, respectively , ... .

[0084] Understandably, big data can be used to roughly determine the total number of electric bicycles currently in the service area. For example, the target boundary can be determined based on the target area, and then based on the number of electric bicycles located within the target boundary and equipped with signal sensors. Determine the number of electric bicycles in the target area. Among them, the ratio of the number of electric bicycles with signal sensors to the number of electric bicycles without signal sensors is k, and / - =k. In this way, it is not necessary to install sensors on all electric bicycles, which also saves costs.

[0085] Of course, in other embodiments, the total number of electric bicycles can also be obtained through other means. In this embodiment, no limitations are imposed.

[0086] Of course, other calculation methods can be used in other implementations, such as using artificial intelligence for accurate prediction, which is not limited in this embodiment.

[0087] S106: Based on total distribution The allocated inventory is as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any time is predicted. The amount of inventory allocated at any given time.

[0088] In obtaining the total distribution of electric bicycles Then, the inventory of the current target station can be dynamically allocated based on the changes in the number of electric bicycles in the current service area.

[0089] For example, it is possible to obtain Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time, respectively , ... Since most electric bicycles pass by the stations, therefore, , ... The sum is roughly the same as the change, while the total distribution... It is generally positively correlated with the current inventory level. Therefore, in this embodiment, it can be based on the total distribution. The allocated inventory is as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any time is predicted. The amount of inventory allocated at any given time.

[0090] Specifically,

[0091]

[0092] in, for The amount of inventory allocated at any given time.

[0093] Alternatively, in some other embodiments, the inventory ratio can also be determined by the ratio of path lengths.

[0094] For example, the average speed of electric bicycles within a target area can be obtained based on big data. Then, starting from each secondary station in the first connected graph, connect the target stations and obtain multiple path lengths. , ... Then, based on multiple path lengths , ... Determine the average path length And based on average path length and average speed Determine the average commute time and obtain Time to Interval time between Finally, it can be based on average commute time. With interval time The ratio of the target site to the target site is used to determine the target site's location. The inventory quantity is allocated at any time. and Time-based allocation of inventory The ratio of .

[0095] This application proposes a method for predicting the supply and demand of electric bicycle stations based on big data analysis. First, it obtains the operating area of ​​the electric bicycles. Then, it obtains a road network map within the operating area and generates a first connectivity graph based on the road network map. Next, it identifies n stations surrounding a target station as secondary stations. Then, it determines the target area, which is the area formed by multiple secondary stations surrounding the target station. Finally, it determines the area within the target region based on big data analysis. The total number of electric bicycles distributed at any given time, and Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time; finally, based on the total distribution and allocated inventory. For and Time to The number of electric bicycles passing through secondary stations and entering the target area at any time is predicted. The method for predicting the supply and demand of electric bicycle stations based on big data analysis, proposed in this application, divides the station service areas more accurately by combining the actual road network and station distribution, and dynamically adjusts the station's allocated inventory based on changes in the number of vehicles within the service area, thus avoiding redundancy or shortage of inventory vehicles caused by uneven station allocation.

[0096] Based on the same inventive concept, this application also proposes a supply and demand forecasting system for electric bicycle stations based on big data analysis. The supply and demand forecasting system for electric bicycle stations based on big data analysis includes:

[0097] The first submodule is used to obtain the operating area of ​​the electric bicycles. Multiple stations are distributed within the operating area. One station is identified as the target station. The inventory quantity is allocated at any time. ;

[0098] The second submodule is used to obtain the road network traffic map within the operating area and generate the first connected graph based on the road network traffic map. The paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, respectively.

[0099] The third submodule is used to determine the n stations surrounding the target station as secondary stations;

[0100] The fourth submodule is used to determine the target area, which is the area formed by multiple secondary stations surrounding it.

[0101] The fifth submodule is used to determine the target area based on big data. Total number of electric bicycles distributed at any time ,as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time, respectively , ... ;

[0102] The sixth submodule is used for distribution based on total amount. The allocated inventory is as well as Time to The number of electric bicycles that pass through secondary stations and enter the target area at any given time is determined. The amount of inventory allocated at any given time.

[0103] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0104] Based on total distribution The allocated inventory is as well as Time to The number of electric bicycles that pass through secondary stations and enter the target area at any given time is determined. Allocate inventory at any given time, and satisfy the following conditions:

[0105]

[0106] in, for The amount of inventory allocated at any given time.

[0107] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0108] Obtain a road network traffic map within the operating area, and generate a first connected graph based on the road network traffic map. The paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, respectively, including:

[0109] Using the road network traffic map as paths, and road intersections and dead-end points as nodes, an initial connected graph is generated.

[0110] Using any node in the initial connected graph as the start and end point, obtain multiple Eulerian circuits;

[0111] Multiple Euler circuits are superimposed to form the first connected graph.

[0112] Optionally, in conjunction with the second aspect, the system is configured as follows:

[0113] The multiple sites surrounding the target site are identified as secondary sites, including:

[0114] A second connected graph is obtained by using multiple stations as nodes and connecting lines between adjacent stations as paths. Based on the second connected graph, n secondary stations are determined, wherein the connecting path between any two secondary stations is parallel to the path in the first connected graph.

[0115] Optionally, the system is configured as follows:

[0116] The target area is defined as the region formed by multiple secondary stations surrounding it, including:

[0117] The target station and secondary stations are projected onto the first connected graph, and the target region is determined based on the target station and secondary stations in the first connected graph. The target region is the area formed by connecting multiple secondary stations and surrounding the target station.

[0118] Optionally, the system is configured as follows:

[0119] Determining the target area based on big data Total number of electric bicycles distributed at any time ,as well as Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time, respectively , ... ,include:

[0120] Determine the target boundary based on the target region;

[0121] Based on the number of electric bicycles located within the target boundary and equipped with signal sensors. Determine the number of electric bicycles in the target area. Among them, the ratio of the number of electric bicycles with signal sensors to the number of electric bicycles without signal sensors is k, and / - =k.

[0122] Optionally, the system is configured as follows:

[0123] The method also includes:

[0124] The average speed of electric bicycles in the target area is obtained based on big data. ;

[0125] In the first connected graph, starting from each second-level station, connect the target stations and obtain multiple path lengths. , ... ;

[0126] Based on multiple path lengths , ... Determine the average path length And based on average path length and average speed Determine the average commute time and obtain Time to Interval time between ;

[0127] Based on average commute time With interval time The ratio of the target site to the target site is used to determine the target site's location. The inventory quantity is allocated at any time. and Time-based allocation of inventory The ratio of .

[0128] This application proposes a big data analytics-based electric bicycle station supply and demand forecasting system. First, it obtains the operating area of ​​the electric bicycles. Then, it obtains a road network map within the operating area and generates a first connectivity graph based on the road network map. Next, it identifies n stations surrounding a target station as secondary stations. Then, it determines the target area, which is the area formed by multiple secondary stations surrounding the target station. Finally, it uses big data to determine the area within the target region. The total number of electric bicycles distributed at any given time, and Time to The number of electric bicycles passing through secondary stations and entering the target area at any given time; finally, based on the total distribution and allocated inventory. For and Time to The number of electric bicycles passing through secondary stations and entering the target area at any time is predicted. The system proposes a big data analytics-based electric bicycle station supply and demand forecasting system. By combining the actual road network and station distribution, it divides the station service areas with greater precision. Simultaneously, it dynamically adjusts the station's allocated inventory based on changes in the number of vehicles within the service area, thus avoiding redundancy or shortage of inventory vehicles caused by uneven station allocation.

[0129] Based on the same inventive concept, embodiments of this application also propose an electronic device, which includes:

[0130] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the automatic overheat protection method based on the universal testing machine according to the embodiments of this application.

[0131] In addition, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the automatic overheat protection method based on a universal testing machine according to embodiments of this application.

[0132] The following is a detailed introduction to the various components of the electronic device:

[0133] In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0134] Alternatively, the processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0135] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0136] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device; the embodiments of the present invention do not specifically limit this.

[0137] A transceiver is used to communicate with network devices or with terminal devices.

[0138] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0139] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not specifically limit this.

[0140] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the data transmission method in the above method embodiments, and will not be repeated here.

[0141] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0142] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0143] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0144] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0145] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0146] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0147] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A method for predicting the supply and demand of electric bicycle stations based on big data analysis, characterized in that, The method includes: The operating area of ​​the electric bicycles is obtained, and multiple stations are distributed within the operating area. One of these stations is identified as the target station. The target station is located in... The allocated inventory of electric bicycles is ; Obtain a road network traffic map within the operating area, and generate a first connected graph based on the road network traffic map, wherein the paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, including: Using the roads in the road network traffic map as paths, and the intersections of the roads and the ends of dead-end roads as nodes, an initial connected graph is generated. Using any node in the initial connected graph as the start and end point, multiple Eulerian circuits are obtained; Multiple Euler circuits are superimposed to form the first connected graph; The n stations surrounding the target station are identified as secondary stations; Determine the target area, which is the area formed by the multiple secondary stations surrounding each other in the first connectivity graph; The target area was determined based on big data. The total distribution of electric bicycles at the specified time and from Time to The number of electric bicycles that pass through the secondary station and enter the target area at any given time are respectively , ... The process of determining the number of electric bicycles entering the target area includes: Determine the target boundary based on the target region; Based on the number of electric bicycles located within the target boundary and equipped with signal sensors. Determine the number of electric bicycles in the target area. The ratio of the number of electric bicycles equipped with the signal sensor to the number of electric bicycles without the signal sensor is k, and / - =k; Based on the total distribution The allocated inventory quantity and the Time to The number of electric bicycles passing through the secondary stations and entering the target area at any given time is predicted. The inventory quantity is allocated at each moment, and satisfies: in, For the The allocated inventory at that time.

2. The method for predicting the supply and demand of electric bicycle stations based on big data analysis according to claim 1, characterized in that, Multiple sites surrounding the target site are identified as secondary sites, including: A second connected graph is obtained by using multiple stations as nodes and connecting lines between adjacent stations as paths. Based on the second connected graph, n secondary stations are determined, wherein the connecting path between any two secondary stations is parallel to the path in the first connected graph.

3. The method for predicting the supply and demand of electric bicycle stations based on big data analysis according to claim 1, characterized in that, The target area is defined as the region formed by the multiple secondary stations surrounding each other in the first connectivity graph, including: The target station and the secondary stations are projected onto the first connected graph, and the target region is determined in the first connected graph based on the target station and the secondary stations, wherein the connection path between any two secondary stations is parallel to the path in the first connected graph.

4. The method for predicting the supply and demand of electric bicycle stations based on big data analysis according to claim 1, characterized in that, The method further includes: The average speed of electric bicycles within the target area is obtained based on big data. ; In the first connected graph, starting from each of the secondary stations, the target stations are connected, and multiple path lengths are obtained. , ... ; Based on multiple path lengths , ... Determine the average path length And based on the average path length and the average speed Determine the average commute time and obtain the Time to Interval time between ; Based on the average commute time With the interval time The ratio of the two values ​​determines the target site at [location]. real-time inventory allocation With the The allocated inventory at that time The ratio of .

5. A supply and demand forecasting system for electric bicycle stations based on big data analysis, characterized in that, For executing the electric bicycle station supply and demand forecasting method based on big data analysis as described in any one of claims 1-4, the electric bicycle station supply and demand forecasting system based on big data analysis comprises: The first submodule is used to obtain the operating area of ​​the electric bicycle, where multiple stations are distributed, and to determine one of these stations as the target station. The allocated inventory of electric bicycles is ; The second submodule is used to obtain the road network traffic map within the operating area and generate a first connected graph based on the road network traffic map, wherein the paths and nodes in the first connected graph correspond to the roads and road intersections in the road network traffic map, respectively. The third submodule is used to determine the n stations surrounding the target station as secondary stations; The fourth submodule is used to determine the target area, which is the area formed by the surrounding of multiple secondary stations; The fifth submodule is used to determine the target area based on big data. The total distribution of electric bicycles at the specified time and from Time to The number of electric bicycles that pass through the secondary station and enter the target area at any given time are respectively , ... ; The sixth submodule is used to base its distribution on the total amount. The allocated inventory quantity is and the Time to The number of electric bicycles passing through the secondary stations and entering the target area at any given time is predicted. The amount of inventory allocated at any given time.

6. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, which are executed by at least one of the processors to enable the at least one of the processors to perform a method for predicting the supply and demand of electric bicycle stations based on big data analysis as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a method for predicting the supply and demand of electric bicycle stations based on big data analysis as described in any one of claims 1-4.

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