A method and system for selecting standard charging piles based on spatiotemporal coverage networks

Abstract

This invention discloses a method and system for selecting standard charging piles based on a spatiotemporal coverage network, belonging to the field of charging pile metering and verification technology. The method includes: acquiring charging order data and constructing a many-to-many relationship network between charging piles and vehicles; establishing a time series characterizing the commissioning sequence of charging piles based on their first charging time; defining a coverage contribution index to quantify the number of charging piles indirectly associated with a single charging pile through the vehicles it serves; setting a target coverage threshold; and employing a temporal greedy selection algorithm to select the earliest commissioned charging pile with a high coverage contribution as the standard charging pile, while satisfying the target coverage constraint. This invention achieves metering coverage of the charging network with minimal initial investment and in the fastest time, while ensuring the connectivity of the metering transmission network, thus improving the scientific and economic efficiency of standard charging pile selection.

Description

Technical Field

[0001] This invention relates to the field of metering and verification technology for electric vehicle charging facilities, specifically to a method and system for selecting standard charging piles based on a spatiotemporal coverage network, which is particularly suitable for standard pile selection in a propagation-based remote verification scenario. Background Technology

[0002] With the rapid development of electric vehicle charging infrastructure, the accuracy of charging pile measurement has become crucial to ensuring fair transactions. Traditional on-site verification methods are costly and inefficient, making it difficult to meet the demands of the rapidly increasing number of charging piles. Remote verification technology has emerged to address this need. Among them, the propagation verification method enables online verification of a large number of charging piles by transferring measurement values ​​between a small number of standard charging piles and the vehicle's BMS system.

[0003] In existing technologies, the selection of standard charging stations is mostly based on experience or simple rules, such as selecting charging stations with high usage frequency (see CN202110701575.6), or optimizing terminal configuration based on the charging station's connectivity rate (see CN202411182908.9). However, these methods do not fully consider the synergy between the charging station's time attributes and network coverage capabilities, which may lead to problems such as late deployment of standard charging stations, low coverage efficiency, and resource waste.

[0004] Therefore, there is an urgent need for a scientific and efficient method for selecting standard charging piles, which can achieve coverage of the entire charging network in the earliest time and with the least investment, while ensuring verification coverage. Summary of the Invention

[0005] This invention aims to address the shortcomings of existing standard pile selection methods in terms of time efficiency and coverage efficiency, and proposes a method and system for optimizing charging pile standard piles based on spatiotemporal coverage networks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method and system for optimizing standard charging piles based on spatiotemporal coverage networks, comprising:

[0008] Step S1: Obtain charging order data, which includes at least the charging pile code, vehicle VIN code, and charging start time;

[0009] Step S2: Based on the charging order data, construct a many-to-many relationship network between charging piles and vehicles;

[0010] Step S3: Calculate the first charging time for each charging station and establish a time series of the charging stations based on the first charging time;

[0011] Step S4: Define the coverage contribution of each charging station, where the coverage contribution refers to the number of all charging stations visited by all vehicles served by the charging station in the historical charging record.

[0012] Step S5: Set the target coverage threshold, where the target coverage refers to the proportion of the number of charging piles indirectly covered by vehicles served by the selected standard charging piles to the total number of all charging piles.

[0013] Step S6: Using a time-sequential greedy selection algorithm, the charging piles are traversed according to the time sequence, and the charging piles are selected in turn to be added to the standard pile set until the cumulative coverage of the selected standard piles reaches the target coverage threshold.

[0014] Step S7: Output the set of standard piles as the preferred result.

[0015] Furthermore, the many-to-many relationship network in step S2 is constructed in the following manner:

[0016] Establish a mapping set from vehicles to charging stations Each vehicle corresponds to all the charging stations it has charged;

[0017] Establish a mapping set from charging stations to vehicles. Each charging station corresponds to all the vehicles it has served.

[0018] Furthermore, in step S3, the time series for establishing charging piles is calculated using the charging order data obtained in step S1, the first charging time of each charging pile is calculated, and the time series for charging piles is established based on the first charging time.

[0019] Furthermore, the coverage contribution in step S4 The calculation formula is: in, Indicates charging station A collection of vehicles that have been serviced. Indicates vehicle A collection of charging stations that have been charged.

[0020] Furthermore, the formula for calculating the target coverage (R) in step S5 is as follows: in, For the selected set of standard piles, This represents the total number of charging stations.

[0021] Furthermore, the time-sequential greedy selection algorithm in step S6 includes the following sub-steps:

[0022] Sort all charging stations in ascending order by first charging time;

[0023] Initialize the selected standard pile set The charging station collection has been covered. ;

[0024] Iterate through the sorted list of charging stations, and for each charging station... :

[0025] 1) join in ;

[0026] 2) Update ;

[0027] 3) Calculate the current coverage rate ;

[0028] 4) If Then stop iterating and output the result. ;

[0029] 5) Otherwise, continue iterating through the next charging station.

[0030] Furthermore, if multiple charging piles have the same initial charging time during the traversal process, the charging pile with the higher coverage contribution will be selected first.

[0031] Furthermore, a method and system for selecting standard charging piles based on spatiotemporal coverage networks also includes:

[0032] 1) Data acquisition module, used to acquire charging order data;

[0033] 2) Relationship network construction module, used to build a many-to-many relationship network between charging piles and vehicles;

[0034] 3) Time series calculation module, used to calculate and sort the first charging time of the charging pile;

[0035] 4) Coverage contribution calculation module, used to calculate the coverage contribution of each charging pile;

[0036] 5) Target setting module, used to set the target coverage threshold;

[0037] 6) The optimal algorithm execution module is used to execute the time-sequential greedy selection algorithm and output a set of standard stakes;

[0038] 7) Result output module, used to output the preferred results.

[0039] Furthermore, a method and system for selecting standard charging piles based on a spatiotemporal coverage network also includes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, implements the method as described in any of the methods within the scope of this specification.

[0040] Furthermore, a method and system for selecting standard charging piles based on a spatiotemporal coverage network also includes a computer-readable storage medium storing a computer program thereon, characterized in that the program, when executed by a processor, implements any of the methods described in this specification.

[0041] Advantages of the invention

[0042] High time efficiency: The verification network is quickly established based on the earliest put into use, shortening the construction cycle of standard piles.

[0043] Excellent coverage efficiency: It quantifies the propagation capability of the stakes by measuring the coverage contribution, achieving maximum coverage with the fewest stakes.

[0044] Highly practical: Based on real charging data, the algorithm can be directly embedded into existing remote metering monitoring and verification platforms.

[0045] Good scalability: It supports dynamic data updates and can adapt to changes in the charging pile network. Attached Figure Description

[0046] Figure 1 The flowchart of the standard pile selection method shows the complete process from data acquisition to result output.

[0047] Figure 2 A diagram illustrating the relationship between charging stations and vehicles, showing a many-to-many connection.

[0048] Figure 3 A diagram illustrating the time-series greedy selection process, showing the gradual selection and coverage growth process. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0050] like Figure 1 As shown, the method provided in this embodiment of the invention includes the following steps:

[0051] S1: Obtain charging order data, including charging pile code, vehicle VIN code, charging start time, etc.

[0052] S2: Construct a many-to-many relationship network between charging piles and vehicles. For example... Figure 2As shown, the charging piles (P1-P6) and vehicles (V1-V4) form a complex network relationship, where one vehicle can access multiple charging piles and one charging pile can serve multiple vehicles.

[0053] S3: Calculate the first charging time for each charging station and sort them in ascending order of time.

[0054] S4: Calculate the coverage contribution of each charging station. For example, if charging station P1 has served vehicles V1 and V2, V1 has visited P1, P2, and P3, and V2 has visited P1 and P4, then the coverage contribution of P1 is {P1, P2, P3, P4}, a total of 4 charging stations.

[0055] S5: Set the target coverage threshold, for example, 95%.

[0056] S6: Execute the time-sequential greedy selection algorithm. For example... Figure 3 As shown, stubs are selected in chronological order, and the coverage set is updated for each selected stub until the coverage reaches the threshold.

[0057] 1) Arrange charging stations in ascending order of their first charging time;

[0058] 2) Select charging piles in sequence to add to the standard pile set, and update the set of covered charging piles;

[0059] 3) Stop selecting when the cumulative coverage reaches the target threshold.

[0060] S7: Output the list of selected standard stakes.

[0061] In this embodiment, the algorithm is implemented in a Python environment and validated using real charging order data. Experiments show that, compared with traditional methods, the method of this invention accurately calculates the number of standard piles and the specific list of standard piles while achieving the target coverage rate, greatly improving the efficiency of standard pile construction and saving a significant amount of standard pile construction costs.

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method and system for optimal charging station selection based on spatiotemporal overlay network, characterized in that, Includes the following steps: Step S1: Obtain charging order data, which includes at least the charging pile code, vehicle VIN code, and charging start time; Step S2: Based on the charging order data, construct a many-to-many relationship network between charging piles and vehicles; Step S3: Calculate the first charging time for each charging station and establish a time series of the charging stations based on the first charging time; Step S4: Define the coverage contribution of each charging station, where the coverage contribution refers to the number of all charging stations visited by all vehicles served by the charging station in the historical charging record. Step S5: Set the target coverage threshold, where the target coverage refers to the proportion of the number of charging piles indirectly covered by vehicles served by the selected standard charging piles to the total number of all charging piles. Step S6: Using a time-sequential greedy selection algorithm, the charging piles are traversed according to the time sequence, and the charging piles are selected in turn to be added to the standard pile set until the cumulative coverage of the selected standard piles reaches the target coverage threshold. Step S7: Output the set of standard piles as the preferred result.

2. The method of claim 1, wherein, The many-to-many relationship network described in step S2 is constructed in the following way: Establishing a mapping set of vehicles to charging poles where each vehicle corresponds to all charging poles it has charged at; Establishing a mapping set of charging piles to vehicles where each charging pile corresponds to all vehicles it has served.

3. The method of claim 1, wherein, The time series for establishing charging piles mentioned in step S3 is calculated by using the charging order data obtained in step S1 to calculate the first charging time of each charging pile, and then establishing the time series of charging piles based on the first charging time.

4. The method according to claim 1, characterized in that, The coverage contribution degree in step S4 The calculation formula is: Wherein, Indicates the charging pile The vehicle set served, Indicates the vehicle The charging pile set charged.

5. The method according to claim 1, characterized in that, The formula for calculating the target coverage (R) in step S5 is as follows: in, For the selected set of standard piles, This represents the total number of charging stations.

6. The method according to claim 1, characterized in that, Step S6, the time-sequential greedy selection algorithm, includes the following sub-steps: Sort all charging stations in ascending order by first charging time; Initialize the selected standard pile set The charging station collection has been covered. ; Iterate through the sorted list of charging stations, and for each charging station... : 1) join in ; 2) Update ; 3) Calculate the current coverage rate ; 4) If Then stop iterating and output the result. ; 5) Otherwise, continue iterating through the next charging station.

7. The method according to claim 5, characterized in that, If multiple charging stations have the same initial charging time during the traversal, the charging station with the higher coverage contribution will be selected first.

8. A method and system for selecting standard charging piles based on spatiotemporal coverage networks, characterized in that, include: The data acquisition module is used to acquire charging order data; The relationship network construction module is used to build a many-to-many relationship network between charging piles and vehicles; The time series calculation module is used to calculate and sort the first charging time of the charging piles; The coverage contribution calculation module is used to calculate the coverage contribution of each charging pile. The target setting module is used to set the target coverage threshold; The optimization algorithm execution module is used to execute the time-sequential greedy selection algorithm and output a set of standard stakes. The results output module is used to output the preferred results.

9. A method and system for selecting standard charging piles based on spatiotemporal coverage networks, characterized in that, An electronic device is included, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, implements the method as described in any one of claims 1 to 7.

10. A method and system for selecting standard charging piles based on spatiotemporal coverage networks, characterized in that, The invention includes a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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