Charging pile data processing system and method

Abstract

The invention provides a charging pile data processing system and method, and the system comprises M charging piles, the M charging piles are divided into N different regions according to the positions of the M charging piles, M and N are integers, and N is greater than or equal to M and greater than 1; the N edge data centers are configured in the N areas in a one-to-one correspondence mode, and all the charging piles in each area are in communication connection with the edge data center corresponding to the area; the N edge data centers are in communication connection with the cloud data center, each charging pile sends charging pile data of the charging pile to the edge data center in communication connection with the charging pile, and each edge data center processes and analyzes the received charging pile data and sends the processed charging pile data to the cloud data center; and an analysis result is sent to the cloud data center. According to the invention, the data of the charging pile can be rapidly, reliably, safely and flexibly processed, and the bandwidth resource demand is low.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a charging pile data processing system and method. Background Art

[0002] With the rapid growth of the electric vehicle market, the number of charging piles has increased dramatically, resulting in a surge in charging data, which poses a severe challenge to data computing. Traditional cloud computing processing methods have limitations in many aspects, such as 1. High latency: the data transmission distance is long, resulting in increased response time, affecting real-time application performance; 2. Bandwidth bottleneck: with the increase of IoT devices, the amount of data has surged, and too much data uploaded to the cloud will lead to insufficient bandwidth; 3. Data security and privacy: data is stored on centralized cloud servers, and there is a risk of leakage, especially when processing sensitive information; 4. Single point failure risk: relying on centralized data centers, if a failure occurs, it may cause service interruption; 5. Insufficient flexibility: for some specific application scenarios, the flexibility and adaptability of cloud computing may be insufficient to meet real-time processing requirements. How to break through the above limitations is an urgent problem to be solved. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a charging pile data processing system and method, which can process charging pile data quickly, reliably, safely and flexibly, and has low bandwidth resource requirements.

[0004] The technical solution adopted by the present invention is as follows: A charging pile data processing system comprises: M charging piles, wherein the M charging piles are divided into N different areas according to their respective locations, wherein M and N are both integers, and M≥N>1; N edge data centers, wherein the N edge data centers are respectively configured in the N areas in a one-to-one correspondence, and all charging piles in each area are communicatively connected to the edge data center corresponding to the area; and a cloud data center, wherein the N edge data centers are communicatively connected to the cloud data center, wherein each charging pile sends its own charging pile data to the edge data center communicatively connected thereto, and each edge data center processes and analyzes the received charging pile data, and sends the analysis results to the cloud data center.

[0005] Furthermore, the edge data center also sends control instructions to the corresponding charging pile according to the analysis results.

[0006] Furthermore, the edge data center is a router, a gateway or an edge server in a corresponding area.

[0007] Furthermore, the cloud data center also monitors the operating status of each of the edge data centers.

[0008] Furthermore, the load status of each edge data center is adjusted through elastic scaling or load migration.

[0009] A charging pile data processing method, M charging piles are divided into N different areas according to their respective locations, N edge data centers are configured in a one-to-one correspondence in the N areas, all charging piles in each area are communicatively connected with the edge data center corresponding to the area, and the N edge data centers are communicatively connected with a cloud data center, wherein M and N are both integers, and M≥N>1, and the method comprises: each of the charging piles sends its own charging pile data to the edge data center communicatively connected with it; each of the edge data center processes and analyzes the received charging pile data, and sends the analysis results to the cloud data center.

[0010] Furthermore, the charging pile data processing method also includes: the edge data center sends a control instruction to the corresponding charging pile according to the analysis result.

[0011] Furthermore, the edge data center is a router, a gateway or an edge server in a corresponding area.

[0012] Furthermore, the charging pile data processing method also includes: the cloud data center monitors the operating status of each of the edge data centers.

[0013] Furthermore, the charging pile data processing method also includes: adjusting the load state of each edge data center through elastic scaling or load migration.

[0014] Beneficial effects of the present invention: The present invention divides charging piles into location-based partitions and sets an edge data center for each partition, so that the edge data center can process the charging pile data of the corresponding partition. Relatively speaking, the edge data center can process data at a location closer to the data source, significantly reducing the data transmission time, and is suitable for real-time applications; by processing and filtering data in the edge data center, the amount of data that needs to be transmitted to the cloud is reduced, thereby reducing bandwidth consumption; processing data locally reduces the risk of transmitting sensitive data to the cloud, thereby improving data security and privacy protection; each edge data center is dispersed, and the failure of a node will not affect the operation of the entire system, thereby improving overall reliability; the edge data center can realize real-time data analysis and support rapid decision-making and response. Therefore, the charging pile data can be processed quickly, reliably, safely, and flexibly, and the bandwidth resource requirements are low. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of a charging pile data processing system according to an embodiment of the present invention.

[0016] Figure 2 The figure is a flow chart of a charging pile data processing method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the charging pile data processing system of the embodiment of the present invention includes M charging piles 10, N edge data centers 20 and a cloud data center 30. The M charging piles 10 are divided into N different areas according to their respective locations, wherein M and N are integers, and M≥N>1; the N edge data centers 20 are respectively configured in the N areas in a one-to-one correspondence, and all the charging piles 10 in each area are connected to the edge data center 20 corresponding to the area in communication; the N edge data centers 20 are connected to the cloud data center 30 in communication. Among them, each charging pile 10 sends its own charging pile data to the edge data center 20 connected to it in communication, and each edge data center 20 processes and analyzes the received charging pile data, and sends the analysis results to the cloud data center 30.

[0019] In one embodiment of the present invention, each charging pile 10, when registering, in addition to providing basic information such as identity identification and performance parameters to the cloud data center 30, also provides location information to the cloud data center 30, and the location information may be the longitude and latitude of the location of the charging pile 10. The cloud data center 30 may group the charging piles 10 according to the longitude and latitude of each charging pile 10, based on administrative divisions and other zoning methods. For example, all charging piles 10 located in a province are divided into the province area. At the same time, an edge data center 20 is configured in the province area, and all charging piles 10 in the province area establish communication connections with the edge data center 20, and the edge data center 20 configured in each province area establishes communication connections with the cloud data center.

[0020] In one embodiment of the present invention, the edge data center 20 is a router, gateway or edge server in the corresponding area, and has functions such as communication and data processing, storage, and analysis.

[0021] After the charging pile data processing system of the embodiment of the present invention is constructed, if the user sends a charging request at the vehicle end or mobile terminal, the cloud data center 30 can recommend the charging pile 10 closest to the user based on the user's current location, and select the edge data center 20 in the area where the charging pile 10 is located to process the relevant data.

[0022] In one embodiment of the present invention, the data processing flow of the charging pile data processing system is as follows: first, the data acquisition module in the charging pile 10 can collect charging pile data, including real-time current, real-time voltage, operation log and other data, and send the charging pile data to the edge data center 20 that is communicatively connected to it, that is, the edge data center 20 in the area where the charging pile 10 is located; then, the edge data center 20 can pre-process the received data, such as data cleaning, filtering and format conversion, etc., to reduce the amount of data and improve the subsequent processing efficiency, and the edge data center 20 can perform real-time analysis on the received data to obtain analysis results, such as overcurrent abnormality analysis based on real-time current; then, the edge data center 20 can send the analysis results and some important data, such as real-time current, real-time voltage, cumulative charging amount, cumulative cost, etc. to the cloud data center 30, so that the cloud data center 30 can perform further processing or long-term storage, and the further processing includes result verification and in-depth analysis.

[0023] In addition, the edge data center 20 may have a local database, and the pre-processed data may be stored in the local database for subsequent access or analysis.

[0024] Furthermore, in one embodiment of the present invention, the edge data center 20 can also send control instructions to the corresponding charging piles 10 according to the analysis results to achieve system closed-loop control and system self-adjustment. For example, when the edge data center 20 analyzes that the overall charging power in a certain area during a certain period is high and exceeds a set threshold, a power reduction instruction can be generated and sent to the charging piles 10 with higher charging power. After receiving the power reduction instruction, these charging piles 10 can choose to reduce their own charging power to avoid excessive pressure on the power grid or system.

[0025] In one embodiment of the present invention, the cloud data center 30 can also monitor the operating status of each edge data center 20, specifically, the request response time and load of the edge data center 20 in each region can be monitored to ensure that the edge computing resources in each region are effectively utilized. For example, indicators such as the number of requests, response time, error rate, CPU and memory usage of the edge data center 20 in each region can be collected. If an indicator is abnormal, the relevant operation and maintenance personnel can be notified by SMS, phone call, etc.; or, user experience monitoring tools such as Google Lighthouse, Pingdom, etc. can be used to test the system page loading time and availability in different regions to ensure consistent user experience.

[0026] In one embodiment of the present invention, the load state of each edge data center 20 can also be adjusted by elastic scaling or load migration. Specifically, the computing resources of each edge data center 20 can be adjusted by elastic scaling, or the charging piles 10 in the area with high resource utilization can be migrated to the area with low resource utilization according to the resource utilization of the edge data centers 20 in each region. In this way, the stable operation of the entire system can be guaranteed.

[0027] In summary, according to the charging pile data processing system of the embodiment of the present invention, by partitioning the charging piles based on location and setting an edge data center corresponding to each partition, the charging pile data of the corresponding partition is processed through the edge data center. Relatively speaking, the edge data center can process data at a location closer to the data source, significantly reducing the data transmission time, and is suitable for real-time applications; by processing and filtering data at the edge data center, the amount of data that needs to be transmitted to the cloud is reduced, thereby reducing bandwidth consumption; processing data locally reduces the risk of transmitting sensitive data to the cloud, thereby improving data security and privacy protection; each edge data center is dispersed, and the failure of a node will not affect the operation of the entire system, thereby improving overall reliability; the edge data center can realize real-time data analysis and support rapid decision-making and response. Therefore, the charging pile data can be processed quickly, reliably, safely, and flexibly, and the bandwidth resource requirements are low.

[0028] Corresponding to the charging pile data processing system of the above embodiment, the present invention also proposes a charging pile data processing method.

[0029] like Figure 2 As shown, the charging pile data processing method of the embodiment of the present invention includes the following steps: S1, each charging pile sends its own charging pile data to the edge data center connected to it.

[0030] S2, each edge data center processes and analyzes the received charging pile data and sends the analysis results to the cloud data center.

[0031] In one embodiment of the present invention, the edge data center is a router, gateway or edge server in the corresponding area, having functions such as communication and data processing, storage, and analysis.

[0032] First, the data acquisition module in the charging pile can collect charging pile data, including real-time current, real-time voltage, operation log and other data, and send the charging pile data to the edge data center that is connected to it for communication, that is, the edge data center in the area where the charging pile is located; then, the edge data center can pre-process the received data, such as data cleaning, filtering and format conversion, etc., to reduce the amount of data and improve the efficiency of subsequent processing, and the edge data center can analyze the received data in real time to obtain analysis results, such as overcurrent abnormality analysis based on real-time current; then, the edge data center can send the analysis results and some important data, such as real-time current, real-time voltage, cumulative charging amount, cumulative cost, etc. to the cloud data center for further processing or long-term storage by the cloud data center, and further processing includes result verification and in-depth analysis.

[0033] In addition, the edge data center may have a local database in which the pre-processed data may be stored for subsequent access or analysis.

[0034] Furthermore, in one embodiment of the present invention, the edge data center can also send control instructions to the corresponding charging piles according to the analysis results to achieve closed-loop control and self-adjustment of the system. For example, when the edge data center analyzes that the overall charging power in a certain area during a certain period of time is high and exceeds a set threshold, a power reduction instruction can be generated and sent to the charging piles with higher charging power. After receiving the power reduction instruction, these charging piles can choose to reduce their own charging power to avoid excessive pressure on the power grid or system.

[0035] In one embodiment of the present invention, the cloud data center can also monitor the operating status of each edge data center, specifically the request response time and load of the edge data centers in each region, so as to ensure that the edge computing resources in each region are effectively utilized. For example, the number of requests, response time, error rate, CPU and memory usage and other indicators of the edge data centers in each region can be collected. If an indicator is abnormal, the relevant operation and maintenance personnel can be notified by SMS, phone call, etc.; or, user experience monitoring tools such as Google Lighthouse and Pingdom can be used to test the system page loading time and availability in different regions to ensure consistent user experience.

[0036] In one embodiment of the present invention, the load state of each edge data center can also be adjusted by elastic scaling or load migration. Specifically, the computing resources of each edge data center can be adjusted by elastic scaling, or charging piles in areas with high resource utilization can be migrated to areas with low resource utilization according to the resource utilization of edge data centers in each region. In this way, the stable operation of the entire system can be guaranteed.

[0037] In summary, according to the charging pile data processing method of the embodiment of the present invention, by partitioning the charging piles based on location and setting an edge data center corresponding to each partition, the charging pile data of the corresponding partition is processed through the edge data center. Relatively speaking, the edge data center can process data at a location closer to the data source, significantly reducing the data transmission time, and is suitable for real-time applications; by processing and filtering data in the edge data center, the amount of data that needs to be transmitted to the cloud is reduced, thereby reducing bandwidth consumption; processing data locally reduces the risk of transmitting sensitive data to the cloud, thereby improving data security and privacy protection; each edge data center is dispersed, and the failure of a node will not affect the operation of the entire system, thereby improving overall reliability; the edge data center can realize real-time data analysis and support rapid decision-making and response. Therefore, the charging pile data can be processed quickly, reliably, safely, and flexibly, and the bandwidth resource requirements are low.

[0038] In the description of the present invention, “plurality” means two or more than two, unless otherwise clearly and specifically defined.

[0039] The execution order of each step shown in the flowchart is a preferred implementation mode. In other embodiments of the present invention, the steps may be adjusted according to the functions involved in each step, for example, they may be executed simultaneously or in reverse order.

[0040] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing the logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device. For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transmit a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0041] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0042] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0043] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A charging pile data processing system, characterized in that: include: M charging piles, the M charging piles are divided into N different areas according to their respective locations, wherein M and N are both integers, and M≥N>1; N edge data centers, the N edge data centers are respectively configured in the N areas in a one-to-one correspondence, and all charging piles in each area are communicatively connected with the edge data center corresponding to the area; A cloud data center, wherein the N edge data centers are all connected to the cloud data center in communication, Each of the charging piles sends its own charging pile data to an edge data center that is communicatively connected thereto, and each of the edge data centers processes and analyzes the received charging pile data and sends the analysis results to the cloud data center.

2. The charging pile data processing system according to claim 1, characterized in that: The edge data center also sends control instructions to the corresponding charging pile according to the analysis results.

3. The charging pile data processing system according to claim 1, characterized in that: The edge data center is a router, a gateway or an edge server in a corresponding area.

4. The charging pile data processing system according to claim 1, characterized in that: The cloud data center also monitors the operating status of each of the edge data centers.

5. The charging pile data processing system according to claim 1, characterized in that: The load status of each edge data center is adjusted through elastic scaling or load migration.

6. A charging pile data processing method, characterized in that: M charging piles are divided into N different areas according to their respective locations, and N edge data centers are configured in a one-to-one correspondence in the N areas. All charging piles in each area are connected to the edge data center corresponding to the area in communication, and the N edge data centers are connected to the cloud data center in communication, wherein M and N are both integers, and M≥N>1, and the method includes: Each of the charging piles sends its own charging pile data to an edge data center connected to it in communication; Each of the edge data centers processes and analyzes the received charging pile data, and sends the analysis results to the cloud data center.

7. The charging pile data processing method according to claim 6, characterized in that: Also includes: The edge data center sends a control instruction to the corresponding charging pile according to the analysis result.

8. The charging pile data processing method according to claim 6, characterized in that: The edge data center is a router, a gateway or an edge server in a corresponding area.

9. The charging pile data processing method according to claim 6, characterized in that: Also includes: The cloud data center monitors the operating status of each of the edge data centers.

10. The charging pile data processing method according to claim 6, characterized in that: Also includes: The load status of each edge data center is adjusted through elastic scaling or load migration.

Images