Charging pile cluster data remote online monitoring system based on 5g communication

Abstract

The present application relates to the technical field of charging pile monitoring, and provides a charging pile cluster data remote online monitoring system based on 5G communication, which comprises a data monitoring system including a monitoring module, an acquisition module and a data recognition module; the monitoring module can monitor the index parameters of the battery in the charging pile in the charging pile cluster, and the index parameters include battery characteristics, charging and discharging characteristics, operation period characteristics, temperature and humidity; the acquisition module is used for acquiring abnormal key parameters in the index parameters.The present application is based on 5G and edge computing for remote monitoring technology of the charging pile cluster, proposes a cloud-edge-end remote monitoring method based on 5G and edge computing, understands and predicts the real-time power consumption situation and trend, finally integrates and classifies the obtained information, makes a prediction and judgment on the development situation, so as to accurately grasp the charging and discharging demand and power supply resource situation of the charging pile cluster to cope with it.

Description

Technical Field

[0001] This invention belongs to the field of charging pile monitoring technology, and specifically relates to a charging pile monitoring method. Background Technology

[0002] Electric vehicles are vehicles that use onboard power sources to drive their wheels with electric motors and meet all road traffic and safety regulations. Because they have a relatively smaller environmental impact compared to traditional vehicles, their future prospects are widely viewed favorably.

[0003] As an energy replenishment device for electric vehicles, the charging performance of an electric vehicle charging station directly impacts the battery pack's lifespan and charging time. A charging station consists of a rectifier that converts input AC power to DC power and a power converter that adjusts the DC power output. By inserting a plug with a power cord into a compatible socket on the electric vehicle, DC power is supplied to the battery to charge it. The charger features a locking lever to facilitate plug insertion and removal, and the lever also provides a signal to confirm that it is locked for safety. Through communication between the charger and the vehicle's battery management system, the power converter can adjust the DC charging power online, and the charger can display charging voltage, charging current, charging amount, and charging cost.

[0004] As electric vehicle charging piles (plugs) on the power distribution side of the power grid, their unique structure dictates that automated communication systems require numerous and dispersed measurement points, wide coverage, and short communication distances. Furthermore, with urban development, network topologies demand flexible and scalable structures. Currently, large-scale applications of new energy vehicle charging pile clusters are emerging. However, due to the typical nonlinear operating characteristics of charging piles, their extensive connection will generate power grid harmonics and low voltage issues, leading to equipment damage and significantly impacting power grid safety and users' lives and livelihoods. Therefore, designing a 5G-based remote online monitoring system for charging pile cluster data is essential to address these problems. Summary of the Invention

[0005] In view of this, the present invention aims to provide a remote online monitoring system for charging pile cluster data based on 5G communication, so as to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A 5G-based remote online monitoring system for charging pile cluster data includes:

[0008] A data monitoring system, which includes a monitoring module, a data acquisition module, and a data recognition module;

[0009] The monitoring module can monitor the index parameters of the batteries in the charging piles in the charging pile cluster. The index parameters include battery characteristics, charge and discharge characteristics, operating period characteristics, temperature and humidity.

[0010] The acquisition module is used to collect abnormal key parameters in the indicator parameters;

[0011] The data identification module can synchronize, filter, store and analyze key parameters and manage data copies using edge computing, while establishing a 5G-oriented communication data parsing protocol to send parameter data.

[0012] The status assessment system can receive parameter data sent by 5G communication data and establish a monitoring and assessment model to conduct real-time status assessment and future trend assessment of the power consumption status of the charging pile cluster. The status assessment system includes a first analysis system and a second analysis system.

[0013] The first analysis system is used to analyze and calculate the power consumption and trend of AC power in the charging pile cluster as assessed by the monitoring and evaluation model;

[0014] The second analysis system is used to analyze and calculate the electricity consumption and trend of the charging pile cluster as assessed by the monitoring and evaluation model, and to combine the analysis results of the first and second analysis systems to conduct a comprehensive evaluation of the electricity consumption metering of the charging pile cluster.

[0015] Preferably, the monitoring module includes a battery sensor, a temperature sensor, and a humidity sensor installed in the charging pile;

[0016] Battery sensors are used to monitor changes in battery voltage, current flow during charging and discharging, and the start and end times of battery charging within the charging station.

[0017] Temperature sensors are used to monitor temperature changes in the batteries inside the charging station;

[0018] Humidity sensors are used to monitor humidity changes in the batteries inside the charging piles, enabling real-time monitoring of various parameters of the charging pile cluster.

[0019] Preferably, the monitoring module includes a leakage current sensor installed in the charging pile, which is used to monitor whether there is leakage during charging between the charging pile and the electric vehicle, and can stop the charging pile from continuing to charge the electric vehicle when it detects leakage, so as to ensure that the charging pile can stop supplying power to the electric vehicle in time in the event of leakage, and prevent further accidental disasters.

[0020] Preferably, the data recognition module identifies key parameters based on convolutional neural networks, and performs synchronization, filtering, storage and analysis of the parameter data. Using convolutional neural networks to identify and analyze parameter data is a suitable choice.

[0021] Preferably, the data monitoring system also includes a data storage module. The data storage module uses DAS to store the parameter data and recognition process identified by the data recognition module. The direct-connection storage method can avoid data loss or leakage due to network attacks.

[0022] Preferably, the parameter data transmitted using 5G communication data includes offline data from previous verifications, historical data from online monitoring, and real-time monitoring data. The monitoring and evaluation model is composed of offline data, historical data, and real-time monitoring data, which is a more reasonable design.

[0023] Preferably, the status assessment system uses the Monte Carlo method and grey relational theory to predict the electric vehicle charging load curves of different land use types, so as to analyze and warn of the future trend of the charging pile cluster, thereby predicting the power consumption status of the charging pile cluster.

[0024] Preferably, both the first and second analysis systems employ power flow calculation methods for uncertain loads of electric vehicle charging pile clusters to perform power flow calculations and predict the spatiotemporal distribution of power flow in the distribution network. This enables the measurement of AC and DC power in the charging pile clusters, thereby achieving the goal of accurately understanding the charging and discharging demands of the charging pile clusters and the status of power supply resources.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] This invention proposes a cloud-edge-device remote monitoring system based on 5G and edge computing for remote monitoring of charging pile clusters. This system adaptively identifies and collects various indicator parameters of the charging pile cluster in real time. Furthermore, it extracts relevant factors related to changes in charging pile cluster demand, understands and predicts real-time electricity consumption and trends, and integrates and classifies the obtained information to predict and judge the development situation. This allows for accurate understanding of the charging and discharging demand and power supply resource status of the charging pile cluster, enabling proactive responses and preventing equipment damage caused by power grid harmonics and low voltage resulting from a large influx of charging piles. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1This is a schematic diagram of the principle of a remote online monitoring system for charging pile cluster data based on 5G communication provided in an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram illustrating the principle of the monitoring and evaluation model provided in this embodiment of the invention;

[0030] Figure 3 A schematic diagram illustrating the principle of a state assessment system provided in an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0032] Please see Figure 1-3 This invention provides a remote online monitoring system for charging pile cluster data based on 5G communication. The system includes a data monitoring system comprising a monitoring module, a data acquisition module, and a data identification module. The monitoring module monitors the parameters of the batteries within the charging piles in the cluster, including battery characteristics, charge / discharge characteristics, operating characteristics, temperature, and humidity. The data acquisition module collects abnormal key parameters, primarily including overcurrent during battery charging, battery temperature, and charging characteristics at various charge levels. The data identification module synchronizes, filters, stores, and analyzes the key parameters and manages data replicas using edge computing. It also establishes a 5G-oriented communication data parsing protocol to send parameter data; currently, the protocol uses... The standard is based on a variant of IEC 60870-5-104, facilitating the timeliness and security of IoT data transmission. The status assessment system receives parameter data transmitted via 5G communication and establishes a monitoring and assessment model to perform real-time status assessment and future trend assessment of the charging pile cluster's power consumption. The status assessment system includes a first analysis system and a second analysis system. The first analysis system analyzes and calculates the AC power consumption and trends of the charging pile cluster as assessed by the monitoring and assessment model. The second analysis system analyzes and calculates the DC power consumption and trends of the charging pile cluster as assessed by the monitoring and assessment model. The results of the first and second analysis systems are combined to comprehensively assess the power metering of the charging pile cluster.

[0033] First, this design utilizes 5G and edge computing for remote monitoring of charging pile clusters. It proposes a cloud-edge-device remote monitoring system based on 5G and edge computing, enabling adaptive identification and real-time collection of various indicator parameters of the charging pile cluster. A data identification module is used to achieve unified management and control of system computing, network, and storage resources. Second, regarding the information collection and processing architecture for electric vehicle charging pile clusters, this design extracts relevant factors related to changes in charging pile cluster demand, understands and predicts real-time electricity consumption and trends, and finally integrates and classifies the obtained information to predict and judge development trends. This allows for accurate understanding of the charging and discharging demand and power supply resource status of the charging pile cluster, enabling appropriate responses and preventing damage to equipment caused by power grid harmonics and low voltage resulting from a large influx of charging piles.

[0034] Specifically, the monitoring module includes a battery sensor, a temperature sensor, and a humidity sensor installed inside the charging pile. The battery sensor is used to monitor the voltage changes of the battery inside the charging pile, the current flow direction of charging and discharging, and the start and end times of battery charging. The temperature sensor is used to monitor the temperature changes of the battery inside the charging pile. The humidity sensor is used to monitor the humidity changes of the battery inside the charging pile, so as to monitor various indicators and parameters of the charging pile cluster in real time and provide them to the data identification module for analysis and processing.

[0035] Meanwhile, the monitoring module also includes a leakage current sensor installed in the charging pile, which is used to monitor whether there is leakage during charging between the charging pile and the electric vehicle. If leakage is detected, the charging pile can stop charging the electric vehicle, ensuring that the charging pile can stop supplying power to the electric vehicle in time in the event of leakage, cutting off the circuit, preventing further accidents and improving the safety of the entire charging pile cluster.

[0036] It should be noted that the data recognition module identifies key parameters based on convolutional neural networks, and performs synchronization, filtering, storage and analysis of the parameter data. Convolutional neural networks are a type of feedforward neural network that includes convolutional computation and has a deep structure. They are one of the representative algorithms of deep learning, and using convolutional neural networks to identify and analyze parameter data is a suitable choice.

[0037] Furthermore, the data monitoring system also includes a data storage module. The data storage module uses DAS to store the parameter data and recognition process identified by the data recognition module. The direct-connection storage method can avoid data loss or leakage due to network attacks, and can further improve the security of stored parameter data.

[0038] Specifically, the parameter data transmitted using 5G communication data includes offline data from previous verifications, historical data from online monitoring, and real-time monitoring data. The monitoring and evaluation model is composed of offline data, historical data, and real-time monitoring data, enabling the status assessment system to evaluate the real-time status and future trends of the charging pile cluster based on the monitoring and evaluation model.

[0039] The specific method for assessing the future trend of the power consumption status of charging pile clusters is as follows: The status assessment system uses the Monte Carlo method and grey relational theory to predict the electric vehicle charging load curves of different land use types, so as to analyze and warn of the future trend of the charging pile clusters, thereby predicting the power consumption status of the charging pile clusters and assessing the future power consumption trend of the charging pile clusters.

[0040] Both the first and second analysis systems employ power flow calculation methods for uncertain loads in electric vehicle charging pile clusters. These methods perform power flow calculations and predict the spatiotemporal distribution of power flow. The power flow calculation is based on the given power grid structure, parameters, and operating conditions of components such as generators and loads. It determines the steady-state operating parameters of each part of the power system to measure the AC and DC power of the charging pile clusters and combine them for comprehensive evaluation. This achieves the goal of accurately understanding the charging and discharging demand of the charging pile clusters and the status of power supply resources.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote online monitoring system for charging pile cluster data based on 5G communication, characterized in that, include: A data monitoring system, which includes a monitoring module, a data acquisition module, and a data recognition module; The monitoring module can monitor the index parameters of the batteries in the charging piles in the charging pile cluster. The index parameters include battery characteristics, charge and discharge characteristics, operating period characteristics, temperature and humidity. The acquisition module is used to collect abnormal key parameters in the indicator parameters; The data identification module can synchronize, filter, store and analyze key parameters and manage data copies using edge computing, while establishing a 5G-oriented communication data parsing protocol to send parameter data. The status assessment system can receive parameter data sent by 5G communication data and establish a monitoring and assessment model to conduct real-time status assessment and future trend assessment of the power consumption status of the charging pile cluster. The status assessment system includes a first analysis system and a second analysis system. The first analysis system is used to analyze and calculate the power consumption and trend of AC power in the charging pile cluster as assessed by the monitoring and evaluation model; The second analysis system is used to analyze and calculate the electricity consumption and trend of the charging pile cluster DC power as assessed by the monitoring and evaluation model, and to combine the analysis results of the first and second analysis systems to conduct a comprehensive evaluation of the electricity consumption metering of the charging pile cluster. The status assessment system uses the Monte Carlo method and grey relational theory to predict the electric vehicle charging load curves in areas with different land use types, so as to analyze and warn of the future trend of charging pile clusters. Both the first and second analysis systems employ power flow calculation methods for uncertain loads on electric vehicle charging pile clusters to perform power flow calculations and predict the spatiotemporal distribution of power flow in the distribution network.

2. The remote online monitoring system for charging pile cluster data based on 5G communication according to claim 1, characterized in that: The monitoring module includes a battery sensor, a temperature sensor, and a humidity sensor installed inside the charging station; Battery sensors are used to monitor changes in battery voltage, current flow during charging and discharging, and the start and end times of battery charging within the charging station. Temperature sensors are used to monitor temperature changes in the batteries inside the charging station; Humidity sensors are used to monitor humidity changes in the batteries inside the charging station.

3. The remote online monitoring system for charging pile cluster data based on 5G communication according to claim 1, characterized in that: The monitoring module includes a leakage current sensor installed in the charging pile, which is used to monitor whether there is leakage during charging between the charging pile and the electric vehicle, and can stop the charging pile from continuing to charge the electric vehicle if leakage is detected.

4. The remote online monitoring system for charging pile cluster data based on 5G communication according to claim 1, characterized in that: The data recognition module uses a convolutional neural network to identify key parameters and perform synchronization, filtering, storage, and analysis of the data.

5. The remote online monitoring system for charging pile cluster data based on 5G communication according to claim 1, characterized in that: The data monitoring system also includes a data storage module, which uses DAS to store the parameter data and recognition process identified by the data recognition module.

6. The remote online monitoring system for charging pile cluster data based on 5G communication according to claim 1, characterized in that: The parameter data transmitted using 5G communication data includes offline data from previous verifications, historical data from online monitoring, and real-time monitoring data. The monitoring and evaluation model is composed of offline data, historical data, and real-time monitoring data.

7. A remote online monitoring device for charging pile cluster data based on 5G communication, characterized in that, The device includes a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor implements the 5G-based charging pile cluster data remote online monitoring system according to the instructions of the computer program as described in any one of claims 1-6.

8. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the 5G-based remote online monitoring system for charging pile cluster data as described in any one of claims 1-6.

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