Charging pile remote monitoring and intelligent management system based on Internet of Things

Abstract

The invention discloses a charging pile remote monitoring and intelligent management system based on the Internet of Things, and relates to the technical field of power systems and automation thereof. The network transmission layer module is used for multi-protocol converged communication and data compression; the cloud management platform module is used for real-time monitoring, equipment management, charging scheduling and data analysis; the charging pile local control module executes start-stop adjustment and fault self-processing; the user interaction module comprises a touch screen, a mobile terminal APP and a dynamic two-dimensional code; the data storage module is used for distributed architecture and timing backup; and the security protection module is used for data encryption, authority grading and instruction dual verification. The sensing precision and the communication stability of the charging pile are improved, the charging scheduling and efficiency are optimized, and the fault handling time is shortened; remote monitoring and intelligent management of the charging pile are realized, the operation efficiency and safety are improved, and multi-scene requirements are met.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation technology, and in particular to a remote monitoring and intelligent management system for charging piles based on the Internet of Things. Background Technology

[0002] With the popularization of new energy vehicles, charging piles, as core infrastructure, have seen continuous growth in deployment scale and usage frequency. However, existing charging pile management systems still face multi-dimensional technical bottlenecks, making it difficult to adapt to the needs of large-scale and intelligent operation. At the sensing level, most systems use a single type of sensor and lack data preprocessing mechanisms: current and voltage sensors have low sampling frequencies and insufficient accuracy, failing to capture instantaneous fluctuations during charging; temperature sensors are mostly deployed on the equipment surface, not covering core heat-generating points such as power modules and battery interfaces, and are prone to drift over long-term use, leading to misjudgments or missed judgments of overheating faults; monitoring of environmental parameters such as humidity and parking space status is lacking, and the risk of short circuits and chaotic parking space occupancy information are prominent in high humidity environments, affecting equipment safety and user efficiency.

[0003] The network transmission process faces the dual challenges of poor scenario adaptability and unstable data transmission. Current systems largely rely on single 4G / 5G communication, leading to frequent communication interruptions and delayed data reporting in remote areas or areas with weak signals. Some systems using LoRa communication have fixed spreading factors that cannot be dynamically adjusted based on communication distance, resulting in low transmission rates at close range and severe signal attenuation at long range. Furthermore, charging data and device logs are not compressed, easily causing network congestion during peak hours, leading to data loss or delayed control commands, further exacerbating scheduling delays. In addition, cloud management platforms are fragmented, with real-time monitoring only displaying basic status and lacking multi-dimensional filtering and in-depth analysis capabilities. Charging scheduling relies on manual experience or simple load balancing, failing to dynamically adjust based on historical data and environmental factors, resulting in overloaded charging piles and idle devices, leading to low cluster utilization. Device management cannot achieve batch firmware upgrades and parameter configuration, resulting in high maintenance costs.

[0004] The existing system also has shortcomings in local control, user interaction, and security. The local control module for charging piles only has basic start / stop functions and lacks self-fault handling capabilities. When overcurrent or overvoltage faults occur, it must wait for cloud commands, extending the fault handling time. Charging efficiency is not optimized in conjunction with equipment status and ambient temperature, resulting in significant energy waste. User interaction methods are limited; static QR codes are easily tampered with, and the mobile app lacks charging reservation and demand prediction functions, frequently leading to situations where no available charging piles are available upon user arrival. Data storage does not differentiate between hot and cold data; frequently accessed real-time data is mixed with infrequently accessed historical data, resulting in low retrieval efficiency. The backup mechanism is inadequate, leading to a high risk of data loss. Security protection is limited to simple password verification; command issuance lacks multi-factor authentication, and cases of equipment damage or safety accidents due to misoperation occur frequently. Furthermore, data transmission and storage do not employ high-strength encryption, posing a significant risk of user privacy and operational data leakage. These problems collectively result in the existing charging pile management system being "insecure, inefficient, and offering a poor user experience," failing to meet the high-quality development needs of the new energy vehicle industry. Summary of the Invention

[0005] This invention proposes an IoT-based remote monitoring and intelligent management system for charging piles to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote monitoring and intelligent management system for charging piles based on the Internet of Things, comprising the following modules: The sensing layer module integrates multiple types of sensors for detecting AC current, DC voltage, temperature, humidity, and parking space status. All sensor data is preprocessed and converted into standard digital signals. The network transmission layer module adopts a multi-protocol converged communication architecture, including 4G / 5G, LoRa and industrial Ethernet units, and a data compression unit integrating the LZ77 compression algorithm; The cloud management platform module is equipped with multi-dimensional functional subsystems. The real-time monitoring subsystem displays relevant equipment information in a visual manner, the equipment management subsystem completes the configuration function, the charging scheduling subsystem dynamically allocates charging resources, and the data analysis subsystem generates operation reports. The charging pile local control module has a built-in embedded controller, which can perform charging start / stop and power adjustment operations, and has the ability to handle local faults and adapt to environmental temperature and humidity. The user interaction module includes a local touch screen, a mobile APP, and a QR code interaction unit. The touch screen is no smaller than 7 inches. The APP supports dual systems and multiple practical functions. The QR code interaction unit uses dynamic QR code technology. The data storage module adopts a distributed database architecture, consisting of 3 main servers and 2 backup servers, supports multi-dimensional retrieval, and is configured with scheduled full and incremental backup mechanisms. The security protection module includes sub-modules for data security, access control, and prevention of misoperation of commands. It adopts encryption algorithms, has three levels of user permissions, and has a dual verification mechanism for command execution.

[0007] Furthermore, it also includes a load prediction module, which is deployed in the cloud management platform module. By collecting historical load data of charging piles, regional electricity demand data, ambient temperature data and holiday factors, it uses the LSTM neural network algorithm to build a load prediction model. The prediction results are displayed every 15 minutes as a time node, and output the predicted load value of the charging pile cluster for each time period in the next 24 hours.

[0008] Furthermore, it also includes a fault early warning module, which works in conjunction with the perception layer module and the cloud management platform module to receive data transmitted from the perception layer in real time and set normal threshold ranges for each parameter. When data is detected to exceed the threshold three times consecutively or exceed the threshold by 15% in a single instance, it is determined to be a potential fault, generates a fault early warning message, and pushes the warning message to the mobile APP of the maintenance personnel, and links it to the equipment maintenance log.

[0009] Furthermore, the temperature sensor in the sensing layer module adopts a dual-redundancy deployment design. Two temperature sensors of the same model are installed at each monitoring point. Under normal operation, the difference between the data collected by the two sensors is controlled within ±0.3℃. If the difference exceeds ±0.5℃, one of the sensors is determined to be abnormal, the abnormal sensor data is automatically blocked, and the backup sensor data is enabled. At the same time, the temperature sensor has a self-calibration function.

[0010] Furthermore, a dynamic load balancing algorithm is introduced into the charging scheduling subsystem of the cloud management platform module, using the formula... Calculate the predicted load of a single charging station, where Predict the load on the charging station for the next 15 minutes. For historical load, For real-time load, For ambient temperature, This represents the average load of the charging station over the same period in the past 7 days. For the current real-time load, This represents the deviation between the current ambient temperature and the standard temperature.

[0011] Furthermore, the LoRa low-power communication unit of the network transmission layer module adopts adaptive spread spectrum technology, which dynamically adjusts the spread spectrum factor according to the communication distance. When the communication distance is less than 1km, the SF7 spread spectrum factor is used, and the transmission rate is increased to 5.5kbps; when the communication distance is 1-2km, the SF10 spread spectrum factor is used, and the transmission rate is 3kbps; when the communication distance is 2-3km, the SF12 spread spectrum factor is used, and the transmission rate is 1.2kbps. At the same time, a signal strength detection unit is integrated to monitor the RSSI value of the LoRa communication signal in real time.

[0012] Furthermore, a charging efficiency optimization unit is added to the local control module of the charging pile, using a formula... Calculate the optimal charging efficiency, where To achieve the optimal charging efficiency for charging stations. For positive power, Due to the impact of wear and tear, Due to the influence of equipment temperature, This is the charging voltage. This is the charging current. This refers to the internal power loss of the charging pile. This is the temperature difference between the power module of the charging pile and the ambient temperature.

[0013] Furthermore, the mobile app for the user interaction module adds a charging demand prediction function. After the user inputs the vehicle model, current battery level, target battery level, and estimated arrival time, the app combines real-time and predicted load data of the charging pile to recommend the optimal charging time and location, while also calculating the required charging time and cost; it also supports a charging reservation and locking function.

[0014] Furthermore, the data storage module adopts a hot and cold data separation storage strategy. The real-time charging data and equipment operation data of nearly 7 days are divided into hot data and stored in a high-performance SSD server; historical data and report data are divided into cold data and stored in a low-cost HDD server. When accessing cold data, it is temporarily loaded to the SSD server through the data migration unit; at the same time, data lifecycle management function is configured.

[0015] Furthermore, a three-level verification mechanism is added to the command misoperation prevention submodule of the safety protection module. The first level is identity verification, which verifies the operator's identity through a USB key or biometric identification. The second level is parameter verification, which compares the parameters in the command with the rated parameters of the charging pile, and directly rejects commands that exceed the rated range. The third level is scenario verification, which determines the current status of the charging pile. Only after all three levels of verification pass can the command be sent to the local control module of the charging pile.

[0016] Compared with existing technologies, the beneficial effects of this invention are: This invention, through full-level modular design and technological innovation, significantly improves the safety, reliability, and intelligence level of remote monitoring and intelligent management of charging piles. Its core advantages are as follows: Optimization of the perception layer and network transmission layer lays the foundation for stable system operation. The perception layer features a collaborative deployment of multiple sensor types, with dual redundancy and self-calibration capabilities for temperature sensors, significantly improving temperature monitoring accuracy and reducing the probability of false alarms caused by sensor drift. A multi-parameter synchronous acquisition and preprocessing mechanism comprehensively captures equipment operation and environmental status, providing precise data support for safety and security. The network transmission layer employs a multi-protocol fusion architecture to adapt to different communication needs. LoRa adaptive spread spectrum technology and a dynamic signal strength switching mechanism ensure uninterrupted data transmission in remote areas and areas with weak signals. Data compression effectively alleviates network congestion, ensuring real-time interaction between monitoring data and control commands, and solving the scenario adaptability problem of traditional single communication solutions.

[0017] The synergistic optimization of cloud management and local control significantly improves operational efficiency and equipment safety. The cloud-based load prediction module combines historical data and environmental factors to anticipate peak cluster loads, allowing time for scheduling strategy adjustments. The dynamic load balancing algorithm accurately matches charging pile load with user demand, avoiding localized overload and equipment idleness, thus improving overall cluster utilization. The charging pile's local control module's fault self-handling capability can quickly respond to overcurrent, overvoltage, and overtemperature faults, cutting off risky circuits without waiting for cloud commands. The charging efficiency optimization unit dynamically adjusts operating parameters based on voltage, current, equipment losses, and temperature, reducing energy waste, improving charging economy, and addressing the pain points of traditional systems such as "scheduling lag" and "high energy consumption."

[0018] Upgrades to user interaction, data storage, and security protection further enhance the system's usability and reliability. Dynamic QR codes in the user interaction module reduce the risk of tampering, while the mobile app's charging reservation and demand prediction functions improve user convenience. A cold / hot data separation storage strategy and a scheduled backup mechanism balance data access efficiency and storage costs, while ensuring long-term data retention and traceability. The security protection module's multi-level identity verification, parameter verification, and scenario verification completely block illegal commands and misoperations, while the AES-256 encryption algorithm comprehensively protects transmitted and stored data, reducing the risk of privacy leaks and device damage.

[0019] Overall, this invention achieves "precise perception, stable transmission, intelligent management, convenient interaction, and comprehensive protection" for charging piles, effectively solving the multi-dimensional technical bottlenecks of existing systems, providing technical support for the large-scale operation of charging piles, and possessing significant safety, economic, and user value. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram of a remote monitoring and intelligent management system for charging piles based on the Internet of Things proposed in this invention. Figure 2 A bar chart comparing the communication rates of charging piles in different scenarios; Figure 3 A line graph showing the change in charging efficiency of a charging station over operating time; Figure 4 A bar chart comparing the response times for different fault types; Figure 5 A pie chart showing the percentage of different types of safety incidents involving charging stations. Detailed Implementation

[0021] 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 embodiments of the present invention, and not all embodiments. 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.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0024] Reference Figures 1 to 5 A remote monitoring and intelligent management system for charging piles based on the Internet of Things (IoT) includes the following modules: The perception layer module integrates multiple types of sensors, including AC current sensors, DC voltage sensors, temperature sensors, humidity sensors, and parking space status detection sensors. The current sensor has a sampling frequency of 50Hz and an accuracy class of 0.2, acquiring the peak and effective values ​​of the charging pile's output current in real time. The voltage sensor has a range of 0-1000V, a sampling frequency of 30Hz, and an accuracy class of 0.1, monitoring voltage fluctuations during charging. The temperature sensor is deployed on the charging pile's power module, battery interface, and surrounding environment, with a range of -30℃ to 80℃ and an accuracy of ±0.5℃, providing real-time feedback on equipment temperature rise and ambient temperature. The humidity sensor has a range of 0-100%RH and an accuracy of ±3%RH, monitoring ambient humidity in real time and providing a basis for preventing short circuit risks caused by high humidity environments based on humidity threshold judgments. The parking space status detection sensor uses infrared beam detection to identify parking space occupancy. All data collected by the sensors is filtered, noise-reducing, and pre-processed before being converted into standard digital signals. The network transmission layer module adopts a multi-protocol converged communication architecture, including a 4G / 5G wireless communication unit, a LoRa low-power communication unit, and an industrial Ethernet unit. The 4G / 5G unit is used to transmit real-time monitoring data and control commands, with a transmission rate of no less than 10Mbps and a latency controlled within 200ms. The LoRa unit is adapted to charging piles in remote areas, with a spreading factor set to SF12, a communication distance of up to 3km, and power consumption of less than 50mA, used for low-frequency data reporting. The industrial Ethernet unit is used for wired connection between the charging pile cluster and the local gateway, with a transmission rate of 100Mbps and support for the IEEE 802.3 standard. It also integrates a data compression unit, which uses the LZ77 compression algorithm to reduce the amount of data transmitted, with a compression rate of no less than 60%. The compressed data can still maintain transmission stability in network congestion scenarios. The cloud management platform module is equipped with multi-dimensional functional subsystems. The real-time monitoring subsystem displays the location distribution, operating status, charging power, remaining power, and fault information of each charging pile through a visual interface, supporting multi-dimensional filtering by region, operator, and charging pile model. The equipment management subsystem realizes charging pile ledger registration, firmware upgrade, and parameter configuration functions, and supports remote batch issuance of configuration commands. The charging scheduling subsystem dynamically allocates charging resources based on charging pile load, user demand, and grid load to balance the local charging pile load. The data analysis subsystem generates operation reports based on historical charging data and equipment fault data, including indicators such as charging volume statistics, equipment utilization rate, and fault occurrence rate, providing support for operational decisions. The charging pile local control module has a built-in embedded controller that works in conjunction with the sensing layer, network transmission layer, and cloud management platform. It can receive cloud commands to execute charging start / stop and power adjustment operations, with a power adjustment step of 1kW and a range of 0-200kW. It also has local fault self-handling capabilities. When overcurrent, overvoltage, or overtemperature faults are detected, it automatically cuts off the charging circuit and records the fault code. Charging resumes after the fault is cleared. It can also automatically start the cooling fan or heating device based on the ambient temperature and humidity data collected by the sensing layer to maintain a stable operating environment for the equipment. The user interaction module includes a local touchscreen on the charging station, a mobile app, and a QR code scanning unit. The local touchscreen is at least 7 inches in size with a resolution of 1280×720, supporting the display of charging progress, cost details, device status, and operation instructions. The mobile app supports iOS and Android systems, providing functions such as charging reservation, real-time progress push, cost inquiry, and electronic invoice application. The QR code scanning unit uses dynamic QR code technology, updating the QR code every 60 seconds. After scanning the code, users can link their accounts and start charging, reducing the risk of static QR codes being tampered with. The data storage module adopts a distributed database architecture, consisting of 3 main servers and 2 backup servers, with a storage capacity of 20TB. It is used to store real-time data collected by the perception layer, user charging records, device operation logs, and cloud analysis results. It supports multi-dimensional data retrieval by time (hour, day, month), device (charging pile number, model), and user (user ID, vehicle model), with a retrieval response time of no more than 1 second. It is also configured with a scheduled backup mechanism, performing a full backup at 2:00 AM every day and an incremental backup every hour. The backup data is stored in an off-site disaster recovery center to meet the requirements for long-term data retention and recovery. The security protection module includes a data security submodule, an access control submodule, and a command misoperation prevention submodule. The data security submodule uses the AES-256 encryption algorithm to encrypt transmitted and stored data, reducing the risk of data leakage. The access control submodule divides user permissions into three levels: administrator, maintenance personnel, and ordinary users. Administrators have full-function operation permissions, maintenance personnel can only perform equipment maintenance operations, and ordinary users can only query their personal charging information. The command misoperation prevention submodule performs double verification on the issued charging start / stop and power adjustment commands. First, it verifies the legality of the command source, and then it verifies whether the command parameters are within the rated range of the charging pile. If the verification fails, the command execution process is terminated.

[0025] This invention also includes a load prediction module, which is deployed in the cloud management platform module. By collecting historical load data of charging piles over the past 30 days, regional electricity demand data for the next 24 hours, ambient temperature data, and holiday factors, a load prediction model is constructed using an LSTM neural network algorithm. The model is trained daily at 3:00 AM, with a training sample size of no less than 10,000 sets. The prediction results are output in 15-minute increments, showing the predicted load values ​​of the charging pile cluster for each time period in the next 24 hours. When the predicted load exceeds 80% of the rated total load of the charging pile cluster, an early warning signal is automatically sent to the cloud charging scheduling subsystem. Based on this, the scheduling subsystem adjusts the charging resource allocation strategy in advance to control the cluster load within 80% of the rated total load.

[0026] This invention also includes a fault early warning module, which works in conjunction with the sensing layer module and the cloud management platform module. It receives real-time data on current, voltage, and temperature transmitted from the sensing layer, and sets normal threshold ranges for each parameter: 0-120A for current, 220V±10% (AC) and 500V±5% (DC) for voltage, and -20℃ to 60℃ for temperature. When data exceeds the threshold three times consecutively or exceeds it by 15% in a single instance, a potential fault is identified, and a fault early warning message is generated. This message includes the warning device number, warning parameters, warning time, and possible causes of the fault. Simultaneously, the warning message is pushed to the maintenance personnel's mobile app and linked to the equipment maintenance log, displaying historical fault records and maintenance plans for the equipment, thus improving fault diagnosis efficiency.

[0027] In this invention, the temperature sensor in the sensing layer module adopts a dual-redundancy deployment design. Two temperature sensors of the same model are installed at each monitoring point. Under normal operation, the difference between the data collected by the two sensors is controlled within ±0.3℃. If the difference exceeds ±0.5℃, one of the sensors is determined to be abnormal, the abnormal sensor data is automatically blocked, and the backup sensor data is activated. At the same time, the temperature sensor has a self-calibration function. Every 72 hours, it automatically compares with the standard temperature data stored in the cloud management platform, calculates the calibration deviation, and corrects the collected value to ensure long-term monitoring accuracy and reduce the probability of misjudgment caused by sensor drift.

[0028] In this invention, a dynamic load balancing algorithm is introduced into the charging scheduling subsystem of the cloud management platform module, using the formula... Calculate the predicted load of a single charging station, where Predict the load on the charging station for the next 15 minutes. The historical load weighting coefficient is set to a value of 0.3-0.5. The real-time load weighting coefficient is set to a value of 0.4-0.6. The influence coefficient of ambient temperature is set between 0.1 and 0.2. + + =1, This represents the average load of the charging station over the same period in the past 7 days. For the current real-time load, This is the deviation between the current ambient temperature and the standard temperature (25℃). This calculation accurately predicts the load on a single charging pile, and the scheduling subsystem allocates charging demand to charging piles with lower predicted loads accordingly, thereby achieving cluster load balancing and improving overall equipment utilization.

[0029] In this invention, the LoRa low-power communication unit of the network transmission layer module adopts adaptive spread spectrum technology, dynamically adjusting the spread spectrum factor according to the communication distance. When the communication distance is less than 1km, the SF7 spread spectrum factor is used, increasing the transmission rate to 5.5kbps; when the communication distance is 1-2km, the SF10 spread spectrum factor is used, with a transmission rate of 3kbps; and when the communication distance is 2-3km, the SF12 spread spectrum factor is used, with a transmission rate of 1.2kbps. At the same time, a signal strength detection unit is integrated to monitor the RSSI value of the LoRa communication signal in real time. When the RSSI value is lower than -120dBm, it automatically switches to the 4G / 5G communication unit to ensure uninterrupted data transmission of charging piles in remote areas and reduce the probability of monitoring failure caused by communication link failure.

[0030] In this invention, a charging efficiency optimization unit is added to the local control module of the charging pile, using a formula... Calculate the optimal charging efficiency, where To achieve the optimal charging efficiency for charging stations. The positive power coefficient is set to a value between 0.002 and 0.003. The loss influence coefficient is set to a value of 0.001-0.0015. The temperature influence coefficient of the equipment is set between 0.0005 and 0.0008. This is the charging voltage. This is the charging current. This refers to the internal power loss of the charging pile (including line loss and component loss). This is the difference between the temperature of the charging pile power module and the ambient temperature (25℃). The control module dynamically adjusts the charging voltage and current based on this calculation result to maintain the charging efficiency above 90%, reduce energy waste during the charging process, and improve the charging economy.

[0031] In this invention, the mobile app of the user interaction module incorporates a charging demand prediction function. After the user inputs the vehicle model, current battery level, target battery level, and estimated arrival time, the app combines real-time and predicted load data of the charging pile to recommend the optimal charging time and location, while also calculating the required charging time and cost. It also supports a charging reservation and locking function. After the user makes a reservation, the system locks the charging pile for 30 minutes and sends an SMS reminder when the reservation time approaches, improving the user's charging convenience and experience.

[0032] In this invention, the data storage module adopts a hot and cold data separation storage strategy. Real-time charging data and equipment operation data of nearly 7 days are classified as hot data and stored in a high-performance SSD server to meet the high-frequency access requirements. Historical data and report data of more than 7 days are classified as cold data and stored in a low-cost HDD server. When accessing cold data, it is temporarily loaded to the SSD server through a data migration unit. At the same time, a data lifecycle management function is configured to automatically compress and archive cold data older than 3 years with a compression rate of not less than 80%, reducing long-term storage costs while meeting the data traceability requirements.

[0033] In this invention, the instruction misoperation prevention submodule of the safety protection module incorporates a three-level verification mechanism. The first level is identity verification, which verifies the operator's identity through a USB key or biometric identification (fingerprint, face), and only verified personnel can initiate instructions. The second level is parameter verification, which compares the charging power, voltage, and other parameters in the instruction with the rated parameters of the charging pile, and directly rejects instructions that exceed the rated range. The third level is scenario verification, which determines whether the current charging pile is in an idle, charging, or faulty state. For example, the charging start instruction will not be executed in a faulty state. Only after all three levels of verification pass can the instruction be sent to the local control module of the charging pile, significantly reducing the probability of equipment damage or safety accidents caused by misoperation.

[0034] The following two examples further illustrate the specific implementation of this system: Example 1: Application of charging pile clusters in urban business districts This embodiment applies to a cluster of 20 DC fast charging piles in a downtown business district, serving new energy vehicle users in surrounding shopping malls and office buildings. The average daily charging volume is about 800kWh. It focuses on accurate monitoring, intelligent scheduling, and user experience optimization under high load scenarios. The deployment and operation details of each module of the system are as follows.

[0035] I. System Module Deployment and Parameter Configuration In the sensing layer module, each charging pile is equipped with one AC current sensor, one DC voltage sensor, three temperature sensors, one humidity sensor, and one parking space status detection sensor. The current sensor has a sampling frequency of 50Hz and an accuracy of 0.2 class. The probe is connected in series in the charging pile output circuit to collect the peak and effective values ​​of the current. The voltage sensor has a range of 0-1000V, a sampling frequency of 30Hz, and an accuracy of 0.1 class. It is connected in parallel to the DC output terminal to monitor charging voltage fluctuations. The temperature sensors are deployed near the IGBT of the power module, at the battery interface plug, and on the outside of the charging pile cabinet. They have a range of -30℃ to 80℃ and an accuracy of ±0.5℃. Two temperature sensors of the same model are installed at each power module for redundancy. During normal operation, the difference between the two collected data is controlled within ±0.3℃. The humidity sensor is installed inside the cabinet, with a range of 0-100%RH and an accuracy of ±3%RH. The parking space status detection sensor uses an infrared beam device and is installed on both sides of the parking space next to the charging pile to identify the parking space occupancy status. All sensor data, after being processed by Kalman filtering for noise reduction, is converted into RS485 standard digital signals and uploaded to the network transmission layer.

[0036] The network transmission layer module adopts a dual-link architecture of "4G / 5G + Industrial Ethernet". Each charging pile has a built-in 4G / 5G communication unit and an industrial Ethernet interface. The 4G / 5G unit has a transmission rate of 15Mbps and a latency of less than 150ms, which is used for real-time uploading of charging data and receiving control commands. The industrial Ethernet unit is connected to the local gateway through a switch, with a transmission rate of 100Mbps and supports the IEEE 802.3 standard. It is used for batch distribution of firmware upgrade packages and parameter configuration commands. The data compression unit is integrated into the local gateway and uses the LZ77 algorithm to compress the uploaded data. The compression rate is maintained at 65%. During the peak charging periods of 10:00-12:00 and 18:00-20:00 every day, the network bandwidth usage is reduced by 40% compared with the uncompressed data.

[0037] The cloud management platform module is deployed on the operator's private cloud. The real-time monitoring subsystem displays the location distribution of 20 charging piles through a web interface, based on a GIS map. It also displays operating status, charging power, remaining battery power, and fault codes. Operating status includes three types: idle, charging, and fault. Multi-dimensional filtering is supported based on criteria such as "charging power greater than 100kW" and "fault type: overheating." The equipment management subsystem stores charging pile ledger information, including the charging pile number, model, and installation date. A firmware upgrade command is sent to the cluster at 3:00 AM on the 1st of each month. During the upgrade process, 10% of the charging piles are reserved as backups to avoid cluster service interruptions. The charging scheduling subsystem introduces a dynamic load balancing algorithm, using a formula... Calculate the predicted load of a single charging station, where Values ​​0.4 Values ​​0.5 The value is 0.1, representing the average load of a charging pile over the past 7 days. The current real-time load is 80kW. The power is 75kW, and the current ambient temperature is 30℃. The difference between the current ambient temperature and the standard temperature of 25℃, which is 5℃, is used to calculate the result. =0.4×80+0.5×75+0.1×5=32+37.5+0.5=70kW. Based on this, the scheduling subsystem allocates new charging demand to the charging pile. The data analysis subsystem generates daily operation reports, which include single-pile charging volume, cluster utilization rate, and fault occurrence rate. The cluster utilization rate target is set to be no less than 85%, and the fault occurrence rate target is controlled to be no more than 1%.

[0038] The charging pile's local control module uses a 32-bit embedded controller, which can receive cloud commands to execute charging start / stop and power adjustment operations. The power adjustment step size is 1kW, and the adjustment range covers 0-180kW. When an overcurrent exceeding 120A, an overvoltage exceeding 525V, or an overtemperature exceeding 60℃ is detected, the charging circuit is cut off within 100ms, and a fault code is recorded. E01 represents an overcurrent fault, and E02 represents an overtemperature fault. Charging is automatically resumed after the fault is cleared. Based on data collected by the humidity sensor, the control module activates the cabinet dehumidification device when the ambient humidity exceeds 85%RH and activates the heating device when the ambient temperature is below 0℃ to maintain a stable operating environment for the equipment.

[0039] In the user interaction module, the charging pile's local touchscreen is 7 inches with a resolution of 1280×720. It supports displaying charging progress, cost details, and device status. The charging progress is presented as a percentage, and the cost details include the charging unit price, charging time, and total cost. The mobile app supports iOS 12.0 and above and Android 8.0 and above. Users can input their vehicle model (e.g., Model 3), current battery level (e.g., 30%), target battery level (e.g., 80%), and estimated arrival time (e.g., 12:00). The app combines real-time and predicted load data of the charging pile to recommend charging pile number 15 for the 12:10-12:40 time slot. The predicted load of this charging pile is 65kW, and the app calculates the required charging time as 30 minutes and the charging cost as 25 yuan. The QR code interaction unit updates the QR code every 60 seconds. Users can scan the QR code with WeChat or Alipay, link their personal account, and click the "Start Charging" button to trigger the charging process.

[0040] The data storage module consists of 3 main SSD servers and 2 backup HDD servers. Each main SSD server has a capacity of 8TB, and each backup HDD server has a capacity of 10TB. Real-time charging data and equipment operation data from the past 7 days are classified as hot data and stored on the SSD servers, with a data retrieval response time of 0.8 seconds. Historical data and report data from more than 7 days ago are classified as cold data and stored on the HDD servers. When accessing cold data, it is temporarily loaded onto the SSD servers through a data migration unit. The data storage module is configured with a scheduled backup mechanism, performing a full backup at 2:00 AM every day and an incremental backup every hour. The backup data is transmitted to an off-site disaster recovery center 200km away via a dedicated link.

[0041] In the security protection module, the data security submodule uses the AES-256 encryption algorithm to encrypt transmitted and stored data; the access control subsystem divides user permissions into three levels: administrator, maintenance personnel, and ordinary users. Administrators have full-function operation permissions, maintenance personnel can only perform equipment maintenance-related operations, and ordinary users can only query personal charging information; the command misoperation prevention submodule performs three levels of verification. The first level is identity verification, which confirms the operator's identity through administrator fingerprint verification; the second level is parameter verification, which ensures that the issued charging power does not exceed the rated power of the charging pile of 180kW; and the third level is scenario verification, which refuses to execute charging start commands for charging piles in a faulty state.

[0042] II. Data Representation and Interpretation Table 1 Comparison of key indicators between the traditional urban business district system and this system. Comparison indicators Traditional system This invention system Temperature monitoring accuracy ±1.2℃ ±0.3℃ Communication interruption rate 8% 0.5% Charging pile cluster utilization rate 62% 88% Fault handling time 5 minutes 100 milliseconds Average user waiting time 25 minutes 8 minutes Table 1 shows that traditional charging pile management systems are limited by insufficient sensor accuracy in the sensing layer, a single network transmission method, a temperature monitoring deviation of ±1.2℃, and a communication interruption rate of 8%, making it impossible to accurately capture equipment status and ensure data transmission. Charging scheduling lacks scientific algorithm support, with a cluster utilization rate of only 62%. Fault handling relies on manual on-site inspection, taking up to 5 minutes. User interaction lacks a reservation function, with an average waiting time of 25 minutes. The system of this invention improves temperature monitoring accuracy to ±0.3℃ through redundant deployment and self-calibration mechanism of temperature sensors; reduces the communication interruption rate to 0.5% through multi-protocol fusion communication and data compression technology; optimizes resource allocation through dynamic load balancing algorithm, achieving a cluster utilization rate of 88%; shortens the handling time to 100 milliseconds through local fault self-processing function; and effectively reduces user waiting time to 8 minutes through mobile APP reservation function, fully adapting to high-load and high-user-demand scenarios in business districts, achieving a dual improvement in operational efficiency and user experience.

[0043] Example 2: Application of charging piles in highway service areas This embodiment applies to 10 DC fast charging piles in a service area on one side of a highway, serving long-distance new energy vehicles with an average daily charging capacity of approximately 500 kWh. It focuses on communication stability, charging efficiency optimization, and fault warning in remote scenarios, and the system modules are designed to adapt to the characteristics of the service area.

[0044] I. System Module Deployment and Parameter Configuration In the perception layer module, in addition to the temperature sensors deployed on the power module, battery interface plug, and outside the cabinet, an extra temperature sensor is installed at the charging gun cable to monitor the temperature rise of the charging gun cable; the current sensor has a sampling frequency of 50Hz, which can accurately capture the instantaneous current peak during the fast charging process of long-distance vehicles; the parking space status detection sensor combines video recognition technology, with the camera installed on the top of the charging pile to assist the infrared beam detector in identifying the parking space occupancy status and avoid interference from the infrared beam detector in bad weather; all temperature sensors automatically compare with the standard temperature data stored in the cloud every 72 hours. The standard temperature data is set at 25℃. The calibration deviation is calculated and the collected value is corrected by comparing the values. For example, if a temperature sensor collects a value of 26℃, the deviation from the standard temperature of 25℃ is 1℃. Then, the sensor will automatically subtract the deviation value of 1℃ in the next collection to ensure long-term monitoring accuracy.

[0045] The network transmission layer module adopts a "LoRa+4G / 5G" adaptive architecture. The charging piles within the service area are spaced 100 meters apart, and the LoRa communication unit uses an SF7 spreading factor, increasing the transmission rate to 5.5kbps. Two backup charging piles are set 500 meters outside the service area, with a communication distance of 1.5km from the gateway. The LoRa communication unit uses an SF10 spreading factor, achieving a transmission rate of 3kbps. The signal strength detection unit monitors the RSSI value of the LoRa communication signal in real time. When the RSSI value is below -120dBm, it automatically switches to the 4G / 5G communication unit within 1 second. The data compression unit sets the compression rate of uploaded data to 70%, effectively reducing the amount of data during long-distance transmission and minimizing bandwidth usage.

[0046] The load prediction module of the cloud management platform collects multi-dimensional data, including historical load data of charging piles in the past 30 days (where the weekend load is 1.5 times that of weekdays), regional electricity demand data for the next 24 hours (traffic flow on holidays increases by 30% compared to weekdays), ambient temperature data (low temperatures in winter affect charging efficiency), and holiday factors. The load prediction model is built using the LSTM neural network algorithm, with the model training cycle set at 3:00 AM every day, and the number of training samples is no less than 12,000 sets each time. When the predicted load exceeds 80% of the rated total load of the charging pile cluster of 1500kW (i.e., 1200kW), the load prediction module automatically sends an early warning signal to the charging scheduling subsystem. The scheduling subsystem then guides some of the charging demand to two backup charging piles outside the service area in advance to avoid cluster overload.

[0047] The fault early warning module receives current, voltage, and temperature data transmitted from the sensing layer in real time, and sets normal threshold ranges for each parameter. The normal threshold for current is 0-120A, the normal threshold for voltage is 500V±5% (i.e., 475V-525V), and the normal threshold for temperature is -20℃ to 60℃. When the voltage of a charging pile reaches 530V three times consecutively (exceeding the normal voltage threshold by 5V), it is determined that the charging pile has a potential overvoltage fault. The fault early warning module generates an early warning message, which includes the device number FW08, the warning parameter voltage 530V, the warning time 14:30, and the possible cause of the fault, abnormal output voltage. At the same time, the early warning message is pushed to the mobile APP of the maintenance personnel and linked to the equipment maintenance log of the charging pile, displaying the historical overvoltage fault record of the charging pile (occurred once 3 months ago) and the corresponding maintenance plan (replacing the voltage detection module).

[0048] The charging efficiency optimization unit of the charging pile local control module uses the formula Calculate the optimal charging efficiency, where The value is 0.0025. The value is 0.0012. The value is 0.0006. This is the charging voltage. This is the charging current. This refers to the internal power loss of the charging pile (including line loss and component loss). This refers to the temperature difference between the charging pile's power module and the ambient temperature of 25°C; when the charging voltage... 400V, charging current 100A (charging power 40kW), internal power loss It is 1800W, and the power module temperature is 35℃. When (=35-25=10℃), substituting into the formula yields... =0.0025×400×100-0.0012×1800-0.0006×10=100-2.16-0.006=97.834. Based on this calculation result, the control module adjusts the charging voltage to 410V and the charging current to 97.6A to maintain the charging efficiency above 97%.

[0049] The mobile app for the user interaction module has added a "long-distance charging planning" function. Users can enter the starting point and destination of their driving route in the app (e.g., starting point is Beijing and destination is Shanghai). The app will automatically recommend charging stations in service areas along the way and display the real-time load and estimated waiting time of each charging station. The QR code interaction unit has added an anti-tampering verification mechanism. After scanning the QR code, users need to verify the mobile phone verification code. Only after successful verification can the user link their account and start charging, avoiding the risk of QR code theft in long-distance scenarios.

[0050] The data storage module adopts a hot and cold data separation storage strategy. Charging records from the past 7 days are classified as hot data, which is accessed frequently and stored on a high-performance SSD server. Historical data and report data older than 7 days are classified as cold data and stored on a low-cost HDD server. Cold data older than 3 years is automatically compressed and archived with a compression rate of no less than 85%. In addition to the daily full backup at 2:00 AM and hourly incremental backup, a new "instant backup after charging peak" mechanism has been added, which performs an incremental backup immediately after the daily charging peak ends at 6:00 PM to further reduce the risk of data loss.

[0051] The safety protection module's command misoperation prevention submodule adds a "charging gun connection status" judgment during the scenario verification process. When the charging gun is not connected to the vehicle, the charging start command is directly rejected. The data security submodule encrypts user license plate information, charging records, and other private data. Only personnel with administrator privileges can view the complete data, and maintenance personnel and ordinary users cannot access the private information.

[0052] II. Data Representation and Interpretation Table 2 Comparison of key indicators between the traditional system and this system in highway service areas Comparison indicators Traditional system This invention system Communication rate in remote areas 75% 99.8% Charging efficiency 82% 95% Fault warning accuracy 60% 92% Data retrieval speed 3.5 seconds 0.9 seconds Safety accident rate 3% 0.1% Table 2 shows that traditional charging pile management systems in highway service area scenarios suffer from several drawbacks. First, due to poor communication conditions in remote areas, the communication rate is only 75%, and data transmission is frequently interrupted. Second, charging efficiency is not optimized based on equipment status and environmental factors, remaining at 82%, resulting in significant energy waste. Third, fault warnings rely solely on a single parameter threshold, with an accuracy rate of only 60%, leading to frequent missed and false alarms. Fourth, data storage does not differentiate between hot and cold data, resulting in a retrieval speed of 3.5 seconds, making it impossible to quickly obtain the required information. Fifth, safety protection measures are simplistic, with a safety accident rate of 3%, posing risks to equipment damage and personnel safety. This invention's system utilizes LoRa adaptive spread spectrum technology and a 4G / 5G dynamic switching mechanism to increase the communication rate in remote areas to 99.8%, ensuring stable data transmission. The charging efficiency optimization unit adjusts the operating status based on multi-dimensional parameters, achieving a charging efficiency of 95% and reducing energy waste. The fault warning module integrates multi-parameter monitoring and historical data, increasing accuracy to 92% and proactively mitigating fault risks. A cold / hot data separation storage strategy reduces data retrieval speed to 0.9 seconds, improving data access efficiency. Three-level verification and privacy encryption measures reduce the security incident rate to 0.1%, fully adapting to the "remote and high reliability requirements" of highway service areas, ensuring charging safety and efficiency for long-distance users.

[0053] Reference Figure 2This figure visually compares the distribution and percentage of security incident types between the traditional system and the system of this invention. In the traditional system, misoperation accounts for as high as 40%, and data leakage accounts for 25%, representing core security risks, mainly due to the lack of multi-level verification and data encryption mechanisms. The present invention, through three-level identity, parameter, and scenario verification, reduces the proportion of misoperation to 5%, and the use of the AES-256 encryption algorithm reduces the data leakage rate to only 3%. The proportion of incidents such as equipment overheating and communication interruptions is also significantly reduced. The remaining 9% are non-system-related incidents such as line aging. The comprehensive upgrade of the security protection module blocks core security risks at the source, significantly improving the safety of charging pile operation.

[0054] Reference Figure 3 This figure illustrates the difference in handling efficiency between the traditional system and the system of this invention under different fault types. The traditional system relies on manual on-site inspection and cloud command issuance, with handling time for various faults exceeding 4 minutes, and communication interruption faults even reaching 320 seconds, which can easily lead to further equipment damage. The present invention, through the fault self-processing capability of the local control module, cuts off the circuit and records the fault code within 100ms after detecting a fault, without waiting for cloud commands. The handling time for various faults is controlled within 110 seconds, and the fastest handling time for over-temperature faults is only 90 seconds, which significantly shortens the fault response cycle, reduces the safety risks caused by equipment failures, and improves the operational continuity of the charging pile cluster.

[0055] Reference Figure 4 This graph clearly illustrates the changing trend of charging efficiency during continuous operation of a charging pile. Traditional systems do not consider factors such as equipment temperature rise and internal power loss, resulting in a continuous decline in charging efficiency with increasing operating time, reaching only 77% after 5 hours, leading to significant energy waste. This invention, through a charging efficiency optimization unit, dynamically adjusts operating parameters based on voltage, current, power loss, and equipment temperature. Even after 5 hours of continuous operation, the charging efficiency remains above 95%, with only a slight decrease, effectively reducing energy loss during charging and improving the economic efficiency of charging pile operation. It is particularly suitable for scenarios requiring long-term continuous operation, such as highway service areas and commercial districts.

[0056] Reference Figure 5 This figure visually illustrates the difference in communication rates between traditional systems and the system of this invention under different application scenarios. Traditional systems, limited by a single communication scheme, experience significant fluctuations in communication rate depending on the scenario. In remote scenarios such as highway service areas and suburban communities, the communication rate is only 70%-75%, failing to meet the requirements for stable data transmission. This invention, through a multi-protocol fusion communication architecture, LoRa adaptive spread spectrum technology, and a dynamic signal strength switching mechanism, achieves a stable communication rate of over 99% in all scenarios, reaching even 99.8% in highway service areas. This completely solves the communication interruption problem in remote scenarios, ensuring the continuity of charging pile data uploads and command issuance in different scenarios.

[0057] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A remote monitoring and intelligent management system for charging piles based on the Internet of Things, characterized in that, Includes the following modules: The sensing layer module integrates multiple types of sensors for detecting AC current, DC voltage, temperature, humidity, and parking space status. All sensor data is preprocessed and converted into standard digital signals. The network transmission layer module adopts a multi-protocol converged communication architecture, including 4G / 5G, LoRa and industrial Ethernet units, and a data compression unit integrating the LZ77 compression algorithm; The cloud management platform module is equipped with multi-dimensional functional subsystems. The real-time monitoring subsystem displays relevant equipment information in a visual manner, the equipment management subsystem completes the configuration function, the charging scheduling subsystem dynamically allocates charging resources, and the data analysis subsystem generates operation reports. The charging pile local control module has a built-in embedded controller, which can perform charging start / stop and power adjustment operations, and has the ability to handle local faults and adapt to environmental temperature and humidity. The user interaction module includes a local touch screen, a mobile APP, and a QR code interaction unit. The touch screen is no smaller than 7 inches. The APP supports dual systems and multiple practical functions. The QR code interaction unit uses dynamic QR code technology. The data storage module adopts a distributed database architecture, consisting of 3 main servers and 2 backup servers, supports multi-dimensional retrieval, and is configured with scheduled full and incremental backup mechanisms. The security protection module includes sub-modules for data security, access control, and prevention of misoperation of commands. It adopts encryption algorithms, has three levels of user permissions, and has a dual verification mechanism for command execution.

2. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, It also includes a load prediction module, which is deployed on the cloud management platform module. By collecting historical load data of charging piles, regional electricity demand data, ambient temperature data and holiday factors, it uses the LSTM neural network algorithm to build a load prediction model. The prediction results are displayed every 15 minutes as a time node, and output the predicted load value of the charging pile cluster for each time period in the next 24 hours.

3. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, It also includes a fault warning module, which works in conjunction with the perception layer module and the cloud management platform module to receive data transmitted from the perception layer in real time and set normal threshold ranges for each parameter. When data is detected to exceed the threshold three times consecutively or exceed the threshold by 15% in a single instance, it is determined to be a potential fault, generates a fault warning message, and pushes the warning message to the mobile APP of the maintenance personnel, and links it to the equipment maintenance log.

4. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, The temperature sensor in the sensing layer module adopts a dual-redundancy deployment design. Two temperature sensors of the same model are installed at each monitoring point. Under normal operation, the difference between the data collected by the two sensors is controlled within ±0.3℃. If the difference exceeds ±0.5℃, one of the sensors is determined to be abnormal, the abnormal sensor data is automatically blocked, and the backup sensor data is enabled. At the same time, the temperature sensor has a self-calibration function.

5. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, The charging scheduling subsystem of the cloud management platform module introduces a dynamic load balancing algorithm, which is implemented through formulas. Calculate the predicted load of a single charging station, where Predict the load on the charging station for the next 15 minutes. For historical load, For real-time load, For ambient temperature, This represents the average load of the charging station over the same period in the past 7 days. For the current real-time load, This represents the deviation between the current ambient temperature and the standard temperature.

6. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, The LoRa low-power communication unit of the network transmission layer module adopts adaptive spread spectrum technology, which dynamically adjusts the spread spectrum factor according to the communication distance. When the communication distance is less than 1km, the SF7 spread spectrum factor is used, and the transmission rate is increased to 5.5kbps; when the communication distance is 1-2km, the SF10 spread spectrum factor is used, and the transmission rate is 3kbps; when the communication distance is 2-3km, the SF12 spread spectrum factor is used, and the transmission rate is 1.2kbps. At the same time, a signal strength detection unit is integrated to monitor the RSSI value of the LoRa communication signal in real time.

7. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, A charging efficiency optimization unit has been added to the local control module of the charging pile, using the formula... Calculate the optimal charging efficiency, where To achieve the optimal charging efficiency for charging stations. For positive power, Due to the impact of wear and tear, Due to the influence of equipment temperature, This is the charging voltage. This is the charging current. This refers to the internal power loss of the charging pile. This is the temperature difference between the power module of the charging pile and the ambient temperature.

8. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, The mobile app for the user interaction module has added a charging demand prediction function. After the user inputs the vehicle model, current battery level, target battery level, and estimated arrival time, the app combines real-time and predicted load data of the charging station to recommend the optimal charging time and location, and calculates the required charging time and cost. It also supports a charging reservation and locking function.

9. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, The data storage module adopts a hot and cold data separation storage strategy. The real-time charging data and equipment operation data of nearly 7 days are divided into hot data and stored in a high-performance SSD server; historical data and report data are divided into cold data and stored in a low-cost HDD server. When accessing cold data, it is temporarily loaded to the SSD server through the data migration unit; at the same time, data lifecycle management function is configured.

10. The IoT-based remote monitoring and intelligent management system for charging piles according to claim 1, characterized in that, The safety protection module's command misoperation prevention submodule incorporates a three-level verification mechanism. The first level is identity verification, which verifies the operator's identity through a USB key or biometric identification. The second level is parameter verification, which compares the parameters in the command with the charging pile's rated parameters, and directly rejects commands that exceed the rated range. The third level is scene verification, which determines the current status of the charging station; Only after all three levels of verification are passed can the instruction be sent to the local control module of the charging pile.

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