Charging pile remote safety management system based on Internet of Things

Abstract

The invention discloses a charging pile remote safety management system based on the Internet of Things, which relates to the technical field of data monitoring control and comprises an Internet of Things data acquisition module, a live analysis module, a charging scheduling module, a load analysis module and a remote control module. According to the method, the total number of the to-be-charged vehicles is obtained by deploying the Internet of Things sensor array, the actual port charging efficiency of the vehicles is combined, the vehicle charging prediction demand value can be generated, the power grid constrained charging scheduling optimization model is constructed, and the total number of the to-be-charged vehicles, the total number of the charging piles and the total capacity of the power grid are comprehensively considered; comprehensive charging power grid load pressure analysis can be carried out, the optimal charging threshold value of the charging pile is generated, the total active power of the whole charging station cluster is monitored and calculated in real time, the total active power is compared with the safety capacity threshold value provided by the power grid side, the optimal charging threshold value of the charging pile and load early warning signals of different grades are received, and the safety of the charging station cluster is improved. And remote and accurate control of the charging pile is realized.

Description

Technical Field

[0001] This invention relates to the field of data monitoring and control technology, and in particular to a remote safety management system for charging piles based on the Internet of Things. Background Technology

[0002] With the rapid development of the electric vehicle industry, the number of charging piles, as a crucial infrastructure for electric vehicles, is constantly increasing. The emergence of IoT-based remote safety management systems for charging piles provides strong technical support for the effective management and safe operation of charging piles, greatly improving the user charging experience and the efficiency of charging pile operation. Currently, IoT technology is widely used in the field of charging pile management. Through the IoT, charging piles can communicate with the back-end management system in real time, enabling functions such as remote monitoring, fault diagnosis, and billing management. For example, sensor technology can be used to collect parameters such as voltage, current, and temperature of the charging pile in real time, uploading them to the back-end system for analysis and processing, so as to promptly identify potential safety hazards and take corresponding measures. Simultaneously, through intelligent billing systems, reasonable charges are calculated based on charging volume and time, facilitating user settlement.

[0003] However, there is a lack of effective countermeasures for the special situation of excessive charging vehicles during holidays. The existing system mainly focuses on equipment monitoring and basic billing management under normal conditions, and its load balancing and charging volume control functions during peak charging periods are relatively weak. It lacks intelligent charging scheduling algorithms and real-time power allocation strategies, and cannot dynamically adjust the charging volume of charging piles according to actual charging demand and grid conditions to ensure the safe operation and charging efficiency of charging piles. To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0004] The purpose of this invention is to: obtain the total number of vehicles to be charged by deploying an IoT sensor array, and generate a predicted demand value for vehicle charging by combining the actual port charging efficiency of the vehicles; construct a grid-constrained charging scheduling optimization model; comprehensively consider the total number of vehicles to be charged, the total number of charging piles, and the total grid capacity; perform a comprehensive analysis of the charging grid load pressure; generate the optimal charging threshold for the charging piles; monitor and calculate the total active power of the entire charging station cluster in real time and compare it with the safe capacity threshold provided by the grid side; receive the optimal charging threshold of the charging piles and load warning signals of different levels; and realize remote and precise control of the charging piles.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a remote safety management system for charging piles based on the Internet of Things, comprising an Internet of Things data acquisition module, a real-time analysis module, a charging scheduling module, a load analysis module, and a remote control module; The IoT data acquisition module collects vehicle characteristic data, charging pile grid load data, and charging pile parameters in real time through an IoT sensor array deployed on the charging pile. It also identifies vehicle charging needs based on vehicle data acquired by camera equipment to obtain the total number of vehicles waiting to be charged. The real-time analysis module analyzes the charging demand based on the total number of vehicles waiting to be charged and the number of charging piles in normal conditions. It also analyzes the charging efficiency of the actual charging ports of the vehicles to generate predicted charging demand values ​​for the vehicles. Furthermore, it classifies the charging type into different levels based on the charging load of the vehicles, resulting in differentiated charging permission labels of high, medium, and low levels. The charging scheduling module constructs a charging scheduling optimization model constrained by the power grid. Based on the total number of vehicles to be charged, combined with the total number of charging piles and the total capacity of the power grid, it performs a charging grid load pressure analysis, calculates the theoretical charging efficiency in combination with the charging port standard, and generates the optimal charging threshold for the charging pile. The load analysis module monitors and calculates the total active power of the entire charging station cluster in real time and compares it with the safe capacity threshold provided by the grid. When the monitored real-time total power approaches the safe threshold, it generates load warning signals of different levels. The remote control module receives the optimal charging threshold and load warning signals of different levels from the charging pile, compiles them into control commands that the charging pile can execute, and remotely sends them out. It sets a unified control power value for each charging vehicle, and when the charging amount is detected to reach the preset threshold, it triggers automatic power-off protection to stop the power supply.

[0006] Further, vehicle charging demand is identified to obtain the total number of vehicles waiting to be charged. The specific process is as follows: A composite sensor array, including power parameter sensors, environmental sensors, and camera equipment, is deployed in the charging pile body and surrounding area. The system monitors the input voltage, current, and power of charging piles using power parameter sensors, and establishes a historical data storage and analysis database to store the historical operating data of charging piles. The data is divided and stored according to peak, mid-peak, and off-peak periods, and the power consumption and charging time limit of charging piles during holidays are marked. Based on the location map of the charging piles, the charging range is defined with the charging piles as the center point. The charging waiting route is planned and road signs are set. The charging route is planned according to different times, and a real-time charging road planning map of the charging piles is generated, and the number of vehicles waiting normally, the number of vehicles waiting under warning, and the number of vehicles waiting during peak hours are marked. Based on the charging road planning map, the number of waiting vehicles within the planned route is counted to obtain the total number of all vehicles in the waiting area. By collecting images of vehicle types and license plates using camera equipment, the system can summarize the number of vehicles that have entered the charging area but have not yet completed charging in real time, and dynamically update the total number of vehicles waiting to be charged based on the occupancy status of the charging piles.

[0007] Furthermore, based on the total number of vehicles awaiting charging and the number of charging stations in normal use, a charging demand analysis is conducted, specifically including the following: S100: Obtain historical charging pile usage records, vehicle charging time, and charging amount, and perform historical electricity consumption statistical analysis to obtain the average charging demand per vehicle. S101. Total number of vehicles to be charged. Calculate the basic charging demand base. Basic demand base = total number of vehicles to be charged × average charging demand per vehicle. The capacity pressure coefficient is calculated using the formula: Capacity pressure coefficient = Total number of vehicles waiting to be charged / Number of charging piles available under normal conditions; Obtain the theoretical maximum charging power of the currently queued vehicles, and average the theoretical maximum charging power of all vehicles waiting to be charged to obtain the average charging power of the current queue. Forecasted vehicle charging demand = Baseline demand × Capacity pressure coefficient × Efficiency correction factor; S102. Based on the actual charging amount at the vehicle's ports, obtain differentiated charging permission labels of high, medium, and low levels.

[0008] Furthermore, a grid-constrained charging scheduling optimization model is constructed, and the specific process is as follows: S200, Set the constraints of the charging scheduling optimization model with grid constraints, including grid security, charging pile physical constraints, and vehicle demand constraints; S201. Input parameter preparation and standardization: Calculate the real-time load pressure coefficient K, the formula is: K=(total number of vehicles to be charged × average charging demand) / (number of available charging piles × total grid capacity); K comprehensively reflects the ratio of vehicle demand to supply capacity. Based on vehicle charging port standards, it maps the theoretical maximum charging power of different models to a standardized charging efficiency coefficient. S202. Construct an optimization function with the charging threshold as the decision variable. The function is constructed by the real-time load pressure coefficient, the total number of vehicles to be charged, and the safe peak load of the power grid. N = F × (1 - K) × f, where N is the optimal charging threshold for the current vehicle, F is the highest safe charge, and f is the lowest safe charge. The optimal charging threshold for vehicles is the output dimension of the grid-constrained charging scheduling optimization model.

[0009] Furthermore, the charging scheduling module also includes a real-time monitoring unit for real-time monitoring of the charging pile's temperature, employing a sliding window algorithm to smooth the temperature data, and setting a dynamic safety threshold. Normal range: Battery pack surface temperature ≤45℃, interface temperature ≤60℃; Warning threshold: When the temperature exceeds the normal range but does not reach the danger value, a yellow warning is triggered, and power rollback is initiated; Danger threshold: When the charging pile temperature is ≥55℃ and the interface temperature is ≥70℃, a red warning is triggered, charging is immediately stopped and the cooling system is activated.

[0010] Furthermore, the total active power of the entire charging station cluster is monitored and calculated in real time, and compared with the safe capacity threshold provided by the grid side, specifically including the following: S300: Obtain the charging efficiency of the charging pile, accumulate the power data of the charging pile, and calculate the total active power in combination with power factor correction. S301. Compare the real-time total active power with the grid safety capacity threshold, calculate the load rate, and classify the warning level according to the load rate, including the green safety zone: load rate < 80%, no warning is required, and normal charging is maintained. Yellow warning zone: 80%≤load rate<90%, triggering a level 3 warning, initiating power rollback of charging piles, and guiding users to charge during off-peak hours; Orange alert zone: 90% ≤ load rate < 95%, triggering a level 2 alert, restricting new user access, and prioritizing charging for high-priority vehicles; Red Alert Zone: Load rate ≥ 95%, triggering Level 1 alert, initiating emergency current limiting strategy, forcibly reducing the power of all charging piles, and triggering automatic power-off protection.

[0011] Furthermore, the remote control module also includes an early warning control unit, the details of which are as follows: It is responsible for safely and reliably sending charging threshold commands to designated charging piles, receiving status reports from charging piles, and verifying whether the commands have been executed correctly. After receiving control commands from the charging pile, it adjusts the output power and uses sensors to feed back the actual charging amount, grid changes, and temperature parameters to form a closed-loop control. It monitors the changes in total active power in real time, verifies the effectiveness of early warning measures, and further upgrades the early warning level if the load rate continues to rise.

[0012] Furthermore, it also includes a safety execution and emergency module, which monitors the real-time charging process of each charging station. When the vehicle's battery level reaches its dynamically adjusted actual limit, it sends a power supply stop command to the charging station and completes the settlement. At the same time, it performs risk assessment based on the vehicle's remaining range and the load information of the surrounding charging network. For vehicles that are detected to have triggered the range safety threshold, it initiates an emergency exemption process and authorizes them to temporarily increase their charging limit.

[0013] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This IoT-based remote safety management system for charging piles, through the deployment of an IoT sensor array, can collect real-time vehicle characteristic data, charging pile grid load data, and charging pile parameters. Based on camera equipment, it acquires vehicle data and identifies vehicle charging needs, accurately determining the total number of vehicles waiting to be charged. By comparing the total number of vehicles waiting to be charged with the number of charging piles in normal use, and combining this with the actual port charging efficiency of the vehicles, it can generate predicted vehicle charging demand values. Based on the vehicle charging load, it classifies charging types into charging levels, obtaining differentiated charging permission tags for different levels. It constructs a grid-constrained charging scheduling optimization model, comprehensively considering the total number of vehicles waiting to be charged, the total number of charging piles, and the total grid capacity. This allows for comprehensive analysis of the charging grid load pressure, generating the optimal charging threshold for each charging pile. It monitors and calculates the total active power of the entire charging station cluster in real time and compares it with the safe capacity threshold provided by the grid side, enabling timely understanding of the charging station's power consumption. It receives the optimal charging threshold and load warning signals of different levels from the charging piles, compiles them into executable control commands for remote distribution, and achieves remote and precise control of the charging piles. Attached Figure Description

[0014] Figure 1 A schematic diagram of the overall structure of the system steps of the present invention is shown. Detailed Implementation

[0015] 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.

[0016] Example 1: like Figure 1 As shown, a remote safety management system for charging piles based on the Internet of Things (IoT) is characterized by including an IoT data acquisition module, a real-time analysis module, a charging scheduling module, a load analysis module, and a remote control module. The IoT data acquisition module collects vehicle characteristic data, charging pile grid load data, and charging pile parameters in real time through an IoT sensor array deployed on the charging pile. It also identifies vehicle charging needs based on vehicle data acquired by camera equipment to obtain the total number of vehicles waiting to be charged. The real-time analysis module analyzes the charging demand based on the total number of vehicles waiting to be charged and the number of charging piles in normal conditions. It also analyzes the charging efficiency of the actual charging ports of the vehicles to generate predicted charging demand values ​​for the vehicles. Furthermore, it classifies the charging type into different levels based on the charging load of the vehicles, resulting in differentiated charging permission labels of high, medium, and low levels. The charging scheduling module constructs a charging scheduling optimization model constrained by the power grid. Based on the total number of vehicles to be charged, combined with the total number of charging piles and the total capacity of the power grid, it performs a charging grid load pressure analysis, calculates the theoretical charging efficiency in combination with the charging port standard, and generates the optimal charging threshold for the charging pile. The load analysis module monitors and calculates the total active power of the entire charging station cluster in real time and compares it with the safe capacity threshold provided by the grid. When the monitored real-time total power approaches the safe threshold, it generates load warning signals of different levels. The remote control module receives the optimal charging threshold and load warning signals of different levels from the charging pile, compiles them into control commands that the charging pile can execute, and remotely sends them out. It sets a unified control power value for each charging vehicle, and when the charging amount is detected to reach the preset threshold, it triggers automatic power-off protection to stop the power supply.

[0017] The IoT data acquisition module acquires key data in real time and accurately: By deploying an IoT sensor array, it can collect vehicle characteristic data, charging pile grid load data, and charging pile parameters in real time, providing a comprehensive and accurate data foundation for subsequent system analysis. Vehicle characteristic data can help understand the charging needs of different vehicle models; charging pile grid load data helps to grasp the grid operation status; and charging pile parameters can reflect the working status of the charging pile itself and promptly detect potential faults.

[0018] Intelligent identification of charging needs: Based on the acquisition of vehicle data by camera equipment and the identification of vehicle charging needs, the total number of vehicles waiting to be charged can be accurately obtained, which provides an important basis for subsequent scheduling and planning, helps to rationally allocate resources, and avoids situations where charging resources are insufficient or wasted. The real-time analysis module accurately analyzes charging demand: by comparing the total number of vehicles waiting to be charged with the number of charging piles in normal use, and combining the actual port charging efficiency of the vehicles, it can generate a predicted value of vehicle charging demand. It can predict future charging demand in advance so as to make preparations in advance, such as adjusting the configuration of charging resources and arranging personnel for maintenance, thereby improving the response speed and quality of charging services. Implement differentiated charging management: Based on the vehicle's charging load, the charging type is divided into charging levels to obtain different levels of differentiated charging permission tags. This method can reasonably allocate charging for different vehicles according to the actual situation. For example, vehicles that need to travel urgently can be given higher-level charging permissions to ensure their fast charging, thereby improving the flexibility and practicality of the entire charging system and enhancing user satisfaction. The charging scheduling module optimizes charging scheduling by constructing a grid-constrained charging scheduling optimization model. This model comprehensively considers the total number of vehicles waiting to be charged, the total number of charging piles, and the total grid capacity, enabling a comprehensive analysis of the charging grid load pressure. Through this analysis, the optimal charging threshold for each charging pile is generated. This allows for the full utilization of charging pile resources while ensuring the safe and stable operation of the grid, achieving efficient scheduling of vehicle charging, avoiding the impact on the grid caused by excessive charging load, and improving the utilization rate of charging piles. Ensuring coordinated operation between the power grid and charging piles: Calculating theoretical charging efficiency based on charging port standards ensures that the charging process is carried out in compliance with standards, guaranteeing coordinated operation between charging facilities and the power grid, extending the service life of charging piles and power grid equipment, and reducing operation and maintenance costs; The load analysis module provides real-time monitoring and early warning: It monitors and calculates the total active power of the entire charging station cluster in real time and compares it with the safe capacity threshold provided by the power grid, enabling timely understanding of the charging station's power consumption. When the monitored real-time total power approaches the safe threshold, it generates load warning signals of different levels, providing early warning to power grid management and charging station operators, giving them sufficient time to take measures such as adjusting charging plans and activating backup power supplies, effectively preventing power grid overload safety accidents and ensuring the stable operation of the power grid. Preventing power system failures: Early detection of potential power load problems helps to adjust and optimize power resources in a timely manner, prevent damage to power equipment or power outages caused by overload, ensure the continuity and reliability of charging services, and reduce inconvenience and losses to users caused by power problems. The remote control module receives the optimal charging threshold and load warning signals of different levels from the charging pile, and compiles them into control commands executable by the charging pile for remote distribution, enabling remote and precise control of the charging pile. This eliminates the need for on-site manual operation, significantly improving management efficiency and reducing labor costs. Simultaneously, it can promptly respond to system scheduling and warning commands, ensuring the charging process proceeds according to the predetermined strategy.

[0019] Safety power-off protection: A unified control power value is set for each charging vehicle. When the charging amount is detected to reach the preset threshold, the automatic power-off protection is triggered to stop the power supply. This function effectively prevents the vehicle from overcharging, avoids battery damage and fire safety hazards caused by overcharging, ensures the safety of the charging process, and protects the property and personal safety of users.

[0020] The process involves identifying vehicle charging needs to determine the total number of vehicles requiring charging. A composite sensor array, including power parameter sensors, environmental sensors, and camera equipment, is deployed in the charging pile body and surrounding area. The system monitors the input voltage, current, and power of charging piles using power parameter sensors, and establishes a historical data storage and analysis database to store the historical operating data of charging piles. The data is divided and stored according to peak, mid-peak, and off-peak periods, and the power consumption and charging time limit of charging piles during holidays are marked. Based on the location map of the charging piles, the charging range is defined with the charging piles as the center point. The charging waiting route is planned and road signs are set. The charging route is planned according to different times, and a real-time charging road planning map of the charging piles is generated, and the number of vehicles waiting normally, the number of vehicles waiting under warning, and the number of vehicles waiting during peak hours are marked. Based on the charging road planning map, the number of waiting vehicles within the planned route is counted to obtain the total number of all vehicles in the waiting area. By collecting images of vehicle types and license plates using camera equipment, the system can summarize the number of vehicles that have entered the charging area but have not yet completed charging in real time, and dynamically update the total number of vehicles waiting to be charged based on the occupancy status of the charging piles.

[0021] Based on the total number of vehicles waiting to be charged and the number of charging stations in normal use, a charging demand analysis is conducted, specifically including the following: S100: Obtain historical charging pile usage records, vehicle charging time, and charging amount, and perform historical electricity consumption statistical analysis to obtain the average charging demand per vehicle. S101. Total number of vehicles to be charged. Calculate the basic charging demand base. Basic demand base = total number of vehicles to be charged × average charging demand per vehicle. The capacity pressure coefficient is calculated using the formula: Capacity pressure coefficient = Total number of vehicles waiting to be charged / Number of charging piles available under normal conditions; Obtain the theoretical maximum charging power of the currently queued vehicles, and average the theoretical maximum charging power of all vehicles waiting to be charged to obtain the average charging power of the current queue. Forecasted vehicle charging demand = Baseline demand × Capacity pressure coefficient × Efficiency correction factor; S102. Based on the actual charging amount at the vehicle's ports, obtain differentiated charging permission labels of high, medium, and low levels.

[0022] The specific process for constructing a grid-constrained charging scheduling optimization model is as follows: S200, Set the constraints of the charging scheduling optimization model with grid constraints, including grid security, charging pile physical constraints, and vehicle demand constraints; S201. Input parameter preparation and standardization: Calculate the real-time load pressure coefficient K, the formula is: K=(total number of vehicles to be charged × average charging demand) / (number of available charging piles × total grid capacity); K comprehensively reflects the ratio of vehicle demand to supply capacity. Based on vehicle charging port standards, it maps the theoretical maximum charging power of different models to a standardized charging efficiency coefficient. S202. Construct an optimization function with the charging threshold as the decision variable. The function is constructed by the real-time load pressure coefficient, the total number of vehicles to be charged, and the safe peak load of the power grid. N = F × (1 - K) × f, where N is the optimal charging threshold for the current vehicle, F is the highest safe charge, and f is the lowest safe charge. The optimal charging threshold for vehicles is the output dimension of the grid-constrained charging scheduling optimization model.

[0023] The charging scheduling module also includes a real-time monitoring unit for real-time monitoring of the charging pile's temperature. It employs a sliding window algorithm to smooth the temperature data and sets dynamic safety thresholds. Normal range: Battery pack surface temperature ≤45℃, interface temperature ≤60℃; Warning threshold: When the temperature exceeds the normal range but does not reach the danger value, a yellow warning is triggered, and power rollback is initiated; Danger threshold: When the charging pile temperature is ≥55℃ and the interface temperature is ≥70℃, a red warning is triggered, charging is immediately stopped and the cooling system is activated.

[0024] The total active power of the entire charging station cluster is monitored and calculated in real time, and compared with the safe capacity threshold provided by the power grid. Specifically, this includes the following: S300: Obtain the charging efficiency of the charging pile, accumulate the power data of the charging pile, and calculate the total active power in combination with power factor correction. S301. Compare the real-time total active power with the grid safety capacity threshold, calculate the load rate, and classify the warning level according to the load rate, including the green safety zone: load rate < 80%, no warning is required, and normal charging is maintained. Yellow warning zone: 80%≤load rate<90%, triggering a level 3 warning, initiating power rollback of charging piles, and guiding users to charge during off-peak hours; Orange alert zone: 90% ≤ load rate < 95%, triggering a level 2 alert, restricting new user access, and prioritizing charging for high-priority vehicles; Red Alert Zone: Load rate ≥ 95%, triggering Level 1 alert, initiating emergency current limiting strategy, forcibly reducing the power of all charging piles, and triggering automatic power-off protection.

[0025] The remote control module also includes an early warning control unit, the details of which are as follows: It is responsible for safely and reliably sending charging threshold commands to designated charging piles, receiving status reports from charging piles, and verifying whether the commands have been executed correctly. After receiving control commands from the charging pile, it adjusts the output power and uses sensors to feed back the actual charging amount, grid changes, and temperature parameters to form a closed-loop control. It monitors the changes in total active power in real time, verifies the effectiveness of early warning measures, and further upgrades the early warning level if the load rate continues to rise.

[0026] It also includes a safety execution and emergency module, which monitors the real-time charging process of each charging pile. When the vehicle's battery level reaches its dynamically adjusted actual limit, it sends a power supply stop command to the charging pile and completes the settlement. At the same time, it makes a risk assessment based on the vehicle's remaining range and the load information of the surrounding charging network. For vehicles that are detected to have triggered the range safety threshold, it initiates an emergency exemption process and authorizes them to temporarily increase their charging limit. The risk assessment is that the remaining range of the vehicle's battery power is greater than the distance to the next service charging station.

[0027] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0028] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. In the two embodiments provided in this application, it should be understood that the disclosed apparatus and system can be implemented in other ways; for example, the apparatus embodiments described above are merely illustrative, and the division of modules is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; furthermore, the coupling or direct coupling or communication connection between the shown or discussed mutuals can be through some interfaces, and the indirect coupling or communication connection between the apparatus or modules can be electrical, mechanical or other forms. The above description is only a preferred embodiment 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 safety management system for charging piles based on the Internet of Things, characterized in that, It includes an IoT data acquisition module, a real-time analysis module, a charging scheduling module, a load analysis module, and a remote control module; The IoT data acquisition module collects vehicle characteristic data, charging pile grid load data, and charging pile parameters in real time through an IoT sensor array deployed on the charging pile. It also identifies vehicle charging needs based on vehicle data acquired by camera equipment to obtain the total number of vehicles waiting to be charged. The real-time analysis module analyzes the charging demand based on the total number of vehicles waiting to be charged and the number of charging piles in normal conditions. It also analyzes the charging efficiency of the actual charging ports of the vehicles to generate predicted charging demand values ​​for the vehicles. Furthermore, it classifies the charging type into different levels based on the charging load of the vehicles, resulting in differentiated charging permission labels of high, medium, and low levels. The charging scheduling module constructs a charging scheduling optimization model constrained by the power grid. Based on the total number of vehicles to be charged, combined with the total number of charging piles and the total capacity of the power grid, it performs a charging grid load pressure analysis, calculates the theoretical charging efficiency in combination with the charging port standard, and generates the optimal charging threshold for the charging pile. The load analysis module monitors and calculates the total active power of the entire charging station cluster in real time and compares it with the safe capacity threshold provided by the grid. When the monitored real-time total power approaches the safe threshold, it generates load warning signals of different levels. The remote control module receives the optimal charging threshold and load warning signals of different levels from the charging pile, compiles them into control commands that the charging pile can execute, and remotely sends them out. It sets a unified control power value for each charging vehicle, and when the charging amount is detected to reach the preset threshold, it triggers automatic power-off protection to stop the power supply.

2. The IoT-based remote safety management system for charging piles according to claim 1, characterized in that, The process involves identifying vehicle charging needs to determine the total number of vehicles requiring charging. A composite sensor array, including power parameter sensors, environmental sensors, and camera equipment, is deployed in the charging pile body and surrounding area. The system monitors the input voltage, current, and power of charging piles using power parameter sensors, and establishes a historical data storage and analysis database to store the historical operating data of charging piles. The data is divided and stored according to peak, mid-peak, and off-peak periods, and the power consumption and charging time limit of charging piles during holidays are marked. Based on the location map of the charging piles, the charging range is defined with the charging piles as the center point. The charging waiting route is planned and road signs are set. The charging route is planned according to different times, and a real-time charging road planning map of the charging piles is generated, and the number of vehicles waiting normally, the number of vehicles waiting under warning, and the number of vehicles waiting during peak hours are marked. Based on the charging road planning map, the number of waiting vehicles within the planned route is counted to obtain the total number of all vehicles in the waiting area. By collecting images of vehicle types and license plates using camera equipment, the system can summarize the number of vehicles that have entered the charging area but have not yet completed charging in real time, and dynamically update the total number of vehicles waiting to be charged based on the occupancy status of the charging piles.

3. The IoT-based remote safety management system for charging piles according to claim 1, characterized in that, Based on the total number of vehicles waiting to be charged and the number of charging stations in normal use, a charging demand analysis is conducted, specifically including the following: S100: Obtain historical charging pile usage records, vehicle charging time, and charging amount, and perform historical electricity consumption statistical analysis to obtain the average charging demand per vehicle. S101. Total number of vehicles to be charged. Calculate the basic charging demand base. Basic demand base = total number of vehicles to be charged × average charging demand per vehicle. The capacity pressure coefficient is calculated using the formula: Capacity pressure coefficient = Total number of vehicles waiting to be charged / Number of charging piles available under normal conditions; Obtain the theoretical maximum charging power of the currently queued vehicles, and average the theoretical maximum charging power of all vehicles waiting to be charged to obtain the average charging power of the current queue. Forecasted vehicle charging demand = Baseline demand × Capacity pressure coefficient × Efficiency correction factor; S102. Based on the actual charging amount at the vehicle's ports, obtain differentiated charging permission labels of high, medium, and low levels.

4. The IoT-based remote safety management system for charging piles according to claim 1, characterized in that, The specific process for constructing a grid-constrained charging scheduling optimization model is as follows: S200, Set the constraints of the charging scheduling optimization model with grid constraints, including grid security, charging pile physical constraints, and vehicle demand constraints; S201. Input parameter preparation and standardization: Calculate the real-time load pressure coefficient K, the formula is: K=(total number of vehicles to be charged × average charging demand) / (number of available charging piles × total grid capacity); K comprehensively reflects the ratio of vehicle demand to supply capacity. Based on vehicle charging port standards, it maps the theoretical maximum charging power of different models to a standardized charging efficiency coefficient. S202. Construct an optimization function with the charging threshold as the decision variable. The function is constructed by the real-time load pressure coefficient, the total number of vehicles to be charged, and the safe peak load of the power grid. N = F × (1 - K) × f, where N is the optimal charging threshold for the current vehicle, F is the highest safe charge, and f is the lowest safe charge. The optimal charging threshold for vehicles is the output dimension of the grid-constrained charging scheduling optimization model.

5. The IoT-based remote safety management system for charging piles according to claim 1, characterized in that, The charging scheduling module also includes a real-time monitoring unit for real-time monitoring of the charging pile's temperature. It employs a sliding window algorithm to smooth the temperature data and sets dynamic safety thresholds. Normal range: Battery pack surface temperature ≤45℃, interface temperature ≤60℃; Warning threshold: When the temperature exceeds the normal range but does not reach the danger value, a yellow warning is triggered, and power rollback is initiated; Danger threshold: When the charging pile temperature is ≥55℃ and the interface temperature is ≥70℃, a red warning is triggered, charging is immediately stopped and the cooling system is activated.

6. The IoT-based remote safety management system for charging piles according to claim 1, characterized in that, The total active power of the entire charging station cluster is monitored and calculated in real time, and compared with the safe capacity threshold provided by the power grid. Specifically, this includes the following: S300: Obtain the charging efficiency of the charging pile, accumulate the power data of the charging pile, and calculate the total active power in combination with power factor correction. S301. Compare the real-time total active power with the grid safety capacity threshold, calculate the load rate, and classify the warning level according to the load rate, including the green safety zone: load rate < 80%, no warning is required, and normal charging is maintained. Yellow warning zone: 80%≤load rate<90%, triggering a level 3 warning, initiating power rollback of charging piles, and guiding users to charge during off-peak hours; Orange alert zone: 90% ≤ load rate < 95%, triggering a level 2 alert, restricting new user access, and prioritizing charging for high-priority vehicles; Red Alert Zone: Load rate ≥ 95%, triggering Level 1 alert, initiating emergency current limiting strategy, forcibly reducing the power of all charging piles, and triggering automatic power-off protection.

7. The IoT-based remote safety management system for charging piles according to claim 1, characterized in that, The remote control module also includes an early warning control unit, the details of which are as follows: It is responsible for safely and reliably sending charging threshold commands to designated charging piles, receiving status reports from charging piles, and verifying whether the commands have been executed correctly. After receiving control commands from the charging pile, it adjusts the output power and uses sensors to feed back the actual charging amount, grid changes, and temperature parameters to form a closed-loop control. It monitors the changes in total active power in real time, verifies the effectiveness of early warning measures, and further upgrades the early warning level if the load rate continues to rise.

8. The IoT-based remote safety management system for charging piles according to claim 1, characterized in that, It also includes a safety execution and emergency module, which monitors the real-time charging process of each charging station. When the vehicle's battery level reaches its dynamically adjusted actual limit, it sends a power supply stop command to the charging station and completes the settlement. At the same time, it performs risk assessment based on the vehicle's remaining range and the load information of the surrounding charging network. For vehicles that are detected to have triggered the range safety threshold, it initiates an emergency exemption process and authorizes them to temporarily increase their charging limit.

Images