Charging pile operation safety management and control system based on artificial intelligence

Abstract

The invention discloses a charging pile operation safety management and control system based on artificial intelligence, and belongs to the technical field of charging pile operation safety management and control. A charging pile operation safety management and control system based on artificial intelligence comprises a data acquisition module, a physical environment monitoring module, a central processing unit, a safety evaluation model, a fault detection module, a risk warning and execution module, a multi-charging pile collaborative management module and a user interface. The problems that existing charging pile data collection and environment monitoring are not comprehensive, an intelligent safety evaluation mechanism is lacked, the fault detection and response efficiency is low, and resource allocation is unreasonable are solved. Precise monitoring and safety trend prediction of the operation state of the charging pile are achieved through comprehensive data collection and the safety evaluation model, an intelligent fault detection and grading early warning mechanism can rapidly recognize and respond to potential and existing faults, resource allocation is optimized through the multi-charging-pile collaborative management module, the overall performance is improved, and the system is suitable for large-scale popularization and application. The operation safety is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile operation safety management and control, and specifically to a charging pile operation safety management and control system based on artificial intelligence. Background Art

[0002] Traditional charging pile systems often rely on manual inspections and regular maintenance to ensure the normal operation of the equipment. This approach is not only inefficient, but also difficult to detect potential safety hazards in a timely manner. For example, due to the lack of real-time monitoring methods, when current or voltage fluctuations or abnormal temperature increases occur, measures are often not taken immediately, resulting in the accumulation of faults and even safety accidents. In addition, the monitoring of environmental factors around charging piles (such as fire risks, weather changes, etc.) is relatively weak, increasing safety risks.

[0003] Most existing charging pile management systems use simple rule-based judgments to assess safety status, which is inadequate when faced with complex and changing situations. In particular, in terms of predicting future safety trends, traditional methods are difficult to accurately identify potential risk points in advance, resulting in delayed preventive measures and inability to effectively avoid accidents. The response speed after a fault occurs is slow, and it usually requires user reports or staff inspections to discover the problem, during which time a certain degree of damage may have already occurred.

[0004] On the other hand, as the number of charging piles increases, how to effectively manage multiple charging stations scattered in different locations has become a problem. Traditional management methods make it difficult to achieve optimal resource allocation, which can easily cause some stations to be overloaded while other stations are idle, which not only reduces the overall charging efficiency, but may also cause equipment damage due to overuse, posing a safety hazard.

[0005] Therefore, it is particularly necessary to develop a charging pile operation safety management and control system that can comprehensively use artificial intelligence technology for data collection, analysis and processing, and has intelligent early warning and optimized scheduling functions. Summary of the invention

[0006] The purpose of the present invention is to provide a charging pile operation safety management and control system based on artificial intelligence. Through comprehensive data collection and environmental monitoring, and a deep learning-driven safety assessment model, accurate monitoring of the operating status of the charging piles and prediction of safety trends are achieved. The intelligent fault detection and graded early warning mechanism can quickly identify and respond to potential and existing faults. The multi-charging pile collaborative management module optimizes resource allocation, improves overall charging efficiency, and solves the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An artificial intelligence-based charging pile operation safety management and control system, comprising:

[0009] Data acquisition module, used to collect the operation data of the charging pile, including but not limited to current, voltage, temperature and humidity;

[0010] A physical environment monitoring module, which is used to monitor the physical state of the charging pile and its surrounding environment, including but not limited to fire, collision impact and weather conditions. The physical environment monitoring module includes at least a smoke sensor, a temperature sensor, a humidity sensor and an acceleration sensor, which are used to comprehensively monitor the physical state of the charging pile and its surrounding environment;

[0011] The central processing unit is used to receive, store and process the operation data and physical environment data of the charging pile, and coordinate the operation between various functional modules;

[0012] A safety assessment model, constructed based on a deep learning algorithm, for assessing the current safety status of the charging pile based on the operating data and physical environment data, and predicting safety trends in future time periods;

[0013] The fault detection module is used to monitor the operating status of each charging pile in real time through the safety assessment model and identify potential and existing faults; if a fault is detected, the detailed information of the fault is recorded, including at least the fault type, fault time, treatment measures, treatment time and results, and the cause of the fault is analyzed, and finally a fault report is generated;

[0014] The risk warning and execution module is used to determine the current safety risk level, generate corresponding warning information and take measures based on the output results of the safety assessment model and fault detection module;

[0015] The multi-charging pile collaborative management module is used to centrally manage and optimize the operating status of multiple charging piles, ensure information sharing and collaborative scheduling between charging piles, optimize charging resource allocation, and improve overall charging efficiency and safety;

[0016] Preferably, the safety assessment model uses a deep neural network (DNN) for feature extraction and a long short-term memory network (LSTM) for time series analysis, and inputs the real-time collected operation data and physical environment data into the trained model. The model outputs the current safety status of the charging pile and the safety trend in the future time period based on the input data.

[0017] Preferably, the security risk levels are divided into low risk, medium risk and high risk;

[0018] The low-risk classification criteria are: minor abnormal conditions, including but not limited to slight fluctuations in current, slight fluctuations in voltage, slight temperature rise, changes in humidity that do not affect the normal operation of the charging pile, and a brief interruption in communication between the charging pile and the management platform;

[0019] The medium-risk classification criteria are: more serious abnormal conditions, including but not limited to a sharp rise or fall in current values ​​that exceeds the normal range, a sharp rise or fall in voltage values ​​that exceeds the normal range, a significant increase in the internal and external temperature of the charging pile that is close to the warning value, leakage of the charging pile is detected but does not reach a dangerous level, the charging gun is not firmly connected to the vehicle resulting in unstable charging, the battery pack temperature is too high but does not reach the critical value, the charging efficiency is significantly reduced, and sensor failure causes data abnormalities.

[0020] Preferably, the high-risk classification criteria are: serious abnormal conditions, including but not limited to fires around and inside the charging pile, structural damage to the charging pile caused by external impact, a sharp increase in temperature exceeding the warning value, internal short circuit or open circuit of the charging pile, overheating of the battery pack, failure of the cooling system, failure of the charging gun to work properly, damage to the charging line or loose connectors, malfunction of the control system, and the charging pile system being maliciously controlled and destroyed by humans under cyber attacks.

[0021] Preferably, the generating of warning information and taking measures according to the risk level are specifically:

[0022] Low risk: Generate low risk warning information, report to the charging pile management platform, display it through the user interface and suggest users to check the status of the charging pile;

[0023] Medium risk: Generate medium risk warning information, send warning information to users and provide detailed fault self-check suggestions, automatically reduce charging power or suspend charging, and report to the charging pile management platform to arrange professional personnel for timely inspection and repair;

[0024] High risk: A high-risk warning message is generated, the charging pile is immediately powered off and shut down, and an alarm message is sent to the charging pile management platform, fire department, community and individual users.

[0025] Preferably, the method of processing the high-risk warning information is specifically as follows:

[0026] Send high-risk warning information to the charging pile management platform, which records and processes relevant information;

[0027] Send high-risk warning information to the fire department via pre-configured contact information;

[0028] Send high-risk warning information to community managers through the community management system or property management system;

[0029] High-risk warning information is sent to individual users through the user interface, text messages, APP push, etc.

[0030] Preferably, the multi-charging pile collaborative management module includes a data sharing unit for collecting operation data from each charging pile and collecting physical state data of the charging pile and its surrounding environment from the physical environment monitoring module;

[0031] Regularly refresh and synchronize the data of each charging pile to ensure that the data of all charging piles is up to date, and use distributed database technology to improve data availability;

[0032] Preferably, the multi-charging pile collaborative management module includes a load balancing unit for real-time monitoring of the load of each charging pile, including at least the charging power and the charging request amount;

[0033] Summarize the load data of all charging piles through the charging pile management platform;

[0034] Dynamically adjust the allocation of charging resources according to the current load of each charging pile and the charging request, and give priority to allocating charging requests to charging piles with lower loads;

[0035] Use machine learning algorithms and historical data to predict load conditions over a period of time in the future;

[0036] Adjust the allocation of charging resources in advance based on the prediction results.

[0037] Preferably, the multi-charging pile collaborative management module further includes a charging scheduling unit, which is used to receive a user's charging request and record the user's charging demand and the location information of the charging pile;

[0038] Allocate appropriate charging piles based on the user's charging needs and the availability of charging piles;

[0039] Generate an optimal charging plan, including charging time and charging power;

[0040] Push charging plans and recommendations to users through the user interface;

[0041] Monitor the charging progress of each charging pile in real time;

[0042] Adjust the charging plan in time when abnormal situations occur.

[0043] Preferably, the system also includes a user interface for displaying status information, warning information and recommended operating instructions of the charging pile, so that the user can understand the status of the charging pile in a timely manner and respond.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention realizes information sharing among multiple charging piles by setting up a multi-charging pile collaborative management module, and also supports dynamic adjustment of charging resource allocation, giving priority to meeting the needs of charging piles with lower loads. By predicting future load changes through machine learning algorithms, the system can make adjustments before actual demand occurs, effectively avoiding problems caused by uneven loads, improving the overall charging efficiency and ensuring the safe operation of the charging piles.

[0046] 2. The present invention constructs an intelligent safety assessment model through deep neural network technology combined with long short-term memory network. It can accurately evaluate the current safety status of the charging pile based on the data collected in real time, and can predict the safety trend in the future, thereby improving the accuracy of safety assessment and enabling potential risks to be identified in advance and preventive measures to be taken.

[0047] 3. The present invention can monitor the working status of each charging pile in real time, quickly identify potential or existing faults, and record detailed fault information for subsequent analysis. Once a problem is detected, the system will classify it into low risk, medium risk or high risk levels according to the severity of the fault, and generate corresponding warning information and response measures, while notifying all parties to handle it quickly, thereby improving processing efficiency and minimizing losses.

[0048] 4. The present invention can not only collect key data during the operation of the charging pile, such as current, voltage, temperature and humidity, but also integrate multiple sensors to conduct all-round monitoring of the charging pile and its surrounding environment, ensuring that the system can capture any abnormal situation in time, providing a solid data foundation for subsequent safety assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a system module structure diagram of the present invention;

[0050] Figure 2 It is a schematic diagram of the security risk level of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In order to solve the problems of incomplete data collection and environmental monitoring of existing charging piles, lack of intelligent safety assessment mechanism, low efficiency of fault detection and response, and unreasonable resource allocation, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0053] A charging pile operation safety management and control system based on artificial intelligence includes a data acquisition module, a physical environment monitoring module, a central processing unit, a safety assessment model, a fault detection module, a risk warning and execution module, a multi-charging pile collaborative management module and a user interface.

[0054] The data acquisition module is used to collect the operating data of the charging pile, including but not limited to current, voltage, temperature and humidity.

[0055] The physical environment monitoring module is used to monitor the physical state of the charging pile and its surrounding environment, including but not limited to fire, collision impact and weather conditions. The physical environment monitoring module includes at least a smoke sensor, a temperature sensor, a humidity sensor and an acceleration sensor, which are used to comprehensively monitor the physical state of the charging pile and its surrounding environment. Real-time data collection can provide comprehensive data support, which is the basis for safety assessment and fault detection, ensuring that abnormal situations can be discovered in time, and providing a basis for subsequent data analysis and decision-making.

[0056] Setting up a physical environment monitoring module can enable timely measures to be taken in emergency situations such as fire and collision, thus improving the overall safety of the system.

[0057] The current, voltage, temperature, humidity and physical environment data of the charging pile are collected regularly or continuously, and the collected data are transmitted to the central processing unit via wireless communication.

[0058] The central processing unit is used to receive, store and process the operating data and physical environment data, perform preprocessing, cleaning, normalization and other operations on the collected data to prepare for subsequent analysis, and coordinate data transmission and operation between the data acquisition module, physical environment monitoring module, safety assessment model, fault detection module, risk warning and execution module, multi-charging pile collaborative management module and user interface.

[0059] The safety assessment model is built based on a deep learning algorithm and is used to evaluate the current safety status of the charging pile based on the operating data and physical environment data, and predict safety trends in future time periods.

[0060] A deep neural network (DNN) is used for feature extraction, and a long short-term memory network (LSTM) is used for time series analysis. The real-time collected operation data and physical environment data are input into the trained model. The model outputs the current safety status of the charging pile and its safety trend in the future time period based on the input data.

[0061] Deep learning models can capture complex patterns in data, improve the accuracy and reliability of security assessments, and detect potential security risks in advance.

[0062] The fault detection module is used to monitor the operating status of each charging pile in real time through a safety assessment model and identify potential and existing faults; if a fault is detected, the detailed information of the fault is recorded, including at least the fault type, fault time, handling measures, handling time and results, and the cause of the fault is analyzed, and finally a fault report is generated.

[0063] The risk warning and execution module is used to determine the current safety risk level, generate corresponding warning information and take measures based on the output results of the safety assessment model and fault detection module.

[0064] Security risk levels are divided into:

[0065] Low risk: Minor abnormal conditions, including but not limited to slight fluctuations in current, slight fluctuations in voltage, slight temperature rise, changes in humidity that do not affect the normal operation of the charging pile, and brief interruption of communication between the charging pile and the management platform.

[0066] Medium risk: more serious abnormal conditions, including but not limited to current values ​​that rise or fall sharply and exceed the normal range, voltage values ​​that rise or fall sharply and exceed the normal range, the temperature inside and outside the charging pile rises significantly and approaches the warning value, leakage of the charging pile is detected but does not reach a dangerous level, the charging gun is not firmly connected to the vehicle resulting in unstable charging, the battery pack temperature is too high but does not reach the critical value, the charging efficiency is significantly reduced, and sensor failure causes data abnormalities.

[0067] High risk: serious abnormal situations, including but not limited to fire around and inside the charging pile, structural damage of the charging pile due to external impact, rapid temperature increase and exceeding the warning value, internal short circuit or open circuit of the charging pile, overheating of the battery pack, failure of the cooling system, failure of the charging gun to work properly, damage to the charging line or loose connectors, malfunction of the control system, and malicious control and human destruction of the charging pile system due to cyber attacks.

[0068] Generate early warning information and take measures based on risk levels, specifically:

[0069] Low risk: Generate low-risk warning information, report to the charging pile management platform, display it through the user interface and suggest users to check the status of the charging pile.

[0070] Medium risk: Generate medium risk warning information, send warning information to users and provide detailed fault self-check suggestions, automatically reduce charging power or suspend charging, and report to the charging pile management platform to arrange professional personnel for timely inspection and repair.

[0071] High risk: High-risk warning information is generated, the charging pile is immediately powered off and shut down, and an alarm message is sent to the charging pile management platform, fire department, community and individual users. Specifically: the high-risk warning information is sent to the charging pile management platform, and the platform records and processes the relevant information; the high-risk warning information is sent to the fire department through the pre-configured contact information; the high-risk warning information is sent to the community management personnel through the community management system or property management system; the high-risk warning information is sent to individual users through the user interface, SMS, APP push, etc.

[0072] The multi-charging pile collaborative management module is used to centrally manage and optimize the operating status of multiple charging piles, ensure information sharing and collaborative scheduling between charging piles, optimize the allocation of charging resources, and improve the overall charging efficiency and safety. The multi-charging pile collaborative management module includes a data sharing unit, a load balancing unit, and a charging scheduling unit.

[0073] The data sharing unit collects operation data from each charging pile and the physical status data of the charging pile and its surrounding environment from the physical environment monitoring module; it regularly refreshes and synchronizes the data of each charging pile to ensure that the data of all charging piles is up to date, and uses distributed database technology to improve data reliability and availability.

[0074] The load balancing unit monitors the load conditions of each charging pile in real time, including at least the charging power and charging request amount; summarizes the load data of all charging piles through the central management platform; dynamically adjusts the allocation of charging resources according to the current load conditions and charging requests of each charging pile, and gives priority to allocating charging requests to charging piles with lower loads; uses machine learning algorithms and combines historical data to predict the load conditions in the future; and adjusts the allocation of charging resources in advance based on the prediction results, optimizes the overall charging efficiency, and reduces the failure rate of charging piles due to uneven loads.

[0075] Charging scheduling unit, the central management platform receives the user's charging request, records the user's charging needs and the location information of the charging pile; allocates suitable charging piles according to the user's charging needs and the availability of charging piles; generates the optimal charging plan, including at least charging time and charging power; pushes charging plans and suggestions to users through the user interface; monitors the charging progress of each charging pile in real time to ensure a smooth charging process; and adjusts the charging plan in a timely manner when an abnormal situation occurs to ensure that the user's needs are met.

[0076] In order to better understand the operation process of the multi-charging pile collaborative management module, a specific embodiment is provided as follows:

[0077] In a large parking lot, there are 10 charging piles (AJ) in operation. At 9:00 a.m. one day, the system starts to coordinate the management of multiple charging piles.

[0078] Each charging pile uploads operating data and physical environment data to the central processing unit every 5 minutes. For example, the data uploaded by charging pile A includes: current 10A, voltage 220V, temperature 30℃, humidity 50%; no fire, no collision or impact.

[0079] The central processing unit regularly refreshes and synchronizes the data of each charging pile to ensure that the data is up to date.

[0080] The central processing unit monitors the load of each charging pile in real time. For example, the load of charging pile A is 70%, the load of charging pile B is 40%, and the load of charging pile C is 80%.

[0081] User A requests charging, and the system selects charging station B with lower load for allocation based on the load conditions.

[0082] The algorithm predicts the load conditions of the charging piles in the next hour. It is predicted that the load of charging pile A will rise to 85%, and the load of charging pile B will remain at around 40%.

[0083] Adjust the allocation of charging resources in advance according to the prediction results, reduce new charging requests for charging pile A, and increase new charging requests for charging pile B.

[0084] User B requests charging, and the system records the user's charging requirements and location information.

[0085] The system allocates charging pile D to user B based on the availability of charging piles.

[0086] Generate the optimal charging plan, for example, user B will charge at charging pile D, the charging time is 1 hour, the charging power is 50kW, and the charging plan and suggestions are pushed to user B through the APP.

[0087] Monitor the charging progress of charging pile D in real time to ensure the charging process goes smoothly.

[0088] If an abnormal situation occurs at charging pile D, the system will automatically adjust the charging plan, reallocate charging pile E to user B, and notify user B.

[0089] The user interface is used to display the status information, warning information and recommended operation guide of the charging pile, so that the user can understand the status of the charging pile in time and respond. The user interface includes:

[0090] Status display unit: displays the real-time status information of the charging pile, such as current, voltage, temperature, humidity, etc.

[0091] Warning information unit: displays current and historical warning information, including low-risk, medium-risk and high-risk warnings.

[0092] Operation guide unit: Provides detailed fault self-check suggestions and operation guides to help users respond to abnormal situations in a timely manner.

[0093] Charging plan unit: displays the user's charging plan and suggestions, including charging time, charging power, etc.

[0094] Notification module: early warning information and important notifications sent to users via SMS, APP push, etc.

[0095] Feedback module: collect user experience and suggestions to help continuously optimize the system.

[0096] The user interface can clearly display the status information, warning information and recommended operating guidelines of the charging pile, which not only helps users better understand the real-time status of the charging pile, but also guides users on how to correctly respond when encountering problems. In addition, the function of sending important notifications through SMS, APP push, etc. also further enhances the user experience and allows users to feel more considerate service.

[0097] Working principle: The system collects the operating data of the charging pile through the data acquisition module, including current, voltage, temperature and humidity, and monitors the charging pile and its surrounding environment status, such as fire, collision and impact, through the physical environment monitoring module. The central processing unit receives and processes this data, and uses the safety assessment model built by the deep learning algorithm to evaluate the current safety status of the charging pile and predict future safety trends. The fault detection module monitors the operating status of the charging pile in real time, identifies potential faults, and records fault information to generate reports. The risk warning and execution module determines the risk level based on the safety assessment results, generates warning information and takes corresponding measures, such as reducing the charging power or suspending charging. The multi-charging pile collaborative management module optimizes the resource allocation of multiple charging piles, ensures information sharing and collaborative scheduling, and improves the overall charging efficiency and safety. The user interface displays the status information, warning information and operation guide of the charging pile, so that users can understand the status of the charging pile in time and respond.

[0098] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0099] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. An artificial intelligence-based charging pile operation safety management and control system, characterized in that: include: Data acquisition module, used to collect operation data of charging piles; A physical environment monitoring module, used to monitor the physical state of the charging pile and the surrounding environment. The physical environment monitoring module includes at least a smoke sensor, a temperature sensor, a humidity sensor and an acceleration sensor; The central processing unit is used to receive, store and process the operation data and physical environment data of the charging pile, and coordinate the operation between various functional modules; A safety assessment model, constructed based on a deep learning algorithm, for assessing the current safety status of the charging pile based on the operating data and physical environment data, and predicting safety trends in future time periods; The fault detection module is used to monitor the operating status of each charging pile in real time through the safety assessment model and identify potential and existing faults; if a fault is detected, the detailed information of the fault is recorded, including at least the fault type, fault time, treatment measures, treatment time and results, and the cause of the fault is analyzed, and finally a fault report is generated; The risk warning and execution module is used to determine the current safety risk level, generate corresponding warning information and take measures based on the output results of the safety assessment model and fault detection module; The multi-charging pile collaborative management module is used to centrally manage and optimize the operating status of multiple charging piles, ensure information sharing and collaborative scheduling between charging piles, and optimize the allocation of charging resources.

2. According to the artificial intelligence-based charging pile operation safety management and control system of claim 1, it is characterized in that: The safety assessment model uses a deep neural network for feature extraction and a long short-term memory network for time series analysis. The real-time collected operation data and physical environment data are input into the trained model. The model outputs the current safety status of the charging pile and the safety trend in the future time period based on the input data.

3. According to the artificial intelligence-based charging pile operation safety management and control system of claim 2, it is characterized in that: The security risk levels are divided into low risk, medium risk and high risk; The low-risk classification criteria include: including but not limited to current fluctuations, voltage fluctuations, temperature rise, humidity changes that do not affect the normal operation of the charging pile, and a brief interruption in communication between the charging pile and the management platform; The medium-risk classification criteria include but are not limited to: current value rising or falling and exceeding the normal range, voltage value rising or falling and exceeding the normal range, internal and external temperature of the charging pile rising and approaching the warning value, charging pile leakage detected but not reaching a dangerous level, charging gun and vehicle not firmly connected resulting in unstable charging, battery pack temperature is too high but not reaching the critical value, charging efficiency decreases, and sensor failure resulting in data abnormalities.

4. According to the artificial intelligence-based charging pile operation safety management and control system of claim 3, it is characterized in that: The high-risk classification criteria include, but are not limited to, fires around and inside the charging pile, structural damage to the charging pile due to external impact, temperature rise exceeding the warning value, internal short circuit or open circuit of the charging pile, battery pack overheating, cooling system failure, charging gun failure and inability to work properly, charging line damage or loose connectors, control system malfunction, and the charging pile system being maliciously controlled and destroyed by humans due to cyber attacks.

5. The charging pile operation safety management and control system based on artificial intelligence according to claim 4 is characterized in that: The generation of early warning information and taking measures according to the risk level are specifically as follows: Low risk: Generate low risk warning information, report to the charging pile management platform, display it through the user interface and suggest users to check the status of the charging pile; Medium risk: Generate medium risk warning information, send warning information to users and provide detailed fault self-check suggestions, automatically reduce charging power or suspend charging, and report to the charging pile management platform to arrange professional personnel for timely inspection and repair; High risk: A high-risk warning message is generated, the charging pile is immediately powered off and shut down, and an alarm message is sent to the charging pile management platform, fire department, community and individual users.

6. The charging pile operation safety management and control system based on artificial intelligence according to claim 5 is characterized in that: The specific method for processing high-risk warning information is as follows: Send high-risk warning information to the charging pile management platform, which records and processes relevant information; Send high-risk warning information to the fire department via pre-configured contact information; Send high-risk warning information to community managers through the community management system or property management system; High-risk warning information is sent to individual users through the user interface, text messages, APP push, etc.

7. The charging pile operation safety management and control system based on artificial intelligence according to claim 6 is characterized in that: The multi-charging pile collaborative management module includes a data sharing unit for collecting operation data from each charging pile and collecting physical status data of the charging pile and its surrounding environment from the physical environment monitoring module; Regularly refresh and synchronize the data of each charging station, and use distributed database technology to improve data availability.

8. The charging pile operation safety management and control system based on artificial intelligence according to claim 7 is characterized in that: The multi-charging pile collaborative management module includes a load balancing unit for real-time monitoring of the load of each charging pile, including at least the charging power and the charging request amount; Summarize the load data of all charging piles through the charging pile management platform; Dynamically adjust the allocation of charging resources according to the current load of each charging pile and the charging request, and give priority to allocating charging requests to charging piles with lower loads; Use machine learning algorithms and historical data to predict load conditions over a period of time in the future; Adjust the allocation of charging resources in advance based on the prediction results.

9. The charging pile operation safety management and control system based on artificial intelligence according to claim 8 is characterized in that: The multi-charging pile collaborative management module also includes a charging scheduling unit for receiving a user's charging request and recording the user's charging demand and the location information of the charging pile; Allocate appropriate charging piles based on the user's charging needs and the availability of charging piles; Generate an optimal charging plan, including charging time and charging power; Push charging plans and recommendations to users through the user interface; Monitor the charging progress of each charging pile in real time; Adjust the charging plan in time when abnormal situations occur.

10. The charging pile operation safety management and control system based on artificial intelligence according to claim 9 is characterized in that: The charging pile operation safety management and control system also includes a user interface for displaying the status information, warning information and recommended operation guides of the charging pile, so that users can understand the status of the charging pile in a timely manner and respond.

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