Monitoring system and method for charging piles

Abstract

To provide a monitoring system and a method for a charging pile, capable of early warning an abnormal state of the charging pile of an electric vehicle and taking appropriate measures.SOLUTION: A monitoring system 1 for a charging pile for charging an electric vehicle 3 via a power module 20 of the charging pile 2 includes an image capturing module 10 configured to capture at least one environmental image signal related to the electric vehicle 3, and a control module 16 connected to the image capturing module 10 and configured to apply an artificial intelligence technique to analyze the at least one environmental image signal to generate an analysis result and control the power module 20 according to the analysis result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a monitoring system and method for a charging pile, and more particularly to a monitoring system and method that can provide early warning of abnormal conditions in a charging pile and take appropriate measures. [Background technology]

[0002] With the increasing popularity of electric vehicles, the safety of charging piles is receiving increasing attention. As a key facility for refueling electric vehicles, charging pile safety is essential to ensuring the safety of users and vehicles. Modern charging pile technology has developed a series of safety protection measures, including overload protection, leakage protection, and lightning protection. These functions are built on the vehicle's battery management system (BMS) and the charging pile's power detection system, and even if an accident occurs during the charging process, the power can be immediately cut off to prevent an accident.

[0003] However, during the vehicle charging process, in addition to power abnormalities, there are many environmental factors that can cause safety hazards, such as nearby vehicles catching fire, collisions, etc. In particular, public charging stations are generally unmanned, so when a problem occurs, it cannot be reported immediately, which may lead to a more serious accident.

[0004] Therefore, early warning of abnormal conditions in charging piles and taking appropriate measures has become one of the goals the industry is striving for. Summary of the Invention [Problem to be solved by the invention]

[0005] Therefore, the present invention aims to provide a monitoring system and method for a charging pile that can provide early warning of abnormal conditions in the charging pile and take appropriate measures. [Means for solving the problem]

[0006] One embodiment of the present invention discloses a monitoring system for a charging pile, where the charging pile charges an electric vehicle via a power module, including: an image capture module configured to capture at least one environmental image signal related to the electric vehicle; and a control module coupled to the image capture module and configured to apply artificial intelligence techniques to analyze the at least one environmental image signal, generate an analysis result, and control the power module according to the analysis result.

[0007] Another embodiment of the present invention discloses a monitoring method for a charging pile, where the charging pile charges an electric vehicle via a power module, the monitoring method includes the steps of capturing at least one environmental image signal associated with the electric vehicle, applying artificial intelligence techniques to analyze the at least one environmental image signal to generate an analysis result, and controlling the power module according to the analysis result.

[0008] These and other objects of the present invention will no doubt become obvious to those skilled in the art after reading the following detailed description of the preferred embodiment that is illustrated in the various figures and drawings. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic diagram of a monitoring system according to an embodiment of the present invention; [Figure 2] FIG. 2 is a schematic diagram of an operation scenario of the surveillance system shown in FIG. 1. [Figure 3A] 3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 3B] 3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 3C] 3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 3D] 3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 4]3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 5A] 3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 5B] 3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 6] 3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 7] 3A-3C are diagrams illustrating different environmental image signals according to an embodiment of the present invention. [Figure 8] 1 is a schematic diagram of a monitoring method according to an embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0010] Certain terms are used throughout the specification and the claims that follow to refer to particular components. As one skilled in the art will appreciate, hardware manufacturers may refer to components by different names. This document does not intend to distinguish between components that differ in name but function. In the following specification and claims, the terms "include" and "comprise" are used in an open-ended manner and should be interpreted to mean "including, but not limited to...". Additionally, the term "couple" is intended to mean either an indirect or direct electrical connection.

[0011] Please refer to FIG. 1. FIG. 1 is a schematic diagram of a monitoring system 1 according to an embodiment of the present invention. The monitoring system 1 is used in a charging pile 2. The charging pile 2 can charge an electric vehicle 3 via a power module 20. The monitoring system 1 monitors various information during the charging process of the charging pile 2 to provide early warning of abnormal conditions and stop the operation of the power module 20 as necessary. The monitoring system 1 includes an image capture module 10, a temperature sensing module 12, a communication module 14, and a control module 16. The image capture module 10 is used to capture at least one environmental image signal IMG associated with the electric vehicle 3. The temperature sensing module 12 is used to sense a temperature signal TMP of the environment near the electric vehicle 3. The communication module 14 is used to exchange messages with a host. The control module 16 is connected to the image capture module 10, the temperature sensing module 12, the communication module 14 and the power module 20 of the charging pile 2, and is used to determine the environmental information of the electric vehicle 3 based on the environmental image signal IMG or the temperature signal TMP, and to receive messages from an external host or output messages to an external host via the communication module 14.

[0012] More specifically, the image capture module 10 may be a camera installed on the housing of the charging pile 2, which captures images of the electric vehicle 3 and its surrounding area within a certain range in the direction of the electric vehicle 3, generates an environmental image signal IMG, and transmits it to the control module 16. In one embodiment, the image capture module 10 may be a camera located above the parking space, which transmits the environmental image signal IMG to the control module 16 via wired or wireless means. In another embodiment, the image capture module 10 may be composed of multiple cameras located at different positions near the parking space, which capture images of the electric vehicle 3 from multiple angles or directions, and generates an environmental image signal IMG and transmits it to the control module 16. In yet another embodiment, the image capture module 10 includes multiple cameras, each of which can capture images of the electric vehicle 3 at a different wavelength, such as natural light, infrared, or thermal images, and generates an environmental image signal IMG and transmits it to the control module 16. In short, the image capture module 10 can be implemented using any image capture device or equipment that can capture all or part of the electric vehicle 3 and its surrounding area within a certain range, and is not limited to these examples.

[0013] Based on the environmental image signal IMG captured by the image capture module 10, the control module 16 can use artificial intelligence techniques to analyze the environmental image signal IMG, generate an analysis result, and control the power module 20 accordingly. As known in the art, artificial intelligence techniques combine computer science, data analysis, machine learning, and algorithm design techniques to create intelligent systems that can mimic human learning, reasoning, and self-correction. These systems can make decisions and perform tasks without direct human intervention by analyzing and learning from large amounts of data. For example, in the field of image recognition applications, artificial intelligence techniques can determine whether a specific event occurs in an image by learning and identifying specific patterns in complex data sets using deep learning and / or machine learning methods. The present invention utilizes artificial intelligence techniques to analyze the environmental image signal IMG, recognizes specific abnormal situations in the environmental image signal IMG using deep learning and / or machine learning methods, and then determines whether the probability of a dangerous situation occurring is greater than a threshold. If the probability of a dangerous situation occurring exceeds the threshold, the control module 16 stops charging the electric vehicle 3 to avoid greater damage.

[0014] Specifically, please refer to Fig. 2. Fig. 2 is a schematic diagram of an operation scenario of the monitoring system 1. In Fig. 2, a charging pile 2 is charging an electric vehicle 3, and the monitoring system 1 is installed inside the charging pile 2 (hence, not shown in Fig. 2). The image capture module 10 is a camera installed on the housing of the charging pile 2, which captures images of the electric vehicle 3 and its surrounding area within a certain range toward the direction of the electric vehicle 3, and generates and transmits an environmental image signal IMG as shown in Fig. 3A to the control module 16. As can be seen from Fig. 3A, the environmental image signal IMG includes an image of the electric vehicle 3 and its surrounding area within a certain range.

[0015] As described above, the control module 16 can use artificial intelligence technology to analyze the environmental image signal IMG, generate an analysis result, and control the power module 20 accordingly. Take smoke detection as an example. The occurrence of smoke is often accompanied by overheating and is an early sign of a fire, but smoke detection is often falsely determined due to external environmental factors. In this case, the present invention can use artificial intelligence technology to more accurately determine whether smoke is present and avoid false positives.

[0016] For example, FIGS. 3B, 3C, and 3D show different environmental image signals IMG. The environmental image signal IMG in FIG. 3B indicates that smoke is occurring near the bottom battery of the electric vehicle 3 (i.e., within a predetermined range). The artificial intelligence technology can analyze the characteristics of the smoke situation through machine learning. For example, the smoke becomes thicker (less light-transmitting) closer to the battery and thinner (more light-transmitting) further away from the battery, and the smoke is continuously emitted from the bottom battery area. The AI ​​technology can further evaluate the probability of overheating in the bottom battery of the electric vehicle 3. Furthermore, if the analysis result of the AI ​​technology indicates that the probability of fire occurrence is greater than a threshold, the control module 16 may stop charging the electric vehicle 3 to mitigate damage. Furthermore, the control module 16 can notify an administrator via the communication module 14. For example, the control module 16 may be connected to the administrator's messaging software by communicating with a messaging software service host, and transmit the analysis result to notify the administrator of the abnormal situation. In one embodiment, the control module 16 can simultaneously determine whether the ambient temperature near the electric vehicle 3 is abnormal based on the temperature signal TMP from the temperature sensing module 12, to help determine the probability of battery overheating or fire occurrence, and control the operation of the power module 20 in a timely manner.

[0017] The environmental image signal IMG in FIG. 3C shows that the electric vehicle 3 is in a foggy area. Through machine learning, AI technology can analyze the characteristics of the fog, such as whether the fog is evenly distributed in the air and has no obvious flow (a phenomenon that spreads from a smoke point). The AI ​​technology may further determine that the fog is merely a normal weather condition and not an abnormal ambient environment. In this case, the control module 16 does not stop charging the electric vehicle 3. In one embodiment, the control module 16 can simultaneously determine whether the ambient temperature near the electric vehicle 3 is abnormal based on the temperature signal TMP from the temperature sensing module 12 to assist in determining whether the fog is a weather condition.

[0018] The environmental image signal IMG in FIG. 3D shows that the vehicle 4 ahead of the electric vehicle 3 is continuously emitting thick smoke (e.g., due to incomplete combustion or low temperature). AI technology can use machine learning to analyze the characteristics of the thick smoke, such as the smoke exiting from the rear of the vehicle 4 and spraying in a certain direction. The AI ​​technology may further determine that the thick smoke is simply due to the exhaust of the vehicle 4 and not an abnormal ambient environment. In this case, the control module 16 does not stop charging the electric vehicle 3. In one embodiment, the control module 16 can simultaneously determine whether the ambient temperature near the electric vehicle 3 is abnormal based on the temperature signal TMP from the temperature sensing module 12 to assist in determining whether the abnormality is due to the exhaust of the vehicle 4.

[0019] It is important to note that Figures 3B-3D are examples of different smoke conditions, illustrating various scenarios of smoke occurrence. In these conditions, relying solely on a fixed determination method may lead to false positives. In contrast, embodiments of the present invention use artificial intelligence technology, utilizing deep learning and machine learning algorithms to learn and identify specific patterns from complex data sets in images, recognize various forms of smoke, and provide a rapid response at the early stage of a fire. AI technology not only improves detection speed, but also increases accuracy and reduces unnecessary interference caused by false alarms.

[0020] It should be noted that the present invention is not limited to detecting smoke. Those skilled in the art can appropriately derive this to detect other abnormal conditions. For example, the environmental image signal IMG in FIG. 4 indicates that a fire has appeared near the bottom battery of the electric vehicle 3 (i.e., within a predetermined range). AI technology can analyze the characteristics of the fire situation through machine learning. For example, sparks first appear near the battery, then flames appear, and smoke continuously emits from the bottom battery area. AI technology can further evaluate the probability of a fire in the bottom battery of the electric vehicle 3. Furthermore, if the analysis result of the AI ​​technology indicates that the probability of a fire is greater than a threshold, the control module 16 can stop charging the electric vehicle 3 to mitigate damage. Furthermore, the control module 16 can notify an administrator via the communication module 14. For example, the control module 16 can be connected to the administrator's messaging software by communicating with a messaging software service host, and transmit the analysis result to notify the administrator of the abnormal situation. Alternatively, the control module 16 can notify the fire department via the communication module 14, for example, by connecting to a fire alarm receiving system and transmitting the analysis result to the fire brigade to notify relevant parties of the abnormal situation or prompting the fire alarm receiving system to issue a fire alarm so as to evacuate people in advance. In one embodiment, the control module 16 can simultaneously determine whether the ambient temperature near the electric vehicle 3 is abnormal based on the temperature signal TMP from the temperature sensing module 12, to avoid misidentifying a non-fire situation as a fire.

[0021] The environmental image signal IMG in FIG. 5A shows that water has accumulated at the bottom of the electric vehicle 3 (i.e., within a predetermined range). AI technology can analyze the characteristics of the water accumulation through machine learning. For example, if the height or range of the water accumulation does not change significantly over a certain period of time, the AI ​​technology can further determine that the water accumulation is a normal condition and not an abnormal ambient environment. In this case, the control module 16 does not stop charging the electric vehicle 3.

[0022] The environmental image signal IMG in FIG. 5B indicates that the bottom of the electric vehicle 3 is submerged. The AI ​​technology can analyze the characteristics of the submerged situation through machine learning. For example, if the submergence level continues to increase over time and exceeds half the height of the tires, the AI ​​technology can further evaluate the probability of submergence at the location of the electric vehicle 3. Furthermore, if the analysis result of the AI ​​technology indicates that the probability of submergence is greater than a threshold, the control module 16 can stop charging the electric vehicle 3 to mitigate potential damage. Furthermore, the control module 16 can notify an administrator via the communication module 14. For example, the control module 16 can communicate with a messaging software service host via the communication module 14 to connect to the administrator's messaging software and send the analysis results to notify the administrator of the abnormal situation.

[0023] The environmental image signal IMG in FIG. 6 indicates that the electric vehicle 3 has collided with another vehicle 5. The AI ​​technology can use machine learning to analyze collision characteristics, such as whether the electric vehicle 3 has shifted position due to the collision or whether there is a visible change in the appearance of the electric vehicle 3 or vehicle 5. Next, the AI ​​technology can further evaluate the probability of the electric vehicle 3 experiencing a severe collision. Furthermore, if the analysis result of the AI ​​technology indicates that the probability of the electric vehicle 3 experiencing a severe collision is greater than a threshold, the control module 16 can stop charging the electric vehicle 3 to mitigate potential damage. Furthermore, the control module 16 can notify an administrator via the communication module 14. For example, the control module 16 can be connected to the administrator's messaging software by communicating with a messaging software service host, and send the analysis results to notify the administrator of the abnormal situation.

[0024] The environmental image signal IMG in FIG. 7 indicates that an earthquake is occurring in the area where the electric vehicle 3 is located. The AI ​​technology can use machine learning to analyze earthquake situation characteristics, such as whether the electric vehicle 3 has shifted position due to the earthquake and the degree of shaking of the electric vehicle 3. Next, the AI ​​technology can further evaluate the probability of a strong earthquake occurring in the area where the electric vehicle 3 is located. Furthermore, if the analysis result of the AI ​​technology indicates that the probability of a strong earthquake occurring in the area of ​​the electric vehicle 3 is greater than a threshold, the control module 16 can stop charging the electric vehicle 3 to mitigate potential damage. Furthermore, the control module 16 can notify an administrator via the communication module 14, for example, by communicating with a messaging software service host and connecting to the administrator's messaging software to send the analysis result and notify the administrator of the abnormal situation.

[0025] The above-described embodiment shows that the monitoring system 1 can use artificial intelligence technology to analyze the environmental image signal IMG to determine whether an abnormal situation, such as a fire, flooding, a strong earthquake, or a vehicle accident, has occurred inside or around the charging vehicle 3. However, the present invention is not limited to these scenarios. The artificial intelligence technology is continuously updated through machine learning, and is capable of identifying various abnormal situations, self-correcting, and adjusting the basis for its determination, and is therefore not constrained by initial settings. Meanwhile, when determining thresholds related to dangerous situations, the control module 16 preferably uses artificial intelligence technology to optimize the threshold settings, for example, by using convolutional neural networks (CNNs) to dynamically determine the thresholds, but is not limited to this method. In one embodiment, the thresholds may be manually set by an operator, which is also within the scope of the present invention.

[0026] In addition to analyzing the environmental image signal IMG using artificial intelligence techniques, the control module 16 can also control the operation of the power module 20 based on sensing signals from other modules. For example, if the temperature sensing module 12 detects that the temperature signal TMP near the electric vehicle 3 exceeds a temperature threshold, the control module 16 can control the power module 20 to stop charging the electric vehicle 3 or reduce the charging current to avoid danger due to high temperatures. At the same time, the control module 16 can optimize the temperature threshold setting using artificial intelligence techniques, or the temperature threshold can be manually set by an operator. Alternatively, in one embodiment, multiple temperature thresholds can be set for different control purposes. For example, if the temperature signal TMP exceeds a first temperature threshold, the charging current can be reduced by 4 amperes; if the temperature signal TMP exceeds a second temperature threshold, the charging current can be reduced by another 4 amperes; and if the temperature signal TMP exceeds a third temperature threshold, charging of the electric vehicle 3 can be stopped. Such adjustment of charging operations based on different temperature thresholds should be familiar to those skilled in the art.

[0027] Meanwhile, in addition to being used to communicate with the messaging software service host and connect to the administrator's messaging software, in one embodiment, the communication module 14 can also be connected to a strong earthquake notification service host to receive earthquake early warnings issued by the host. In other words, in addition to using artificial intelligence technology to analyze the environmental image signal IMG and determine the earthquake situation, the control module 16 can receive the earthquake early warning via the communication module 14 and, if the location of the electric vehicle 3 is affected by a strong earthquake, proactively control the power module 20 to stop charging the electric vehicle 3 to avoid more serious damage.

[0028] Similarly, in another embodiment, the communication module 14 can be connected to a fire alarm control panel to receive fire alarms issued by the panel. That is, in addition to using artificial intelligence technology to analyze the environmental image signal IMG and determine the fire situation, the control module 16 can receive the fire alarm via the communication module 14 and proactively control the power module 20 to stop charging the electric vehicle 3 in order to avoid more serious damage if the location of the electric vehicle 3 is affected by a fire.

[0029] Furthermore, the monitoring system 1 may include an alarm device connected to the control module 16 and used to issue an alarm signal. For example, the alarm device may be a speaker, an indicator light, a screen, etc. used to emit a sound, a light signal, an image, or a text message. The control module 16 may control the alarm device to issue an alarm signal based on the charge status of the power module 20 or when an abnormal situation occurs.

[0030] The above-described operation of the monitoring system 1 can be summarized as a monitoring method 8, as shown in Figure 8. The monitoring method 8 includes the following steps.

[0031] Step 80: Start.

[0032] Step 82: Capture at least one environmental image signal associated with the electric vehicle.

[0033] Step 84: Applying artificial intelligence techniques to analyze at least one environmental image signal, generating an analysis result, and controlling the power module according to the analysis result.

[0034] Step 86: End.

[0035] For detailed operations and variations of the monitoring method 8, please refer to the previous explanation, and therefore will not be repeated here.

[0036] In the prior art, safety protection measures for charging piles are based on the vehicle's battery management system and power detection system. However, these protection measures can only respond to abnormal power input and output and cannot provide early warnings for environmental factors. In particular, at public charging stations where there are no dedicated inspection staff, problems cannot be reported immediately when they occur, which can lead to serious accidents. In contrast, the monitoring system of the present invention uses various monitoring technologies, particularly artificial intelligence technology, to detect environmental information during the charging process, identify abnormal situations at an early stage, and take measures to prevent serious damage. Therefore, the present invention can enhance the charging safety protection of electric vehicles.

[0037] Those skilled in the art will readily recognize that numerous modifications and variations of the apparatus and method may be made while retaining the teachings of the present invention. Accordingly, the above disclosure should be construed as limited only by the metes and bounds of the appended claims.

Claims

1. 1. A monitoring system for a charging pile, the charging pile charging electric vehicles via power modules, the monitoring system comprising: an image capture module configured to capture at least one environmental image signal associated with the electric vehicle; a control module connected to the image capture module and configured to apply artificial intelligence techniques to analyze the at least one environmental image signal, generate an analysis result, and control the power module in response to the analysis result.

2. The monitoring system of claim 1 , wherein if the analysis result indicates that a probability of an occurrence of a hazardous condition is greater than a threshold, the control module controls the power module to stop charging the electric vehicle.

3. The monitoring system of claim 2 , wherein the at least one environmental image signal indicates a smoke situation or a fire source within a predetermined range of the electric vehicle, and the hazardous condition is a fire.

4. The monitoring system of claim 3 , wherein the artificial intelligence technology uses machine learning to recognize the smoke condition or fire source and determine the occurrence probability of the hazardous condition based on characteristics of the smoke or fire source.

5. The monitoring system of claim 2 , wherein the at least one environmental image signal indicates a submersion condition of the electric vehicle within a predetermined range, and the hazardous condition is submersion.

6. The monitoring system of claim 5 , wherein the artificial intelligence technology uses machine learning to recognize the submersion situation and determine the probability of occurrence of the hazardous condition based on characteristics of the submersion situation.

7. The monitoring system of claim 2 , wherein the at least one environmental image signal indicates that the electric vehicle is experiencing a vibration condition, and the hazardous condition is a strong earthquake.

8. The monitoring system of claim 7 , wherein the artificial intelligence technology uses machine learning to recognize the vibration scenario and determine the probability of occurrence of the hazardous condition based on characteristics of the vibration scenario.

9. The monitoring system of claim 2 , wherein the control module is further configured to set the threshold.

10. The monitoring system of claim 1 , wherein if the analysis result indicates a collision of the electric vehicle, the control module controls the power module to stop charging the electric vehicle.

11. 2. The monitoring system of claim 1, further comprising: a temperature sensing module connected to the control module and configured to sense a temperature signal of an environment near the electric vehicle, the control module further configured to control the power module to stop charging the electric vehicle or reduce a charging current when the temperature signal exceeds a temperature threshold.

12. The monitoring system of claim 11 , wherein the control module is further configured to set the temperature threshold.

13. The monitoring system of claim 1 , further comprising a communication module connected to the control module, the control module using the communication module to exchange messages with a host.

14. 14. The monitoring system of claim 13, wherein the host is a strong earthquake notification service host used to issue strong earthquake notifications, and when the communication module receives a strong earthquake notification and the strong earthquake notification indicates that a location of the electric vehicle is affected by a strong earthquake, the control module is further configured to control the power module to stop charging the electric vehicle.

15. 14. The monitoring system of claim 13, wherein the host is a fire alarm control panel used to issue fire alarms, and when the communications module receives a fire alarm and the fire alarm indicates that the location of the electric vehicle is affected by a fire, the control module is further configured to control the power module to stop charging the electric vehicle.

16. 14. The monitoring system of claim 13, wherein the host is a communications software service host connected to at least one communications software of at least one administrator, and the control module is further configured to transmit the analysis results to the at least one communications software of the at least one administrator via the communications module and the communications software service host.

17. 2. The monitoring system of claim 1, further comprising: an alarm device connected to the control module and configured to issue an alarm signal, the control module further configured to control the alarm device to issue the alarm signal based on a charge state of the power module.

18. 1. A monitoring method for a charging pile, the charging pile charging an electric vehicle via a power module, the monitoring method comprising: capturing at least one environmental image signal associated with the electric vehicle; applying artificial intelligence techniques to analyze the at least one environmental image signal to generate an analysis result; and controlling the power module in response to the analysis result.

19. 20. The monitoring method of claim 18, further comprising controlling the power module to stop charging the electric vehicle if the analysis result indicates that a probability of an unsafe condition occurring is greater than a threshold.

20. 20. The monitoring method of claim 19, wherein the at least one environmental image signal indicates a smoke situation or a fire source within a predetermined range of the electric vehicle, and the hazardous condition is a fire.

21. 21. The monitoring method of claim 20, wherein the artificial intelligence technology uses machine learning to recognize the smoke condition or fire source and determine the probability of occurrence of the hazardous condition based on characteristics of the smoke or fire source.

22. 20. The method of claim 19, wherein the at least one environmental image signal indicates a submersion condition of the electric vehicle within a predetermined range, and the hazardous condition is submersion.

23. 23. The monitoring method of claim 22, wherein the artificial intelligence technique uses machine learning to recognize the submersion situation and determine the probability of occurrence of the hazardous condition based on characteristics of the submersion situation.

24. 20. The monitoring method of claim 19, wherein the at least one environmental image signal indicates that the electric vehicle is experiencing a vibration condition, and the hazardous condition is a strong earthquake.

25. 25. The monitoring method of claim 24, wherein the artificial intelligence technique uses machine learning to recognize the vibration scenario and determine the probability of occurrence of the hazardous condition based on characteristics of the vibration scenario.

26. 20. The method of claim 19, further comprising the step of setting the threshold.

27. 20. The monitoring method of claim 18, further comprising controlling the power module to stop charging the electric vehicle if the analysis indicates a crash of the electric vehicle.

28. 20. The monitoring method of claim 18, further comprising the steps of: sensing a temperature signal of an environment near the electric vehicle; and controlling the power module to stop charging the electric vehicle or reduce a charging current if the temperature signal exceeds a temperature threshold.

29. 30. The method of claim 28, further comprising the step of setting the temperature threshold.

30. 20. The monitoring method of claim 18, further comprising the step of exchanging messages with the host.

31. 31. The monitoring method of claim 30, wherein the host is a strong earthquake notification service host used to issue strong earthquake notifications, and the monitoring method further includes the step of controlling the power module to stop charging the electric vehicle when the strong earthquake notification is received and indicates that the location of the electric vehicle is affected by a strong earthquake.

32. 31. The monitoring method of claim 30, wherein the host is a fire alarm control panel used to issue fire alarms, and the monitoring method further includes receiving the fire alarm and controlling the power module to stop charging the electric vehicle if the fire alarm indicates that the location of the electric vehicle is affected by a fire.

33. 31. The monitoring method of claim 30, wherein the host is a communications software service host connected to at least one communications software of at least one administrator, and the monitoring method further comprises the step of transmitting the analysis results to the at least one communications software of the at least one administrator via the communications software service host.

34. 20. The monitoring method of claim 18, further comprising controlling an alarm device to issue an alarm signal based on the state of charge of the power module.

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