A remote charging online monitoring system and method for electric vehicles

Abstract

This invention discloses an online monitoring system and method for remote charging of electric vehicles, relating to the field of electric vehicle charging. It includes: a data acquisition module for real-time acquisition of electric vehicle charging status information, and for real-time adjustment of the data acquisition cycle based on this information; a visualization unit for receiving and displaying the electric vehicle charging status information acquired by the acquisition module; and a monitoring module for acquiring the accumulated electric vehicle charging status information and monitoring the overall charging risk based on this information. This invention significantly improves the efficiency and relevance of data acquisition by real-time acquisition of key parameters such as voltage, current, and battery temperature, and intelligently adjusting the acquisition frequency based on parameter fluctuation characteristics. This reduces data redundancy when the charging status is stable and increases the monitoring frequency when parameters fluctuate abnormally.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, specifically to an online monitoring system and method for remote charging of electric vehicles. Background Technology

[0002] Charging monitoring is a technology that uses intelligent devices or systems to track and manage the charging process in real time. It can monitor parameters such as charging voltage, current, power level, and temperature, display the charging progress in real time, and provide early warnings for abnormal conditions such as overcharging, overheating, and short circuits, ensuring charging safety. It is widely used in electric vehicle charging.

[0003] Patent application number 202111296915.8 discloses an online monitoring system for remote charging of electric vehicles, including a cloud server located at a remote end, user terminals and control terminals remotely connected to the cloud server, and a smart meter connected to the cloud server via a gateway. The smart meter is connected to several "electricity managers" for charging electric vehicles. Each electricity manager includes a mounting plate for wall mounting, a housing fixedly mounted on the mounting plate, a metal pin with one end inside the housing and the other end extending out of the housing, and a sliding plate slidably mounted inside the housing. The sliding plate is equipped with... The metal clip corresponding to the metal pin is used to clamp the metal pin for conduction after the moving plate moves up. An iron core is provided at the bottom of the moving plate, and an electromagnet with demagnetization is provided at the bottom of the outer shell corresponding to the iron core. A limit plate is provided inside the outer shell, and a guide rod is provided between the limit plate and the bottom of the outer shell. A guide hole is provided on the moving plate, and the guide hole on the moving plate slides with the guide rod. This application aims to solve the problem that "the charging facilities do not provide comprehensive charging data for electric vehicles, cannot accurately analyze whether the charging status is normal, and the power supply is still connected after the electric vehicle has finished charging, which increases the probability of danger".

[0004] However, existing technologies do not provide comprehensive charging monitoring for the electric vehicle itself in electric vehicle charging scenarios, in order to fully ensure the safety of electric vehicle charging.

[0005] To address this, a remote charging online monitoring system and method for electric vehicles is proposed. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides an online monitoring system and method for remote charging of electric vehicles, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions;

[0008] This invention discloses an online monitoring system for remote charging of electric vehicles, comprising:

[0009] The system comprises the following modules: a data acquisition module for real-time collection of electric vehicle (EV) charging status information and a visualization unit for receiving and displaying this information; a monitoring module for acquiring accumulated charging status information and monitoring overall charging risks; a judgment module for setting charging risk thresholds, obtaining monitoring results from the monitoring module, and determining a risk when the monitoring results exceed the threshold; an early warning module for triggering an alert when the judgment module determines a charging risk; and an emergency maintenance unit for receiving judgment results from the judgment module and disconnecting the charging power supply from the EV when a charging risk is identified.

[0010] Furthermore, the acquisition module is equipped with a marking unit and a storage unit. The marking unit is used to receive the electric vehicle charging status information collected by the acquisition module and mark the electric vehicle charging status information with a collection timestamp. The storage unit is used to retrieve the electric vehicle charging status information that has been marked with a collection timestamp from the marking unit and sort and store the electric vehicle charging status information based on the collection timestamp marked on the electric vehicle charging status information.

[0011] The acquisition module is set with an initial operating cycle. Based on the initial operating cycle, the acquisition module runs three times continuously while the electric vehicle is connected to the power source, and then the acquisition cycle is adjusted.

[0012] Furthermore, the electric vehicle charging status information collected by the acquisition module includes: voltage, current, battery temperature, remaining charge, and battery health status (SOH). When the voltage, current, battery temperature, remaining charge, and battery health status (SOH) of the electric vehicle charging status information are stored in the storage unit, they are all stored as line graphs representing the changing trends.

[0013] The periodic adjustment of the data acquisition module for collecting electric vehicle charging status information follows the following rules:

[0014]

[0015] In the formula: d is the next collection cycle for electric vehicle charging status information by the acquisition module; d0 is the initial operating cycle; n is the total number of historical collections of electric vehicle charging status information; v i v i+1 Let be the voltage values ​​collected at the i-th and i+1-th times; min(v i ,v i+1 () indicates taking the minimum value within the parentheses; I i Ii+1 C represents the current values ​​collected in the i-th and i+1th samplings. i C i+1 These are the stable values ​​collected in the i-th and i+1th samplings; SOH i SOH i+1 ω(v), ω(I), ω(C), and ω(SOH) are the SOH values ​​collected for the i-th and i+1th time, respectively; ω(v), ω(I), ω(C), and ω(SOH) are the weights of SOH for the corresponding voltage, current, battery temperature, and battery health status.

[0016] When d is used to collect electric vehicle charging status information, the electric vehicle charging status information is collected at the beginning of d, and the next electric vehicle charging status information collection cycle is requested again based on the above formula.

[0017] Furthermore, ω(v), ω(I), ω(C), and ω(SOH) are all positive numbers and their sum is 1, and they obey the following:

[0018]

[0019] Furthermore, the visualization unit is integrated into a mobile computer device with display function. The visualization unit is interactively connected to the storage unit, retrieves the electric vehicle charging status information stored in the form of a line graph from the storage unit, and refreshes and displays the line graph representing the electric vehicle charging status information in real time.

[0020] When displaying line graphs representing electric vehicle charging status information, the visualization unit simultaneously identifies the risk index of each line graph corresponding to the electric vehicle charging status information. The line graph with the highest risk index is selected as the display target, and the display command is executed. Each time the line graph data is updated, the identification operation of the risk index of each line graph corresponding to the electric vehicle charging status information is refreshed, and the replacement of the displayed line graph is completed based on the identification results.

[0021] Furthermore, the logical representation of the risk index for identifying the electric vehicle charging status information corresponding to the line graph based on the line graph is as follows:

[0022] The nodes in the linear graph are sampled and denoted as the set of sample points;

[0023] When the target is identified as voltage or current:

[0024]

[0025] When the target is battery temperature or battery health status (SOH):

[0026]

[0027] In the formula: RI is the risk index; S is the standard deviation of volatility determined based on the corresponding values ​​of each sampling point in the sampling point set; k is the trend slope determined based on the line segments between each sampling point in the sampling point set; R is the rate of change of the trend determined based on the line segments between each sampling point in the sampling point set; max(S), max(|k|), and max(R) are the maximum values ​​of the standard deviation of volatility, the absolute value of the trend slope, and the rate of change of the trend in the historical information. This indicates taking the maximum value of each summation term in the numerator of the fractional part of the above expression;

[0028] in, Indicates to The operation of calculating the mean.

[0029] Furthermore, during the operation of the monitoring module, the charging status information of the electric vehicle accumulated by the acquisition module is obtained, and the operation of monitoring the comprehensive charging risk of the electric vehicle based on the charging status information is as follows:

[0030] The risk index corresponding to the charging status information of each electric vehicle is obtained during the operation of the visualization unit. The risk index corresponding to the charging status information of each electric vehicle is used to monitor the comprehensive charging risk of electric vehicles. The monitoring logic is expressed as follows:

[0031] Each time, the risk index, denoted as RI, is calculated based on the corresponding voltage, current, battery temperature, and battery health status (SOH). v RI I RI C RI SOH ;

[0032]

[0033] In the formula: RI syn The overall risk of electric vehicle charging; u represents the cumulative number of times the risk index corresponding to the current charging status information of each electric vehicle has been calculated; max s (RI v RI I RI C RI SOH ) represents the maximum risk index corresponding to the charging status information of each electric vehicle obtained in the s-th calculation;

[0034] Based on the updated risk index corresponding to the charging status information of each electric vehicle, the RI is repeatedly calculated. syn Then there is RI syn (1) RI syn (2), ..., RI syn (x-1), RI syn (x), always apply RI syn (1) RI syn (2), ..., RI syn (x-1), RIsyn The latest four calculation results in (x) determine whether there is a risk in charging electric vehicles;

[0035] RI syn (x-2)-RI syn (x-3)<RI syn (x-1)-RI syn (x-2)<RI syn (x)-RI syn (x-1);

[0036] If the above formula is true, it is determined that there is a risk in charging electric vehicles. The above determination logic and the logic of whether there is a risk in charging electric vehicles in the determination module are applied synchronously in the system.

[0037] Furthermore, when the determination module determines that there is no risk in charging the electric vehicle, it refreshes the system operation.

[0038] The early warning module is connected to the electrical equipment owned by the user of the electric vehicle via a network. When triggered, it sends a preset early warning message to the electrical equipment owned by the user of the electric vehicle via the network, thus completing the operation of issuing an early warning.

[0039] Among them, the emergency maintenance unit has a higher priority than the early warning module in the system.

[0040] Furthermore, the acquisition module is interconnected with a tagging unit and a storage unit via a wireless network. The acquisition module is interconnected with a visualization unit via a wireless network. The visualization unit is interconnected with the storage unit via a wireless network. The visualization unit is interconnected with a monitoring module and a judgment module via a wireless network. The judgment module is interconnected with an early warning module via a wireless network. The early warning module and the judgment module are interconnected with an emergency maintenance unit via a wireless network.

[0041] On the other hand, a method for online monitoring of remote charging of electric vehicles includes:

[0042] Configure a data collection period, collect electric vehicle charging status information in real time based on the collection period, and record the collected electric vehicle charging status information as a line graph showing trend changes; identify the electric vehicle charging risk index according to the recorded charging status information of each type of electric vehicle; monitor the comprehensive electric vehicle charging risk by combining the risk index identification results corresponding to the historical charging status information of each type of electric vehicle; set the electric vehicle charging risk judgment logic, obtain the comprehensive electric vehicle charging risk monitoring results, and determine whether there is a risk in electric vehicle charging based on the monitoring results and the electric vehicle charging risk judgment logic; issue an early warning when it is determined that there is a risk in electric vehicle charging, and simultaneously disconnect the connection between the electric vehicle charging power supply and the electric vehicle.

[0043] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0044] This invention provides an online monitoring system and method for remote charging of electric vehicles. During implementation, the system and method collect key parameters such as voltage, current, and battery temperature in real time, and intelligently adjust the collection frequency based on parameter fluctuation characteristics. This not only reduces data redundancy when the charging state is stable, but also increases the monitoring frequency when parameters fluctuate abnormally, significantly improving the efficiency and relevance of data collection.

[0045] At the visualization level, the system breaks through by presenting multi-dimensional data in real time in the form of dynamic line graphs, and through risk index intelligent identification technology, it automatically captures the parameter dimension with the highest current risk and displays it first, helping users quickly identify charging hazards;

[0046] Meanwhile, the risk monitoring process integrates multi-parameter risk indices and constructs a comprehensive risk model through historical data comparison and dynamic weighting algorithms. This model can not only provide real-time early warnings of abnormal states, but also complete a comprehensive maintenance strategy from monitoring to early warning and then to handling through an emergency power outage mechanism. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0048] Figure 1 A schematic diagram of a remote charging online monitoring system for electric vehicles;

[0049] Figure 2 This is a flowchart illustrating a method for online monitoring of remote charging of electric vehicles. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0051] The present invention will be further described below with reference to embodiments.

[0052] Example 1:

[0053] This embodiment provides an online monitoring system for remote charging of electric vehicles, such as... Figure 1 As shown, it includes:

[0054] The data acquisition module is used to collect electric vehicle charging status information in real time, and adjust the period of data acquisition based on the electric vehicle charging status information.

[0055] The acquisition module is equipped with a marking unit and a storage unit. The marking unit is used to receive the electric vehicle charging status information collected by the acquisition module and mark the electric vehicle charging status information with a collection timestamp. The storage unit is used to retrieve the electric vehicle charging status information that has been marked with a collection timestamp from the marking unit and sort and store the electric vehicle charging status information based on the collection timestamp marked on the electric vehicle charging status information.

[0056] The acquisition module is set with an initial operating cycle. The acquisition module runs three times continuously when the electric vehicle is connected to the power source based on the initial operating cycle, and then the acquisition cycle is adjusted.

[0057] The electric vehicle charging status information collected by the acquisition module includes: voltage, current, battery temperature, remaining charge, and battery health status (SOH). When the voltage, current, battery temperature, remaining charge, and battery health status (SOH) of the electric vehicle charging status information are stored in the storage unit, they are all stored as line graphs showing the trend of change.

[0058] The periodic adjustment of the data acquisition module for collecting electric vehicle charging status information follows the following rules:

[0059]

[0060] In the formula: d is the next collection cycle for electric vehicle charging status information by the acquisition module; d0 is the initial operating cycle; n is the total number of historical collections of electric vehicle charging status information; v i v i+1 Let be the voltage values ​​collected at the i-th and i+1-th times; min(v i ,v i+1 () indicates taking the minimum value within the parentheses; I i I i+1 C represents the current values ​​collected in the i-th and i+1th samplings. i C i+1 These are the stable values ​​collected in the i-th and i+1th samplings; SOH i SOH i+1 ω(v), ω(I), ω(C), and ω(SOH) are the SOH values ​​collected for the i-th and i+1th time, respectively; ω(v), ω(I), ω(C), and ω(SOH) are the weights of SOH for the corresponding voltage, current, battery temperature, and battery health status.

[0061] Wherein, when d is used to collect electric vehicle charging status information, the electric vehicle charging status information is collected at the beginning of d, and the next electric vehicle charging status information collection cycle is requested again based on the above formula.

[0062] ω(v), ω(I), ω(C), and ω(SOH) are all positive numbers and their sum is 1, and they follow the following rules:

[0063]

[0064] Through the logical formulas in the above embodiments, the cycle of the system acquisition module collecting electric vehicle charging status information is adaptively adjusted, making the collected electric vehicle charging status information more referential, thereby improving the accuracy of the subsequent module operation in this embodiment.

[0065] The visualization unit is used to receive and display the electric vehicle charging status information collected by the acquisition module.

[0066] The visualization unit is integrated by a mobile computer device with display function. The visualization unit is interactively connected with the storage unit. It retrieves the electric vehicle charging status information stored in the form of a line graph from the storage unit and refreshes and displays the line graph representing the electric vehicle charging status information in real time.

[0067] The visualization unit, when displaying a line graph representing the charging status information of electric vehicles, simultaneously identifies the risk index of the electric vehicle charging status information corresponding to each line graph based on the line graph, takes the line graph with the highest risk index as the display target, executes the display command, and refreshes the identification operation of the risk index of the electric vehicle charging status information corresponding to each line graph each time the line graph data is updated, and completes the replacement of the displayed line graph based on the identification results.

[0068] The logical representation of the risk index based on the line graph identification of electric vehicle charging status information is as follows:

[0069] The nodes in the linear graph are sampled and denoted as the set of sample points;

[0070] When the target is identified as voltage or current:

[0071]

[0072] When the target is battery temperature or battery health status (SOH):

[0073]

[0074] In the formula: RI is the risk index; S is the standard deviation of volatility determined based on the corresponding values ​​of each sampling point in the sampling point set; k is the trend slope determined based on the line segments between each sampling point in the sampling point set; R is the rate of change of the trend determined based on the line segments between each sampling point in the sampling point set; max(S), max(|k|), and max(R) are the maximum values ​​of the standard deviation of volatility, the absolute value of the trend slope, and the rate of change of the trend in the historical information. This indicates taking the maximum value of each summation term in the numerator of the fractional part of the above expression;

[0075] in, Indicates to The operation of calculating the mean;

[0076] The above logical formula is used to limit the calculation method of the risk index corresponding to the charging status information of various types of electric vehicles;

[0077] The monitoring module is used to acquire the electric vehicle charging status information accumulated by the acquisition module, and to monitor the comprehensive risks of electric vehicle charging based on the electric vehicle charging status information.

[0078] During the monitoring module's operation, it acquires the electric vehicle charging status information accumulated by the acquisition module, and monitors the overall charging risk of electric vehicles based on this charging status information.

[0079] The risk index corresponding to the charging status information of each electric vehicle is obtained during the operation of the visualization unit. The risk index corresponding to the charging status information of each electric vehicle is used to monitor the comprehensive charging risk of electric vehicles. The monitoring logic is expressed as follows:

[0080] Each time, the risk index, denoted as RI, is calculated based on the corresponding voltage, current, battery temperature, and battery health status (SOH). v RI I RI C RI SOH ;

[0081]

[0082] In the formula: RI syn The overall risk of electric vehicle charging; u represents the cumulative number of times the risk index corresponding to the current charging status information of each electric vehicle has been calculated; max s (RI v RI I RI C RI SOH ) represents the maximum risk index corresponding to the charging status information of each electric vehicle obtained in the s-th calculation;

[0083] Based on the updated risk index corresponding to the charging status information of each electric vehicle, the RI is repeatedly calculated. syn Then there is RI syn(1) RI syn (2), ..., RI syn (x-1), RI syn (x), always apply RI syn (1) RI syn (2), ..., RI syn (x-1), RI syn The latest four calculation results in (x) determine whether there is a risk in charging electric vehicles;

[0084] RI syn (x-2)-RI syn (x-3)<RI syn (x-1)-RI syn (x-2)<RI syn (x)-RI syn (x-1);

[0085] If the above formula is true, it is determined that there is a risk in electric vehicle charging. The above determination logic and the logic of whether there is a risk in electric vehicle charging in the determination module are applied synchronously in the system.

[0086] The above formula outputs the electric vehicle charging status information in digital form, and provides another electric vehicle charging risk judgment logic. This logic, together with the judgment module, provides more effective risk monitoring of the electric vehicle charging process.

[0087] The judgment module is used to set the risk judgment threshold for electric vehicle charging, obtain the comprehensive risk monitoring results of electric vehicle charging from the monitoring module, and judge that there is a risk in electric vehicle charging when the monitoring results are greater than the judgment threshold.

[0088] The early warning module is used to trigger operation and issue an early warning when the judgment module determines that there is a risk in charging electric vehicles.

[0089] The emergency maintenance unit is used to receive the judgment result from the judgment module and disconnect the electric vehicle charging power supply from the electric vehicle when the judgment result indicates that there is a risk in charging the electric vehicle.

[0090] When the determination module determines that there is no risk in charging electric vehicles, it refreshes the system operation.

[0091] The early warning module connects to the electrical equipment owned by the user of the electric vehicle via the network. When triggered, it sends a preset early warning message to the electrical equipment owned by the user of the electric vehicle via the network, thus completing the operation of issuing an early warning.

[0092] Among them, the emergency maintenance unit has a higher priority than the early warning module in the system;

[0093] The acquisition module is connected to the tagging unit and storage unit via a wireless network. The acquisition module is also connected to the visualization unit via a wireless network. The visualization unit is connected to the storage unit via a wireless network. The visualization unit is also connected to the monitoring module and the judgment module via a wireless network. The judgment module is connected to the early warning module via a wireless network. The early warning module and the judgment module are connected to the emergency maintenance unit via a wireless network.

[0094] In this embodiment, the acquisition module collects electric vehicle charging status information in real time and adjusts the collection cycle based on the electric vehicle charging status information. The marking unit synchronously receives the electric vehicle charging status information collected by the acquisition module and marks the collection timestamp of the electric vehicle charging status information. The storage unit retrieves the electric vehicle charging status information that has been marked with the collection timestamp in the marking unit in real time and sorts and stores the electric vehicle charging status information based on the collection timestamp. The visualization unit receives the electric vehicle charging status information collected by the acquisition module and displays the electric vehicle charging status information. Then, the monitoring module obtains the electric vehicle charging status information accumulated by the acquisition module and monitors the comprehensive risk of electric vehicle charging based on the electric vehicle charging status information.

[0095] The judgment module sets the electric vehicle charging risk judgment threshold, obtains the comprehensive risk monitoring results of electric vehicle charging from the monitoring module, compares the monitoring results with the judgment threshold, and judges that there is a risk to electric vehicle charging when the monitoring results are greater than the judgment threshold. Finally, the early warning module is triggered to issue an early warning when the judgment module judges that there is a risk to electric vehicle charging. The emergency maintenance unit receives the judgment results from the judgment module in real time and disconnects the connection between the electric vehicle charging power supply and the electric vehicle when the judgment result is that there is a risk to electric vehicle charging.

[0096] The system described in the above embodiments provides comprehensive charging monitoring services for electric vehicles in charging scenarios, ensuring the stability of the charging process and responding promptly to prevent the risk from escalating when abnormal risks occur.

[0097] Example 2:

[0098] At the implementation level, based on Example 1, this example refers to... Figure 2 A further detailed description of the online monitoring system for remote charging of electric vehicles in Example 1 is provided below:

[0099] A method for online monitoring of remote charging of electric vehicles includes:

[0100] Step 1: Configure the data collection period, collect electric vehicle charging status information in real time based on the data collection period, and record the collected electric vehicle charging status information in a line graph that shows the trend change.

[0101] Step 2: Identify the charging risk index of each type of electric vehicle based on the recorded charging status information.

[0102] Step 3: Combine historical charging status information of various types of electric vehicles with corresponding risk index identification results to monitor the comprehensive charging risk of electric vehicles.

[0103] Step 4: Set up the electric vehicle charging risk assessment logic, obtain the comprehensive risk monitoring results of electric vehicle charging, and determine whether there is a risk in electric vehicle charging based on the monitoring results and the electric vehicle charging risk assessment logic.

[0104] Step 5: Issue a warning when it is determined that there is a risk in charging the electric vehicle, and simultaneously disconnect the electric vehicle charging power supply from the electric vehicle.

[0105] In summary, the system and method described in the above embodiments, during execution, collect key parameters such as voltage, current, and battery temperature in real time and intelligently adjust the collection frequency based on parameter fluctuation characteristics. This reduces data redundancy when the charging state is stable and increases the monitoring frequency when parameters fluctuate abnormally, significantly improving the efficiency and relevance of data collection. At the visualization level, the system innovatively presents multi-dimensional data in real time in the form of dynamic line graphs. Through intelligent risk index identification technology, it automatically prioritizes displaying the parameter dimension with the highest current risk, helping users quickly identify potential charging hazards. At the same time, in the risk monitoring stage, it integrates multi-parameter risk indices and constructs a comprehensive risk model through historical data comparison and dynamic weighting algorithms. This not only provides real-time warnings of abnormal states but also completes a comprehensive maintenance strategy from monitoring to warning and then to handling through an emergency power-off mechanism.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An online monitoring system for remote charging of electric vehicles, characterized in that, include: The data acquisition module is used to collect electric vehicle charging status information in real time, and adjust the period of data acquisition based on the electric vehicle charging status information. The acquisition module is equipped with a marking unit and a storage unit. The marking unit is used to receive electric vehicle charging status information collected by the acquisition module and mark the electric vehicle charging status information with a collection timestamp. The storage unit is used to retrieve the electric vehicle charging status information that has been marked with a collection timestamp from the marking unit and sort and store the electric vehicle charging status information based on the collection timestamp marked on the electric vehicle charging status information. The acquisition module is set with an initial operating cycle. The acquisition module runs three times continuously when the electric vehicle is connected to the power source based on the initial operating cycle, and then the acquisition cycle is adjusted. The electric vehicle charging status information collected by the acquisition module includes: voltage, current, battery temperature, remaining power, and battery health status (SOH). When the voltage, current, battery temperature, remaining power, and battery health status (SOH) of the electric vehicle charging status information are stored in the storage unit, they are all stored as line graphs showing the trend of change. The periodic adjustment of the data acquisition module for collecting electric vehicle charging status information follows the following rules: ； In the formula: This marks the next cycle for the data acquisition module to collect electric vehicle charging status information. This is the initial running cycle; Total number of historical data collections of electric vehicle charging status information; These are the voltage values ​​collected at the i-th and i+1-th times; This indicates taking the minimum value within the parentheses; This indicates taking the minimum value within the parentheses; This indicates taking the minimum value within the parentheses; This indicates taking the minimum value within the parentheses; These are the current values ​​collected at the i-th and i+1-th times; These are the stable values ​​collected in the i-th and i+1th samplings; These are the SOH values ​​collected for the i-th and i+1th time. The weights for corresponding voltage, current, battery temperature, and battery health status (SOH) are determined. in, When used for collecting charging status information of electric vehicles, The charging status information of electric vehicles is collected at the beginning stage, and the next cycle of collecting charging status information of electric vehicles is requested based on the above formula. The visualization unit is used to receive and display the electric vehicle charging status information collected by the acquisition module. The monitoring module is used to acquire the electric vehicle charging status information accumulated by the acquisition module, and to monitor the comprehensive risks of electric vehicle charging based on the electric vehicle charging status information. The judgment module is used to set the risk judgment threshold for electric vehicle charging, obtain the comprehensive risk monitoring results of electric vehicle charging from the monitoring module, and judge that there is a risk in electric vehicle charging when the monitoring results are greater than the judgment threshold. The early warning module is used to trigger operation and issue an early warning when the judgment module determines that there is a risk in charging electric vehicles. The emergency maintenance unit is used to receive the judgment result from the judgment module and disconnect the electric vehicle charging power supply from the electric vehicle when the judgment result indicates that there is a risk in charging the electric vehicle.

2. The online monitoring system for remote charging of electric vehicles according to claim 1, characterized in that, The All numbers are positive and their sum is 1, and they follow the following rules: .

3. The online monitoring system for remote charging of electric vehicles according to claim 1, characterized in that, The visualization unit is integrated by a mobile computer device with display function. The visualization unit is interactively connected to the storage unit, and retrieves the electric vehicle charging status information stored in the form of a line graph from the storage unit, and refreshes and displays the line graph representing the electric vehicle charging status information in real time. When displaying line graphs representing electric vehicle charging status information, the visualization unit simultaneously identifies the risk index of each line graph corresponding to the electric vehicle charging status information. The line graph with the highest risk index is selected as the display target, and the display command is executed. Each time the line graph data is updated, the identification operation of the risk index of each line graph corresponding to the electric vehicle charging status information is refreshed, and the replacement of the displayed line graph is completed based on the identification results.

4. The online monitoring system for remote charging of electric vehicles according to claim 1, characterized in that, During the operation of the monitoring module, the charging status information of electric vehicles accumulated by the acquisition module is obtained, and the operation of monitoring the comprehensive charging risk of electric vehicles based on the charging status information is as follows: The risk index corresponding to the charging status information of each electric vehicle is obtained during the operation of the visualization unit. The risk index corresponding to the charging status information of each electric vehicle is used to monitor the comprehensive charging risk of electric vehicles. The monitoring logic is expressed as follows: Each time, the risk index is calculated based on the corresponding voltage, current, battery temperature, and battery health status (SOH), and denoted as [missing information]. ; ； In the formula: Comprehensive risks associated with charging electric vehicles; The cumulative number of times the risk index corresponding to the current charging status information of each electric vehicle is calculated. This indicates that the maximum risk index corresponding to the charging status information of each electric vehicle obtained in the s-th calculation is taken. Based on the updated risk index corresponding to the charging status information of each electric vehicle, the calculation is repeated. Then there is Always apply The latest four calculation results determine whether there are risks associated with charging electric vehicles; ； If the above formula is true, it is determined that there is a risk in charging electric vehicles. The above determination logic and the logic of whether there is a risk in charging electric vehicles in the determination module are applied synchronously in the system.

5. The online monitoring system for remote charging of electric vehicles according to claim 1, characterized in that, When the determination result indicates that there is no risk in charging the electric vehicle, the determination module refreshes the system operation. The early warning module is connected to the electrical equipment owned by the user of the electric vehicle via a network. When triggered, it sends a preset early warning message to the electrical equipment owned by the user of the electric vehicle via the network, thus completing the operation of issuing an early warning. Among them, the emergency maintenance unit has a higher priority than the early warning module in the system.

6. The online monitoring system for remote charging of electric vehicles according to claim 1, characterized in that, The acquisition module is interconnected with a tagging unit and a storage unit via a wireless network. The acquisition module is interconnected with a visualization unit via a wireless network. The visualization unit is interconnected with a monitoring module and a judgment module via a wireless network. The judgment module is interconnected with an early warning module via a wireless network. The early warning module and the judgment module are interconnected with an emergency maintenance unit via a wireless network.

7. A method for online monitoring of remote charging of electric vehicles, wherein the method is an implementation method of the online monitoring system for remote charging of electric vehicles as described in any one of claims 1-6, characterized in that, include: Step 1: Configure the data collection period, collect electric vehicle charging status information in real time based on the data collection period, and record the collected electric vehicle charging status information in a line graph that shows the trend change. Step 2: Identify the charging risk index of each type of electric vehicle based on the recorded charging status information. Step 3: Combine historical charging status information of various types of electric vehicles with corresponding risk index identification results to monitor the comprehensive charging risk of electric vehicles. Step 4: Set up the electric vehicle charging risk assessment logic, obtain the comprehensive risk monitoring results of electric vehicle charging, and determine whether there is a risk in electric vehicle charging based on the monitoring results and the electric vehicle charging risk assessment logic. Step 5: Issue a warning when it is determined that there is a risk in charging the electric vehicle, and simultaneously disconnect the electric vehicle charging power supply from the electric vehicle.

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