A charging pile error monitoring method based on vehicle-pile data interaction and related equipment

Abstract

The application discloses a charging pile error monitoring method based on vehicle-pile data interaction and related equipment, and key electric energy data of a new energy vehicle and a direct current charging pile are acquired in real time through a charging pile remote monitoring platform to obtain a charging pile metering error index average value; a deviation threshold of the charging pile metering error index average value is determined according to a metering grade of the charging pile, fixed loss of a vehicle-pile charging process and deviation caused by a charging pile aging factor; the charging pile metering error index average value is compared with the deviation threshold of the charging pile metering error index average value to determine whether the charging pile is credible, and then state information of the charging pile is updated. Through multiple verification, whether the metering information of the charging pile is credible is determined, online monitoring of the charging pile is realized, and false alarms caused by part of remote monitoring data noise are avoided.

Description

Technical Field

[0001] This invention relates to a method and related equipment for monitoring charging pile errors based on vehicle-charging pile data interaction, belonging to the field of power equipment monitoring technology. Background Technology

[0002] Currently, my country is vigorously developing the new energy vehicle industry, and the domestic charging infrastructure industry is growing rapidly. As an essential charging device for new energy vehicles, the accuracy of metering of new energy charging piles has become a focus of attention for new energy vehicle consumers. Summary of the Invention

[0003] Purpose of the invention: In order to address the issue of the reliability of metering information of charging piles, this invention provides a charging pile error monitoring method based on vehicle-charging pile data interaction.

[0004] Technical solution: To achieve the above objectives, the technical solution adopted by this invention is as follows:

[0005] A charging pile error monitoring method based on vehicle-charging pile data interaction is proposed. This method acquires key electrical energy data of new energy vehicles and DC charging piles in real time through a remote monitoring platform, monitors the dynamic changes and interactive behavior of vehicle-charging pile data, calculates the electrical energy metering data error index of the charging piles in real time, and issues an alert when the vehicle-charging pile electrical energy data error index exceeds a threshold. The method focuses on the recent operating data and vehicle-charging pile electrical energy data error coefficients of charging piles included in the alert list. Whether manual verification is required is determined based on events where the electrical energy metering error index exceeds the threshold and the trend of electrical energy metering error changes. The specific steps include:

[0006] Step 1: The charging pile remote monitoring platform records information from the charging pile metering unit in real time, and simultaneously receives metering information from the vehicle-side BMS transmitted to the charging pile. Based on the information recorded by the charging pile metering unit, the platform obtains the calculated electrical energy data. Based on the metering information transmitted to the vehicle-side BMS, the platform obtains the electrical energy data measured by the vehicle-side BMS transmitted to the charging pile.

[0007] Step 2: Based on the electrical energy data recorded and calculated by the charging pile metering unit, the electrical energy information calculated by the vehicle-side BMS transmitted to the charging pile, and the charging cycle, the average value of the charging pile metering error index is obtained.

[0008] Step 3: Determine the deviation threshold of the average value of the charging pile metering error index based on the metering level of the charging pile, the fixed loss during the vehicle-charging process, and the aging factors of the charging pile.

[0009] Step 4: Compare the average value of the charging pile's metering error index with the deviation threshold of the average value of the charging pile's metering error index. If the average value of the charging pile's metering error index exceeds the deviation threshold of the average value of the charging pile's metering error index, then the reliability of the charging pile is determined. If the average value of the charging pile's metering error index is within the deviation threshold range of the average value of the charging pile's metering error index, then the charging pile is considered reliable.

[0010] Step 5: Complete the online monitoring process for the metering accuracy of the charging pile and update the status information of the charging pile.

[0011] Preferably, the formula for the deviation threshold of the average value of the metering error index of the charging pile is as follows:

[0012] ;

[0013] in, This is the deviation threshold of the average value of the metering error index for charging piles. The coefficient for energy deviation caused by the metering verification level of the charging pile. This indicates the charging start time. This indicates the end time of charging. This represents the metering current value at the charging terminal during the charging cycle. This refers to the resistance value from the AC-DC conversion unit of the charging pile to the charging gun. For charging time, The metering unit of the charging pile records the calculated electrical energy data. This represents the vehicle-side BMS that transmits the energy information to the charging station for calculation. This represents the difference in energy consumption between older charging stations and standard charging stations when measuring electricity. This is the threshold scaling factor.

[0014] Preferred method for determining the credibility of charging piles is as follows:

[0015] Step 41, Condition 1: Analyze the subsequent n working data of the charging pile. If it is found that the average value of the charging pile measurement error index exceeds the deviation threshold of the average value of the charging pile measurement error index again, then the charging pile will be manually inspected and its measurement accuracy will be verified on-site.

[0016] Step 42, Scenario 2: First, analyze the subsequent n operational data of the charging pile. It is found that the average value of the charging pile's measurement error index in these n instances did not exceed the deviation threshold again. Then, analyze the n operational errors before the threshold warning for the charging pile. Analyze this data and perform linear regression fitting. The analysis shows that the fitting curve for the charging pile's 2n+1 data points shows an upward trend. Therefore, this charging pile is placed under close monitoring. If any further events exceeding the threshold occur, manual on-site verification of its measurement accuracy will be arranged.

[0017] Preferably, the metering unit records information including charging current, charging voltage, and energy information measured by the metering unit inside the charging pile, and transmits vehicle-side BMS metering information to the charging pile including vehicle-side metered charging current, charging voltage, and energy information.

[0018] Preferably, the formula for the average value of the metering error index of the charging pile is:

[0019] ;

[0020] In the formula: This represents the average value of the charging pile's metering error index during the charging cycle. This indicates the charging start time. This represents the end time of charging. The metering unit of the charging pile records the calculated electrical energy data. This represents the electrical energy data measured by the vehicle-side BMS and transmitted to the charging station.

[0021] Another objective of this invention is to provide a charging pile error monitoring system based on vehicle-charging pile data interaction, comprising a charging pile remote monitoring platform, a deviation threshold determination unit, a deviation threshold comparison unit, and a status update unit, wherein:

[0022] The charging pile remote monitoring platform is used to record information from the charging pile metering unit in real time, and simultaneously receive metering information from the vehicle-mounted BMS transmitted to the charging pile. Based on the information recorded by the charging pile metering unit, the platform obtains the calculated electrical energy data. Based on the metering information transmitted to the vehicle-mounted BMS, it obtains the electrical energy data measured by the vehicle-mounted BMS. Based on the calculated electrical energy data from the charging pile metering unit, the calculated electrical energy information from the vehicle-mounted BMS, and the charging cycle, the platform obtains the average value of the charging pile metering error index.

[0023] The deviation threshold determination unit is used to determine the deviation threshold of the average value of the charging pile metering error index based on the metering level of the charging pile, the fixed loss in the vehicle-charging process, and the aging factors of the charging pile.

[0024] The deviation threshold comparison unit is used to compare the average value of the charging pile metering error index with the deviation threshold of the average value of the charging pile metering error index. If the average value of the charging pile metering error index exceeds the deviation threshold of the average value of the charging pile metering error index, the reliability of the charging pile is determined. If the average value of the charging pile metering error index is within the deviation threshold range of the average value of the charging pile metering error index, it indicates that the charging pile is reliable.

[0025] The state update unit is used to update the state of the charging pile based on the reliable charging pile obtained by the deviation threshold comparison unit.

[0026] Preferably, the system includes a reliability determination unit, which is used to determine the reliability of the charging pile. It analyzes the charging pile's operational data for the next n times and finds that the average value of the charging pile's measurement error index exceeds the deviation threshold again. If this occurs, the charging pile will be manually inspected on-site to verify its measurement accuracy. Alternatively, the system first analyzes the charging pile's operational data for the next n times and finds that the average value of the charging pile's measurement error index does not exceed the deviation threshold again. Then, it analyzes the n operational errors prior to the threshold warning, analyzes these data and performs linear regression fitting. If the fitting curve for the charging pile's 2n+1 data points shows an upward trend, the charging pile will be placed under close observation. If any subsequent events exceeding the threshold occur, manual on-site verification of its measurement accuracy will be arranged.

[0027] Another object of the present invention is to provide an electronic device comprising: at least one processor, at least one memory, and a communication interface. The processor, memory, and communication interface communicate with each other. The memory stores program instructions executable by the processor, which invokes the program instructions to execute the aforementioned charging pile error monitoring method based on vehicle-charging pile data interaction.

[0028] Another object of the present invention is to provide a server including a processor and a memory, wherein the memory is used to store program instructions, and the processor is configured to invoke the program instructions to execute the charging pile error monitoring method based on vehicle-charging pile data interaction.

[0029] Another object of the present invention is to provide a computer-readable storage medium storing program instructions that, when executed by a processor, cause the processor to perform the aforementioned charging pile error monitoring method based on vehicle-charging pile data interaction.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] 1. This method combines vehicle-side BMS information and charging pile-side metering information to construct a charging pile working error index. Multiple verifications are used to determine whether the charging pile's metering information is reliable. This not only achieves online monitoring of the charging pile but also avoids false alarms caused by noise in some remote monitoring data.

[0032] 2. This invention studies the metering accuracy of charging piles based on the interaction between vehicle and charging pile data. It uses the ratio of the difference between vehicle and charging pile data to the metering value of the charging facility as the metering error index of the charging facility, which makes it easier to set deviation thresholds for vehicle collision data deviations under different charging power conditions. Attached Figure Description

[0033] Figure 1 A process for judging the working error of charging piles based on vehicle-charging pile data interaction;

[0034] Figure 2 This outlines the process for determining the reliability of charging pile data status. Detailed Implementation

[0035] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0036] A charging pile error monitoring method based on vehicle-charging pile data interaction is proposed. This method acquires key energy metering data of new energy vehicles and DC charging piles in real time through a remote monitoring platform. It utilizes distributed computing and big data processing methods to monitor the vehicle-charging pile charging data interaction behavior in real time and calculates the vehicle-charging pile energy data error coefficient. An alert is issued when the vehicle-charging pile energy data error coefficient exceeds a set threshold. The specific workflow is as follows: Figure 1 As shown, it includes the following steps:

[0037] Step 1: The charging pile remote monitoring platform records information from the charging pile metering unit in real time, and simultaneously receives metering information from the vehicle-mounted BMS transmitted to the charging pile. Based on the information recorded by the charging pile metering unit, the platform obtains the calculated electrical energy data. Based on the metering information transmitted to the vehicle-mounted BMS, the platform obtains the electrical energy data measured by the vehicle-mounted BMS. In another embodiment, the metering unit recording information includes charging current, charging voltage, and electrical energy information measured by the metering unit within the charging pile, and the vehicle-mounted BMS metering information transmitted to the charging pile includes vehicle-mounted metered charging current, charging voltage, and electrical energy information.

[0038] Step 2: Based on the energy data recorded and calculated by the charging pile metering unit, the energy information calculated by the vehicle-side BMS transmitted to the charging pile, and the charging cycle, calculate the charging pile metering error, the charging pile metering error index, and the average value of the charging pile metering error index in a complete charging cycle.

[0039] In another embodiment, the charging pile metering error is calculated. The formula is:

[0040] ;

[0041] Charging pile metering error index The formula is:

[0042] ;

[0043] The formula for the average value of the metering error index of the charging pile is:

[0044] ;

[0045] In the formula: This represents the average value of the charging pile's metering error index during the charging cycle. This indicates the charging start time. This represents the end time of charging. Indicates one complete charging cycle. The metering unit of the charging pile records the calculated electrical energy data. This represents the electrical energy data measured by the vehicle-side BMS and transmitted to the charging station.

[0046] Step 3: Determine the deviation threshold of the average value of the charging pile metering error index based on the metering level of the charging pile, the fixed loss during the vehicle-charging process, and the aging factors of the charging pile.

[0047] In another embodiment, the threshold value of the average value of the charging pile metering error index It is determined by taking into account multiple factors, including the metering level of the charging pile, the fixed losses during the vehicle-to-pile charging process, and the aging of the charging pile, as expressed as:

[0048] ;

[0049] in, The coefficient for energy deviation caused by the metering verification level of charging piles. The fixed loss energy deviation coefficient in the process of power transmission between charging piles and vehicles. This represents the electricity metering error coefficient caused by the aging of charging piles. This is the threshold scaling factor.

[0050] The fixed loss electrical energy deviation Line loss is caused by heat dissipated into the environment as part of the electrical energy transmitted by components such as DC charging guns and cables. Since DC charging equipment has high power, this part of the line loss cannot be ignored. The root mean square current method is used to calculate the line loss for one charging cycle, as follows:

[0051] ;

[0052] in, The resistance value from the AC-DC conversion unit of the charging pile to the charging gun end. For the metering current value at the charging end during the charging cycle, This refers to the charging time. This refers to the fixed losses and the terminal electrical energy during the charging cycle. and The ratio of the average:

[0053] ;

[0054] The power metering error coefficient This is caused by the aging of the charging piles. The calculation method for this coefficient is as follows:

[0055] ;

[0056] in This represents the difference in energy consumption between older charging piles and the standard charging pile's measurement value. It is a statistical value, calculated by selecting a certain number of charging piles of various service years at regular time intervals and measuring the average measurement error of each batch using a standard charging pile calibration instrument. Finally, a function of the charging pile measurement error value under different service times is fitted to obtain the aforementioned error coefficient. .

[0057] Furthermore, the threshold value of the average value of the charging pile metering error index. The calculation method for one charging cycle is as follows:

[0058] ;

[0059] Therefore, the formula for the deviation threshold of the average value of the charging pile metering error index is as follows:

[0060] ;

[0061] in, This is the deviation threshold of the average value of the metering error index for charging piles. The coefficient for energy deviation caused by the metering verification level of the charging pile. This indicates the charging start time. This represents the end time of charging. This represents the metering current value at the charging terminal during the charging cycle. This refers to the resistance value from the AC-DC conversion unit of the charging pile to the charging gun. For charging time, The metering unit of the charging pile records the calculated electrical energy data. This represents the vehicle-side BMS that transmits the energy information to the charging station for calculation. This represents the difference in energy consumption between older charging stations and standard charging stations when measuring electricity. This is the threshold scaling factor.

[0062] Step 4: Compare the average value of the charging pile's metering error index with the deviation threshold of the average value of the charging pile's metering error index in real time, and decide whether to proceed with the next step of inspection based on the charging pile's status. If the average value of the charging pile's metering error index exceeds the deviation threshold of the average value of the charging pile's metering error index, then the charging pile's reliability is determined. If the average value of the charging pile's metering error index is within the deviation threshold range of the average value of the charging pile's metering error index, then the charging pile is considered reliable.

[0063] In another embodiment, the reliability of charging pile metering data is determined by comparing and analyzing the recent average metering error index of the target charging pile. First, the average metering error index of the pile over the past five times is read, and then the average metering error index of the five tasks following the reliability determination process is analyzed. For example... Figure 2 As shown, the method for determining the credibility of charging piles is as follows:

[0064] Step 41, Scenario 1: Analyzing the subsequent n operational data of the charging pile, if it is found that the average value of the charging pile's measurement error index exceeds the deviation threshold again, then the charging pile will undergo manual inspection to verify its measurement accuracy on-site. In another embodiment, after analyzing the subsequent 5 operational data of the charging pile, it is found that the average value of the measurement error index exceeds the threshold again.

[0065] Step 42, Scenario 2: First, analyze the subsequent n operational data of the charging pile. It is found that the average value of the charging pile's measurement error index in these n instances did not exceed the deviation threshold again. Then, analyze the n operational errors before the over-threshold warning. Analyze these data and perform linear regression fitting. The analysis shows that the fitting curve for the charging pile's 2n+1 data points shows an upward trend. Therefore, this charging pile is placed under close monitoring. If any further events exceeding the threshold occur, manual on-site verification of its measurement accuracy will be arranged. In another embodiment, analyze the subsequent 5 operational data of the charging pile. It is found that the average value of the measurement error index in these 5 instances did not exceed the threshold again. Combining this with the 5 operational errors before the over-threshold warning, analyze these data and perform linear regression fitting. The analysis shows that the fitting curve for the charging pile's 11 data points shows an upward trend.

[0066] Step 5: Complete the online monitoring process for the metering accuracy of the charging pile and update the status information of the charging pile.

[0067] In this embodiment, real-time charging data of new energy vehicles and metering data of charging piles are acquired through a remote monitoring platform. Distributed computing and big data processing methods are used to monitor each charging pile connected to the platform network in real time. Metering and testing personnel only need to verify the individual charging piles that are flagged by the remote monitoring platform system on-site, achieving the effects of resource saving and efficient supervision.

[0068] In another embodiment, a charging pile error monitoring system based on vehicle-charging pile data interaction is provided, including a charging pile remote monitoring platform, a deviation threshold determination unit, a deviation threshold comparison unit, and a status update unit, wherein:

[0069] The charging pile remote monitoring platform is used to record information from the charging pile metering unit in real time, and simultaneously receive metering information from the vehicle-mounted BMS transmitted to the charging pile. Based on the information recorded by the charging pile metering unit, the platform obtains the calculated electrical energy data. Based on the metering information transmitted to the vehicle-mounted BMS, it obtains the electrical energy data measured by the vehicle-mounted BMS. Based on the calculated electrical energy data from the charging pile metering unit, the calculated electrical energy information from the vehicle-mounted BMS, and the charging cycle, the platform obtains the average value of the charging pile metering error index.

[0070] The deviation threshold determination unit is used to determine the deviation threshold of the average value of the charging pile metering error index based on the metering level of the charging pile, the fixed loss in the vehicle-charging process, and the aging factors of the charging pile.

[0071] The deviation threshold comparison unit is used to compare the average value of the charging pile metering error index with the deviation threshold of the average value of the charging pile metering error index. If the average value of the charging pile metering error index exceeds the deviation threshold of the average value of the charging pile metering error index, the reliability of the charging pile is determined. If the average value of the charging pile metering error index is within the deviation threshold range of the average value of the charging pile metering error index, it indicates that the charging pile is reliable.

[0072] The state update unit is used to update the state of the charging pile based on the reliable charging pile obtained by the deviation threshold comparison unit.

[0073] In another embodiment, a reliability determination unit is included, which is used to determine the reliability of the charging pile. This unit analyzes the charging pile's operational data from the next n times. If it finds that the average value of the charging pile's measurement error index exceeds the deviation threshold of the average value of the charging pile's measurement error index again, then the charging pile will undergo manual inspection to verify its measurement accuracy on-site. First, the unit analyzes the charging pile's operational data from the next n times and finds that the average value of the charging pile's measurement error index does not exceed the deviation threshold of the average value of the charging pile's measurement error index again. Then, it analyzes the n operational errors before the threshold warning, analyzes these data and performs linear regression fitting. If the analysis shows that the fitting curve of the charging pile's 2n+1 data points shows an upward trend, then the charging pile will be placed under close observation. If any subsequent events exceeding the threshold occur, then manual on-site verification of its measurement accuracy will be arranged.

[0074] In another embodiment, an electronic device is provided, comprising: at least one processor, at least one memory, and a communication interface. The processor, memory, and communication interface communicate with each other. The memory stores program instructions executable by the processor, which invokes the program instructions to execute the described charging pile error monitoring method based on vehicle-charging pile data interaction.

[0075] In another embodiment, a server is provided, including a processor and a memory, wherein the memory is used to store program instructions, and the processor is configured to invoke the program instructions to execute the charging pile error monitoring method based on vehicle-charging pile data interaction.

[0076] In another embodiment, a computer-readable storage medium is provided, the computer storage medium storing program instructions that, when executed by a processor, cause the processor to perform the charging pile error monitoring method based on vehicle-charging pile data interaction.

[0077] In this embodiment, a remote monitoring platform for charging piles is used to acquire key electrical energy data of new energy vehicles and DC charging piles in real time, monitor the dynamic changes and interactive behavior of vehicle-pile charging data, and calculate the electrical energy metering data error index of charging piles in real time. The platform issues an alert when the vehicle-pile electrical energy data error index exceeds a threshold. Special attention is paid to the recent operating data and vehicle-pile electrical energy data error coefficients of charging piles included in the alert list. Whether manual verification is required is determined based on events where the electrical energy metering error index exceeds the threshold and the trend of electrical energy metering error changes. The remote monitoring platform for charging piles comprises two main parts: hardware and software. The hardware part refers to the installation of a remote communication module in each DC charging pile, which can transmit various electrical metering data information under charging conditions in real time. The software part includes establishing a remote monitoring software platform and an information exchange protocol for the vehicle-side BMS. The vehicle-pile electrical energy data error coefficient calculated based on the charging condition electrical energy data is related to the error level of the charging pile metering module, the error level of the vehicle BMS metering module, and the loss value of the vehicle-pile charging model, achieving the effect of remotely monitoring the operating error of charging piles and quickly identifying problematic charging piles.

[0078] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring charging pile errors based on vehicle-charging pile data interaction, characterized in that, Includes the following steps: Step 1: The charging pile remote monitoring platform records information in real time by the charging pile metering unit, and at the same time receives the vehicle-side BMS metering information transmitted to the charging pile. The electrical energy data calculated by the charging pile metering unit is obtained based on the information recorded by the charging pile metering unit. The electrical energy data measured by the vehicle-side BMS is obtained based on the metering information transmitted to the charging pile. Step 2: Based on the energy data recorded and calculated by the charging pile metering unit, the energy information calculated by the vehicle-side BMS transmitted to the charging pile, and the charging cycle, the average value of the charging pile metering error index is obtained. Step 3: Determine the deviation threshold of the average value of the charging pile metering error index based on the metering level of the charging pile, the fixed loss during the vehicle-charging process, and the aging factors of the charging pile. The formula for the deviation threshold of the average value of the metering error index of the charging pile is as follows: ； in, This is the deviation threshold of the average value of the metering error index for charging piles. The coefficient for energy deviation caused by the metering verification level of the charging pile. This indicates the charging start time. This indicates the end time of charging. This represents the metering current value at the charging terminal during the charging cycle. This refers to the resistance value from the AC-DC conversion unit of the charging pile to the charging gun. For charging time, The metering unit of the charging pile records the calculated electrical energy data. This represents the vehicle-side BMS that transmits the energy information to the charging station for calculation. This represents the difference in energy consumption between older charging stations and standard charging stations when measuring electricity. This is the threshold scaling factor; Step 4: Compare the average value of the charging pile metering error index with the deviation threshold of the average value of the charging pile metering error index. If the average value of the charging pile metering error index exceeds the deviation threshold of the average value of the charging pile metering error index, then the reliability of the charging pile is determined. If the average value of the charging pile metering error index is within the deviation threshold range of the average value of the charging pile metering error index, then the charging pile is considered reliable. Step 5: Complete the online monitoring process for the metering accuracy of the charging pile and update the status information of the charging pile.

2. The charging pile error monitoring method based on vehicle-charging pile data interaction according to claim 1, characterized in that: The methods for determining the credibility of charging piles are as follows: Step 41, Condition 1: Analyze the working data of the charging pile in the next n times. If it is found that the average value of the charging pile measurement error index in these n times exceeds the deviation threshold of the average value of the charging pile measurement error index again, then the charging pile will be manually inspected and its measurement accuracy will be verified on site. Step 42, Working Condition 2: First, analyze the working data of the charging pile in the subsequent n times, and find that the average value of the charging pile metering error index in these n times did not exceed the deviation threshold of the average value of the charging pile metering error index again. Further analysis of the n working errors prior to the charging pile exceeding the threshold warning was conducted. These data were analyzed and linear regression fitting was performed. The analysis revealed that the fitting curve of the charging pile for the 2n+1 times showed an upward trend. Therefore, the charging pile was subsequently placed under key observation. If the event of exceeding the threshold occurs again, manual on-site verification of its measurement accuracy will be arranged.

3. The charging pile error monitoring method based on vehicle-charging pile data interaction according to claim 2, characterized in that: The metering unit records information including charging current, charging voltage, and energy information measured by the metering unit inside the charging pile, and transmits vehicle-side BMS metering information including vehicle-side metered charging current, charging voltage, and energy information.

4. The charging pile error monitoring method based on vehicle-charging pile data interaction according to claim 3, characterized in that: The formula for the average value of the metering error index of the charging pile is: ； In the formula: This represents the average value of the charging pile's metering error index during the charging cycle. This indicates the charging start time. This indicates the end time of charging. The metering unit of the charging pile records the calculated electrical energy data. This represents the electrical energy data measured by the vehicle-side BMS and transmitted to the charging station.

5. A charging pile error monitoring system based on the vehicle-charging pile data interaction method according to claim 1, characterized in that: It includes a charging pile remote monitoring platform, a deviation threshold determination unit, a deviation threshold comparison unit, and a status update unit, wherein: The charging pile remote monitoring platform is used to record information by the charging pile metering unit in real time, and simultaneously receive metering information from the vehicle-side BMS transmitted to the charging pile; it obtains the energy data recorded and calculated by the charging pile metering unit based on the information recorded by the charging pile metering unit, and obtains the energy data measured by the vehicle-side BMS transmitted to the charging pile based on the metering information transmitted to the charging pile; it obtains the average value of the charging pile metering error index based on the energy data recorded and calculated by the charging pile metering unit, the energy information calculated by the vehicle-side BMS transmitted to the charging pile, and the charging cycle. The deviation threshold determination unit is used to determine the deviation threshold of the average value of the charging pile metering error index based on the metering level of the charging pile, the fixed loss in the vehicle-charging process, and the aging factors of the charging pile. The deviation threshold comparison unit is used to compare the average value of the charging pile metering error index with the deviation threshold of the average value of the charging pile metering error index. If the average value of the charging pile metering error index exceeds the deviation threshold of the average value of the charging pile metering error index, the reliability of the charging pile is determined. If the average value of the charging pile metering error index is within the deviation threshold range of the average value of the charging pile metering error index, it indicates that the charging pile is reliable. The state update unit is used to update the state of the charging pile based on the reliable charging pile obtained by the deviation threshold comparison unit.

6. The charging pile error monitoring system with vehicle-charging pile data interaction according to claim 5, characterized in that: The system includes a reliability determination unit, which is used to determine the reliability of the charging pile. It analyzes the charging pile's operational data from subsequent n operations and finds that the average value of the charging pile's measurement error index exceeds the deviation threshold of the average value again. If this occurs, the charging pile is then manually inspected to verify its measurement accuracy on-site. Alternatively, it analyzes the charging pile's operational data from subsequent n operations and finds that the average value of the charging pile's measurement error index does not exceed the deviation threshold of the average value again. Further analysis of the n working errors prior to the charging pile exceeding the threshold warning was conducted. These data were analyzed and linear regression fitting was performed. The analysis revealed that the fitting curve of the charging pile for the 2n+1 times showed an upward trend. Therefore, the charging pile was subsequently placed under key observation. If the event of exceeding the threshold occurs again, manual on-site verification of its measurement accuracy will be arranged.

7. An electronic device, characterized in that, include: The system includes at least one processor, at least one memory, and a communication interface; the processor, memory, and communication interface communicate with each other. The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the charging pile error monitoring method based on vehicle-charging pile data interaction as described in any one of claims 1 to 4.

8. A server, characterized in that, It includes a processor and a memory, wherein the memory is used to store program instructions, and the processor is configured to invoke the program instructions to execute the charging pile error monitoring method based on vehicle-charging pile data interaction as described in any one of claims 1-4.

9. A computer-readable storage medium, characterized in that, The computer storage medium stores program instructions, which, when executed by a processor, cause the processor to perform the charging pile error monitoring method based on vehicle-charging pile data interaction as described in any one of claims 1-4.

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