Automatic processing method and device for charging equipment exception

Abstract

The present application relates to the field of charging equipment, and particularly relates to an automatic processing method and device for charging equipment exception. An automatic processing method for charging equipment exception comprises the following steps: setting a charging equipment exception rule library; when the charging equipment is in an exception rule in the exception rule library, automatically executing a preset processing method for the exception rule. The response speed of operation and maintenance is improved, the previous manual intervention processing is changed into a response speed of seconds, the shutdown loss is greatly reduced, the false alarm rate is greatly reduced from 30% to 5%, the exception rule maintenance efficiency is greatly improved, the time consumption of new equipment rule configuration is reduced from 2 hours to 5 minutes, the labor cost is greatly reduced, the proportion of automatic recovery of the equipment is greatly improved, the automatic recovery rate reaches 95%, the availability rate is improved to 99.5%, and the customer satisfaction is improved.

Description

Technical Field

[0001] This invention relates to the field of charging equipment, and in particular to an automatic method and apparatus for handling abnormalities in charging equipment. Background Technology

[0002] In the field of modern Industrial Internet of Things (IIoT), especially in the operation and maintenance management of basic charging equipment and facilities such as charging piles, ensuring stable equipment operation and timely fault handling is crucial. However, traditional charging equipment anomaly monitoring systems employ static threshold alarm mechanisms, which face significant challenges such as fixed rules, separate handling, lack of false alarm processing, poor flexibility, and lack of self-healing capabilities. This results in response delays, high false alarm rates, and increased maintenance costs, making it difficult to meet the large-scale, complex, and ever-changing operation and maintenance needs of charging piles. The core technical challenge is how to construct an intelligent diagnostic and self-healing control system that can dynamically adapt to environmental changes, automate fault handling, and significantly reduce false alarm rates. Summary of the Invention

[0003] To address the technical problem of reducing false alarm rates, this invention provides an automatic processing method and an automatic processing device for charging equipment malfunctions.

[0004] Firstly, an automatic handling method for abnormalities in charging devices is provided, comprising the following steps: Set up a rule base for abnormal charging devices; When the charging device falls under an abnormal rule in the abnormal rule library, a preset processing method for the abnormal rule is automatically executed.

[0005] Preferably, the method further includes the step of: modifying the abnormal rules in the abnormal rule base of the charging device according to the historical behavior of the charging device, so as to prevent the charging device from unnecessarily entering the automatic execution of the preset processing method.

[0006] Preferably, the step of changing the abnormal rules in the abnormal rule base of the charging device based on the historical behavior of the charging device includes the following steps: Extract feature vectors of historical behavior of charging devices; An improved density clustering algorithm is used to obtain differences in the behavior patterns of charging devices based on the feature vectors. The charging devices are automatically grouped according to the features of their historical behavior, and a differentiated alarm strategy is customized for the automatically grouped charging devices. The abnormal rules in the abnormal rule base of the charging device are changed according to the differentiated alarm strategy.

[0007] Preferably, the setting of the charging device exception rule base includes: Set up a direct rule base for charging device anomalies. The direct rule base includes rules on whether the current, voltage, and combined parameters of the charging device are in an abnormal state. An indirect rule base for charging device anomalies is established. The indirect rule base includes local micro-temperature difference, micro-impedance change, electromagnetic noise power spectral density of specific frequency bands, and communication message jitter rate of the charging device.

[0008] Preferably, the execution frequency of the charging device abnormal indirect rule base is lower than the execution frequency of the charging device abnormal direct rule base.

[0009] Secondly, an automatic handling device for abnormalities in charging equipment is also provided, comprising: Setting unit: Used to set the exception rule base for charging equipment; Execution unit: When the charging device falls under an abnormal rule in the abnormal rule library, it automatically executes a preset processing method for the abnormal rule.

[0010] Preferred options also include: Whitelist unit: Used to modify the abnormal rules in the abnormal rule base of the charging device according to the historical behavior of the charging device, so as to prevent the charging device from unnecessarily entering the automatic execution of the preset processing method.

[0011] Preferably, the whitelist unit includes: Extraction module: Used to extract feature vectors of the historical behavior of charging devices; Grouping module: Used to obtain differences in the behavior patterns of charging devices based on the feature vector using an improved density clustering algorithm, automatically group the charging devices according to the features of their historical behavior, and customize differentiated alarm strategies for the automatically grouped charging devices. Change module: Used to change the exception rules in the exception rule base of the charging device according to the differentiated alarm strategy.

[0012] Preferably, the setting of the charging device exception rule base includes: Set up a direct rule base for charging device anomalies. The direct rule base includes rules on whether the current, voltage, and combined parameters of the charging device are in an abnormal state. An indirect rule base for charging device anomalies is established. The indirect rule base includes local micro-temperature difference, micro-impedance change, electromagnetic noise power spectral density of specific frequency bands, and communication message jitter rate of the charging device.

[0013] Preferably, the execution frequency of the charging device abnormal indirect rule base is lower than the execution frequency of the charging device abnormal direct rule base.

[0014] Beneficial effects: 1. Improve the speed of operation and maintenance response, changing from manual intervention to a response speed measured in seconds, greatly reducing downtime losses; 2. The false alarm rate has been significantly reduced, from 30% to 5%; 3. The efficiency of abnormal rule maintenance has been greatly improved. The time to configure new device rules has been reduced from 2 hours to 5 minutes, resulting in a significant reduction in labor costs. 4. The proportion of automatic equipment recovery has been greatly improved, with an automatic recovery rate of 95% and an availability rate of 99.5%, resulting in increased customer satisfaction. Attached Figure Description

[0015] Figure 1 This is a first embodiment of an automatic handling method for abnormalities in charging devices; Figure 2 This is a second embodiment of an automatic handling method for abnormalities in charging devices; Figure 3 This is an embodiment of modifying the exception rules in the exception rule base of the charging device based on the historical behavior of the charging device; Figure 4 An example of setting up an exception rule base for charging devices; Figure 5 This is a first embodiment of an automatic handling device for abnormalities in charging equipment; Figure 6 This is a second embodiment of an automatic handling device for abnormalities in charging equipment; Figure 7 This is an example of a whitelist unit; Figure 8 An example of setting up an exception rule base for charging devices.

[0016] Explanation of reference numerals in the attached figures: 1. An automatic processing device for charging equipment anomalies; 2. A charging equipment anomaly rule base; 11. Setting Unit; 12. Execution Unit; 13. Whitelist Unit; 131. Extraction Module; 132. Grouping Module; 133. Change Module; 21. Setting Direct Rule Base for Charging Device Abnormalities; 22. Setting Indirect Rule Base for Charging Device Abnormalities. Detailed Implementation

[0017] To address the technical problem of reducing false alarm rates, this invention provides an automatic processing method and an automatic processing device for charging equipment malfunctions.

[0018] Firstly, such as Figure 1 As shown, an automatic handling method for abnormalities in charging devices is provided, including the following steps: S1: Setting up an anomaly rule base for charging equipment; Charging equipment is indispensable in modern life. In this embodiment, the charging equipment mainly includes: high-power charging equipment and car charging piles. High-power charging equipment can be used for temporary power supply in remote areas; while car charging piles are used for charging electric vehicles or hybrid vehicles in various scenarios. Currently, car charging piles are widely distributed in cities and rural areas, gradually becoming an indispensable device in life. Due to the wide coverage of car charging piles, the time and labor costs of their maintenance have also increased significantly. If the maintenance cost of car charging piles is not reduced, the charging piles will experience various malfunctions due to inadequate maintenance, resulting in the loss of their due functions, inevitably causing electric vehicles to be unable to obtain the expected charging service at the expected location. If car charging piles can automatically handle faults in the event of anomalies, the operation and maintenance costs will be greatly reduced, not only in terms of time but also in terms of labor costs. In this embodiment, by setting up an anomaly rule base for possible charging equipment, possible anomalies of the charging equipment can be considered in advance, and a database of anomaly rules that conform to the anomaly rules can be set up. The purpose is to collect as many anomaly rules as possible together to facilitate subsequent solutions. Anomaly rules can include: voltage ripple instability, such as voltage ripple greater than 15% for 20 minutes; excessive current, such as current exceeding the safe current value provided by the charging pile by 20% for 5 minutes. All of the above can be considered anomalies of the charging equipment. Therefore, compiling these anomalies into a library is called an anomaly rule library. Setting up the charging equipment anomaly rule library involves the following steps: Utilizing a visual dynamic configuration system, a dual interface of a web console or mobile terminal is provided at the front-end interaction layer, supporting drag-and-drop logic components and setting real-time syntax validation; for example, when the input statement is: voltage fluctuation rate > 15% AND duration > 120 seconds, the parameter validity is automatically checked; a back-end rule parser is set up at the back-end to sequentially perform word segmentation processing on the statement input at the front-end interaction layer, generate an abstract syntax tree, and convert it into executable binary rules.

[0019] S2: When the charging device falls under an abnormal rule in the abnormal rule library, a preset processing method for that abnormal rule is automatically executed. The more comprehensive the abnormal rules in the abnormal rule library, the more preset processing methods will be automatically executed. Of course, not all abnormalities can be resolved by automatic execution steps. Therefore, the abnormal rules are determined based on the range that can be automatically resolved. As mentioned earlier, if abnormalities such as voltage ripple and current values ​​occur, automatic execution can resolve them. For example, if the voltage ripple is too large, the rectifier parameters can be controlled to reduce the ripple; if the current value is too large, the output current can be reduced. Therefore, the preset processing methods executed automatically are for abnormal rules in the abnormal rule library that can be resolved. In order to automatically execute the preset processing methods for abnormal rules, it is necessary to predict the possible abnormal rules in advance and to perform intelligent detection for the charging pile. For example, monitoring the output voltage and current, and monitoring the real-time output power. Based on these monitoring metrics, the system automatically executes preset processing methods to control the devices that generate these anomalies, enabling them to return to normal operation and allowing the entire charging pile to resume normal functioning. This prevents the parameters from entering the anomaly rule base again. The automatic execution of preset processing methods for these anomaly rules employs a multi-level action execution chain. An example of this multi-level action execution chain is as follows: Step 1 (highest priority): Remotely restart the device. If the restart is not completed within 30 seconds, it is considered a failure. If it fails, the above steps are repeated (maximum of 2 times). Step 2: Switch to backup power, with the prerequisite of verifying voltage stability (to prevent secondary failures during switching). Step 3: Generate a maintenance work order, linking the notification to the maintenance team. This multi-level action execution chain implements dynamic machine management. The dynamic machine includes the following states: pending, executing, success, failed. The transition conditions are: action triggered, timeout / API error code returned, device status verification passed, retry count exhausted. The transition states are: EXECUTING, FAILED, CLOSED, ESCALATED.

[0020] Preferred, such as Figure 2As shown, the method also includes step S3: Based on the historical behavior of the charging device, modify the abnormal rules in the abnormal rule base of the charging device to prevent the charging device from unnecessarily entering the automatic execution of the preset processing method. The aforementioned method steps are all for the initially configured charging device. However, if the initially configured abnormal rule base is still used after the charging device has been running for a long time, it will reduce maintenance efficiency and increase maintenance costs. For example, if a charging device is in normal working condition for a long time, with current, voltage, and power stable within a reasonable range, then the rules in the abnormal rule base can be relaxed to raise the threshold for the charging device to enter the abnormal rule base. This would prevent occasional voltage ripples greater than 15% and lasting for 20 minutes from becoming a condition for the charging device to automatically execute the preset processing method. The above-mentioned behavior of modifying the abnormal rules in the abnormal rule base based on the historical behavior of the charging device can be called whitelisting behavior. That is, when conditions permit, certain behaviors of certain charging devices that meet the conditions are ignored, and the preset processing method is not automatically executed, reducing the workload of the charging device and improving its working efficiency.

[0021] Preferred, such as Figure 3 As shown: The step of changing the exception rules in the exception rule base of the charging device based on the historical behavior of the charging device includes the following steps: S31: Extract the feature vector of the charging device's historical behavior; including the charging device's normal operating status, normal operating duration, load status during normal operation, and various parameters of the charging device, including current ripple, voltage ripple, extreme values ​​of current and voltage, stability, etc. Set the above parameters as the feature vector of the charging device's historical behavior.

[0022] S32: Using an improved density clustering algorithm, the behavior patterns of charging devices are differentiated based on the feature vectors. The charging devices are automatically grouped according to their historical behavior characteristics, and differentiated alarm strategies are customized for each group. Considering the characteristics of charging device installation—such as several devices installed in one small area and several in another—grouping of charging devices is possible. For example, voltage ripples exhibiting the same problem might appear in one small area, while current extremes exceeding warning limits might appear in another. This only reflects problems occurring in different areas; it's also possible for the same problem to occur simultaneously in several small areas. This is why differentiated alarm strategies need to be customized for each group. With grouping, different warning strategies can be applied to groups with different behavior patterns, facilitating the optimization and management of the entire charging device system.

[0023] S33: Change the exception rules in the exception rule base of the charging device according to the differentiated alarm strategy.

[0024] Preferred, such as Figure 4 As shown, the setting of the charging device exception rule base includes: S11: Establish a direct rule base for charging device anomalies. This direct rule base includes rules regarding whether the charging device's current, voltage, and combined current and voltage parameters are in an abnormal state. These rules directly affect the abnormal state of the charging device, such as parameters like current, voltage, and power. These parameters directly and immediately reflect the charging device's state.

[0025] S12: Establish an indirect rule base for charging equipment anomalies. This indirect rule base includes parameters such as local micro-temperature differences, micro-impedance changes, electromagnetic noise power spectral density in specific frequency bands, and communication message jitter rate. This indirect rule base indirectly reflects the charging equipment's status. For example, a change in the local micro-temperature difference might indicate a change in the internal environment of the charging equipment; however, the voltage and current, which are rules in the direct rule base, are not immediately triggered. As a whole, changes in the direct rule base directly reflect information directly related to the charging equipment. The indirect rule base, however, does not reflect information directly related to the charging equipment. While these indirect rules do not directly reflect charging equipment information, they should still be noted by charging equipment maintenance personnel. For example, a significant increase in the electromagnetic noise power spectral density in a specific frequency band might be caused by electromagnetic interference around the charging equipment, which could also affect its normal operation.

[0026] Preferably, the execution frequency of the charging device anomaly indirect rule library is lower than that of the charging device anomaly direct rule library. The charging device anomaly direct rule library is detected and executed at a frequency of minutes or hours. The charging device anomaly indirect rule library, however, is detected and executed at a frequency greater than hours. This is because the rules in the charging device direct rule library directly relate to parameters such as voltage, current, and power of the device being charged. The anomaly rules in the charging device indirect rule library, on the other hand, concern indirect parameters of the charging device other than voltage, current, and power; these parameters do not directly affect the charging state of the device being charged. For example, a slight change in impedance within the charging device may be caused by continuous charging, but if it does not temporarily affect the use of the charging device and the device being charged, then maintenance of the charging device is unnecessary; maintenance can be performed after the device being charged is fully charged. Therefore, the detection and execution frequency of anomaly rules in the charging device indirect rule library is lower than that in the charging device direct rule library.

[0027] Secondly, such as Figure 5 As shown, an automatic handling device 1 for abnormalities in charging equipment is also provided, comprising: Setting unit 11: Used to set the abnormal rule base for charging equipment; Execution unit 12: When the charging device is in an abnormal rule in the abnormal rule library, it automatically executes a preset processing method for the abnormal rule.

[0028] Preferred, such as Figure 6 As shown, it also includes: Whitelist unit 13: Used to change the abnormal rules in the abnormal rule base of the charging device according to the historical behavior of the charging device, so as to prevent the charging device from unnecessarily entering the automatic execution of the preset processing method.

[0029] Preferred, such as Figure 7 As shown, the whitelist unit 13 includes: Extraction module 131: Used to extract feature vectors of historical behavior of charging devices; Grouping module 132: Used to obtain differences in the behavior patterns of charging devices based on the feature vector using an improved density clustering algorithm, automatically group the charging devices according to the features of their historical behavior, and customize differentiated alarm strategies for the automatically grouped charging devices. Modification module 133: Used to modify the exception rules of the exception rule base of the charging device according to the differentiated alarm strategy.

[0030] Preferred, such as Figure 8 As shown, the setting of the charging device exception rule base 2 includes: Set up a direct rule base 21 for abnormal charging devices. The direct rule base includes rules on whether the current, voltage, and combined parameters of the charging device are in an abnormal state. Set up an indirect rule base 22 for abnormal charging equipment. The indirect rule base includes local micro-temperature difference, micro-impedance change, electromagnetic noise power spectral density of specific frequency bands, and communication message jitter rate of the charging equipment.

[0031] Preferably, the execution frequency of the charging device abnormal indirect rule base is lower than the execution frequency of the charging device abnormal direct rule base.

[0032] Beneficial effects: 1. Improve the speed of operation and maintenance response, changing from manual intervention to a response speed measured in seconds, greatly reducing downtime losses; 2. The false alarm rate has been significantly reduced, from 30% to 5%; 3. The efficiency of abnormal rule maintenance has been greatly improved. The time to configure new device rules has been reduced from 2 hours to 5 minutes, resulting in a significant reduction in labor costs. 4. The proportion of automatic equipment recovery has been greatly improved, with an automatic recovery rate of 95% and an availability rate of 99.5%, resulting in increased customer satisfaction.

[0033] Finally, it should be noted that any modification or equivalent substitution of some or all of the technical features based on the device structure and the technical solutions of the embodiments of the present invention, without departing from the corresponding technical solutions of the present invention, shall fall within the patent scope of the device structure and the embodiments of the present invention.

Claims

1. An automatic handling method for abnormalities in charging equipment, characterized in that, Includes the following steps: Set up a rule base for abnormal charging devices; When the charging device falls under an abnormal rule in the abnormal rule library, a preset processing method for the abnormal rule is automatically executed.

2. The automatic handling method for charging equipment malfunctions according to claim 1, characterized in that, It also includes the step of: changing the abnormal rules in the abnormal rule base of the charging device according to the historical behavior of the charging device, so as to avoid the charging device from unnecessarily entering the automatic execution of the preset processing method.

3. The automatic handling method for charging equipment malfunctions according to claim 2, characterized in that, The step of changing the exception rules in the exception rule base of the charging device based on the historical behavior of the charging device includes the following steps: Extract feature vectors of historical behavior of charging devices; An improved density clustering algorithm is used to obtain differences in the behavior patterns of charging devices based on the feature vectors. The charging devices are automatically grouped according to the features of their historical behavior, and a differentiated alarm strategy is customized for the automatically grouped charging devices. The abnormal rules in the abnormal rule base of the charging device are changed according to the differentiated alarm strategy.

4. The automatic handling method for charging equipment malfunctions according to claim 1, characterized in that, The set charging device exception rule base includes: Set up a direct rule base for charging device anomalies. The direct rule base includes rules on whether the current, voltage, and combined parameters of the charging device are in an abnormal state. An indirect rule base for charging device anomalies is established. The indirect rule base includes local micro-temperature difference, micro-impedance change, electromagnetic noise power spectral density of specific frequency bands, and communication message jitter rate of the charging device.

5. The automatic handling method for charging equipment malfunctions according to claim 4, characterized in that, The execution frequency of the indirect rule base for charging device anomalies is lower than that of the direct rule base for charging device anomalies.

6. An automatic handling device for abnormalities in charging equipment, characterized in that, include: Setting unit: Used to set the exception rule base for charging equipment; Execution unit: When the charging device falls under an abnormal rule in the abnormal rule library, it automatically executes a preset processing method for the abnormal rule.

7. The automatic handling device for charging equipment malfunctions according to claim 6, characterized in that, Also includes: Whitelist unit: Used to modify the abnormal rules in the abnormal rule base of the charging device according to the historical behavior of the charging device, so as to prevent the charging device from unnecessarily entering the automatic execution of the preset processing method.

8. The automatic handling device for charging equipment malfunctions according to claim 7, characterized in that, The whitelist unit includes: Extraction module: Used to extract feature vectors of the historical behavior of charging devices; Grouping module: Used to obtain differences in the behavior patterns of charging devices based on the feature vector using an improved density clustering algorithm, automatically group the charging devices according to the features of their historical behavior, and customize differentiated alarm strategies for the automatically grouped charging devices. Change module: Used to change the exception rules in the exception rule base of the charging device according to the differentiated alarm strategy.

9. The automatic handling device for charging equipment malfunctions according to claim 6, characterized in that, The set charging device exception rule base includes: Set up a direct rule base for charging device anomalies. The direct rule base includes rules on whether the current, voltage, and combined parameters of the charging device are in an abnormal state. An indirect rule base for charging device anomalies is established. The indirect rule base includes local micro-temperature difference, micro-impedance change, electromagnetic noise power spectral density of specific frequency bands, and communication message jitter rate of the charging device.

10. The automatic handling device for charging equipment malfunctions according to claim 9, characterized in that, The execution frequency of the indirect rule base for charging device anomalies is lower than that of the direct rule base for charging device anomalies.

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