Charging equipment off-line data processing method and device

Abstract

The embodiment of the invention discloses a charging equipment offline data processing method and device. The method comprises the following steps: acquiring offline data of the charging equipment according to a first preset time period, analyzing the offline data to determine an offline index, acquiring a plurality of preset offline fault levels and a corresponding offline fault level reference range, and determining the offline fault level of the charging equipment according to the offline index and the plurality of offline fault level reference ranges. Determining a current offline fault level corresponding to the charging device, obtaining a highest offline fault level of the charging device in a current offline detection period, the current offline detection period being a time period from the last recovery of the network to the current time, and responding to the current offline fault level being higher than the highest offline fault level. And generating corresponding offline prompt information, and sending the offline prompt information to the charging equipment and / or a user terminal bound with the charging equipment. According to the method, the network fault of the charging equipment can be automatically identified, so that the equipment operation and maintenance can be accurately driven by data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for processing offline data of a charging device. Background Art

[0002] With the popularity of electric vehicles, network management and fault identification of charging equipment have become increasingly important. Currently, the identification of charging equipment network faults faces many challenges, especially in the context of large-scale charging infrastructure, and the network fault troubleshooting process often requires a lot of human resources.

[0003] Traditional troubleshooting usually relies on manual monitoring and maintenance by operation and maintenance personnel. Operation and maintenance personnel must detect the pattern of communication interruption between each charging device and platform one by one, and then determine whether there is a network failure based on experience. This results in high labor costs and slow response speed. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a charging device offline data processing method and apparatus to automatically identify network faults of the charging device, improve network fault identification efficiency, and reduce network fault identification costs.

[0005] In a first aspect, a method for processing offline data of a charging device is provided, the method comprising: Acquiring offline data of the charging device according to a first preset time period; Determine a corresponding offline indicator according to the offline data; Acquire a plurality of preset offline fault levels and corresponding offline fault level reference ranges; Determining an offline identification result of the charging device according to the offline indicator and the multiple offline fault level reference ranges, wherein the offline identification result includes a current offline fault level; Obtaining the highest offline fault level of the charging device in a current offline detection cycle, where the current offline detection cycle is a time period from the last time the network was restored to normal to the current time; In response to the current offline fault level being higher than the highest offline fault level, generating corresponding offline prompt information; The offline prompt information is sent to the charging device and / or a user terminal bound to the charging device.

[0006] In a second aspect, a charging equipment offline data processing device is provided, the device comprising: A first acquisition module, used to acquire offline data of the charging device according to a first preset time period; A first determining module, used to determine a corresponding offline indicator according to the offline data; A second acquisition module, used to acquire a plurality of preset offline fault levels and corresponding offline fault level reference ranges; A second determination module, configured to determine an offline identification result of the charging device according to the offline indicator and the multiple offline fault level reference ranges, wherein the offline identification result includes a current offline fault level; A third acquisition module is used to obtain the highest offline fault level of the charging device in a current offline detection period, where the current offline detection period is a time period from the last time the network was restored to normal to the current time; A generating module, configured to generate corresponding offline prompt information in response to the current offline fault level being higher than the highest offline fault level; The sending module is used to send the offline prompt information to the charging device and / or the user terminal bound to the charging device.

[0007] According to a third aspect, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method described in the first aspect above.

[0008] According to a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0009] In a fifth aspect, a computer program product is provided, comprising a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the method described in the first aspect is implemented.

[0010] The embodiment of the present invention obtains offline data of the charging device according to a first preset time period, analyzes the offline data to determine offline indicators, obtains multiple pre-set offline fault levels and corresponding offline fault level reference ranges, determines the current offline fault level corresponding to the charging device according to the offline indicators and multiple offline fault level reference ranges, obtains the highest offline fault level of the charging device in the current offline detection cycle, and obtains the highest offline fault level of the charging device in the current offline detection cycle. The current offline detection cycle is the time period from the last time the network was restored to normal to the current time. In response to the current offline fault level being higher than the highest offline fault level, a corresponding offline prompt message is generated, and the offline prompt message is sent to the charging device and / or the user terminal bound to the charging device. The above method can realize automatic identification of network faults of charging devices, improve network fault identification efficiency, reduce network fault identification costs, and accurately drive network fault operation and maintenance of charging devices with data. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which: Figure 1 A schematic diagram of an offline data processing system for a charging device according to an embodiment of the present invention; Figure 2 A flowchart of an offline fault level classification method according to an embodiment of the present invention; Figure 3 A flowchart of an offline portrait generation method according to an embodiment of the present invention; Figure 4 A schematic diagram of updating a first offline portrait according to an embodiment of the present invention; Figure 5 A schematic diagram of updating a second offline portrait according to an embodiment of the present invention; Figure 6 It is a flow chart of a charging device offline data processing method according to an embodiment of the present invention; Figure 7 A flowchart of an offline data acquisition method according to an embodiment of the present invention; Figure 8 A flowchart of an offline data acquisition method according to an embodiment of the present invention; Fig. 9 A schematic diagram of an off-line data processing device for a charging device according to an embodiment of the present invention; Fig.10 Schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The present application is described below based on embodiments, but the present application is not limited to these embodiments. In the detailed description of the present application below, some specific details are described in detail. It is possible for those skilled in the art to fully understand the present application without the description of these details. In order to avoid confusing the essence of the present application, known methods, processes, flows, components and circuits are not described in detail.

[0013] In addition, persons of ordinary skill in the art will appreciate that the drawings provided herein are for illustration purposes and are not necessarily drawn to scale.

[0014] Unless the context clearly requires otherwise, the words "include", "comprising" and similar words throughout the application should be interpreted as including rather than exclusive or exhaustive; that is, the meaning is "including but not limited to".

[0015] In the description of this application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" is two or more.

[0016] Figure 1 FIG. 1 is a schematic diagram of an offline data processing system for a charging device according to an embodiment of the present invention. Figure 1 As shown, the charging device offline data processing system includes a server 11 and a plurality of charging devices 12 .

[0017] In this embodiment, the server 11 is a general data processing device that provides computing or application services for the charging platform client. It can be a single computer, a cluster of multiple computers, or a cloud server that can flexibly adjust computing resources through cloud technology. The charging device 12 is a device that can provide charging services for electric vehicles, such as a charging pile.

[0018] In a possible implementation, the charging device offline data processing system further includes a user terminal 13. The user terminal 13 is a terminal device held by the operator of the charging device 12. The user terminal 13 is bound to the charging device 12. Specifically, the user terminal 13 is a general terminal that can run charging platform client applications and applets. For example, the terminal can be a mobile phone, computer, tablet computer or other terminal device. The user terminal 13 is used to receive offline fault prompt information and network recovery prompt information.

[0019] In this embodiment, the server 11 exchanges information with multiple charging devices 12 through a network to obtain offline data of the charging devices 12 , and implements offline fault level classification and offline fault identification of the charging devices 12 based on the offline data.

[0020] In the offline fault level classification stage, the server 11 exchanges information with multiple charging devices 12 through the network to obtain a certain amount of offline data. Then, the server 11 uses the day as the time unit and regularly analyzes the offline data of the charging devices of the previous day in the early morning of each day to obtain the corresponding offline indicators, and then obtains the offline snapshot corresponding to the day. The offline indicators include at least one of the offline indicators such as the maximum offline duration, the cumulative offline duration, and the number of offline times. By performing big data analysis on a large number of offline snapshots, the distribution of all charging devices in at least one of the above offline indicators in a duration interval (for example, one month or one year, or a longer period of time) is observed, and then the corresponding offline portrait is determined. According to the offline portrait and the offline fault level control parameters, multiple offline fault levels and corresponding offline fault level reference ranges are determined.

[0021] In a possible implementation manner, the offline indicator may further include indicators such as average offline duration, offline distribution ratio, etc., and the offline indicator types may be added or deleted according to actual conditions.

[0022] In the offline fault identification stage, the server 11 takes the last time when the network is restored to normal as the starting point, obtains the offline data of the charging device 12 according to the first preset time period, analyzes the offline data to determine the corresponding offline index, and then determines the offline identification result of the charging device 12 according to the offline index and the multiple offline fault levels determined above and the corresponding offline fault level reference range, and the offline identification result includes the current offline fault level of the charging device 12. Then, the highest offline fault level of the charging device 12 in the current offline detection cycle is obtained, and the current offline fault level is compared with the highest offline fault level. If the current offline fault level is higher than the highest offline fault level, the corresponding offline prompt information is generated and sent to the charging device 12 and / or the user terminal 13 bound to the charging device 12. If the current offline fault level is not higher than the highest offline fault level, the offline fault detection result is ignored.

[0023] The charging device offline data processing system of the embodiment of the present invention obtains the offline data of the charging device according to the first preset time period, analyzes the offline data to determine the offline index, obtains multiple pre-set offline fault levels and corresponding offline fault level reference ranges, determines the current offline fault level corresponding to the charging device according to the offline index and multiple offline fault level reference ranges, obtains the highest offline fault level of the charging device in the current offline detection cycle, and obtains the highest offline fault level of the charging device in the current offline detection cycle. The current offline detection cycle is the time period from the last time the network was restored to normal to the current time. In response to the current offline fault level being higher than the highest offline fault level, a corresponding offline prompt message is generated, and the offline prompt message is sent to the charging device and / or the user terminal bound to the charging device. The above method can realize automatic identification of network faults of charging devices, improve network fault identification efficiency, reduce network fault identification costs, and accurately drive network fault operation and maintenance of charging devices with data.

[0024] The charging device offline data processing method of the embodiment of the present invention may include an offline fault level division part and an offline fault identification part. First, multiple offline fault levels are divided through big data analysis of massive offline data, and the corresponding offline fault level reference range is determined, thereby completing the preliminary data preparation for offline fault identification, and then after receiving the offline data of the charging device, the received offline data is analyzed based on the preliminary data preparation to determine whether the charging device has an offline fault.

[0025] Figure 2 FIG. 1 is a flow chart of the offline fault level classification method according to an embodiment of the present invention. Figure 2 As shown, the offline fault level classification method includes the following steps: Step S201, obtaining an offline data sample set.

[0026] The offline data sample set includes a large amount of offline data as sample data, and the offline data is generated according to the network connection and disconnection data between the server and the charging device.

[0027] Specifically, the network on-off data between the server and the charging device is collected, and then the corresponding network on-off data is generated according to a predetermined data format, and the offline data is obtained by analyzing the network on-off data.

[0028] In a possible implementation, heartbeat monitoring may be used to determine network connectivity data.

[0029] Optionally, the heartbeat detection may be that the server periodically sends a heartbeat signal to the charging device. If the server can receive feedback information from the charging device within the first predetermined time, it is determined that the network of the charging device is normal at this time. If the server does not receive feedback information from the charging device within the first predetermined time, it is determined that the network of the charging device is normal at this time and a network failure occurs. The first predetermined time may be determined according to the signal transmission time and the charging device response time, for example, 1 second, 5 seconds or 10 seconds.

[0030] Optionally, heartbeat detection can be that the charging device periodically sends heartbeat messages to the server. When the server can normally receive the heartbeat message reported by the charging device, it means that the network of the charging device is normal and there is no offline fault. When the server does not receive the heartbeat message reported by the charging device within the second scheduled time, it means that a network fault has occurred in the charging device. The second scheduled time can be determined based on the offline fault detection accuracy. The smaller the second scheduled time, the higher the offline fault detection accuracy. For example, the second scheduled time can be 1 minute, 2 minutes or 5 minutes.

[0031] In one possible implementation, SNMP monitoring (Simple Network Management Protocol) can be used to determine network connectivity data. The server uses the SNMP protocol to monitor the network status of the charging device and can obtain the connection status information of the charging device in a timely manner. When the charging device actively disconnects from the server, the server can monitor and perceive the disconnection behavior through the SNMP protocol. When the charging device actively reconnects to the server, the server can also monitor and perceive the connection behavior of the charging device through the SNMP protocol.

[0032] In a possible implementation, the TCP connection state can be used to determine the network connection data. By detecting the state of the TCP connection between the server and the charging device, if the TCP connection state is CLOSE_WAIT or TIME_WAIT, it may mean that the connection has been disconnected.

[0033] In a possible implementation, the network connection and disconnection data may also be determined through log analysis. The server may periodically analyze the communication log with the charging device to identify network connection failure and network recovery events, such as error codes or abnormal disconnection records to determine network failures.

[0034] There are many methods for collecting the network connection and disconnection data between the server and the charging device. In addition to the above method, it can also be implemented in other ways. The method for collecting the network connection and disconnection data is not limited here.

[0035] Based on the above network connection and disconnection data collection method, the status of the charging device can be divided into two categories, namely, non-offline and offline. At the same time, when the status changes, the corresponding offline status change time is also recorded. Because the charging device status change may be in milliseconds, the offline status change time can be accurate to milliseconds.

[0036] In a possible implementation manner, the predetermined data format of the offline data may be [offline status, offline status change time], and the offline data of each charging device is arranged in chronological order.

[0037] In a possible implementation, the predetermined data format of offline data may be single-link data. Single-link data generally refers to data in a single linked list. A single linked list is a common data structure consisting of a series of nodes, each of which contains two parts of information: one is the data of the node itself, and the other is the data pointing to the next node in the list. Since each node only contains a pointer to the next node, this linked list structure can only be traversed in one direction. Exemplarily, the offline data of the single-link data structure may be [current offline status, current offline status change time; next offline status, next offline status change time].

[0038] In one possible implementation, the predetermined data format of offline data may be a double-link data structure, and the double-link may refer to a double-link connection (Double Link Connection), especially in the connection between servers or devices. A double-link is a common data structure, consisting of a series of nodes, each of which contains not only a data part, but also two pointers: one pointing to the previous node (the previous offline state change point), and the other pointing to the next node (the next offline state change point). This structure enables the double-linked list to support bidirectional access, that is, it can be traversed from front to back or from back to front, so as to improve reliability and stability. Exemplarily, the offline data of the double-link data structure may be [current offline state, current offline state change time; previous offline state, previous offline state change time; next offline state, next offline state change time].

[0039] The above method can be used to obtain offline data, which provides a data foundation for subsequent offline data analysis.

[0040] Step S202: determining a plurality of offline snapshots from the offline data sample set according to a third preset time period.

[0041] The third preset time period is a predetermined time period, such as every day, which is used to define the time range of the offline snapshot. The offline data is analyzed with one day as the time window to obtain the corresponding offline index, and then the corresponding offline snapshot is obtained.

[0042] Specifically, by analyzing offline data, a variety of offline indicators that can reflect the severity of offline faults are obtained, such as the maximum offline duration, cumulative offline duration, number of offline times, average offline duration, and offline situation distribution ratio. The maximum offline duration refers to the longest offline behavior selected from all offline data. A single long offline time often has a significant impact on the charging device. The cumulative offline duration refers to the offline duration accumulated from all offline data, which represents the overall offline impact degree within the entire observed time interval. For example, when the device network signal line or SIM card has poor contact, there will often be a feature of a certain frequency and each offline duration for a certain period of time. The cumulative offline duration can be used for effective identification. The number of offline times refers to the sum of the cumulative number of offline times, indicating the existence of high-frequency and short-duration device offline behaviors. The average offline duration refers to the average offline duration within the entire observed time interval, which can also represent the overall offline impact degree. The offline situation distribution ratio refers to the distribution of the offline behavior occurrence time within the entire observed time interval.

[0043] There are obvious correlations between the above-mentioned offline indicators. For example, there is an inverse proportional relationship between the number of offline times and the average offline duration, and there is a positive correlation between the cumulative offline duration and the number of offline times and the average offline duration, etc. At least one offline indicator can be selected for subsequent analysis according to actual needs.

[0044] Step S203: Generate a corresponding offline portrait according to the offline snapshot.

[0045] Figure 3 FIG. 1 is a flow chart of an offline portrait generation method according to an embodiment of the present invention. Figure 3 As shown, the offline fault level classification method includes the following steps: Step S301 : determining an indicator baseline according to the multiple offline snapshots, wherein the indicator baseline includes at least one of a number baseline, a maximum duration baseline, and a cumulative duration baseline.

[0046] Specifically, by performing big data analysis on offline snapshots, the distribution of corresponding offline indicators of all charging devices within a duration interval is observed, and the baseline of the corresponding offline indicators of a predetermined percentage of charging devices is set as the indicator baseline, which can also be called the health baseline. The predetermined percentage can be 90%, 95% or 98%, etc. For example, it is observed that the number of offline times of more than 95% of charging devices in a year is less than 50 times, so 50 times is set as the indicator baseline of the number of offline times of charging devices. The indicator baseline can change over time and should be based on the latest data. When all indicators in the offline indicators are within the indicator baseline, it can be said that the charging device has no offline faults.

[0047] Step S302, determining whether there is an offline portrait.

[0048] If it exists, go to step S304; if it does not exist, go to step S303.

[0049] Step S303: determine the first offline snapshot as the offline portrait.

[0050] The offline snapshots are sorted in chronological order.

[0051] Step S304: determine the target offline snapshot in chronological order.

[0052] Step S305: determine whether each offline indicator in the target offline snapshot is smaller than the corresponding indicator baseline.

[0053] If yes, go to step S306; if no, go to step S307.

[0054] Step S306: Update the offline index of each portrait in the offline portrait to the corresponding offline index in the target offline snapshot.

[0055] Step S307, determining whether any offline indicator in the target offline snapshot is greater than the corresponding portrait offline indicator in the offline portrait.

[0056] Step S308: Update the portrait offline indicator to the corresponding offline indicator in the target offline snapshot.

[0057] Step S309, determining whether any offline index in a number of consecutive offline snapshots is smaller than the corresponding portrait offline index.

[0058] If yes, go to step S310; if no, go to step S304.

[0059] Step S310: updating the offline indicators of each portrait in the offline portrait to the corresponding offline indicators in the target offline snapshot.

[0060] Figure 4is a schematic diagram of updating a first offline portrait according to an embodiment of the present invention, Figure 5 This is a schematic diagram of updating the second offline portrait according to an embodiment of the present invention. Figure 4 , Figure 5 The above method is explained with corresponding examples.

[0061] When the device has no offline portrait, the first offline snapshot is defined as the offline portrait. After confirming that the offline portrait exists, the offline snapshot is continued to be used to update the offline portrait. For the convenience of explanation, the target offline snapshot is used to represent the offline snapshot currently in use, and the offline indicators are divided into three items, namely the number of offline times, the longest offline time and the cumulative offline time.

[0062] Specifically, when the offline index of the target offline snapshot is respectively higher than, equal to, or lower than the corresponding index baseline or the portrait offline index, the corresponding updating method is different.

[0063] When all offline indicators of the target offline snapshot are equal to the corresponding offline indicators of the portrait, the offline portrait is not updated.

[0064] When all offline indicators of the target offline snapshot are less than the corresponding indicator baseline, all offline indicators of the offline portrait are directly updated to the corresponding offline indicators. Figure 4 As shown, sub-graph 41 is the indicator baseline corresponding to the three offline indicators (D, E, F), the three offline indicators corresponding to the offline portrait are (A, B, C), and sub-graph 42 is the three offline indicators (a, b, c) corresponding to the target offline snapshot. By analyzing sub-graph 42, it is easy to conclude that a is smaller than D, b is smaller than E, and c is smaller than F, that is, all offline indicators of the target offline snapshot are smaller than the corresponding indicator baselines. Therefore, updating the offline portrait can obtain sub-graph 43, and sub-graph 43 indicates that the updated offline portrait indicators are (a, b, c).

[0065] When the target offline snapshot has an offline indicator that is not less than the corresponding indicator baseline, the update method is determined by comparing the offline indicator of the target offline snapshot with the corresponding portrait offline indicator in the offline portrait.

[0066] When any offline index of the target offline snapshot is greater than the corresponding offline index of the portrait, the portrait offline index is updated to the corresponding offline index in the target offline snapshot. Figure 5 As shown, sub-graph 51 is the three offline indicators corresponding to the offline portrait (A, B, C), and sub-graph 52 is the three offline indicators corresponding to the target offline snapshot (a1, b1, c1). By analyzing sub-graph 52, it is easy to conclude that b1 is greater than B, and c1 is greater than C. Therefore, by updating the offline portrait, sub-graph 53 can be obtained. Sub-graph 43 indicates that the updated offline portrait indicators are (A, b1, c1).

[0067] When any offline indicator of the target offline snapshot is less than the corresponding offline indicator of the portrait, in order to ensure the stability of the offline portrait, the offline indicator that has dropped should be recorded first, and the offline portrait should continue to be updated with the new offline snapshot. If the same offline indicator drops compared with the offline portrait in a predetermined number of consecutive offline snapshots, the offline indicator of the portrait in the offline portrait should be updated to the corresponding offline indicator in the latest offline snapshot. Figure 5 As shown, sub-graph 51 is the 3 offline indicators (A, B, C) corresponding to the offline portrait, and sub-graph 52 is the 3 offline indicators (a1, b1, c1) corresponding to the first offline snapshot. By analyzing sub-graph 52, it is easy to conclude that a1 is less than A. At this time, the number of times the offline indicator a1 decreases is recorded as 1, but the corresponding offline portrait indicator in the offline portrait is not updated. Therefore, the corresponding offline portrait indicator in the updated offline portrait in sub-graph 53 is still A. Sub-graph 54 is the second offline snapshot, in which the parameter corresponding to the same offline indicator is a2, and a2 is still less than A. At this time, it is recorded as The number of times the offline indicator a1 drops is recorded as 2, and the corresponding offline portrait indicator in the offline portrait is still not updated. Therefore, the corresponding offline portrait indicator in the updated offline portrait in sub-graph 55 is still A. Sub-graph 56 is the third offline snapshot, where the parameter corresponding to the same offline indicator is a3, and a3 is still less than A. At this time, the number of times the offline indicator a1 drops is recorded as 3. Assuming the predetermined number is 3, the portrait offline indicator is updated to the offline indicator corresponding to the last offline snapshot. The updated offline portrait is shown in sub-graph 57, and the offline portrait indicator is a3 at this time.

[0068] The offline portrait is updated using the above method until the offline portrait tends to be stable, or the timing of putting the offline portrait into use is determined according to the number of offline snapshots used to update the offline portrait.

[0069] Step S204, determining a plurality of offline fault levels and corresponding offline fault level reference ranges according to the offline portrait and the offline fault level control parameter.

[0070] The offline fault level control parameters may include the number of offline fault levels, the proportion of the corresponding range of each offline fault level to the total offline index, the number of offline indexes between adjacent offline fault levels, and the like.

[0071] Taking the offline indicator as the number of offline times as an example, if the number of offline times is within 50 times, it is within the indicator baseline, which means that the charging equipment is normal and the offline fault level is defined as 0; the offline times between 50-100 times are defined as the offline fault level 1; the offline times between 100-200 times are defined as the offline fault level 2... and so on.

[0072] The level threshold division here can be based on the actual number of offline times distributed in the corresponding number interval. When dividing, the difference between the first and last values ​​of the offline times in each offline fault level interval can be decreasing, increasing, or unchanged, and can be dynamically adjusted according to the actual business scenario needs.

[0073] In a possible implementation, when the quality of charging devices is uniform, the number of charging devices with higher offline fault levels should be the least, so the higher the offline fault level, the smaller the difference between the first and last values ​​of the corresponding offline times.

[0074] In a possible implementation manner, in order to objectively reflect charging devices at different offline fault levels, the difference between the first and last values ​​of the offline times corresponding to each offline fault level may be set to be the same.

[0075] pass Figure 2 After the offline fault level is obtained by the method shown, offline fault identification can be performed on the charging device.

[0076] Figure 6 Flow chart of the method for processing offline data of a charging device according to an embodiment of the present invention. Figure 6 As shown, the charging device offline data processing method includes the following steps: Step S601: acquiring offline data of a charging device according to a first preset time period.

[0077] Among them, the first preset time period is a detection time window, which can be set and changed according to the identification requirements. For example, the first preset time period can be 3 minutes, 5 minutes or 10 minutes.

[0078] Figure 7 FIG. 1 is a flow chart of an offline data acquisition method according to an embodiment of the present invention. Figure 7 As shown, the offline data acquisition method includes the following steps: Step S701: receiving a heartbeat message reported by the charging device according to a second preset time period.

[0079] The second preset time period can be set according to actual conditions, and the second preset time period is shorter than the first preset time period.

[0080] Step S702: determining multiple offline state change times and corresponding offline states of the charging device within the first preset time period according to the heartbeat message.

[0081] The multiple offline status change times and the corresponding offline statuses are arranged in chronological order.

[0082] Step S703: determining the offline data according to the offline status change time and the corresponding offline status.

[0083] For example, the offline data is [offline, 11:00], [non-offline, 11:02], [offline, 11:05], [offline, 11:06]...

[0084] Figure 8 FIG. 1 is a flow chart of an offline data acquisition method according to an embodiment of the present invention. Figure 8 As shown, the offline data acquisition method includes the following steps: Step S801: Obtain a connection interruption notification and a corresponding timestamp corresponding to the charging device.

[0085] Step S802: determining multiple offline state change times and corresponding offline states of the charging device within the first preset time period according to the connection interruption notification and the corresponding timestamp.

[0086] Step S803: determining the offline data according to the offline status change time and the corresponding offline status.

[0087] Figure 7 and Figure 8 The method shown can be understood in conjunction with the corresponding content of the offline fault classification part, and the method for determining offline data is not only Figure 7 and Figure 8 The method shown may also be other methods, for details, see the corresponding content of the above offline fault level classification part.

[0088] Step S602: determining a corresponding offline indicator according to the offline data.

[0089] The offline indicator includes at least one of the number of offline times, the longest offline time, and the cumulative offline time of the charging device within the first preset time period.

[0090] In a possible implementation manner, the offline indicator may further include indicators such as average offline duration, offline distribution ratio, etc., and the offline indicator types may be added or deleted according to actual conditions.

[0091] The offline indicator determination method in the offline fault identification process is the same as the offline indicator determination method in the above-mentioned offline fault level classification process, which will not be repeated here.

[0092] Step S603, obtaining a plurality of preset offline fault levels and corresponding offline fault level reference ranges.

[0093] The offline fault level reference range includes at least one of an offline times reference range, a longest offline duration reference range, and a cumulative offline duration reference range corresponding to the offline fault level.

[0094] That is, the offline fault level and the corresponding offline fault level reference range obtained by the above offline fault level classification process are obtained. Different offline indicators have different reference ranges. For example, the offline fault level reference range corresponding to offline fault level 1 includes: number of offline times [5 times, 10 times], maximum offline time [30 seconds, 45 seconds], cumulative offline time [50 seconds, 70 seconds]...

[0095] Step S604: determining an offline identification result of the charging device according to the offline indicator and the multiple offline fault level reference ranges, wherein the offline identification result includes a current offline fault level.

[0096] Specifically, determine which offline fault level reference range the offline indicator falls into, and then determine the current offline fault level corresponding to the charging device. If there is only one offline indicator, the current offline fault level is determined according to the offline fault level reference range that the offline indicator falls into. If there are multiple offline indicators, and different offline indicators fall into different offline fault level reference ranges, the current offline fault level is determined according to a preset determination rule. For example, the preset determination rule may be to take the highest offline fault level to determine the current offline fault level, take the Nth-ranked offline fault level as the current offline fault level, or take the weighted sum of the offline fault levels corresponding to each offline indicator to determine the current offline fault level, and so on.

[0097] Step S605: obtaining the highest offline fault level of the charging device in the current offline detection cycle.

[0098] Among them, the current offline detection cycle is the time period from the last time the network returned to normal to the current time. That is to say, from the last offline fault repair, the network recovered as the start time to the current offline fault recovery, the network recovered as the end time, which is an offline detection cycle. At the beginning, the network is normal, then the corresponding highest offline fault level is 0, and then when the offline data is acquired and offline fault identification is performed according to the first preset time period, the highest offline fault level of the current offline detection cycle is recorded and updated. For example, the offline fault level corresponding to the first identification is 1, then the highest offline fault level at this time is 1, the offline fault level corresponding to the second identification is 3, then the highest offline fault level at this time is 3, and the offline fault level corresponding to the third identification is 1, then the highest offline fault level at this time is 3.

[0099] Step S606: In response to the current offline fault level being higher than the highest offline fault level, generating corresponding offline prompt information.

[0100] The offline prompt information may include the current offline fault level, the offline indicator used for this identification, and other information.

[0101] Step S607: Send the offline prompt information to the charging device and / or a user terminal bound to the charging device.

[0102] The user terminal bound to the charging device may be a terminal of the charging device operator, and the offline prompt information may be sent to the user terminal in the form of a mini-program or application software notification, text message, email, etc. The offline prompt information is sent to the charging device and the user terminal to inform the user of offline faults of the charging device and the aggravation of the offline fault level, so as to promote offline maintenance of the charging device.

[0103] And only when the current offline fault level is higher than the highest offline fault level will the corresponding offline prompt information be sent to the user. If the current offline fault level is not higher than the highest offline fault level, no offline prompt information will be sent, thereby avoiding sending meaningless fault warnings to users multiple times and optimizing user experience.

[0104] In a possible implementation, if the offline identification result indicates that the charging device has no offline fault, for example, the offline fault level is 0, then the offline identification result of the charging device in the last first preset time period is determined, and if the offline identification result of the charging device in the last first preset time period indicates that the charging device has an offline fault, a network recovery prompt message is sent to the charging device and / or a user terminal bound to the charging device. In other words, when the offline state of the charging device changes from offline to non-offline, the user should be notified that the network has been restored to prompt the user to continue using the charging device. At the same time, it is determined that the current offline detection cycle ends and enters the next offline detection cycle.

[0105] If the offline identification result of the charging device within the previous first preset time period indicates that the charging device has no offline failure, the process ends. That is to say, if the charging device is always in a non-offline state, there is no need to send a normal network prompt message to the user, thereby reducing unnecessary information disturbance.

[0106] The method of the embodiment of the present invention is to obtain the offline data of the charging device according to the first preset time period, analyze the offline data to determine the offline index, obtain multiple preset offline fault levels and corresponding offline fault level reference ranges, determine the current offline fault level corresponding to the charging device according to the offline index and multiple offline fault level reference ranges, obtain the highest offline fault level of the charging device in the current offline detection cycle, and the current offline detection cycle is the time period from the last time the network was restored to normal to the current time. In response to the current offline fault level being higher than the highest offline fault level, a corresponding offline prompt message is generated, and the offline prompt message is sent to the charging device and / or the user terminal bound to the charging device. The above method can realize automatic identification of network faults of charging devices, improve network fault identification efficiency, reduce network fault identification costs, and accurately drive network fault operation and maintenance of charging devices with data.

[0107] Fig. 9 Schematic diagram of an off-line data processing device for a charging device according to an embodiment of the present invention. Fig. 9 As shown, the charging equipment offline data processing device includes: A first acquisition module 901 is used to acquire offline data of a charging device according to a first preset time period; A first determining module 902, configured to determine a corresponding offline indicator according to the offline data; A second acquisition module 903 is used to acquire a plurality of preset offline fault levels and corresponding offline fault level reference ranges; A second determination module 904, configured to determine an offline identification result of the charging device according to the offline indicator and the multiple offline fault level reference ranges, wherein the offline identification result includes a current offline fault level; The third acquisition module 905 is used to obtain the highest offline fault level of the charging device in the current offline detection period, where the current offline detection period is the time period from the last time the network was restored to normal to the current time; A generating module 906, configured to generate corresponding offline prompt information in response to the current offline fault level being higher than the highest offline fault level; The sending module 907 is used to send the offline prompt information to the charging device and / or the user terminal bound to the charging device.

[0108] The device of the embodiment of the present invention obtains offline data of the charging device according to a first preset time period, obtains a plurality of pre-set offline fault levels and corresponding offline fault level reference ranges, determines an offline identification result of the charging device according to the offline data and the offline fault level reference ranges corresponding to the plurality of offline fault levels, the offline identification result includes a current offline fault level, obtains the highest offline fault level of the charging device in the current offline detection cycle, the current offline detection cycle is a time period from the detection of the charging device being offline to the current time, generates corresponding offline prompt information in response to the current offline fault level being higher than the highest offline fault level, and sends the offline prompt information to the charging device and / or a user terminal bound to the charging device. The device of the embodiment of the present invention can realize automatic identification of network faults of the charging device, improve network fault identification efficiency, reduce network fault identification cost, and drive network fault operation and maintenance of the charging device with data accuracy.

[0109] Fig.10 FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present invention. Fig.10 As shown, Fig.10 The electronic device shown is a general address query device, which includes a general computer hardware structure, which at least includes a processor 1001 and a memory 1002. The processor 1001 and the memory 1002 are connected via a bus 1003. The memory 1002 is suitable for storing instructions or programs executable by the processor 1001. The processor 1001 can be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 1001 executes the instructions stored in the memory 1002 to execute the method flow of the embodiment of the present invention as described above to realize the processing of data and the control of other devices. The bus 1003 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to the display controller 1004 and the display device and the input / output (I / O) device 1005. The input / output (I / O) device 1005 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices known in the art. Typically, the input / output device 1005 is connected to the system through an input / output (I / O) controller 1006.

[0110] It should be understood by those skilled in the art that the embodiments of the present application may be provided as methods, devices (equipment) or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0111] The present application is described with reference to flowcharts of methods, apparatuses (devices) and computer program products according to embodiments of the present application. It should be understood that each process in the flowchart can be implemented by computer program instructions.

[0112] These computer program instructions may be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device that implements the process Figure 1 A function specified in a process or multiple processes.

[0113] These computer program instructions may also be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the instructions for implementing the process Figure 1 A device that specifies functions in a process or multiple processes.

[0114] The embodiment of the present invention is to obtain offline data of the charging device according to a first preset time period, obtain multiple pre-set offline fault levels and corresponding offline fault level reference ranges, determine the offline identification result of the charging device according to the offline data and the offline fault level reference ranges corresponding to the multiple offline fault levels, the offline identification result includes the current offline fault level, obtain the highest offline fault level of the charging device in the current offline detection cycle, the current offline detection cycle is the time period from the detection of the charging device being offline to the current time, and in response to the current offline fault level being higher than the highest offline fault level, generate corresponding offline prompt information, and send the offline prompt information to the charging device and / or the user terminal bound to the charging device. The above method can realize automatic identification of network faults of charging devices, improve network fault identification efficiency, reduce network fault identification costs, and accurately drive network fault operation and maintenance of charging devices with data.

[0115] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program, wherein the computer-readable program is used for a computer to execute part or all of the above method embodiments.

[0116] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by specifying relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0117] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A charging equipment offline data processing method, characterized in that: The method comprises: Acquiring offline data of the charging device according to a first preset time period; Determine a corresponding offline indicator according to the offline data; Acquire a plurality of preset offline fault levels and corresponding offline fault level reference ranges; Determining an offline identification result of the charging device according to the offline indicator and the multiple offline fault level reference ranges, wherein the offline identification result includes a current offline fault level; Obtaining the highest offline fault level of the charging device in a current offline detection cycle, where the current offline detection cycle is a time period from the last time the network was restored to normal to the current time; In response to the current offline fault level being higher than the highest offline fault level, generating corresponding offline prompt information; The offline prompt information is sent to the charging device and / or a user terminal bound to the charging device.

2. The method according to claim 1, characterized in that The offline indicator includes at least one of the number of offline times, the longest offline time, and the cumulative offline time of the charging device within the first preset time period; The offline fault level reference range includes at least one of an offline times reference range, a longest offline duration reference range, and a cumulative offline duration reference range corresponding to the offline fault level.

3. The method according to claim 1, characterized in that The acquiring the offline data of the charging device according to the first preset time period includes: Receiving a heartbeat message reported by the charging device according to a second preset time period; Determine, according to the heartbeat message, a plurality of offline state change times and corresponding offline states of the charging device within the first preset time period; The offline data is determined according to the offline status change time and the corresponding offline status.

4. The method according to claim 1, characterized in that The acquiring the offline data of the charging device according to the first preset time period includes: Obtaining a connection interruption notification and a corresponding timestamp corresponding to the charging device; Determine, according to the connection interruption notification and the corresponding timestamp, a plurality of offline state change times and corresponding offline states of the charging device within the first preset time period; The offline data is determined according to the offline status change time and the corresponding offline status.

5. The method according to claim 2, characterized in that: The method for determining the multiple offline fault levels and the corresponding offline fault level reference ranges includes: Obtain offline data sample set; Determining a plurality of offline snapshots from the offline data sample set according to a third preset time period; Generate a corresponding offline portrait according to the offline snapshot; According to the offline portrait and the offline fault level control parameter, a plurality of offline fault levels and corresponding offline fault level reference ranges are determined.

6. The method according to claim 5, characterized in that Generating a corresponding offline portrait according to the offline snapshot includes: Determine an indicator baseline according to the multiple offline snapshots, the indicator baseline comprising at least one of a number baseline, a maximum duration baseline, and a cumulative duration baseline; In response to the offline portrait not existing, determining the first offline snapshot as the offline portrait, the offline snapshots being sorted in chronological order; The offline portrait is updated using the offline snapshots in the time sequence.

7. The method according to claim 6, characterized in that The updating of the offline portrait using the offline snapshots in the time sequence includes: Determine the target offline snapshot according to the time sequence; Determine whether each offline indicator in the target offline snapshot is less than the corresponding indicator baseline; In response to any offline indicator in the target offline snapshot being greater than a corresponding indicator baseline, determining whether any offline indicator in the target offline snapshot is greater than a corresponding portrait offline indicator in the offline portrait; In response to any offline indicator in the target offline snapshot being greater than the corresponding portrait offline indicator, the portrait offline indicator is updated to the corresponding offline indicator in the target offline snapshot.

8. The method according to claim 7, characterized in that The method further comprises: In response to all offline indicators in the target offline snapshot being smaller than corresponding indicator baselines, each portrait offline indicator in the offline portrait is updated to a corresponding offline indicator in the target offline snapshot.

9. The method according to claim 7, characterized in that: The method further comprises: In response to any offline index in a predetermined number of consecutive offline snapshots being smaller than the corresponding portrait offline index, the portrait offline index is updated to the offline index corresponding to the last offline snapshot.

10. The method according to claim 1, characterized in that The method further comprises: In response to the offline identification result indicating that the charging device has no offline fault, determining an offline identification result of the charging device within a previous first preset time period; In response to the offline identification result of the charging device within the last first preset time period indicating that the charging device has an offline fault, network recovery prompt information is sent to the charging device and / or a user terminal bound to the charging device.

11. The method according to claim 10, characterized in that The method further comprises: Determine that the current offline detection cycle ends and enter the next offline detection cycle.

12. A charging equipment offline data processing device, characterized in that: The device comprises: A first acquisition module, used to acquire offline data of the charging device according to a first preset time period; A first determining module, used to determine a corresponding offline indicator according to the offline data; A second acquisition module, used to acquire a plurality of preset offline fault levels and corresponding offline fault level reference ranges; A second determination module, configured to determine an offline identification result of the charging device according to the offline indicator and the multiple offline fault level reference ranges, wherein the offline identification result includes a current offline fault level; A third acquisition module is used to obtain the highest offline fault level of the charging device in a current offline detection cycle, where the current offline detection cycle is a time period from the last time the network was restored to normal to the current time; A generating module, configured to generate corresponding offline prompt information in response to the current offline fault level being higher than the highest offline fault level; The sending module is used to send the offline prompt information to the charging device and / or the user terminal bound to the charging device.

13. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

15. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 11 is implemented.

Images