Network hibernation exception analysis method and apparatus, server, and storage medium

Abstract

The application discloses a network dormancy anomaly analysis method and device, a server and a readable storage medium. The method comprises the following steps: screening at least one target period meeting a target condition from a plurality of candidate periods; the candidate period is a period in which a vehicle sends a network management message; determining an abnormal period in which the vehicle has a network dormancy anomaly and a network dormancy anomaly category of the vehicle in the abnormal period according to the length of each target period; screening a target abnormal node from abnormal nodes in the vehicle according to the network dormancy anomaly category; the abnormal node is a node sending the network management message in the abnormal period; and determining a network dormancy anomaly analysis result of the vehicle according to the node category of the target abnormal node and / or the sending quantity of the network management message. According to the method, the network dormancy anomaly analysis efficiency is improved, the cost of the network dormancy anomaly analysis is reduced, and then the analysis efficiency of the vehicle power supply reason is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and more specifically, to a network dormancy anomaly analysis method, device, server, and computer-readable storage medium. Background Art

[0002] With the continuous iteration and upgrading of vehicle technology, the number of electrical units in vehicles continues to increase. If a controller fails to go into sleep mode after the vehicle is powered off, it can easily cause the battery to be overcharged, which can lead to the vehicle being unable to start.

[0003] Therefore, there is an urgent need for a method to analyze the vehicle's sleep anomaly so as to determine the cause of the vehicle's power supply based on the sleep anomaly analysis results. Summary of the Invention

[0004] The present application proposes a network sleep anomaly analysis method, device, server and computer-readable storage medium to provide a sleep anomaly analysis means.

[0005] In a first aspect, an embodiment of the present application provides a method for analyzing network dormancy anomalies, the method comprising:

[0006] Selecting at least one target time period that meets the target conditions from multiple candidate time periods; the candidate time period is a time period during which the vehicle sends a network management message;

[0007] According to the duration of each target time period, determine the abnormal time period during which the vehicle's network dormancy anomaly occurs and the type of network dormancy anomaly that the vehicle has within the abnormal time period;

[0008] According to the network dormancy anomaly category, target abnormal nodes are selected from abnormal nodes in the vehicle; abnormal nodes are nodes that send network management messages during the abnormal period;

[0009] The network dormancy anomaly analysis result of the vehicle is determined based on the node category of the target abnormal node and / or the number of network management messages sent.

[0010] In a second aspect, an embodiment of the present application further provides a network sleep anomaly analysis device, the device comprising:

[0011] A time period screening module is used to screen at least one target time period that meets the target conditions from multiple candidate time periods; the candidate time period is the time period during which the vehicle sends the network management message;

[0012] A determination module, configured to determine, based on the duration of each target time period, an abnormal time period during which the vehicle experiences network dormancy anomaly and a type of network dormancy anomaly within the abnormal time period;

[0013] A node screening module is used to screen target abnormal nodes from abnormal nodes in the vehicle according to the network dormancy abnormality category; abnormal nodes are nodes that send network management messages during the abnormal period;

[0014] The result determination module is used to determine the vehicle's network dormancy anomaly analysis result based on the node category of the target abnormal node and / or the number of network management messages sent.

[0015] In a third aspect, an embodiment of the present application further provides a server, which includes: one or more processors; a memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by one or more processors, and the one or more programs are configured to execute the above method.

[0016] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a program code executable by a processor, and when the program code is executed by the processor, the processor executes the above method.

[0017] The present application provides a network sleep anomaly analysis method, device, server and computer-readable storage medium. In the present application, a target time period that meets the target conditions is first screened, and the abnormal time period in which the network sleep anomaly occurs and the network sleep anomaly category within the abnormal time period are determined based on the target time period. Then, the target abnormal node where the abnormality occurs in the vehicle is determined according to the network sleep anomaly category, and the network sleep anomaly analysis result of the vehicle is further determined according to the node category of the target abnormal node and / or the number of network management messages sent. This achieves the purpose of determining the target abnormal node by the network sleep anomaly category within the abnormal time period, and further determining the network sleep anomaly analysis result by the node category of the target abnormal node and / or the number of network management messages sent. Manual analysis is not required to determine the network sleep anomaly analysis result, thereby improving the efficiency of the network sleep anomaly analysis, reducing the cost of the network sleep anomaly analysis, and thereby improving the efficiency of the analysis of the vehicle power feeding cause.

[0018] Other features and advantages of the embodiments of the present application will be described in the following description and, in part, will become apparent from the description or be understood by practicing the embodiments of the present application. The objectives and other advantages of the embodiments of the present application can be achieved and obtained through the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a network sleep anomaly analysis method proposed in one embodiment of the present application is shown.

[0021] Figure 2 Show Figure 1 Steps S130 and S140 of the corresponding embodiment are in a flowchart of an embodiment.

[0022] Figure 3 Show Figure 1 Steps S130 and S140 of the corresponding embodiment are in a flowchart of another embodiment.

[0023] Figure 4 Show Figure 1 Steps S130 and S140 of the corresponding embodiment are in the flowchart of yet another embodiment.

[0024] Figure 5 A schematic diagram showing a network sleep anomaly analysis process in an embodiment of the present application is shown.

[0025] Figure 6 A structural block diagram of a network sleep anomaly analysis device proposed in one embodiment of the present application is shown.

[0026] Figure 7 A structural block diagram of a server provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0028] It should be noted that similar reference numerals and letters refer to like items throughout the accompanying drawings, and once an item is defined in one drawing, it should not require further definition and explanation in the subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are merely used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0029] Referring to Figure 1 , Figure 1 A flowchart of a network hibernation exception analysis method according to an embodiment of the present application is shown, which is used for a server, and the method comprises the following steps:

[0030] S110, selecting at least one target time period meeting a target condition from a plurality of candidate time periods.

[0031] Wherein, the candidate time period is a time period in which the vehicle sends a network management message; that is, the time period in which the vehicle sends the network management message can be regarded as the candidate time period, or some time periods in which the vehicle sends the network management message can be selected as the candidate time period, for example, time periods longer than a specified time threshold are selected as the candidate time period.

[0032] The vehicle in the embodiment can be an electric vehicle or a fuel vehicle, and can be a sedan, an SUV, a bus, a truck, or the like.

[0033] In the present application, the controller in the vehicle which has been network managed can send a network management message to the server through a network (a wireless network, a mobile network, or the like) so as to facilitate the server to analyze the running state of the vehicle according to the network management message sent by the controller. The controller in the vehicle can include a gateway controller and a non-gateway controller, the non-gateway controller refers to other controllers in the vehicle except the gateway controller, and the non-gateway controller can include an air conditioner controller, a light controller, a multimedia controller, or the like.

[0034] It is worth mentioning that the controller in the vehicle which has been network managed needs to comply with one of two standards, AUTORSAR (AUTomotive Open System Architecture, an automotive electronic software standard) or OSEK (an "Automotive Open System and Interface Software Specification for Automotive Electronics" proposed by the German automotive industry in 1993, aiming to enhance the safety, portability of software code, and reduce software development cycle).

[0035] Secondly, the controller which has been network managed writes the network wake-up reason (the reason for sending the network management message) and the network maintenance reason (the reason for maintaining the sending of the network management message) of itself into the network management message.

[0036] Further, the vehicle is configured with a big data uploading function (uploading bus signals (the bus signals include various signals in the vehicle, such as speed signals, light signals, radar signals, etc.) of the vehicle to the server in the form of network management messages), and the frequency of receiving the network management messages in the server is consistent with the cycle of collecting vehicle signals by the vehicle in an ideal case, for example, the vehicle speed signal of the vehicle is refreshed once every 100 ms, and when uploaded to the server, it should also be collected and uploaded once every 100 ms. However, considering the bus load rate, the hardware capability of the corresponding controller, the network traffic occupation and other cost factors, it is actually impossible to achieve such a high frequency of data update, so the frequency of receiving the network management messages by the server can be sent once every 1000 ms.

[0037] The network management message sent by the controller of the vehicle can include a sending time stamp of the sending time point (the time point at which the vehicle sends the network management message) of the network management message in addition to the collected vehicle signals, so that the server can determine the time at which the vehicle sends the network management message according to the sending time stamp in the network management message.

[0038] The network management message sent by the vehicle can be continuous (that is, the vehicle sends a network management message every cycle of collecting vehicle signals), so that the server determines that the sending time points of the network management messages are continuous according to the time stamps in the received network management messages; the network management message sent by the vehicle can also be intermittent (that is, the vehicle does not send a network management message for one or more cycles of collecting vehicle signals), so that the server determines that the sending time points of the network management messages are intermittent according to the time stamps in the received network management messages. Thus, the server can divide a plurality of sending time points with continuous sending time points into a candidate period, thereby obtaining the aforementioned plurality of candidate periods.

[0039] In other words, each candidate period includes a plurality of sending time points, for any one candidate period, if it is determined that each sending time point of the vehicle in the candidate period meets the target condition according to the network management message at the sending time point, it is determined that the candidate period meets the target condition, and if it is determined that at least one sending time point of the vehicle in the candidate period does not meet the target condition according to the network management message at the sending time point, it is determined that the candidate period does not meet the target condition.

[0040] In this application, the target condition includes that the opening and closing state of the vehicle door does not change, the opening and closing state of the vehicle cover does not change, the gear of the vehicle is in the parking gear, the vehicle is not in the air download state, and the vehicle is not in the high voltage state.

[0041] The door's open / closed state remains unchanged, which may include the vehicle's hood lock state remaining unchanged or the vehicle's door switch state remaining unchanged. Similarly, the vehicle's hood's open / closed state remains unchanged, which may include the vehicle's hood lock state remaining unchanged or the vehicle's hood switch state remaining unchanged. In this application, "unchanged" may mean remaining in the same state (e.g., remaining open or closed) for a specified period of time. The specified period of time may be set based on needs, such as 20 minutes.

[0042] Vehicle network dormancy anomalies are only considered when the vehicle is out of use mode (i.e., in Park), not in the high-voltage state, or in the Over-the-Air Technology (OTA) state. This means the server should monitor the vehicle's gear position, OTA status, and high-voltage status. If any of these three signals are set to a valid position (a valid position indicates the occurrence of the signal's state), the network anomaly is not considered a network anomaly.

[0043] Secondly, considering that the user may perform related operations on the vehicle when not in use, which may cause the vehicle bus to remain awake (such as opening and closing the door, or opening and closing the hood, etc.), the server should monitor the switch and lock signals of the vehicle doors and hood. If the above signals jump, it normally indicates that human operation is in progress. Since the network abnormality is determined based on the interval between signal jumps, it is also necessary to consider whether the opening and closing status of the door and the opening and closing status of the hood have not changed to determine whether the target conditions are met.

[0044] S120 : Determine, based on the duration of each target time period, an abnormal time period during which the vehicle experiences a network dormancy anomaly and a network dormancy anomaly category of the vehicle within the abnormal time period.

[0045] After obtaining the sending time period, each target time period can be directly used as an abnormal time period or spliced ​​into abnormal time periods according to the length of each target time period. Then, the network dormancy abnormality category of the vehicle in the abnormal time period can be determined according to the specific content of the network management message in the abnormal time period.

[0046] In this application, the network sleep anomaly category may include abnormal network wakeup, continuous network non-sleep, and frequent network wakeup.

[0047] In some embodiments, S120 may include: if the duration of the target period reaches a first duration threshold, obtaining the target period as an abnormal period, and determining the abnormal category of the vehicle's network dormancy during the abnormal period as the vehicle's network continuously not dormant during the abnormal period. The first duration threshold may be 60 minutes.

[0048] In some other embodiments, S120 may further include: if the duration of the target time period reaches a first duration threshold, obtaining the target time period as an abnormal time period; counting the first node that sends network management messages during the abnormal time period; determining the first node that sends the second largest number of network management messages during the abnormal time period as a second node; if the number of network management messages sent by the second node during the abnormal time period reaches a quantity threshold corresponding to the abnormal time period, determining the network sleep anomaly category of the vehicle during the abnormal time period as the vehicle's network continuously not sleeping during the abnormal time period; if the number of network management messages sent by the second node during the abnormal time period does not reach a quantity threshold corresponding to the abnormal time period, determining the network sleep anomaly category of the vehicle as the vehicle's network frequently waking up during the abnormal time period. The quantity threshold corresponding to the abnormal time period may be determined based on the number of sending time points during the abnormal time period, for example, the quantity threshold for the abnormal time period is the product of a ratio threshold and the number of sending time points during the abnormal time period, wherein the ratio threshold may be set based on demand, for example, the ratio threshold is 90%.

[0049] For any abnormal period, each controller that sends a network management message to the vehicle during the abnormal period is regarded as a first node. Therefore, for any first node, the first node has sent a network management message at at least one sending time point during the abnormal period. Based on this, the number of network management messages sent by each first node during the abnormal period is counted (because each first node sends a network management message at a sending time point, the number of network management messages sent by each first node during the abnormal period is consistent with the number of sending time points when each first node sends network management messages during the abnormal period), and then the first node with the second largest number of network management messages sent during the abnormal period is obtained as the second node.

[0050] Considering that the electronic and electrical architecture of a vehicle generally has a domain control structure, and the entire vehicle generally has a gateway controller with high computing and control capabilities and gateway functions, and this type of controller is generally the node that maintains the network for the longest time on the entire vehicle (the vehicle gateway will only enter sleep mode when it monitors that there are no network requests from other nodes). Therefore, in scenarios where the network is not in sleep mode, the number of network management messages sent by this type of controller is generally the largest and has no reference value. Therefore, in this application, the controller with the second largest number of target network management messages sent (that is, the second node) is selected as the judgment criterion.

[0051] After determining the second node, if the number of network management messages sent by the second node during the abnormal period reaches the corresponding number threshold, it is determined that the second node sends network management messages frequently during the abnormal period, and the period of time for the second node to send network management messages during the abnormal period is longer. Therefore, the network sleep anomaly category of the vehicle is determined to be that the vehicle network continues to be non-sleep during the abnormal period; if the number of network management messages sent by the second node during the abnormal period does not reach the corresponding number threshold, it is determined that the second node sends network management messages frequently during the abnormal period, and the period of time for the second node to send network management messages during the abnormal period is shorter. Therefore, the network sleep anomaly category of the vehicle is determined to be that the vehicle network is frequently awakened during the abnormal period.

[0052] In some further embodiments, S120 further includes: selecting intermediate time periods within each target time period whose duration does not reach a first duration threshold; merging intermediate time periods whose time intervals are not greater than a second duration threshold into one time period, to obtain at least one merged time period; the first duration threshold being greater than the second duration threshold; for any merged time period, if the duration of the merged time period reaches the first duration threshold, obtaining the merged time period as an abnormal time period, and determining the abnormality category of the vehicle's network dormancy during the abnormal time period as abnormal network awakening of the vehicle during the abnormal time period. The second duration threshold can be set based on demand, for example, 20 minutes.

[0053] In other words, the present application merges the intermediate time periods with smaller time intervals (time intervals less than the second time threshold) into a merged time period, and then for any merged time period, if the duration of the merged time period does not reach the first time threshold, although the time interval difference between each wake-up is small, the total duration of the abnormal network wake-up is small, and the network management message within the merged time period can be discarded, and the network management message within the merged time period is not analyzed, that is, it is determined that the vehicle does not have abnormal network wake-up within the merged time period; if the duration of the merged time period reaches the first time threshold, it is determined that the total duration of the vehicle network wake-up is longer, and the time interval difference between each wake-up is small. Therefore, the network sleep anomaly category of the vehicle is determined to be abnormal network wake-up of the vehicle within the merged time period, and the merged time period is regarded as an abnormal time period.

[0054] In short, use A i In the case where the first duration threshold is 60 minutes, if A1+A2+···+A n ≥60min, then the vehicle is determined to be at A1+A2+···+A n If the network wakes up abnormally during the combined period, the combined period will be regarded as an abnormal period.

[0055] S130 : Filter target abnormal nodes from abnormal nodes in the vehicle according to the network dormancy abnormality category.

[0056] An abnormal node is a node that sends network management messages during an abnormal period. This node refers to the aforementioned controller in the vehicle. A single controller can be considered a node. For example, a gateway controller can be considered a node. A node that sends a network management message at at least one time point during an abnormal period is considered an abnormal node.

[0057] For each abnormal period, target abnormal nodes are filtered from abnormal nodes within the abnormal period according to the network dormancy abnormality category of the vehicle within the abnormal period.

[0058] For example, during abnormal periods when the network wakes up abnormally, the node that sends the largest number of network messages is selected as the target abnormal node. For another example, during abnormal periods when the network is continuously not in sleep mode, the node that sends the second largest number of network management messages is selected as the target abnormal node. For another example, during abnormal periods when the network wakes up frequently, the node that sends the second largest number of network messages is selected as the target abnormal node.

[0059] S140 : Determine a network dormancy anomaly analysis result of the vehicle according to the node type of the target abnormal node and / or the number of network management messages sent.

[0060] After determining the target abnormal node, the vehicle's network dormancy anomaly analysis result can be determined based on the target abnormal node's node category, or based on the number of network management messages sent by the target abnormal node. Of course, the vehicle's network dormancy anomaly analysis result can also be determined based on both the target abnormal node's node category and the number of network management messages sent.

[0061] In this embodiment, the target time period that meets the target conditions is first screened, and the abnormal time period in which the network sleep anomaly occurs and the network sleep anomaly category within the abnormal time period are determined based on the target time period. Then, the target abnormal node where the abnormality occurs in the vehicle is determined according to the network sleep anomaly category, and the network sleep anomaly analysis result of the vehicle is further determined according to the node category of the target abnormal node and / or the number of network management messages sent. This achieves the purpose of determining the target abnormal node according to the network sleep anomaly category within the abnormal time period, and further determining the network sleep anomaly analysis result according to the node category of the target abnormal node and / or the number of network management messages sent. There is no need for manual analysis to determine the network sleep anomaly analysis result, which improves the efficiency of the network sleep anomaly analysis, reduces the cost of the network sleep anomaly analysis, and thereby improves the efficiency of the analysis of the vehicle power feeding cause.

[0062] In addition, according to the duration of different target time periods, the vehicle's network sleep anomaly categories are divided into three categories: continuous network non-sleep, frequent network wake-up, and abnormal network wake-up, which achieves accurate classification of the vehicle's network sleep anomaly, thereby making the accuracy of the determined vehicle's network sleep anomaly category higher, thereby improving the accuracy of the network sleep anomaly analysis results determined according to the network sleep anomaly category.

[0063] In one embodiment, if Figure 2 As shown, S130 may include S210-S220 as follows:

[0064] S210: If the network dormancy anomaly category of the vehicle is that the vehicle continues to be in the network non-dormant state during the abnormal period, count the number of network management messages sent by each abnormal node during the abnormal period.

[0065] For each abnormal period, if the vehicle does not sleep in the network during the abnormal period, the abnormal node in the abnormal period is determined, and the number of network management messages sent by the abnormal node is counted.

[0066] Generally speaking, an abnormal node sends a network management message once at each sending time point. For each abnormal node, the number of sending time points at which the abnormal node sends the network management message during the abnormal period can be counted as the number of network management messages sent by the abnormal node.

[0067] S220 : Determine, from among the abnormal nodes, an abnormal node whose number of network management messages sent reaches a number threshold corresponding to the abnormal period, as a target abnormal node.

[0068] For each abnormal time period, the description of the quantity threshold corresponding to the abnormal time period refers to the description of S120 in the above embodiment and will not be repeated here.

[0069] That is, for each abnormal period, if the vehicle continues to be active during the period, all abnormal nodes within the abnormal period whose number of network management messages sent reaches the threshold corresponding to the abnormal period are selected as the target abnormal nodes for the abnormal period. This process is repeated for each abnormal period to determine the target abnormal node for each abnormal period.

[0070] Accordingly, S140 includes S230-S240 as follows:

[0071] S230: If the node type of the target abnormal node is a gateway controller type, determine the network dormancy abnormality analysis result of the vehicle according to the reason why the target abnormal node maintains network wake-up.

[0072] S240. If the node category of the target abnormal node includes a gateway controller category and a non-gateway controller category, determine the network sleep abnormality analysis result of the vehicle based on the maintenance reason of the first target abnormal node belonging to the gateway controller category maintaining network wake-up and the node description information of the second target abnormal node belonging to the non-gateway controller category.

[0073] The node description information of the second target abnormal node includes at least one of a node identifier of the second target abnormal node and a reason why the second target abnormal node maintains network wake-up. The node identifier may be a node code, a node name, or the like.

[0074] The first target abnormal node refers to a target abnormal node belonging to the gateway controller category, that is, the first target abnormal node is a gateway controller, and the second target abnormal node refers to a target abnormal node belonging to the non-gateway controller category, that is, the second target abnormal node is a non-gateway controller.

[0075] The nodes of the gateway controller category may include a gateway controller, and the nodes of the non-gateway controller category may include other nodes except the gateway controller.

[0076] In other words, if the vehicle's network does not sleep continuously during the abnormal period, and only the gateway controller sends network management messages during the abnormal period, the reason why the gateway controller maintains network wake-up is obtained as the vehicle's network sleep abnormality analysis result.

[0077] If the vehicle's network continues to be active during the abnormal period, and both the gateway controller and the non-gateway controller send network management messages during the abnormal period, it is necessary to combine the reason why the gateway controller maintains network wake-up and the node description information of the non-gateway controller to determine the vehicle's network sleep anomaly analysis result. For example, the reason why the gateway controller maintains network wake-up and the node description information of the non-gateway controller can be summarized as the vehicle's network sleep anomaly analysis result.

[0078] For each abnormal period in which the network does not sleep continuously, the vehicle's network sleep abnormality analysis result may be determined according to the aforementioned S210 , S220 , S230 , and S240 .

[0079] In this embodiment, when the network sleep anomaly category of the vehicle is that the network of the vehicle continues to be non-sleep during an abnormal period, the network sleep anomaly analysis result of the vehicle is determined based on the maintenance reason of the gateway controller and the node description information of the non-gateway controller, thereby achieving accurate analysis of the abnormal event of the network continuing to be non-sleep and improving the accuracy of the network sleep anomaly analysis result of the vehicle.

[0080] In yet another embodiment, Figure 3As shown, S130 may include S310-S320 as follows:

[0081] S310: If the network dormancy anomaly category of the vehicle is abnormal network awakening of the vehicle within an abnormal period, determine a first associated period within the abnormal period according to each awakening time point of the vehicle within the abnormal period.

[0082] If the vehicle's network dormancy anomaly category is abnormal network awakening during an abnormal period, the time points of each vehicle awakening during the abnormal period are obtained, and then a period is determined based on the time points of each vehicle awakening, which serves as a first associated period. For example, if the vehicle awakens 10 times during the abnormal period, the first associated period is also 10. The duration of the first associated period can be a first preset duration, such as 10 seconds.

[0083] In some embodiments, multiple target time periods used to constitute the abnormal time period can be obtained, and then the starting time point of each target time period is used as the time point for each vehicle wake-up; for example, if the multiple target time periods used to constitute the abnormal time period are 4, the vehicle wakes up 4 times, and thus the first associated time period is also 4.

[0084] In some other implementations, the time point at which the vehicle sends a network management message each time during the abnormal period may be obtained as the time point at which the vehicle wakes up each time.

[0085] S320: Filter abnormal nodes that are not passively awakened from abnormal nodes that send network management messages within the first association period as target abnormal nodes.

[0086] Among them, non-passive wake-up (also called active wake-up) means that the vehicle is awakened by the vehicle itself instead of through user operation or instructions.

[0087] That is to say, when the network dormancy anomaly category of the vehicle is abnormal network awakening of the vehicle within an abnormal period, after determining the first associated period, the abnormal nodes that send network management messages within the first associated period are determined, and then the non-passively awakened abnormal nodes are screened out from the abnormal nodes that send network management messages within the first associated period as target abnormal nodes.

[0088] Accordingly, S140 includes: determining a network dormancy anomaly analysis result of the vehicle according to a target abnormal node that sends the largest number of network management messages during the abnormal period.

[0089] The number of network management messages sent by the target abnormal node during the abnormal period can be counted, and then the target abnormal node with the largest number of network management messages sent during the abnormal period can be determined. After that, the node identifier of the target abnormal node with the largest number of network management messages sent during the abnormal period can be obtained, and a descriptive text indicating the abnormal awakening of the node network can be generated through the node identifier as the network sleep anomaly analysis result of the vehicle.

[0090] For each abnormal period of abnormal network awakening, the vehicle's network dormancy abnormality analysis result may be determined according to the aforementioned S310 , S320 , and S140 .

[0091] In this embodiment, when the network sleep anomaly category of the vehicle is abnormal network awakening of the vehicle within an abnormal period, multiple first associated time periods are determined. Then, non-passively awakened abnormal nodes are screened from the abnormal nodes that send network management messages within the first associated time period as target abnormal nodes, and the network sleep anomaly analysis results of the vehicle are determined based on the target abnormal node that sends the largest number of network management messages within the abnormal period, thereby achieving accurate analysis of abnormal events of abnormal network awakening and improving the accuracy of the network sleep anomaly analysis results of the vehicle.

[0092] In yet another embodiment, Figure 4 As shown, S140 may include S410-S420 as follows:

[0093] S410: If the network dormancy anomaly category of the vehicle is that the vehicle frequently wakes up from the network within the abnormal period, determine a second associated period within the abnormal period according to the time point of each vehicle wake-up within the abnormal period.

[0094] If the vehicle's network dormancy anomaly is classified as frequent network awakenings during an abnormal period, the time points of each vehicle awakening during the abnormal period are obtained, and then a period is determined based on the time points of each vehicle awakening as a second associated period. For example, if the vehicle awakens 10 times during the abnormal period, the second associated period is also 10. The duration of the second associated period can be a second preset duration, such as 10 seconds or 12 seconds.

[0095] The time point of each vehicle awakening is determined by referring to the aforementioned S310 and will not be described in detail.

[0096] S420: Filter abnormal nodes that are not passively awakened from abnormal nodes that send network management messages within the second association period as target abnormal nodes.

[0097] That is to say, when the network dormancy anomaly category of the vehicle is that the vehicle frequently wakes up the network during the abnormal period, after determining the second associated period, the abnormal nodes that send network management messages during the second associated period are determined, and then the abnormal nodes that are not passively awakened are screened out from the abnormal nodes that send network management messages during the second associated period as target abnormal nodes.

[0098] Accordingly, S140 includes: determining a network dormancy anomaly analysis result of the vehicle according to a target abnormal node that belongs to a non-gateway controller category and has the largest number of network management messages sent during the abnormal period.

[0099] The number of network management messages sent by each target abnormal node during the abnormal period can be counted, and then the target abnormal node that belongs to the non-gateway controller category and has the largest number of network management messages sent during the abnormal period can be determined, and the node identification of the target abnormal node that belongs to the non-gateway controller category and has the largest number of network management messages sent during the abnormal period can be determined. A descriptive text indicating that the target abnormal node network is frequently awakened is generated through the node identification as the vehicle's network sleep anomaly analysis result.

[0100] For each abnormal period of frequent network wake-up, the vehicle's network dormancy abnormality analysis result may be determined according to the aforementioned S410 , S420 , and S140 .

[0101] In this embodiment, when the network sleep anomaly category of the vehicle is that the vehicle frequently wakes up the network during an abnormal period, a second associated period is determined, and the network sleep anomaly analysis result of the vehicle is determined based on the node identifier of the target abnormal node that belongs to a non-gateway controller and sends the largest number of network management messages during the abnormal period, thereby achieving accurate analysis of abnormal events of frequent network wake-ups and improving the accuracy of the network sleep anomaly analysis results of the vehicle.

[0102] In an example scenario, the vehicle controller sends a network management message according to the set sending cycle. After the server receives the network management message, it stores the network management message. After that, the user specifies any historical period based on the needs and obtains the network management message within the historical period to perform the network dormancy anomaly analysis process of this application, such as Figure 5 As shown:

[0103] First, all candidate time periods within the specified historical period are traversed. A candidate time period is a period with consecutive transmission time points within the historical period. For example, if the transmission period is 1 second, the transmission time points are the 1st second, the 2nd second, the 3rd second, etc., a network management message is sent every second from the 1st second to the 10th second, no network management message is sent in the 11th second and the 12th second, and a network management message is sent every second from the 13th second to the 50th second. In this case, the first candidate time period determined is the candidate time period from the 1st second to the 10th second, and the candidate time period from the 13th second to the 50th second.

[0104] Determine whether the vehicle meets the target condition within each candidate time period, and retain the candidate time period in which the vehicle meets the target condition as the target time period; for each target time period, then determine whether the duration of the target time period reaches a first duration threshold;

[0105] If the duration of the target time period does not reach the first duration threshold, the intermediate time periods that do not reach the first duration threshold and whose interval is less than the second duration threshold are merged to obtain the abnormal time period, and then determine whether the abnormal time period reaches the first duration threshold. If the abnormal time period does not reach the first duration threshold, the segment is removed (that is, the merged time period is removed). If the abnormal time period reaches the first duration threshold, the network sleep anomaly category is determined to be network abnormal awakening, and then the first associated time period is determined, and the abnormal nodes awakened in the first associated time period are traversed, and the abnormal nodes that are not passively awakened in the first associated time period are obtained as target abnormal nodes, and the node identifier of the target abnormal node with the largest number of network management message transmissions is reported to obtain the network sleep anomaly analysis result;

[0106] If the duration of the target period reaches the first duration threshold, it is predetermined that the network is continuously not dormant, and the target period is determined as an abnormal period, and then the sending status of the target network management message in the abnormal period is traversed to determine the second node;

[0107] If the number of target network management messages sent by the second node during the abnormal period reaches a corresponding number threshold, the second node is determined to be a target abnormal node, and then a network sleep abnormality analysis result is determined based on the node category of the target abnormal node: if the target abnormal node is a gateway controller, a maintenance reason for the gateway controller to maintain network wake-up is obtained as the network sleep abnormality analysis result of the vehicle; if the target abnormal node includes a gateway controller and a non-gateway controller, the network sleep abnormality analysis result of the vehicle is determined according to the maintenance reason for the gateway controller to maintain network wake-up and the node description information of the non-gateway controller;

[0108] If the number of target network management messages sent by the second node during the abnormal period does not reach the corresponding quantity threshold, it is determined that the network is frequently awakened, and then the second associated period is determined, and the abnormal nodes awakened during the second associated period are traversed, and the abnormal nodes that are not passively awakened during the second associated period are obtained as target abnormal nodes, and the node identifier of the target abnormal node that belongs to the non-gateway controller category and has the largest number of network management message transmissions is reported to obtain the network sleep abnormality analysis result.

[0109] See attached Figure 6 , Figure 6 The following is a block diagram of a network sleep anomaly analysis device proposed in one embodiment of the present application. Applied to a server, the device 800 includes:

[0110] The time period screening module 810 is used to screen at least one target time period that meets the target conditions from multiple candidate time periods; the candidate time period is the time period during which the vehicle sends the network management message;

[0111] A determination module 820 is configured to determine, based on the duration of each target time period, an abnormal time period during which the vehicle experiences a network dormancy anomaly and a type of network dormancy anomaly within the abnormal time period;

[0112] The node screening module 830 is used to screen target abnormal nodes from abnormal nodes in the vehicle according to the network dormancy abnormality category; the abnormal node is the node that sends network management messages during the abnormal period;

[0113] The result determination module 840 is used to determine the vehicle's network dormancy anomaly analysis result based on the node type of the target abnormal node and / or the number of network management messages sent.

[0114] Optionally, the node screening module 830 is further used to count the number of network management messages sent by each abnormal node during the abnormal period if the network sleep anomaly category of the vehicle is that the network of the vehicle continues to be non-sleep during the abnormal period; determine from each abnormal node the abnormal node whose number of network management messages sent reaches the number threshold corresponding to the abnormal period as the target abnormal node; accordingly, the result determination module 840 is further used to determine the network sleep anomaly analysis result of the vehicle according to the maintenance reason of the target abnormal node maintaining network wake-up if the node category of the target abnormal node is the gateway controller category; if the node category of the target abnormal node includes the gateway controller category and the non-gateway controller category, determine the network sleep anomaly analysis result of the vehicle according to the maintenance reason of the first target abnormal node belonging to the gateway controller category maintaining network wake-up and the node description information of the second target abnormal node belonging to the non-gateway controller category; the node description information of the second target abnormal node includes the node identifier of the second target abnormal node and at least one of the maintenance reason of the second target abnormal node maintaining network wake-up

[0115] Optionally, the node screening module 830 is also used to determine a first associated time period from the abnormal time period based on the time point of each vehicle awakening during the abnormal time period if the network dormancy anomaly category of the vehicle is abnormal network awakening of the vehicle during the abnormal time period; and to screen non-passively awakened abnormal nodes from the abnormal nodes that send network management messages during the first associated time period as target abnormal nodes; accordingly, the result determination module 840 is also used to determine the network dormancy anomaly analysis result of the vehicle based on the target abnormal node that sends the largest number of network management messages during the abnormal time period.

[0116] Optionally, the node screening module 830 is also used to determine a second associated time period from the abnormal time period based on the time point of each vehicle wake-up during the abnormal time period if the network sleep anomaly category of the vehicle is that the vehicle frequently wakes up the network during the abnormal time period; and screen out non-passively woken-up abnormal nodes from the abnormal nodes that send network management messages during the second associated time period as target abnormal nodes; accordingly, the result determination module 840 is also used to determine the network sleep anomaly analysis result of the vehicle based on the target abnormal node that belongs to the non-gateway controller category and has the largest number of network management messages sent during the abnormal time period.

[0117] Optionally, the determination module 820 is further configured to obtain the target period as an abnormal period if the duration of the target period reaches a first duration threshold, and determine the network sleep anomaly category of the vehicle during the abnormal period as the vehicle's network continuously not sleeping during the abnormal period.

[0118] Optionally, the determination module 820 is also used to obtain the target time period as an abnormal time period if the duration of the target time period reaches a first duration threshold; count the first node that sends network management messages during the abnormal time period; determine the first node with the second largest number of network management messages sent during the abnormal time period as the second node; if the number of network management messages sent by the second node during the abnormal time period reaches a number threshold corresponding to the abnormal time period, determine the network sleep anomaly category of the vehicle during the abnormal time period as the vehicle's network continues to not sleep during the abnormal time period; if the number of network management messages sent by the second node during the abnormal time period does not reach the number threshold corresponding to the abnormal time period, determine the network sleep anomaly category of the vehicle as the vehicle's network frequently wakes up during the abnormal time period.

[0119] Optionally, the determination module 820 is also used to select an intermediate time period within each target time period whose duration does not reach the first duration threshold; merge the intermediate time periods whose time intervals are not greater than the second duration threshold into one time period to obtain at least one merged time period; the first duration threshold is greater than the second duration threshold; for any merged time period, if the duration of the merged time period reaches the first duration threshold, obtain the merged time period as an abnormal time period, and determine the network dormancy anomaly category of the vehicle during the abnormal time period as abnormal network wake-up of the vehicle during the abnormal time period.

[0120] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In addition, the functions in the various embodiments of the present application may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0122] Please refer to Figure 6 , which shows a structural block diagram of a server provided according to an embodiment of the present application. The server 500 can be a server capable of running applications, such as a smartphone, tablet computer, e-book, and vehicle. The server 500 in the present application may include one or more of the following components: a processor 510, a memory 520, and one or more applications. The one or more applications may be stored in the memory 520 and configured to be executed by the one or more processors 510, and the one or more programs are configured to execute the method described in the aforementioned method embodiment.

[0123] The processor 510 may include one or more processing cores. The processor 510 utilizes various interfaces and circuits to connect various components within the server 500. It executes instructions, programs, code sets, or instruction sets stored in the memory 520, as well as accesses data stored in the memory 520, to perform various server 500 functions and process data. Optionally, the processor 510 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 510 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 510 and may be implemented separately via a communications chip.

[0124] The memory 520 may include a random access memory (RAM) or a read-only memory (ROM). The memory 520 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 520 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the following various method embodiments, etc. The data storage area may also store data (such as a phone book, audio and video data, chat history data) created by the server 500 during use.

[0125] In addition, the functions in the various embodiments of the present application may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0126] On the other hand, the present application also provides a computer-readable storage medium, which stores program code. The program code can be called by a processor to execute the method described in the above method embodiment.

[0127] The computer-readable storage medium can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a cluster of ROMs. Alternatively, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for program code for executing any of the method steps described above. The program code can be read from or written to one or more computer program products. The program code can be compressed, for example, in a suitable form.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A network dormancy anomaly analysis method, characterized in that: The method comprises: At least one target time period meeting target conditions is selected from a plurality of candidate time periods; the candidate time period is a time period during which the vehicle sends network management messages; the target conditions include: the vehicle door remains open or closed, the vehicle hood remains open or closed, the vehicle is in a parking position, the vehicle is not in an over-the-air download state, and the vehicle is in a non-high voltage state; Determining the abnormal time period during which the vehicle experienced a network dormancy anomaly based on the duration of each target time period, and determining the network dormancy anomaly category of the vehicle during the abnormal time period based on the duration of the abnormal time period, or determining the network dormancy anomaly category of the vehicle during the abnormal time period based on the duration of the abnormal time period and the number of network management messages during the abnormal time period; the abnormal time period is one target time period or a combination of multiple target time periods; screening target abnormal nodes from abnormal nodes in the vehicle according to the network dormancy abnormality category; the abnormal nodes are nodes that send network management messages during the abnormal period; The network dormancy anomaly analysis result of the vehicle is determined according to the node category of the target abnormal node and / or the number of network management messages sent.

2. The method according to claim 1, characterized in that The step of screening target abnormal nodes from abnormal nodes in the vehicle according to the network dormancy abnormality category includes: If the network dormancy anomaly category of the vehicle is that the vehicle's network continues to be active during the abnormal period, counting the number of network management messages sent by each abnormal node during the abnormal period; Determine, from among the abnormal nodes, an abnormal node whose number of network management messages sent reaches a number threshold corresponding to the abnormal period, as the target abnormal node; The determining of the network dormancy anomaly analysis result of the vehicle according to the node category of the target abnormal node and / or the number of network management messages sent includes: If the node category of the target abnormal node is a gateway controller category, determining a network sleep anomaly analysis result of the vehicle according to a maintenance reason for the target abnormal node maintaining network wake-up; If the node category of the target abnormal node includes a gateway controller category and a non-gateway controller category, the network sleep abnormality analysis result of the vehicle is determined based on the maintenance reason of the first target abnormal node belonging to the gateway controller category maintaining network wake-up and the node description information of the second target abnormal node belonging to the non-gateway controller category; the node description information of the second target abnormal node includes the node identifier of the second target abnormal node and at least one of the maintenance reason for the second target abnormal node maintaining network wake-up.

3. The method according to claim 1, characterized in that The step of screening target abnormal nodes from abnormal nodes in the vehicle according to the network dormancy abnormality category includes: If the network dormancy anomaly category of the vehicle is abnormal network awakening of the vehicle within the abnormal period, determining a first associated time period within the abnormal period according to each awakening time point of the vehicle within the abnormal period; Selecting a non-passively awakened abnormal node from the abnormal nodes that send network management messages within the first association period as the target abnormal node; The determining of the network dormancy anomaly analysis result of the vehicle according to the node category of the target abnormal node and / or the number of network management messages sent includes: The network dormancy anomaly analysis result of the vehicle is determined according to the target abnormal node that sends the largest number of network management messages during the abnormal period.

4. The method according to claim 1, wherein The step of screening target abnormal nodes from abnormal nodes in the vehicle according to the network dormancy abnormality category includes: If the network dormancy anomaly category of the vehicle is that the vehicle frequently wakes up from the network within the abnormal period, determining a second associated time period within the abnormal period based on the time point of each vehicle wakeup within the abnormal period; Selecting a non-passively awakened abnormal node from the abnormal nodes that send network management messages within the second association period as the target abnormal node; The determining of the network dormancy anomaly analysis result of the vehicle according to the node category of the target abnormal node and / or the number of network management messages sent includes: The network dormancy anomaly analysis result of the vehicle is determined according to a target abnormal node that belongs to a non-gateway controller category and sends the largest number of network management messages during the abnormal period.

5. The method according to claim 1, wherein The determining, based on the duration of each target time period, the abnormal time period during which the vehicle experiences a network dormancy abnormality, and determining, based on the duration of the abnormal time period, the network dormancy abnormality category of the vehicle within the abnormal time period, includes: If the duration of the target time period reaches a first duration threshold, the target time period is obtained as an abnormal time period, and the network dormancy abnormality category of the vehicle during the abnormal time period is determined as the vehicle continuously not sleeping during the abnormal time period.

6. The method according to claim 1, characterized in that Determining the abnormal time period during which the vehicle experiences a network dormancy abnormality based on the duration of each target time period, and determining the network dormancy abnormality category of the vehicle during the abnormal time period based on the duration of the abnormal time period and the number of network management messages during the abnormal time period, includes: If the duration of the target time period reaches a first duration threshold, obtaining the target time period as an abnormal time period; Counting the first node that sends the network management message during the abnormal period; Determine a first node that sends the second largest number of network management messages during the abnormal period as the second node; If the number of network management messages sent by the second node during the abnormal period reaches a number threshold corresponding to the abnormal period, determining that the network dormancy anomaly category of the vehicle during the abnormal period is that the vehicle continuously does not sleep during the abnormal period; If the number of network management messages sent by the second node during the abnormal period does not reach the number threshold corresponding to the abnormal period, it is determined that the network dormancy abnormality category of the vehicle is that the vehicle frequently wakes up the network during the abnormal period.

7. The method according to claim 1, characterized in that The determining, based on the duration of each target time period, the abnormal time period during which the vehicle experiences a network dormancy abnormality, and determining, based on the duration of the abnormal time period, the network dormancy abnormality category of the vehicle within the abnormal time period, includes: Selecting an intermediate time period within each target time period whose duration does not reach the first duration threshold; Merge the intermediate time periods whose time intervals are not greater than a second time threshold into one time period, to obtain at least one merged time period; wherein the first time threshold is greater than the second time threshold; For any merged time period, if the duration of the merged time period reaches the first duration threshold, the merged time period is obtained as an abnormal time period, and the network dormancy abnormality category of the vehicle in the abnormal time period is determined as abnormal network awakening of the vehicle in the abnormal time period.

8. A network dormancy anomaly analysis device, characterized in that: The device comprises: a time period screening module, configured to screen at least one target time period that meets target conditions from a plurality of candidate time periods; the candidate time period being a time period during which the vehicle sends network management messages; the target conditions comprising: the vehicle doors being in an unchanged state, the vehicle hood being in an unchanged state, the vehicle being in a parking position, the vehicle not being in an over-the-air download state, and the vehicle being in a non-high voltage state; a determination module, configured to determine, based on the duration of each target time period, an abnormal time period during which the vehicle experiences a network dormancy anomaly, and determine, based on the duration of the abnormal time period, a network dormancy anomaly category of the vehicle within the abnormal time period, or, based on the duration of the abnormal time period and the number of network management messages within the abnormal time period, determine the network dormancy anomaly category of the vehicle within the abnormal time period; the abnormal time period being one target time period or a concatenation of multiple target time periods; a node screening module, configured to screen target abnormal nodes from abnormal nodes in the vehicle according to the network dormancy abnormality category; the abnormal nodes are nodes that send network management messages during the abnormal period; The result determination module is used to determine the network dormancy anomaly analysis result of the vehicle according to the node category of the target abnormal node and / or the number of network management messages sent.

9. A server, characterized in that: include: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program codes executable by a processor, and when the program codes are executed by the processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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