Vehicle cloud platform monitoring alarm method and system, medium and electronic equipment

Abstract

The invention discloses a vehicle cloud platform monitoring alarm method and system, a medium and electronic equipment, and the method comprises the steps: obtaining various state data, the multiple kinds of state data comprise first state data of the virtual machine, second state data of the container cluster, third state data of the target service and fourth state data of the third-party service; for each type of state data, preprocessing the state data according to a preset target index type to obtain first index data; and storing the multiple first index data in an index storage engine, dynamically monitoring the first index data on the index storage engine based on a preset target threshold rule, and giving an alarm when the first index data does not meet the target threshold rule. According to the invention, unified monitoring is carried out on the vehicle cloud platform through the index storage engine, the monitoring difficulty is reduced, and the operation and maintenance efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle cloud platform, in particular, relates to a vehicle cloud platform monitoring alarm method, system, medium and electronic equipment. BACKGROUND

[0002] The vehicle cloud platform based on the Internet of Things is located between a vehicle application program and a TBOX (Telematics BOX) of a vehicle, and is used for interfacing various demands and third-party services.

[0003] At present, the vehicle cloud platform runs in a container cluster, depends on multiple component services and external third-party services, and involves a wide range. When the vehicle cloud platform fails, it is difficult to troubleshoot. SUMMARY

[0004] Embodiments of the present application provide a vehicle cloud platform monitoring alarm method, system, medium and electronic equipment, which are used to solve the technical problem of difficult troubleshooting of the vehicle cloud platform.

[0005] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0006] According to a first aspect of the present application, a vehicle cloud platform monitoring alarm method is provided, the vehicle cloud platform comprising a virtual machine, a container cluster being built on the virtual machine, the container cluster being used for running target services and calling third-party services, and the method comprising: acquiring multiple state data, the multiple state data comprising first state data of the virtual machine, second state data of the container cluster, third state data of the target services, and fourth state data of the third-party services; for each of the state data, pre-processing the state data according to a preset target index type to obtain first index data; storing the multiple first index data into an index storage engine, and dynamically monitoring the first index data based on a preset target threshold rule on the index storage engine, and alarming when the first index data does not meet the target threshold rule.

[0007] In some embodiments, based on the foregoing scheme, the state data is the first state data, the second state data, or the fourth state data, and the pre-processing of the state data according to the preset target index type to obtain the first index data comprises: format-converting the state data according to a preset target format to obtain second index data; According to the target index type, the second index data is aggregated to obtain the first index data corresponding to the target index type.

[0008] In some embodiments, based on the foregoing scheme, the state data is the third state data, and the preprocessing of the state data according to the preset target index type to obtain the first index data includes: According to the preset specification format, the third index data is filtered from the state data; According to the target index type, a target field is determined, and the target field is extracted from the third index data to obtain the first index data corresponding to the target index type, the target field including at least one of a host, a service name, a uniform resource identifier, a status code, and a time consumption.

[0009] In some embodiments, based on the foregoing scheme, the method further includes: Obtaining a target operating environment of the vehicle cloud platform; Based on a preset environment rule mapping relationship, the target threshold rule is determined according to the target operating environment and the target index type, the environment rule mapping relationship including a plurality of data groups and a threshold rule corresponding to each data group, each data group including an operating environment and an index type.

[0010] In some embodiments, based on the foregoing scheme, the method further includes: For each first index data, the first index data is counted according to a preset statistical rule to obtain fourth index data, the statistical rule including at least one of a count statistical rule, a sum statistical rule, a fluctuation statistical rule, and a distribution statistical rule.

[0011] In some embodiments, based on the foregoing scheme, the target threshold rule includes a target threshold range, and the first index data does not satisfy the target threshold rule, and an alarm is performed, including: If the first index data is not in the target threshold range, an alarm is performed.

[0012] According to a second aspect of the present application, a vehicle cloud platform system is provided, including a vehicle cloud platform and a vehicle cloud platform monitoring and alarm terminal, the vehicle cloud platform including a virtual machine, the virtual machine having a container cluster built thereon, the container cluster being used to run a target service and call a third-party service, the vehicle cloud platform monitoring and alarm terminal being in communication connection with the vehicle cloud platform, and the vehicle cloud platform monitoring and alarm terminal being used to execute the method of any one of the embodiments of the first aspect of the present application.

[0013] In some embodiments, based on the foregoing scheme, the vehicle cloud platform monitoring alarm terminal further comprises a collection agent module, a first plug-in, a second plug-in, a third plug-in and a fourth plug-in, the first plug-in is adapted to the virtual machine, the second plug-in is adapted to the container cluster, the third plug-in is adapted to the target service, and the fourth plug-in is adapted to the third-party service, and when the plurality of state data is acquired, the vehicle cloud platform monitoring alarm terminal is further configured to: acquire the first state data based on the collection agent module and the first plug-in, acquire the second state data based on the collection agent module and the second plug-in, acquire the third state data based on the collection agent module and the third plug-in, and acquire the fourth state data based on the collection agent module and the fourth plug-in.

[0014] According to a third aspect of the present application, a computer readable storage medium is provided, which stores a computer program including executable instructions, when the executable instructions are executed by a processor, the method according to any one of the embodiments of the first aspect of the present application is implemented.

[0015] According to a fourth aspect of the present application, an electronic device is provided, comprising: one or more processors; a memory for storing executable instructions of the processor, when the executable instructions are executed by the one or more processors, the one or more processors implement the method according to any one of the embodiments of the first aspect of the present application.

[0016] The beneficial effects of the present application are as follows: According to the target index type, the state data is preprocessed to obtain first index data, the first index data is stored to the index storage engine, and the vehicle cloud platform is uniformly monitored based on the target threshold rule through the index storage engine, which reduces the monitoring difficulty and improves the operation and maintenance efficiency.

[0017] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0018] The drawings herein are incorporated into the specification and form part of the specification, show embodiments consistent with the present application, and together with the specification serve to explain the principles of the present application. It is obvious that the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings from these drawings without creative labor. In the drawings: Figure 1 A flowchart of a vehicle cloud platform monitoring alarm method in an embodiment of the present application is shown; Figure 2A block diagram of a vehicle cloud platform system in an embodiment of the present application is shown. Figure 3 A schematic diagram of a computer readable storage medium in an embodiment of the present application is shown. Figure 4 A schematic diagram of a system structure of an electronic device in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to 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 the embodiments of the present application. Based on the embodiments in the present application, other embodiments of the plurality of burners obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0020] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a sufficient understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be used. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring the aspects of the present application.

[0021] The block diagrams shown in the drawings are only functional entities, which do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0022] The flowcharts shown in the drawings are only exemplary illustrations, and do not necessarily include the contents and operations / steps of the plurality of burners, nor do they necessarily be executed in the order described. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0023] In the description of the present application, it should be understood that the terms "first", "second" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "plurality" is two or more.

[0024] Figure 1A flow chart of a vehicle cloud platform monitoring alarm method in the embodiment of the application is shown, see Figure 1 The application provides a vehicle cloud platform monitoring alarm method, the vehicle cloud platform comprises a virtual machine, a container cluster is built on the virtual machine, the container cluster is used for running a target service and calling a third-party service, and at least comprises steps S1 to S3: In step S1, a plurality of state data is acquired, the plurality of state data comprises first state data of the virtual machine, second state data of the container cluster, third state data of the target service and fourth state data of the third-party service. The first state data comprises state data of CPU, disk, memory and network load.

[0025] In step S2, for each of the state data, the state data is preprocessed according to a preset target index type to obtain first index data. The target index type can include a first type of physical environment problem, a second type of component service problem, and a third type of business function problem machine, wherein the collection range of the first type is a virtual machine or a container cluster, the first type can include network connectivity, CPU usage, memory usage, disk usage, 1-5-15 minute CPU load, port network state, the collection range of the second type includes Mysql, Redis, Kafka and Nginx, the second type can include Mysql QPS, TPS, connection number, slow query, master-slave delay, master-slave state, Redis latency, instantaneous_ops_per_sec, connected_clients, latency is response time, instantaneous_ops_per_sec is the average number of total processing requests per second, connected_clients is the number of client connections, Kafka ActiveControllerCount, BytesInPerSec / BytesOutPerSec, kafka_consumergroup_lag, ActiveControllerCount is the number of active controllers, BytesInPerSec / BytesOutPerSec is the throughput of Kafka, kafka_consumergroup_lag is the message delay of each consumer, Nginx Accepts, Requests, server error rate, request tim, Accepts is the number of accepted client connections, Requests is the total number of requests, request time is the request processing time, the collection range of the third type can be a business project, and the third type can include PV, UV, request success rate, QPS, average time consumption, P99, P75.

[0026] In step S3, the plurality of first index data is stored into an index storage engine, and the first index data is dynamically monitored based on a preset target threshold rule on the index storage engine, and an alarm is given when the first index data does not meet the target threshold rule.

[0027] In some implementations, the state data is the first state data, the second state data, or the fourth state data. The step of preprocessing the state data according to a preset target indicator type to obtain the first indicator data includes: converting the state data according to a preset target format to obtain the second indicator data; and aggregating the second indicator data according to the target indicator type to obtain the first indicator data corresponding to the target indicator type.

[0028] In this way, the status data is converted into scattered second indicator data. The second indicator data has a unified format, which makes it easier to aggregate into the first indicator data.

[0029] In some implementations, the status data is the third status data. Preprocessing the status data according to a preset target indicator type to obtain the first indicator data includes: filtering the status data to obtain the third indicator data according to a preset standard format; determining the target field according to the target indicator type; extracting the target field from the third indicator data to obtain the first indicator data corresponding to the target indicator type. The target field includes at least one of host, service name, Uniform Resource Identifier, status code, and time consumption. This can also be understood as data cleaning according to a project tracking format, using regular expressions to match the format of the status data, and only extracting specific fields from data that matches the format, i.e., the third indicator data. The host is `host`, and the Uniform Resource Identifier is `uri`.

[0030] In this way, the status data is cleaned, and the status data in a standardized format is used as the third indicator data. The target field is then extracted from the third indicator data, which improves the data usability.

[0031] In some embodiments, the method further includes: acquiring the target operating environment of the vehicle cloud platform; and determining the target threshold rule based on a preset environment rule mapping relationship, according to the target operating environment and the target indicator type. The environment rule mapping relationship includes: multiple data groups and a threshold rule corresponding to each data group, each data group including an operating environment and an indicator type. The operating environment can be different physical machine environments, different container environments, or different process environments. Different process environments can be test environments, production environments, or pre-production environments.

[0032] Thus, the upper and lower boundaries of the first indicator data differ for different operating environments, and dynamic queries can be performed based on the operating environment and target indicator type, making the target threshold rule adaptable to the target operating environment.

[0033] It should be noted that dynamic threshold rules can be established by dynamically configuring the rule engine. The specific rules of the rule engine can be version-managed through Nacos, and developers can dynamically modify them through the interface, ultimately achieving dynamic monitoring of data.

[0034] In some embodiments, the method further includes: for each type of the first indicator data, performing statistics on the first indicator data according to a preset statistical rule to obtain fourth indicator data, wherein the statistical rule includes at least one of a counting statistical rule, a summation statistical rule, a fluctuation statistical rule, and a distribution statistical rule. The counting statistical rule is counter, the summation statistical rule is summary, the fluctuation statistical rule is gauge, and the distribution statistical rule is histogram.

[0035] For example, when the status data is first status data, the counting statistics rule is used to count the number of virtual machines in the first target state, the number of daily restarts, and the number of CPU over-threshold alarms. The first target state is one of running, shut down, and faulty. The summation statistics rule is used to count the total CPU utilization, total memory usage, and monthly total virtual machine storage read / write volume of all virtual machines. The fluctuation statistics rule is used to count the CPU utilization fluctuation range, memory usage standard deviation, and minute-level fluctuation of virtual machine network bandwidth of a single virtual machine. The distribution statistics rule is used to count the quantile distribution of virtual machine CPU utilization and the number distribution of virtual machines of different specifications. When the status data is second status data, the counting statistics rule is used to count the number of Pods in the second target state, the number of cluster nodes, the number of daily Pod restarts, the number of container crashes, and the number of node offlines. The second target state is one of running, ready, and not ready. A Pod includes one or more containers. The summation statistics rule is used to count the total cluster CPU quota usage, total storage usage, and total network inbound traffic of all Pods. The fluctuation statistics rule is used to count the minute-level fluctuation of Pod CPU utilization and cluster DNS. The fluctuation range of query volume and node network throughput are analyzed. Distribution statistics rules are used to statistically analyze the distribution of container CPU utilization, the distribution of Pod counts in different namespaces, and the P90 quantile of Pod memory usage. When the status data is third-state data, counting statistics rules are used to statistically analyze the number of vehicles, trips, users, and maintenance. The vehicle dimension includes the number of online vehicles, offline vehicles, and vehicles with fault alarms. The trip dimension includes the number of trips completed and trips interrupted daily. The user dimension includes the number of times the vehicle owner's APP is logged in and the number of times the vehicle is remotely controlled daily. The maintenance dimension includes the number of vehicles scheduled for maintenance and the number of maintenance orders completed daily. The summation statistics rules are used to statistically analyze the total daily power consumption, total fuel consumption, total charging amount, total daily mileage, total driving time, total daily data collection from vehicle sensors, total daily remote diagnostic time, and total OTA data for all vehicles. The upgrade package distribution volume and fluctuation statistics rules are used to count the minute-level fluctuations in the number of online vehicles, the daily average power consumption fluctuations of a single vehicle model, the hour-level fluctuations in the number of remote vehicle control times, and the daily fluctuations in the number of battery fault alarms. The distribution statistics rules are used to count the regional distribution of online vehicles, the percentile of power consumption per 100 kilometers for all vehicles, the distribution of single trip mileage, and the distribution of vehicle fault types.When the status data is the fourth status data, the counting statistics rules are used to count the daily number of GPS positioning interface calls, the number of GPS positioning failures, the number of daily vehicle network service fee payment calls, the number of payment timeouts, the number of daily vehicle insurance query interface calls, the number of interface return errors, the number of daily road condition and weather data retrievals, and the number of data missing events. The summation statistics rules are used to count the daily total call cost of third-party services, the total traffic consumption, the daily total response time of third-party interfaces, the daily total payment amount completed through the payment service, and the daily total insurance intention amount generated through the insurance interface. The fluctuation statistics rules are used to count the minute-level call volume fluctuation of the map positioning interface, the fluctuation of the payment interface response time, and the fluctuation of the insurance query interface success rate. The distribution statistics rules are used to count the response time distribution: the quantile of the map interface response time, the time period distribution of third-party services, the distribution of payment interface failure types, and the cost proportion of each third-party service.

[0036] In some implementations, the method further includes: in response to a query statement input from the front end, retrieving first indicator data for multiple target time periods from the indicator storage engine, wherein the multiple target time periods are different from each other; and displaying a trend chart of the first indicator data on a target interface based on the multiple first indicator data. The trend chart enables data visualization on the interface, facilitating viewing by operations and maintenance personnel. The query statement can be an SQL statement, thereby allowing access to the indicator storage engine API via query alerts.

[0037] In some implementations, the target threshold rule includes a target threshold range, and an alarm is triggered when the first indicator data does not meet the target threshold rule, including: if the first indicator data is not within the target threshold range, then an alarm is triggered.

[0038] For example, the target indicator type of the first indicator data is CPU utilization, and the target threshold range is 50% to 80%. If the first indicator data is 90%, then the first indicator data is not within the target threshold range and an alarm is triggered. If the first indicator data is 75%, then the first indicator data is within the target threshold range and no alarm is triggered.

[0039] In some implementations, the target threshold rule includes a target threshold, and an alarm is triggered when the first indicator data does not meet the target threshold rule, including: if the first indicator data is greater than the target threshold, then an alarm is triggered.

[0040] For example, the target indicator type of the first indicator data is memory usage, and the target threshold is 8GB. If the first indicator data is 9GB, then the first indicator data is greater than the target threshold and an alarm is triggered. If the first indicator data is 6GB, then the first indicator data is less than the target threshold and no alarm is triggered.

[0041] In some implementations, after issuing an alarm when the first indicator data does not meet the target threshold rule, the method further includes: generating alarm information and sending the alarm information to a target user. The alarm information can be sent to the target user via email API, Lark webhook, WeChat, or DingTalk. The target user can be operations and maintenance personnel, making it convenient for them to view the information. The alarm information can be alarm data for multiple target indicator types or alarm data for a single target indicator type.

[0042] In summary, on the one hand, for vehicle cloud platforms, a comprehensive monitoring and alarm mechanism is established, encompassing hardware resources, component status, business monitoring, and third-party services. Compared to existing technologies, this provides a wider overall monitoring scope, offering more information to operations and maintenance personnel and significantly improving troubleshooting efficiency. On the other hand, various interface tools, such as indicator visualization, indicator monitoring and alarms, and log query and analysis, support for operations and maintenance is achieved. Existing technical solutions are geared towards not only operations and maintenance personnel but also R&D personnel, business leaders, and others, thus requiring them to assume more roles. Furthermore, this application presents a standardized and compliant system monitoring solution. Compared to existing technologies, business access only requires generating logs according to a standardized format without modifying business logic, resulting in lower overall intrusiveness to the business. Therefore, this application offers lower costs and faster speed for rapid access and expansion.

[0043] In this application, the status data is preprocessed according to the target indicator type to obtain the first indicator data, and the first indicator data is stored in the indicator storage engine. Based on the target threshold rules, the vehicle cloud platform is uniformly monitored through the indicator storage engine, which reduces the monitoring difficulty and improves the operation and maintenance efficiency.

[0044] Figure 2 A block diagram of a vehicle cloud platform system according to an embodiment of this application is shown. See also: Figure 2 According to a second aspect of this application, a vehicle cloud platform system 100 is provided, including a vehicle cloud platform 101 and a vehicle cloud platform monitoring and alarm terminal 102. The vehicle cloud platform includes a virtual machine, on which a container cluster is built. The container cluster is used to run target services and call third-party services. The vehicle cloud platform monitoring and alarm terminal is communicatively connected to the vehicle cloud platform and is used to execute the method described in any embodiment of the first aspect of this application.

[0045] In some implementations, the vehicle cloud platform monitoring and alarm terminal further includes a data acquisition agent module, a first plugin, a second plugin, a third plugin, and a fourth plugin. The first plugin is adapted to the virtual machine, the second plugin is adapted to the container cluster, the third plugin is adapted to the target service, and the fourth plugin is adapted to the third-party service. When acquiring multiple status data, the vehicle cloud platform monitoring and alarm terminal is also used to: acquire the first status data based on the data acquisition agent module and the first plugin, acquire the second status data based on the data acquisition agent module and the second plugin, acquire the third status data based on the data acquisition agent module and the third plugin, and acquire the fourth status data based on the data acquisition agent module and the fourth plugin.

[0046] Thus, when collecting data in the first state, the first plugin is compatible with the virtual machine's collection environment, facilitating the data collection work of the data collection agent module. When collecting data in the second state, the second plugin is compatible with the container cluster's collection environment, enabling the data collection agent module to perform data collection smoothly. When collecting data in the third state, the third plugin is compatible with the target business, allowing the data collection agent module to complete the data collection work. When collecting data in the fourth state, the fourth plugin is compatible with the third-party service, enabling the data collection work of the data collection agent module to proceed quickly.

[0047] In some implementations, the data collection agent module is an agent, the first plugin is linux-agent, the second plugin is k8s-agent, the third plugin includes Mysql-agent, kafka-agent, and redis-agent, and the fourth plugin is service-agent.

[0048] Thus, the first plugin is linux-agent, which can read hardware resource information by running linux commands; the second plugin is k8s-agent, which can pull cluster status information by accessing the kubestate API; the third plugin includes MySQL-agent, kafka-agent, and redis-agent, which can pull business log information by using the tail command within the agent; and the fourth plugin is service-agent, which can determine the overall status of the current third-party service by periodically accessing the health check interface API within the agent.

[0049] In this application, the adaptation of the data collection environment is achieved by deploying a set of fluent-agent + plugin type configuration. The type configuration is divided into: env-type environment type, data-source data collection source, data-path data directory address, and data-filter preliminary data preprocessing process. In data-filter, a custom Python script can be specified to perform preliminary data adjustment. The script logic will be as concise as possible and cannot be multi-threaded.

[0050] Based on the same inventive concept, as a third aspect, this application also provides a computer-readable storage medium storing a computer program thereon, the computer program including executable instructions that, when executed by a processor, implement the method described in any embodiment of the first aspect of this application.

[0051] In some possible implementations, various aspects of this application may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this application.

[0052] refer to Figure 3 As shown, a program product 200 for implementing the above-described method according to an embodiment of this application is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product of this application is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0053] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0054] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0055] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0056] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0057] In another respect, this application also provides an electronic device capable of implementing the above-described method.

[0058] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0059] The following reference Figure 4 To describe an electronic device 300 according to this embodiment of the present application. Figure 4 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0060] like Figure 4As shown, the electronic device 300 is presented in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, and a bus 330 connecting different system components (including storage unit 320 and processing unit 310).

[0061] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in the "Embodiment Methods" section above according to various exemplary embodiments of this application.

[0062] Storage unit 320 may include readable media in the form of volatile storage units, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0063] Storage unit 320 may also include a program / utility 324 having a set (at least one) of program modules 325, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0064] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0065] Electronic device 300 can also communicate with one or more external devices 400 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with the electronic device 300, and / or with any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. Figure 4 As shown, network adapter 360 communicates with other modules of electronic device 300 via bus 330. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0066] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0067] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A vehicle cloud platform monitoring and alarm method, characterized in that, The vehicle cloud platform includes virtual machines, on which a container cluster is built. The container cluster is used to run target services and call third-party services. The method includes: Acquire multiple state data, including the first state data of the virtual machine, the second state data of the container cluster, the third state data of the target business, and the fourth state data of the third-party service; For each type of state data, the state data is preprocessed according to a preset target indicator type to obtain the first indicator data; Multiple first indicator data are stored in an indicator storage engine. The first indicator data is dynamically monitored on the indicator storage engine based on a preset target threshold rule. An alarm is triggered when the first indicator data does not meet the target threshold rule.

2. The vehicle cloud platform monitoring and alarm method according to claim 1, characterized in that, The status data is the first status data, the second status data, or the fourth status data. The step of preprocessing the status data according to a preset target indicator type to obtain the first indicator data includes: The status data is converted according to a preset target format to obtain the second indicator data; Based on the target indicator type, the second indicator data is aggregated to obtain the first indicator data corresponding to the target indicator type.

3. The vehicle cloud platform monitoring and alarm method according to claim 1, characterized in that, The state data is the third state data. The preprocessing of the state data according to a preset target indicator type to obtain the first indicator data includes: According to a preset standard format, the third indicator data is obtained by filtering from the status data; The target field is determined according to the target indicator type, and the target field is extracted from the third indicator data to obtain the first indicator data corresponding to the target indicator type. The target field includes at least one of host, service name, uniform resource identifier, status code and time consumption.

4. The vehicle cloud platform monitoring and alarm method according to claim 1, characterized in that, The method further includes: Obtain the target operating environment of the vehicle cloud platform; Based on a preset environment rule mapping relationship, the target threshold rule is determined according to the target operating environment and the target indicator type. The environment rule mapping relationship includes: multiple data groups and the threshold rule corresponding to each data group. Each data group includes an operating environment and an indicator type.

5. A vehicle cloud platform monitoring and alarm method according to claim 1, characterized in that, The method further includes: For each type of the first indicator data, the first indicator data is statistically analyzed according to a preset statistical rule to obtain the fourth indicator data. The statistical rule includes at least one of the following: counting statistical rule, summation statistical rule, fluctuation statistical rule, and distribution statistical rule.

6. The vehicle cloud platform monitoring and alarm method according to claim 1, characterized in that, The target threshold rule includes a target threshold range. An alarm is triggered when the first indicator data does not meet the target threshold rule, including: If the first indicator data is not within the target threshold range, an alarm will be triggered.

7. A vehicle cloud platform system, characterized in that, The system includes a vehicle cloud platform and a vehicle cloud platform monitoring and alarm terminal. The vehicle cloud platform includes a virtual machine, on which a container cluster is built. The container cluster is used to run target services and call third-party services. The vehicle cloud platform monitoring and alarm terminal is communicatively connected to the vehicle cloud platform and is used to execute the method described in any one of claims 1-6.

8. A vehicle cloud platform system according to claim 7, characterized in that, The vehicle cloud platform monitoring and alarm terminal further includes a data acquisition agent module, a first plugin, a second plugin, a third plugin, and a fourth plugin. The first plugin is adapted to the virtual machine, the second plugin is adapted to the container cluster, the third plugin is adapted to the target business, and the fourth plugin is adapted to the third-party service. When acquiring multiple status data, the vehicle cloud platform monitoring and alarm terminal is also used for: The first state data is obtained based on the acquisition agent module and the first plugin; the second state data is obtained based on the acquisition agent module and the second plugin; the third state data is obtained based on the acquisition agent module and the third plugin; and the fourth state data is obtained based on the acquisition agent module and the fourth plugin.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program includes executable instructions that, when executed by a processor, implement the method of any one of claims 1-6.

10. An electronic device, characterized in that, include: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1-6.

Images