Log processing method and device

Abstract

The embodiment of the invention provides a log processing method and device. The method comprises the steps that operation log data of a vehicle production system in the operation process and production log data corresponding to vehicle production tasks included in the vehicle production system are determined; determining operation state data of the vehicle production system according to the operation log data; detecting the production log data according to a log detection rule corresponding to the vehicle production task to obtain a detection result; and generating early warning information under the condition that the running state data is determined to meet a first preset condition, or generating the early warning information under the condition that the production log data is determined to meet a second preset condition according to the detection result. Related personnel can be timely notified when abnormal operation is found, so that real-time monitoring of the vehicle production system is realized, faults of the vehicle production system can be timely found, the influence of the faults on vehicle production business is reduced, and the production efficiency is ensured.

Description

Technical Field

[0001] The embodiments of the present specification relate to the field of computer technology, and in particular to a log processing method. Background Art

[0002] With the development of computer technology, it is no longer necessary to manually control the production line during vehicle production. Computers can be used to control the operation of the vehicle production system to achieve automation and intelligence in vehicle production. The vehicle production system can include a production control system and a process or product quality management system. The production control system can be used to supervise, control and manage the entire production line. On a highly automated production line, the vehicle production system plays a core control role. Once a vehicle production system fails, the production of the entire production line and its upstream and downstream production lines will stagnate, thereby affecting production efficiency. Therefore, an effective technical solution is urgently needed to monitor the operation process of the vehicle production system. Summary of the invention

[0003] In view of this, an embodiment of this specification provides a log processing method. One or more embodiments of this specification also relate to a log processing device, a computing device, a computer-readable storage medium and a computer program to solve the technical defects existing in the prior art.

[0004] According to a first aspect of an embodiment of this specification, a log processing method is provided, including:

[0005] Determining operation log data of a vehicle production system during operation, and production log data corresponding to a vehicle production task included in the vehicle production system;

[0006] Determining operation status data of the vehicle production system according to the operation log data;

[0007] According to the log detection rule corresponding to the vehicle production task, the production log data is detected to obtain a detection result;

[0008] generating warning information when determining that the operating status data meets the first preset condition, or

[0009] When it is determined according to the detection result that the production log data meets the second preset condition, early warning information is generated.

[0010] According to a second aspect of an embodiment of this specification, a log processing device is provided, including:

[0011] A first determination module is configured to determine operation log data of a vehicle production system during operation, and production log data corresponding to a vehicle production task included in the vehicle production system;

[0012] a second determination module, configured to determine operation status data of the vehicle production system according to the operation log data;

[0013] A detection module is configured to detect the production log data according to the log detection rule corresponding to the vehicle production task to obtain a detection result;

[0014] a generating module configured to generate warning information when it is determined that the operating status data meets the first preset condition, or

[0015] When it is determined according to the detection result that the production log data meets the second preset condition, early warning information is generated.

[0016] According to a third aspect of an embodiment of this specification, a computing device is provided, including:

[0017] Memory and processor;

[0018] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the above-mentioned log processing method are implemented.

[0019] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned log processing method are implemented.

[0020] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned log processing method.

[0021] An embodiment of the present specification provides a log processing method, which determines the operation log data of a vehicle production system during operation, and the production log data corresponding to the vehicle production tasks included in the vehicle production system; determines the operation status data of the vehicle production system based on the operation log data; detects the production log data according to the log detection rules corresponding to the vehicle production tasks to obtain detection results; generates warning information when it is determined that the operation status data meets a first preset condition, or generates warning information when it is determined that the production log data meets a second preset condition based on the detection result.

[0022] The above method obtains the operation log data and production log data of the vehicle production system, and detects the operation log data and production log data, thereby realizing real-time monitoring of the vehicle production system from multiple dimensions. The operation status data determined by the operation log data can reflect the operation status of the vehicle production system itself, thereby judging whether the vehicle production system is operating normally. By detecting the production log data according to the log detection rules, it can be determined whether the operation of the production task of the vehicle production line complies with the business rules, and when the operation status data meets the first preset condition or the production log data meets the second preset condition, an early warning information is generated, so as to facilitate timely notification of relevant personnel when an abnormal operation is found, thereby realizing real-time monitoring of the vehicle production system and vehicle production tasks, timely discovering faults of the vehicle production system, reducing the impact of faults on vehicle production business, and ensuring production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of an application scenario of a log processing method provided by an embodiment of this specification;

[0024] Figure 2 is a flow chart of a log processing method provided by an embodiment of this specification;

[0025] Figure 3 It is a schematic diagram of log data acquisition in a log processing method provided in one embodiment of this specification;

[0026] Figure 4 It is a schematic diagram of template matching in a log processing method provided by an embodiment of this specification;

[0027] Figure 5 It is a processing flow chart of a log processing method provided by an embodiment of this specification;

[0028] Figure 6 It is a structural diagram of a log processing device provided by an embodiment of this specification;

[0029] Figure 7 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0030] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0031] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0032] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0033] In addition, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0034] First, the terms involved in one or more embodiments of this specification are explained.

[0035] Vehicle production system: A system that supports the production of complete vehicles and powertrains, including production control systems and process or product quality management systems, such as engine block casting control systems, engine machining control systems, engine assembly process production control systems, engine assembly process quality management systems, body stamping control systems, body welding control systems, vehicle assembly systems, vehicle quality management systems, and vehicle electronic and electrical testing systems.

[0036] PLC: Programmable Logic Controller, a digital computing electronic device used in industrial production, such as automated production line equipment controlled by a PLC controller.

[0037] Production control system: a general term for a type of software system that manages the entire production process of the production line, including production process management, material management, production plan implementation, and repair and scrap management.

[0038] CPU: Central Processing Unit, the computing and control core of the computer system.

[0039] MySQL: A relational database management system that stores data in different tables.

[0040] Kafka: A high-throughput distributed publish-subscribe messaging system that can handle all action stream data.

[0041] Hadoop: A distributed system infrastructure that can be used to develop distributed programs and perform data computing and storage.

[0042] Flink: A distributed processing engine and framework for stateful computation on bounded and unbounded data streams, used to process streaming data.

[0043] SQL: Structured Query Language, a database language with multiple functions such as data manipulation and data definition.

[0044] ES cluster: It is a distributed document database in which each field can be indexed and the data in each field can be searched. It can store, search and analyze large amounts of data in a very short time.

[0045] XML: Extensible Markup Language, which can be used to mark data and define data types.

[0046] LCS: Longest Common Subsequence, a similarity-based matching algorithm.

[0047] DTW: Dynamic Time Warping, a dynamic time warping algorithm, can be used to calculate the distance between two samples in time series data.

[0048] Topic: a collection of data.

[0049] MES: Manufacturing Execution System, a set of production information management system for the execution layer of the workshop of manufacturing enterprises. MES can provide enterprises with management modules including manufacturing data management, planning and scheduling management, production scheduling management, inventory management, quality management, human resources management, work center / equipment management, tool and fixture management, procurement management, cost management, project kanban management, production process control, bottom-level data integration analysis, and upper-level data integration decomposition. It is a manufacturing collaborative management platform.

[0050] In this specification, a log processing method is provided. This specification also relates to a log processing device, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0051] See also Figure 1 , Figure 1 A schematic diagram of an application scenario of a log processing method provided according to an embodiment of the present specification is shown. Figure 1 The vehicle production system 102 and the log processing platform 104 are included. The vehicle production system 102 includes a PLC device 1022 and a server 1024, and the control of the vehicle production in the vehicle production workshop can be realized through the information interaction between the PLC device 1022 and the server 1024.

[0052] In a specific implementation, the log processing platform 104 can be used to collect log data of the vehicle production system 102 , and cluster and analyze the log data, thereby realizing abnormality detection of the log data.

[0053] like Figure 1 As shown, the log processing platform 104 can obtain the production log data and operation log data of the vehicle production system 102, and store and analyze the production log data and operation log data. When the production log data is detected for abnormality, the production log data can be detected according to the log detection rules determined by the business rules to determine whether the production log data meets the business requirements. When the operation log data is detected for abnormality, the abnormality detection can be performed according to the operation status data recorded in the operation log data and the preset operation status threshold value set in advance. When it is determined that the operation status data is greater than the preset operation status threshold value, it is indicated that the vehicle production system 102 has an abnormality. At this time, an early warning message can be generated, and the early warning message can be sent to the operator of the vehicle production system 102 through a preset communication method. For example, the early warning message can be sent to the operator through an email, or the early warning message can be sent to the vehicle production system 102, and the vehicle production system 102 can display it to the operator through the display interface of the terminal. Thereby, the abnormality detection of the vehicle production system is realized.

[0054] See also Figure 2 , Figure 2 A flow chart of a log processing method provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0055] Step 202: Determine the operation log data of the vehicle production system during operation, and the production log data corresponding to the vehicle production tasks included in the vehicle production system.

[0056] Specifically, the log processing method provided in the embodiments of this specification can be applied to a log processing platform to analyze logs, thereby detecting production lines in the field of product production, such as detecting log data of a vehicle production system, thereby determining whether the vehicle production system is operating normally. In addition, it can also be used in other product production systems, which is not limited in the embodiments of this specification.

[0057] Among them, the operation log data of the vehicle production system during operation can be understood as a log used to record the performance indicators of the vehicle production system during operation. The performance indicators of the vehicle production system include but are not limited to indicators such as the CPU occupancy rate, memory, disk throughput, network throughput and system response time of the vehicle production system. The vehicle production tasks included in the vehicle production system can be understood as vehicle parts production tasks and vehicle parts assembly tasks. The production log data corresponding to the vehicle production tasks can be understood as a log used to record the production data of the vehicle production system when executing the vehicle production tasks. The production data includes but is not limited to the execution time of the vehicle production tasks, the execution order between vehicle production tasks, etc.

[0058] In a specific implementation, the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined, including:

[0059] Obtain initial log data from the vehicle production system;

[0060] The initial log data is clustered, and the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined according to the clustering result.

[0061] The initial log data of the vehicle production system can be understood as all log data of the vehicle production system, and the initial log data can include multiple types of log data, such as but not limited to operation log data and production log data. Clustering the initial log data can be understood as determining log data of the same type in all log data.

[0062] Based on this, all log data of the vehicle production system can be obtained, and all log data can be clustered, and the operation log data and production log data can be determined from all log data according to the clustering results.

[0063] In another embodiment of the present specification, when collecting log data, the log data can be collected according to the type of log data. Specifically, the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system also include:

[0064] According to the type of log data, the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are collected.

[0065] In practical applications, the log collection interface can be used to communicate with the vehicle production system, so as to obtain all log data of the vehicle production system through the log collection interface. It can be understood that the log collection interface can also be connected to other systems to realize log collection of other systems, so as to realize log collection for different data sources.

[0066] See also Figure 3 , Figure 3 FIG. 1 is a schematic diagram showing the acquisition of log data in a log processing method according to an embodiment of the present specification. Figure 3 As shown, the log processing platform can receive a variety of raw data, such as files and directories, relational databases, system operation logs, communication protocols, message queues and script collection, and collect various types of log data into the log processing platform for centralized processing.

[0067] Specifically, the log analysis module included in the log processing platform sends the collection task configuration to the mysql database, the log analysis module sends the data collector deployment task to the cell, the cell sends the collection task configuration, realizes the deployment of the data collector, uses the first data collector to obtain the log file, the system operation log and the communication protocol, uses the second data collector to obtain the message queue and the database, and outputs it to the kafka cluster through the router, and stores it in the mysql database through the cell. Among them, the log collection task configuration data can be stored in the mysql database, and the collected log data can be stored in the Eleasticsearch database. The log analysis module can submit tasks to the data center, and the data center submits the flink task to the Hadoop cluster, and executes the data parsing task and the data storage task in the Hadoop cluster. When the Hadoop cluster executes the data parsing task, it can read data from the kafka cluster and write back a new topic to the kafka cluster. When the Hadoop cluster executes the data storage task, it can read data from the kafka cluster and store the read data in the ES cluster. The log analysis module can also convert the SQL query into an ES query through the processor, so as to query data from the ES cluster.

[0068] In summary, by obtaining all log data of the vehicle production system, it is possible to comprehensively detect whether the operation of the vehicle production system is normal, so that system failures can be discovered in a timely manner.

[0069] In practical applications, log pattern parsing is an algorithm that parses log data from semi-structured data to structured data. In the process of automated analysis of log data, this algorithm can be used as an intermediate step to serve the subsequent anomaly detection task of log data. The specific implementation method is as follows:

[0070] The clustering process is performed on the initial log data, and the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined according to the clustering result, including:

[0071] Performing character extraction processing on the initial log data to obtain target characters corresponding to the initial log data;

[0072] Determining the operation log data of the vehicle production system during operation according to the matching result between the preset operation log template and the target character;

[0073] According to the matching result between the preset production log template and the target character, the production log data corresponding to the vehicle production task included in the vehicle production system is determined.

[0074] Among them, the target characters can be understood as constant characters included in the initial log data. For example, if the initial log data is "CPU occupancy rate is 98%", the target characters can be, for example, "CPU" and "occupancy rate". The preset operation log template can be understood as the template corresponding to the operation log data, for example, "CPU occupancy rate is *". The preset production log template can be understood as the template corresponding to the production log data, for example, "Vehicle chassis assembly time is * minutes".

[0075] Based on this, the initial log data can be subjected to character extraction processing using a word segmentation algorithm according to pre-set keywords, constant characters in the initial log data can be extracted, and the constant characters can be matched with a preset operation log template. The operation log data can be determined in the initial log data based on the matching results. At the same time, the constant characters can also be matched with a preset production log template. The production log data can be determined in the initial log data based on the matching results.

[0076] It can be understood that the initial log data may include multiple log data, and the target character corresponding to each log data can be matched with the preset operation log template and the preset production log template, so as to distinguish the operation log data and the production log data from the multiple log data.

[0077] In addition, for other types of log data, corresponding log data templates can also be determined, and the distinction and acquisition of other types of log data can be achieved according to the aforementioned matching process.

[0078] In practical applications, a log corpus can be established, and based on the keywords stored in the log corpus, a word segmentation algorithm optimized for log characteristics can be used to extract characters from the initial log data to obtain target characters. In addition, time, SQL statements, XML and other contents can also be identified based on the initial log data, and keywords and word segmentation algorithms can be considered as variable settings. When matching, it can be implemented based on the LCS matching algorithm based on similarity, and the inverted list and template tree can be further used to accelerate template matching.

[0079] See also Figure 4 , Figure 4A schematic diagram of template matching in a log processing method provided according to an embodiment of the present specification is shown. Specifically, for the initial log data "The CPU occupancy rate of disk 1 is 90%", the initial log data is split and replaced according to the word segmentation algorithm, and the target characters corresponding to the initial log data are obtained as {CPU, occupancy rate, disk 1, is, NUM}. Specifically, the word segmentation algorithm can perform keyword extraction, regular variable replacement, word segmentation, Chinese word segmentation, data processing and filtering on the initial log data to obtain the target characters. Afterwards, the target character and the preset template can be linearly and quickly matched according to the template tree. In the case of successful matching, template identification 1 is obtained, and the template corresponding to the template identification 1 is: The CPU occupancy rate of disk * is NUM%, variable: * is 1, and NUM is 90. In the case of failed matching, further matching can be performed according to the inverted list, so as to determine the corresponding template according to the matching result, and determine which type of log data the initial log data is according to the template.

[0080] In summary, similar log data can be divided into the same cluster through template matching, so as to obtain multiple types of log data, which is convenient for subsequent detection of different types of log data according to different detection methods.

[0081] In a specific implementation, before determining the operation log data of the vehicle production system during operation according to the matching result between the preset operation log template and the target character, the method further includes:

[0082] Performing character extraction processing on historical operation log data of the vehicle production system during operation to obtain target characters corresponding to the historical operation log data;

[0083] According to the preset characters, other characters in the historical operation log data except the target characters are replaced to obtain a preset operation log template including the preset characters and the target characters.

[0084] Specifically, historical operation log data of the vehicle production system during operation can be obtained, and character extraction processing can be performed on the historical operation log data to obtain target characters corresponding to the historical operation log data, and preset characters can be used to replace other characters in the historical operation log data except the target characters, thereby obtaining a preset operation log template containing preset characters and target characters.

[0085] Among them, other characters in the historical operation log data except the target character can be understood as variables in the historical operation log data.

[0086] In practical applications, the preset characters may be placeholders, for example.

[0087] For example, for the historical operation log data "CPU occupancy rate is 90%", the target characters "CPU", "occupancy rate", "is" and "%" corresponding to the historical operation log data can be obtained. Then, the placeholder "*" can be used to replace other characters "90" in the historical operation log data except the target character, and the preset operation log template containing the target character and preset characters is obtained as "CPU occupancy rate is *%".

[0088] In one embodiment of the present specification, there may be multiple historical operation log data, and the same characters (i.e., target characters) in the multiple historical operation log data may be retained, and different characters (i.e., other characters) may be replaced with preset characters, thereby obtaining a preset operation log template corresponding to the historical operation log data. For example, for the historical operation log data "CPU occupancy rate is 10%", "CPU occupancy rate is 50%", and "CPU occupancy rate is 90%", the same characters "CPU", "occupancy rate", "for", and "%" may be retained, and the preset character "*" may be used to replace different characters "10", "50", and "90", thereby obtaining a preset operation log template of "CPU occupancy rate is *%".

[0089] In summary, by grouping similar logs into the same cluster, for each cluster, retain the characters that remain unchanged in all logs in the cluster, and replace the different characters in the cluster with placeholders, so as to obtain the representative template of each cluster, and thus obtain the log template corresponding to each type of log data.

[0090] Accordingly, before determining the production log data corresponding to the vehicle production task included in the vehicle production system according to the matching result between the preset production log template and the target character, the method further includes:

[0091] Performing character extraction processing on the historical production log data corresponding to the vehicle production system to obtain target characters corresponding to the historical production log data;

[0092] According to the preset characters, other characters in the historical production log data except the target characters are replaced to obtain a preset production log template including the preset characters and the target characters.

[0093] Specifically, the historical production log data corresponding to the vehicle production system can be obtained, and character extraction processing can be performed on the historical production data to obtain the target characters corresponding to the historical production log data. The preset characters can be used to replace other characters in the historical production log data except the target characters, thereby obtaining a preset production log template containing the preset characters and the target characters.

[0094] It can be understood that the process of obtaining the preset production log template is similar to the process of obtaining the aforementioned preset operation log template, and will not be repeated in the embodiments of this specification.

[0095] In practical applications, after obtaining the initial log data of the vehicle production system, the method further includes:

[0096] Performing format conversion on the initial log data to obtain initial log data in a preset format;

[0097] Accordingly, clustering the initial log data and determining the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system according to the clustering result include:

[0098] The initial log data in the preset format is clustered, and the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined according to the clustering result.

[0099] Specifically, in order to facilitate identification of the initial log data, the initial log data may be formatted to obtain initial log data in a preset format that can be identified by the log processing platform. Accordingly, the process of clustering the initial log data in the preset format is similar to the above, and will not be repeated here.

[0100] Step 204: Determine the operation status data of the vehicle production system according to the operation log data.

[0101] Specifically, the operation status data of the vehicle production system can be obtained from the content recorded in the operation log data, wherein the operation status data can be understood as the performance index of the aforementioned vehicle production system during operation.

[0102] Step 206: According to the log detection rule corresponding to the vehicle production task, the production log data is detected to obtain a detection result.

[0103] The log detection rule can be understood as a log detection rule determined according to the task execution requirements of the vehicle production task. For example, for the vehicle parts production task and the parts assembly task, in order to ensure the operation of the production line, for the same vehicle part, the parts production task usually needs to be executed before the parts assembly task. Then, the log detection rule can be, for example, that the execution order of the parts production task is before the parts assembly task.

[0104] Based on this, the log detection rule corresponding to the vehicle production task can be determined according to the task execution requirements of the vehicle production task, and the production log data can be detected according to the log detection rule to obtain the detection result. The detection result can be used to indicate whether the production log data meets the log detection rule.

[0105] In addition, a log detection model can be constructed based on the log detection rules and topology map data, and the log detection model can be used to detect the production log data to obtain the detection results.

[0106] Step 208: Generate warning information when it is determined that the operating status data meets the first preset condition, or

[0107] When it is determined according to the detection result that the production log data meets the second preset condition, early warning information is generated.

[0108] Among them, the running status data meets the first preset condition, which can be understood as the running status data is greater than the preset running status threshold. The preset status threshold can be understood as the running status data when the vehicle production system is operating normally. When the running status data is greater than the preset status threshold, it means that the vehicle production system is operating abnormally at this time. The production log data meets the second preset condition, which can be understood as the production log data meets the task execution requirements of the vehicle production task, such as the task execution order meets the requirements or the task execution time meets the requirements, etc.

[0109] Specifically, generating warning information when determining that the operating status data meets the first preset condition includes:

[0110] In the case where it is determined that the operating status data is greater than a preset operating status threshold, determining that the operating status data satisfies a first preset condition;

[0111] Generate warning information based on the operating status data.

[0112] Specifically, when it is determined that the operating status data is greater than a preset operating status threshold, it is determined that the operating status data meets the first preset condition, that is, the vehicle production system is operating abnormally at this time, then a warning information is generated according to the abnormal operating status data.

[0113] The warning information may include operating status data and the abnormal cause and processing strategy generated for the abnormality.

[0114] For example, the running status data is a CPU occupancy rate of 98%, and the preset running status threshold is 90%. This indicates that the running status data is abnormal, that is, the CPU occupancy rate is abnormal. Then, an early warning message can be generated based on the running status data.

[0115] In summary, by detecting the operating status data, it is possible to monitor the operation of the vehicle production system.

[0116] In a specific implementation, generating warning information according to the operating status data includes:

[0117] Determining, according to the running status data, associated status data associated with the running status data;

[0118] Determining whether the association status data satisfies the first preset condition;

[0119] If so, generating warning information according to the operating status data and the associated status data;

[0120] If not, generate warning information based on the operating status data.

[0121] The associated state data may be understood as performance indicator data at the same time point as the operating state data. The operating log data of the vehicle production system may record the operating state data and the time corresponding to the operating state data.

[0122] Based on this, the associated state data at the same time point as the operating state data can be determined according to the operating state data, and it can be judged whether the associated state data is greater than the preset operating state threshold. If so, it means that the associated state data is also abnormal, and an early warning message is generated according to the operating state data and the associated state data. If not, it means that the associated state data is normal, and an early warning message is generated only according to the operating state data.

[0123] For example, the running status data is a CPU occupancy rate of 98%, and the preset running status threshold is 90%. At this time, it means that the running status data CPU occupancy rate is abnormal. The system response time of the associated status data at the same time point as the running status data is determined to be 1 second. The preset running status threshold is the system response time threshold a seconds. It is determined whether the system response time is greater than a. If so, an early warning message is generated based on the CPU occupancy rate and the system response time. If not, an early warning message is generated only based on the abnormal CPU occupancy rate.

[0124] In summary, by judging whether the associated status data is abnormal, a comprehensive judgment on the operation of the vehicle production system can be achieved, which is convenient for the subsequent determination of the cause of the abnormality and the corresponding processing strategy, and further ensures the timely discovery and processing of faults.

[0125] Further, the determining, based on the running status data, associated status data associated with the running status data includes:

[0126] Determining, according to the running status data, the running time corresponding to the running status data;

[0127] According to the running time, the associated state data corresponding to the running time is determined, and the associated state data is determined as the associated state data associated with the running state data.

[0128] Specifically, the running time corresponding to the running status data may be determined according to the running status data, thereby determining the associated status data corresponding to the running time, and the associated status data is the associated status data associated with the running status data.

[0129] It can be understood that the associated status data may also be operating status data.

[0130] In practical applications, the associated state data can also be determined based on the relationship between unsupervised time series events and time series data. Specifically, it can be determined whether two running state data come from the same distribution, and the correlation between the two running state data can be calculated according to the DTW algorithm to determine whether the two running state data are associated.

[0131] In summary, by determining the associated status data associated with the operating status data, a data basis is provided for the subsequent judgment on the operation of the vehicle production system.

[0132] In practical applications, the vehicle production tasks are at least two;

[0133] The detecting the production log data according to the log detection rule corresponding to the vehicle production task to obtain the detection result includes:

[0134] determining a reference execution order between the at least two vehicle production tasks according to log detection rules corresponding to the at least two vehicle production tasks;

[0135] Determining an execution order between the at least two vehicle production tasks according to the production log data corresponding to the at least two vehicle production tasks;

[0136] According to the reference execution sequence, the execution sequence between the at least two vehicle production tasks is detected to obtain a detection result.

[0137] The reference execution sequence may be understood as a theoretical execution sequence between at least two vehicle production tasks. Vehicle production may be realized by executing the vehicle production tasks according to the reference execution sequence.

[0138] Based on this, the theoretical execution order between the at least two vehicle production tasks can be determined according to the log detection rules corresponding to the at least two vehicle production tasks, and the actual execution order between the at least two vehicle production tasks can be determined according to the production log data corresponding to the at least two vehicle production tasks, and it is judged whether the theoretical execution order and the actual execution order are consistent, so as to obtain the detection result. If the theoretical execution order and the actual execution order are consistent, the detection result can be normal, and if the theoretical execution order and the actual execution order are inconsistent, the detection result can be abnormal.

[0139] In addition, by detecting the actual execution time of the vehicle production task, it is also possible to determine whether the actual execution time of the vehicle production task is normal, and whether there is a problem of stagnation or overspeed of the production line. Specifically, the reference execution time of the vehicle production task can be determined according to the log detection rule corresponding to the vehicle production task, and the actual execution time of the vehicle production task can be determined according to the production log data corresponding to the vehicle production task, and the detection result can be determined according to the reference execution time and the actual execution time. Specifically, when it is determined that the difference between the reference execution time and the actual execution time is less than or equal to the preset time threshold, the detection result can be normal. When it is determined that the difference between the reference execution time and the actual execution time is greater than the preset time threshold, the detection result can be abnormal.

[0140] In summary, by setting log detection rules and abstracting the execution order and execution time between vehicle production tasks according to the log detection rules, the actual execution time and actual execution order of vehicle production tasks on the production line can be detected, thereby realizing the detection of production line operation and facilitating timely discovery of production line operation failures.

[0141] Then, when it is determined according to the detection result that the production log data meets the second preset condition, generating warning information includes:

[0142] When it is determined according to the detection result that the execution order between the at least two vehicle production tasks is different from the reference execution order, a warning message is generated.

[0143] Specifically, when it is determined that the actual execution order between at least two vehicle production tasks is different from the reference execution order, it means that the production line is operating abnormally. At this time, early warning information can be generated based on the at least two vehicle production tasks.

[0144] In addition, the generated warning information can also be sent to the operators of the vehicle production system through a preset communication method, and the preset communication method includes but is not limited to email, application push deployed on the mobile terminal, etc.

[0145] To sum up, the above method obtains the operation log data and production log data of the vehicle production system, and detects the operation log data and production log data, thereby realizing real-time monitoring of the vehicle production system from multiple dimensions. The operation status data determined by the operation log data can reflect the operation status of the vehicle production system itself, thereby judging whether the vehicle production system is operating normally. By detecting the production log data according to the log detection rules, it can be determined whether the operation of the production task of the vehicle production line complies with the business rules, and when the operation status data meets the first preset condition or the production log data meets the second preset condition, an early warning information is generated, so as to facilitate timely notification of relevant personnel when an abnormal operation is found, thereby realizing real-time monitoring of the vehicle production system and vehicle production tasks, timely discovering faults of the vehicle production system, reducing the impact of faults on vehicle production business, and ensuring production efficiency.

[0146] The following combination Figure 5 , taking the application of the log processing method provided in this specification in a vehicle production system as an example, the log processing method is further described. Figure 5 A processing flow chart of a log processing method provided by an embodiment of the present specification is shown, which specifically includes the following steps.

[0147] Step 502: Acquire initial log data of the vehicle production system.

[0148] Specifically, a log data collector may be used to obtain all log data of the vehicle production system.

[0149] Step 504: clustering the initial log data, and determining the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system according to the clustering result.

[0150] The initial log data may include multiple log data, and the target character corresponding to each log data may be matched with a preset operation log template and a preset production log template, so as to distinguish the operation log data and the production log data from the multiple log data.

[0151] Specifically, the initial log data can be subjected to character extraction processing using a word segmentation algorithm based on pre-set keywords, constant characters in the initial log data can be extracted, and the constant characters can be matched with a preset operation log template. The operation log data can be determined in the initial log data based on the matching results. At the same time, the constant characters can also be matched with a preset production log template. The production log data can be determined in the initial log data based on the matching results.

[0152] For example, for the initial log data "CPU occupancy rate is 98%", the target characters may be "CPU" and "occupancy rate". The preset operation log template may be understood as the template corresponding to the operation log data, such as "CPU occupancy rate is *". According to the matching result, it can be determined that the initial log data is the operation log data. For the initial log data "Vehicle chassis assembly time is 1 minute", it can be determined according to the matching result that it corresponds to the preset production log template "Vehicle chassis assembly time is * minutes", which means that the initial log data is the production log data.

[0153] In addition, according to another embodiment of the present specification, when log collection is accessed, it is possible to distinguish between the operation log data and production log data of the vehicle production system being collected. Based on this, it is also possible to directly collect the operation log data of the vehicle production system during operation, as well as the production log data corresponding to the vehicle production tasks included in the vehicle production system.

[0154] Step 506: Determine the operating status data of the vehicle production system according to the operating log data.

[0155] For example, the CPU occupancy rate (ie, the operating status data) may be determined to be 98% based on the operating log data “CPU0 occupancy rate is 98%”.

[0156] Step 508: When it is determined that the operating status data is greater than a preset operating status threshold, determine that the operating status data meets a first preset condition; and generate warning information according to the operating status data.

[0157] For example, the running status data is a CPU occupancy rate of 98%, and the preset running status threshold is 90%. This indicates that the running status data is abnormal, that is, the CPU occupancy rate is abnormal. Then, an early warning message can be generated based on the running status data.

[0158] Furthermore, the associated state data at the same time point as the operating state data can be determined based on the operating state data, and it can be determined whether the associated state data is greater than a preset operating state threshold. If so, it means that the associated state data is also abnormal, and an early warning message is generated based on the operating state data and the associated state data. If not, it means that the associated state data is normal, and an early warning message is generated only based on the operating state data.

[0159] For example, the running status data is a CPU occupancy rate of 98%, and the preset running status threshold is 90%. At this time, it means that the running status data CPU occupancy rate is abnormal. The system response time of the associated status data at the same time point as the running status data is determined to be 1 second. The preset running status threshold is the system response time threshold a seconds. It is determined whether the system response time is greater than a. If so, an early warning message is generated based on the CPU occupancy rate and the system response time. If not, an early warning message is generated only based on the abnormal CPU occupancy rate.

[0160] Step 510: Detect the production log data according to the log detection rule corresponding to the vehicle production task to obtain a detection result.

[0161] According to the log detection rules corresponding to at least two vehicle production tasks, the theoretical execution order between at least two vehicle production tasks is determined, and according to the production log data corresponding to at least two vehicle production tasks, the actual execution order between the at least two vehicle production tasks is determined, and it is judged whether the theoretical execution order and the actual execution order are consistent, so as to obtain the detection result. If the theoretical execution order and the actual execution order are consistent, the detection result may be normal, and if the theoretical execution order and the actual execution order are inconsistent, the detection result may be abnormal.

[0162] In addition, by detecting the actual execution time of the vehicle production task, it is also possible to determine whether the actual execution time of the vehicle production task is normal, and whether there is a problem of stagnation or overspeed of the production line. Specifically, the reference execution time of the vehicle production task can be determined according to the log detection rule corresponding to the vehicle production task, and the actual execution time of the vehicle production task can be determined according to the production log data corresponding to the vehicle production task, and the detection result can be determined according to the reference execution time and the actual execution time. Specifically, when it is determined that the difference between the reference execution time and the actual execution time is less than or equal to the preset time threshold, the detection result can be normal. When it is determined that the difference between the reference execution time and the actual execution time is greater than the preset time threshold, the detection result can be abnormal.

[0163] Step 512: When it is determined according to the detection result that the production log data meets the second preset condition, generate warning information.

[0164] Specifically, when it is determined that the actual execution order between at least two vehicle production tasks is different from the reference execution order, it means that the production line is operating abnormally. At this time, early warning information can be generated based on the at least two vehicle production tasks.

[0165] In addition, the generated warning information can also be sent to the operators of the vehicle production system through a preset communication method, and the preset communication method includes but is not limited to email, application push deployed on the mobile terminal, etc.

[0166] To sum up, the above method obtains the operation log data and production log data of the vehicle production system, and detects the operation log data and production log data, thereby realizing real-time monitoring of the vehicle production system from multiple dimensions. The operation status data determined by the operation log data can reflect the operation status of the vehicle production system itself, thereby judging whether the vehicle production system is operating normally. By detecting the production log data according to the log detection rules, it can be determined whether the operation of the production task of the vehicle production line complies with the business rules, and when the operation status data meets the first preset condition or the production log data meets the second preset condition, an early warning information is generated, so as to facilitate timely notification of relevant personnel when an abnormal operation is found, thereby realizing real-time monitoring of the vehicle production system and vehicle production tasks, timely discovering faults of the vehicle production system, reducing the impact of faults on vehicle production business, and ensuring production efficiency.

[0167] Corresponding to the above method embodiment, this specification also provides a log processing device embodiment, Figure 6 FIG. 1 shows a schematic diagram of a log processing device provided by an embodiment of the present specification. Figure 6 As shown, the device comprises:

[0168] A first determination module 602 is configured to determine operation log data of a vehicle production system during operation, and production log data corresponding to a vehicle production task included in the vehicle production system;

[0169] A second determination module 604 is configured to determine the operation status data of the vehicle production system according to the operation log data;

[0170] The detection module 606 is configured to detect the production log data according to the log detection rule corresponding to the vehicle production task to obtain a detection result;

[0171] The generating module 608 is configured to generate warning information when it is determined that the operating status data meets the first preset condition, or

[0172] When it is determined according to the detection result that the production log data meets the second preset condition, early warning information is generated.

[0173] In an optional embodiment, the first determining module 602 is further configured to:

[0174] Obtain initial log data from the vehicle production system;

[0175] The initial log data is clustered, and the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined according to the clustering result.

[0176] In an optional embodiment, the first determining module 602 is further configured to:

[0177] Performing character extraction processing on the initial log data to obtain target characters corresponding to the initial log data;

[0178] Determining the operation log data of the vehicle production system during operation according to the matching result between the preset operation log template and the target character;

[0179] According to the matching result between the preset production log template and the target character, the production log data corresponding to the vehicle production task included in the vehicle production system is determined.

[0180] In an optional embodiment, the first determining module 602 is further configured to:

[0181] Performing character extraction processing on historical operation log data of the vehicle production system during operation to obtain target characters corresponding to the historical operation log data;

[0182] According to the preset characters, other characters in the historical operation log data except the target characters are replaced to obtain a preset operation log template including the preset characters and the target characters.

[0183] In an optional embodiment, the first determining module 602 is further configured to:

[0184] Performing character extraction processing on the historical production log data corresponding to the vehicle production system to obtain target characters corresponding to the historical production log data;

[0185] According to the preset characters, other characters in the historical production log data except the target characters are replaced to obtain a preset production log template including the preset characters and the target characters.

[0186] In an optional embodiment, the first determining module 602 is further configured to:

[0187] Performing format conversion on the initial log data to obtain initial log data in a preset format;

[0188] The initial log data in the preset format is clustered, and the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined according to the clustering result.

[0189] In an optional embodiment, the generating module 608 is further configured to:

[0190] In the case where it is determined that the operating status data is greater than a preset operating status threshold, determining that the operating status data satisfies a first preset condition;

[0191] Generate warning information based on the operating status data.

[0192] In an optional embodiment, the generating module 608 is further configured to:

[0193] Determining, according to the running status data, associated status data associated with the running status data;

[0194] Determining whether the association status data satisfies the first preset condition;

[0195] If so, generating warning information according to the operating status data and the associated status data;

[0196] If not, generate warning information based on the operating status data.

[0197] In an optional embodiment, the generating module 608 is further configured to:

[0198] Determining, according to the running status data, the running time corresponding to the running status data;

[0199] According to the running time, the associated state data corresponding to the running time is determined, and the associated state data is determined as the associated state data associated with the running state data.

[0200] In an optional embodiment, the number of vehicle production tasks is at least two; the detection module 606 is further configured to:

[0201] determining a reference execution order between the at least two vehicle production tasks according to log detection rules corresponding to the at least two vehicle production tasks;

[0202] Determining an execution order between the at least two vehicle production tasks according to the production log data corresponding to the at least two vehicle production tasks;

[0203] According to the reference execution sequence, the execution sequence between the at least two vehicle production tasks is detected to obtain a detection result.

[0204] In an optional embodiment, the generating module 608 is further configured to:

[0205] When it is determined according to the detection result that the execution order between the at least two vehicle production tasks is different from the reference execution order, a warning message is generated.

[0206] To summarize, the above-mentioned device obtains the operation log data and production log data of the vehicle production system, and detects the operation log data and production log data, thereby realizing real-time monitoring of the vehicle production system from multiple dimensions. The operation status data determined by the operation log data can reflect the operation status of the vehicle production system itself, thereby judging whether the vehicle production system is operating normally. By detecting the production log data according to the log detection rules, it can be determined whether the operation of the production tasks of the vehicle production line complies with the business rules, and when the operation status data meets the first preset condition or the production log data meets the second preset condition, an early warning information is generated, so as to facilitate timely notification of relevant personnel when an abnormal operation is found, thereby realizing real-time monitoring of the vehicle production system and vehicle production tasks, timely discovering faults of the vehicle production system, reducing the impact of faults on vehicle production business, and ensuring production efficiency.

[0207] The above is a schematic scheme of a log processing device of this embodiment. It should be noted that the technical scheme of the log processing device and the technical scheme of the log processing method described above are of the same concept, and the details not described in detail in the technical scheme of the log processing device can be found in the description of the technical scheme of the log processing method described above.

[0208] Figure 7 The block diagram of a computing device 700 according to an embodiment of the present specification is shown. The components of the computing device 700 include but are not limited to a memory 710 and a processor 720. The processor 720 is connected to the memory 710 via a bus 730, and the database 750 is used to store data.

[0209] The computing device 700 also includes an access device 740 that enables the computing device 700 to communicate via one or more networks 760. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 740 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0210] In one embodiment of the present application, the above components of the computing device 700 and Figure 7 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 7 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as needed.

[0211] The computing device 700 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 700 may also be a mobile or stationary server.

[0212] The processor 720 is used to execute the following computer executable instructions, which implement the steps of the above-mentioned log processing method when executed by the processor.

[0213] The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the log processing method described above are of the same concept, and the details not described in detail in the technical scheme of the computing device can be found in the description of the technical scheme of the log processing method described above.

[0214] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions, which can implement the steps of the above-mentioned log processing method when executed by a processor.

[0215] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the log processing method described above are of the same concept, and the details not described in detail in the technical scheme of the storage medium can be found in the description of the technical scheme of the log processing method described above.

[0216] An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned log processing method.

[0217] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the log processing method described above are of the same concept, and the details not described in detail in the technical scheme of the computer program can be found in the description of the technical scheme of the log processing method described above.

[0218] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0219] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0220] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0221] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0222] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A log processing method, characterized in that: include: Determining operation log data of a vehicle production system during operation, and production log data corresponding to a vehicle production task included in the vehicle production system; Determining operation status data of the vehicle production system according to the operation log data; According to the log detection rule corresponding to the vehicle production task, the production log data is detected to obtain a detection result; generating warning information when determining that the operating status data meets the first preset condition, or When it is determined according to the detection result that the production log data meets the second preset condition, early warning information is generated.

2. The method according to claim 1, characterized in that The determining of the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system includes: Obtain initial log data from the vehicle production system; The initial log data is clustered, and the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined according to the clustering result.

3. The method according to claim 2, characterized in that The clustering process is performed on the initial log data, and the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined according to the clustering result, including: Performing character extraction processing on the initial log data to obtain target characters corresponding to the initial log data; Determining the operation log data of the vehicle production system during operation according to the matching result between the preset operation log template and the target character; According to the matching result between the preset production log template and the target character, the production log data corresponding to the vehicle production task included in the vehicle production system is determined.

4. The method according to claim 3, characterized in that Before determining the operation log data of the vehicle production system during operation according to the matching result between the preset operation log template and the target character, the method further includes: Performing character extraction processing on historical operation log data of the vehicle production system during operation to obtain target characters corresponding to the historical operation log data; According to the preset characters, other characters in the historical operation log data except the target characters are replaced to obtain a preset operation log template including the preset characters and the target characters.

5. The method according to claim 3, characterized in that: Before determining the production log data corresponding to the vehicle production task included in the vehicle production system according to the matching result between the preset production log template and the target character, the method further includes: Performing character extraction processing on the historical production log data corresponding to the vehicle production system to obtain target characters corresponding to the historical production log data; According to the preset characters, other characters in the historical production log data except the target characters are replaced to obtain a preset production log template including the preset characters and the target characters.

6. The method according to claim 2, characterized in that After obtaining the initial log data of the vehicle production system, the method further includes: Performing format conversion on the initial log data to obtain initial log data in a preset format; Accordingly, clustering the initial log data and determining the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system according to the clustering result include: The initial log data in the preset format is clustered, and the operation log data of the vehicle production system during operation and the production log data corresponding to the vehicle production tasks included in the vehicle production system are determined according to the clustering result.

7. The method according to claim 1, characterized in that The generating of warning information when determining that the operating status data meets the first preset condition includes: In the case where it is determined that the operating status data is greater than a preset operating status threshold, determining that the operating status data satisfies a first preset condition; Generate warning information based on the operating status data.

8. The method according to claim 7, characterized in that The generating of warning information according to the operating status data includes: Determining, according to the running status data, associated status data associated with the running status data; Determining whether the association status data satisfies the first preset condition; If so, generating warning information according to the operating status data and the associated status data; If not, generate warning information based on the operating status data.

9. The method according to claim 8, characterized in that The step of determining, according to the running status data, associated status data associated with the running status data comprises: Determining, according to the running status data, the running time corresponding to the running status data; According to the running time, the associated state data corresponding to the running time is determined, and the associated state data is determined as the associated state data associated with the running state data.

10. The method according to claim 1, characterized in that The vehicle production tasks are at least two; The detecting the production log data according to the log detection rule corresponding to the vehicle production task to obtain the detection result includes: determining a reference execution order between the at least two vehicle production tasks according to log detection rules corresponding to the at least two vehicle production tasks; Determining an execution order between the at least two vehicle production tasks according to the production log data corresponding to the at least two vehicle production tasks; According to the reference execution sequence, the execution sequence between the at least two vehicle production tasks is detected to obtain a detection result.

11. The method according to claim 10, characterized in that The generating of warning information when it is determined according to the detection result that the production log data meets the second preset condition includes: When it is determined according to the detection result that the execution order between the at least two vehicle production tasks is different from the reference execution order, a warning message is generated.

12. A log processing device, characterized in that: include: A first determination module is configured to determine operation log data of a vehicle production system during operation, and production log data corresponding to a vehicle production task included in the vehicle production system; a second determination module, configured to determine operation status data of the vehicle production system according to the operation log data; A detection module is configured to detect the production log data according to the log detection rule corresponding to the vehicle production task to obtain a detection result; a generating module configured to generate warning information when it is determined that the operating status data meets the first preset condition, or When it is determined according to the detection result that the production log data meets the second preset condition, early warning information is generated.

13. A computing device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the log processing method according to any one of claims 1 to 11 are implemented.

14. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, implement the steps of the log processing method described in any one of claims 1 to 11.

Images