Abnormality analysis method and device, electronic equipment and computer program product

Abstract

The invention discloses an anomaly analysis method and device, electronic equipment and a computer program product, and relates to the technical field of robot process automation, and the method comprises the steps: obtaining a monitoring configuration file; each actuator is controlled to execute task information according to the obtained node parameters in the monitoring configuration file and logic information between the nodes, and burying point data, system collection data and task video information corresponding to each actuator are obtained; when the system monitoring log generates an abnormal prompt, performing alignment processing on the burying point data, the system acquisition data, the task video information and the system monitoring log corresponding to each actuator according to the task identifier and the timestamp to obtain an accident site data set; and displaying the accident scene data set based on a multi-dimensional display mode so as to carry out anomaly analysis on the task execution condition of the actuator according to a multi-dimensional display result. According to the method, the accident site data set is subjected to multi-dimensional display, so that the abnormal point can be quickly positioned.

Description

Technical Field

[0001] The present application relates to the field of robotic process automation technology, and in particular to an abnormality analysis method, device, electronic equipment and computer program product. Background Art

[0002] In today's financial sector, business is developing rapidly, and the demand for automated processing is also rising sharply. Robotic Process Automation (RPA) technology has been widely popularized and applied in financial business scenarios due to its high efficiency and accuracy. Among them, actuators (robots), as one of the core components of RPA products, play a vital role.

[0003] In the actual operation of financial services, the number of executors used is extremely large, and these executors are usually distributed in a complex network environment, rather than deployed on local physical machines. When abnormal information is generated during the execution process, the existing solution for abnormal analysis is usually to use the management and control platform system to use embedded data or send heartbeat information to the executor to perform preliminary abnormal location, and further log in to the designated executor through remote links to mine historical execution logs for abnormality troubleshooting. This abnormal analysis method has the problem of time-consuming abnormal analysis and low abnormal location accuracy. Summary of the invention

[0004] The present application provides an anomaly analysis method, device, electronic device and computer program product, which can improve the accuracy and efficiency of anomaly analysis.

[0005] In a first aspect, the present application provides an abnormality analysis method, comprising:

[0006] Obtain a monitoring configuration file, wherein the monitoring configuration file includes task information corresponding to each executor; the task information includes at least a task identifier, node parameters, and logic information between nodes;

[0007] Control each of the actuators to execute the task information according to the corresponding node parameters and the logic information between the nodes, and obtain the corresponding point data, system collection data and task video information of each actuator;

[0008] When the system monitoring log generates an abnormal prompt, the buried point data, system collected data, task video information and the system monitoring log corresponding to each actuator are aligned according to the task identifier and timestamp to obtain an accident scene data set;

[0009] The accident scene data set is displayed based on a multi-dimensional display method to obtain a multi-dimensional display result, so as to perform an abnormal analysis on the task execution status of the actuator according to the multi-dimensional display result.

[0010] In a second aspect, the present application provides an abnormality analysis device, the device comprising:

[0011] A file acquisition module, used to acquire a monitoring configuration file, wherein the monitoring configuration file includes task information corresponding to each executor; the task information includes at least a task identifier, node parameters, and logic information between nodes;

[0012] A task execution module is used to control each of the executors to execute the task information according to the corresponding node parameters and the logic information between the nodes, and obtain the corresponding point data, system acquisition data and task video information of each executor;

[0013] A data processing module, used for aligning the buried point data, system collected data, task video information and system monitoring log corresponding to each actuator according to the task identifier and timestamp when an abnormal prompt is generated in the system monitoring log, so as to obtain an accident scene data set;

[0014] The multi-dimensional display module is used to display the accident scene data set based on a multi-dimensional display method to obtain a multi-dimensional display result, so as to perform an abnormal analysis on the task execution status of the actuator according to the multi-dimensional display result.

[0015] In a third aspect, the present application further provides an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the anomaly analysis method described in any embodiment of the present application.

[0019] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the anomaly analysis method described in any embodiment of the present application when executed.

[0020] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the anomaly analysis method of any embodiment of the present application when executed by a processor.

[0021] The abnormal analysis solution provided in the embodiment of the present application, when the system monitoring log generates an abnormal prompt, aligns the buried point data, system acquisition data, task video information and system monitoring log corresponding to each actuator according to the task identifier and timestamp, and obtains the accident scene data set, which can comprehensively present detailed information in the task execution process from multiple dimensions. In this solution, through automated data collection, integration and alignment processing, and multi-dimensional display of the accident scene data set based on timestamps, operation and maintenance personnel can intuitively and clearly obtain all relevant information when the actuator performs the task; and combined with the task video information display method, it can assist in quickly locating abnormal points, achieving the beneficial effect of improving the accuracy and efficiency of abnormal analysis.

[0022] It should be noted that the above computer instructions may be stored in whole or in part on a computer-readable storage medium, wherein the computer-readable storage medium may be packaged together with the processor of the anomaly analysis device, or may be packaged separately from the processor of the anomaly analysis device, which is not limited in this application.

[0023] The description of the second, third, fourth and fifth aspects of the present application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, fourth and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0024] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description.

[0025] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, and usage scenarios of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a flowchart of the abnormality analysis method provided in the embodiment of the present application;

[0028] Figure 2is another flow chart of the abnormality analysis method provided in an embodiment of the present application;

[0029] Figure 3 is a structural schematic diagram of an abnormality analysis device provided in an embodiment of the present application;

[0030] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the present application will be clearly and completely described below in conjunction with the drawings in the present embodiment. Obviously, the described embodiment is only a part of the embodiment of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0034] Figure 1 A flowchart of an abnormality analysis method provided in an embodiment of the present application is provided. This embodiment can be applied to the case where an abnormality analysis is performed on the system when executing a business based on robotic process automation technology. The method can be performed by an abnormality analysis device, which can be implemented in the form of hardware and / or software and integrated in an electronic device that executes the method. Preferably, the electronic device in the embodiment of the present application can be a server, or a computer device, etc.

[0035] refer to Figure 1The abnormality analysis method provided in this embodiment includes but is not limited to the following steps:

[0036] S110, obtaining a monitoring configuration file, where the monitoring configuration file includes task information corresponding to each executor; the task information includes at least a task identifier, node parameters, and logic information between nodes.

[0037] The monitoring configuration file is a file that the operation and maintenance personnel pre-configure in the visual interface according to actual needs. The monitoring configuration file contains the detailed instructions and parameter information required for each executor to perform tasks, which is used to instruct each executor what tasks to do and how to do them.

[0038] Among them, the method of obtaining the monitoring configuration file is that after the operation and maintenance personnel complete the configuration data of the task information corresponding to each actuator in the visual interface, the monitoring configuration file can be generated after operating the preset button. The current preset button can be an "OK" button or a "Finish" button in the visual interface.

[0039] When setting the task information corresponding to each executor, it is also necessary to fully configure the task identifier, node parameters and logical information between nodes corresponding to each task information. Among them, the task identifier is used to accurately identify a specific task among many tasks; the node parameters are the specific data or conditions required by the executor for each node in the task execution process; the logical information between nodes is used to describe the order of execution of each node in the task, conditional branches and loop structures, etc., which can be reflected in the form of a node list. For example, in an order processing process, there may be a node that receives order information first, and then there are different processing branches according to the size of the order amount. If the amount is less than a certain value, it will go to the simple review process node, and if it is greater, it will enter the detailed review process node. The order and conditional judgment relationship between these nodes is the logical information between the nodes.

[0040] S120, control each executor to execute task information according to corresponding node parameters and logic information between nodes, and obtain the corresponding point data, system acquisition data and task video information of each executor.

[0041] Each executor initializes the task execution environment according to the parsed node parameters and logic information, and loads the required software libraries and tools. For example, if the task involves web page operation, the browser automation library is loaded; if it involves data processing, the corresponding data processing library is loaded; and the task is executed from the starting node according to the logical order between the nodes. For each node, the executor performs specific operations according to the node parameters. For example, in a data query node, the executor uses the database connection library to connect to the database and perform query operations according to the database query statement in the node parameters to obtain data results.

[0042] During the execution of each node, the executor collects data through the built-in tracking code. In terms of running node information collection, the node name, input parameters and start time are recorded when the node starts to execute. The execution time, output results, number of runs (if there is a loop operation) and exception information when an error occurs are recorded at the end of the node execution (if an error occurs). For resource consumption data, the performance monitoring interface provided by the system is used to regularly (such as every 100 milliseconds) obtain the current central processing unit (CPU), memory, and network bandwidth usage and record them. The execution task details record the task number (usually related to the task identifier), start time and initial execution status at the beginning of the task, and update the execution status according to the situation during the task execution (such as changing to pause, continue, complete or error, etc.).

[0043] The executor establishes a connection with the system's monitoring service and obtains system data periodically (e.g., every second). The business process identification and status information is received through the interface or message queue provided by the system. For example, when the state of the business process to which the task belongs changes (e.g., from running to paused), the information is obtained and recorded in a timely manner. For platform system parameters, such as database connection status and server load, the executor sends a request to the system's database monitoring module and server monitoring module to obtain the current parameter values ​​and record them. Business-related event records generated by the platform are obtained through the event notification mechanism of the subscription system. When a related event occurs (such as a data update event), the detailed information of the event is received and recorded.

[0044] The executor starts the task video recording function and uses the screen recording library technology to record. Before recording begins, set the video recording parameters, such as resolution, frame rate, video encoding format, etc. During the entire process of executing the task, the video recording continues, and the changes in the executor's operation interface are recorded as a video stream, and a timestamp is added to each video frame to match the timestamp of other data. The entire process of the executor's operation interface when executing the task is recorded in the form of a video. For example, in an automated financial report generation task, the task video information will record the entire screen recording process of the executor opening the financial software, entering the account password, selecting the report template, filling in the data, etc., including the movement of the mouse, clicking, keyboard input, and other screens.

[0045] S130. When the system monitoring log generates an abnormal prompt, the buried point data, system collected data, task video information and system monitoring log corresponding to each actuator are aligned according to the task identifier and timestamp to obtain the accident scene data set.

[0046] When the executor starts executing task information according to the corresponding node parameters and the logical information between nodes, a system monitoring log is generated. The system monitoring log is a log file that records various events and states during the operation of the system. It records various information at the system level, such as changes in the use of system resources, the start and stop time of each component in the system, errors and abnormal events that occur in the system, etc.

[0047] When the system monitoring log generates an abnormal prompt, the system first extracts the task identifier in the abnormal prompt. Then, the corresponding point data, system acquisition data, and task video information are searched in the database according to the task identifier. Starting from the earliest timestamp data, the data in the point data, system acquisition data, task video information, and system monitoring log are associated and aligned in chronological order. For the task video information, the precise time corresponding to each frame is calculated based on the timestamp and frame rate of the video frame, and matched with other data at the same timestamp position to obtain the accident scene data set.

[0048] S140, displaying the accident scene data set based on a multi-dimensional display method to obtain a multi-dimensional display result, so as to perform an abnormal analysis on the task execution status of the executor according to the multi-dimensional display result.

[0049] When displaying the accident scene data set based on a multi-dimensional display method, the essence is to open the on-site restoration page of the corresponding execution task in the management and control platform. The management and control platform displays the execution flow chart (the execution sequence of the entire business process, a series of execution paths of logical relationships, and annotated indicators such as status and parameter information), scene execution process (executor operation video and resource usage trend chart above the video playback bar), software and hardware operation information (line chart and list form to show the process status of the executor, number of threads, function call status and visual processing unit usage, memory usage, disk input / output status, network connection status), execution scene log (all log information is displayed in list form, distinguished by business log and system log), etc.

[0050] Thus, the process video, flowchart, associated log and resource status information generated by the accident scene restoration can be displayed in multiple dimensions. Through the graphical user interface design, information of different dimensions is presented in a layered manner, so that users can observe multiple aspects of the RPA task execution process at the same time. In addition, the various display parts are interactively linked, and users can simultaneously obtain the log information and resource usage corresponding to a certain process node when viewing the node. Operation and maintenance personnel can observe the data and execution process displayed by each module, compare the differences between the normal operation status and the accident, and combine business logic and system architecture knowledge to quickly and accurately determine the specific location and possible causes of the problem. For example, by analyzing the abnormal data input and output of a step in the execution flowchart, combined with the corresponding log information and machine operation status, it can be determined whether the accident was caused by factors such as scene design errors, data errors or external interference.

[0051] As a specific application example, suppose a financial enterprise uses the RPA platform to process the loan approval process. During the execution process, an exception suddenly occurs, resulting in business interruption. The following are the specific operation steps of this solution in actual application: When creating a loan approval process template, the operation and maintenance personnel checked the monitoring options such as "Open recording of the number of executors" and "Time period length for retaining the accident scene", and defined the node parameters and node logic of the executor for loan approval; the system automatically records the execution process of the entire loan approval process. When an exception occurs, the operation and maintenance personnel can quickly locate the problem node through the execution flowchart; the system records the execution process of the entire loan approval process, and the operation and maintenance personnel can understand the specific situation when the accident occurs through the playback function; the system collects various indicator data when the machine is running, such as CPU and memory usage. The operation and maintenance personnel analyze these data and find the cause of the exception; the system links the log of the execution scenario with other monitoring data to provide the operation and maintenance personnel with comprehensive accident scene information.

[0052] The abnormal analysis method provided in this embodiment aligns the point data, system acquisition data, task video information and system monitoring log corresponding to each actuator according to the task identifier and timestamp when the system monitoring log generates an abnormal prompt, and obtains the accident scene data set in a way that can comprehensively present detailed information during the task execution process from multiple dimensions. In this solution, through automated data collection, integration and alignment processing, and multi-dimensional display of the accident scene data set based on timestamps, operation and maintenance personnel can intuitively and clearly obtain all relevant information when the actuator performs the task; and combined with the task video information display method, it can assist in quickly locating abnormal points, achieving the beneficial effect of improving the accuracy and efficiency of abnormal analysis.

[0053] Figure 2It is another flow chart of the abnormality analysis method provided by an embodiment of the present application. The embodiment of the present application is optimized on the basis of the above-mentioned embodiments, and the specific optimization is as follows: this embodiment explains in detail the implementation process of "controlling each actuator to execute task information according to the corresponding node parameters and the logical information between each node", the implementation process of "aligning the buried point data, system collected data, task video information and system monitoring log corresponding to each actuator according to the task identifier and timestamp to obtain the accident scene data set", and the implementation process of "displaying the accident scene data set based on a multi-dimensional display method to obtain a multi-dimensional display result, and performing abnormal analysis on the task execution of the actuator according to the multi-dimensional display result".

[0054] See also Figure 2 The abnormality analysis method of this embodiment includes but is not limited to the following steps:

[0055] S210. Obtain a monitoring configuration file, where the monitoring configuration file includes task information corresponding to each executor; the task information includes at least a task identifier, node parameters, and logic information between nodes.

[0056] S220. For the current node, obtain node data generated when the current node executes node parameters, where the node data at least includes running node information, resource consumption data corresponding to the current node, and node execution information.

[0057] When the executor starts to execute the current node, it first records the node information generated by running the current node (i.e., the running node information). The running node information in this embodiment may include the node name (such as in a financial transaction processing flow, there may be a "transaction amount calculation node"); node input parameters (such as in the "transaction amount calculation node", the input parameters may be the original amount of the transaction, the handling fee rate, etc.); during the node execution process, continuously monitor and record the execution status of the node (such as whether it is running normally, whether it is in a state of waiting for other resources or conditions, whether an error pause occurs, etc.); if an error occurs in the node execution, the error type is recorded in detail (whether it is a data type mismatch error, an algorithm execution error, or an external resource access error, etc., as well as an error message and an error code). When the node execution ends, the execution end time and output result are recorded. The output result can be the return value after data processing, the success or failure mark of the operation, etc. At the same time, the cumulative number of runs of the node in the entire task process is recorded. Especially for nodes with cyclic execution, the record of the number of runs is crucial for analyzing the task execution logic and performance.

[0058] During the execution of the current node, the performance monitoring interface provided by the system is used to regularly obtain the resource consumption of the executor. For CPU resources, the CPU usage of the current node is recorded to understand its demand for computing resources; for memory resources, the changes in memory usage are monitored and recorded, including the initial allocation of memory, the peak memory during execution, and the final memory usage after execution. This helps to evaluate the efficiency of memory utilization by node operations and whether there is a risk of memory leakage; for network bandwidth resources, if the current node involves network data transmission, such as downloading data from a remote server or uploading data to a database, the network bandwidth usage is recorded, including the size of the data traffic for upload and download, as well as the delay of the network connection. These data can reflect the degree of dependence of node operations on network resources and the impact of the network environment on task execution.

[0059] Node execution information records the start time and end time of the node task, as well as the execution status of the node (such as success, pause, failure, or waiting, etc.).

[0060] S221. After obtaining the node data, the system operation data corresponding to the operation of the current node is collected based on the monitoring interface. The system operation data at least includes database connection status and system load information.

[0061] The executor interacts with the monitoring interface of the database management system to obtain database connection status information and monitor whether the connection is established normally. If the connection fails, the fault type is recorded, such as connection timeout, username and password error, database server unreachable, etc. The system performance monitoring tool is used to obtain system load information. The system load information can include the average CPU usage rate. If the average CPU usage rate is too high, it may cause slow execution of node tasks or even timeout errors. The average memory usage rate is used to evaluate the tightness of server memory resources. High memory usage may trigger memory swap operations, seriously affecting system performance. The disk I / O busyness records the frequency and data volume of disk read and write operations. If the disk I / O is busy, it may cause data read and write delays, affecting the performance of file operations or database data persistence in node tasks. The network connection status, in addition to the network conditions related to the database connection, also monitors the connection status of the server with other external systems or network devices, such as network packet loss rate, network bandwidth utilization, etc. The specific information contained in the system load information is not limited here.

[0062] S222. When each node finishes executing based on the node parameters and the logic information between the nodes, the buried point data is obtained according to the running node information corresponding to each node, the resource consumption data corresponding to the current node, and the node execution information.

[0063] After all nodes have finished executing based on the node parameters and the logical information between nodes, the running node information, resource consumption data and node execution information collected by each node are constructed into a complete set of embedded data records in the chronological order of node execution.

[0064] S223. Obtain system collection data according to the database connection status and system load information corresponding to each node.

[0065] According to the database connection status and system load information corresponding to each node, these data can be integrated to construct a system collection data record set, which corresponds to the point-of-use data record set. This record set can contain system operation data indexed by time series, such as detailed records of database connection status and system load information corresponding to each time point. Such system collection data can reflect the changes in the external environment and the availability of resources during the task execution process from the system level. Combined with the point-of-use data, it can comprehensively evaluate the overall situation of task execution, especially when analyzing whether task anomalies are related to system environmental factors.

[0066] After obtaining the buried point data and system acquisition data corresponding to each actuator, the task video information of each actuator can be further obtained by executing the following steps S224 and S225. The specific method is as follows:

[0067] S224. Obtain recording trigger parameters corresponding to each executor.

[0068] The recording trigger parameter is a parameter pre-set in the monitoring configuration file. Among them, the recording trigger parameter is a parameter corresponding to the trigger start recording function. In this embodiment, each executor corresponds to a recording trigger parameter. The reason for this setting is that since the video occupies a large storage space, when different executors perform different tasks, only the execution process that is prone to abnormalities can be recorded. Therefore, different recording conditions are set for different executors to start the recording function when the current recording condition is triggered.

[0069] Among them, the node identification can be set as a recording condition, that is, when each executor executes to the current node identification based on the logical information between each node, the video recording function can be triggered.

[0070] S225. In the process where each executor executes the task information according to the corresponding node parameters and the logic information between the nodes, the task execution process of the executor is recorded according to the recording trigger parameters to obtain the task video information.

[0071] Among them, the node identification can be set as the recording condition, that is, for each executor, it can be clear that when the current node identification is executed based on the logical information between each node, the video recording function can be triggered, so that the task video information can be obtained after the task information is executed.

[0072] Optionally, the recording trigger parameters in this embodiment may include start parameters and end parameters, wherein both the start parameters and the end parameters can be set through node identifiers, such as when the first node identifier is executed based on the logical information between each node, the start of the video recording function is triggered; when the second node identifier is executed, the end of the video recording is triggered, etc., so as to further save storage space.

[0073] This solution provides a video recording function, which can clearly show the operation steps of the executor during the task execution process. When an abnormality occurs in the task execution, the task video information can be combined with the embedded point data and system collection data to completely restore the scene when the abnormality occurs, which helps to quickly locate the abnormality. Corresponding recording trigger parameters are set for each executor to save storage space.

[0074] S230, obtaining data format information corresponding to the buried point data, system collected data and task video information respectively.

[0075] Since the data format information contained in the buried point data, system acquisition data and task video information collected in this embodiment can include time format, numerical format and text format at the same time, in order to facilitate data processing in subsequent steps, the solution provided in this embodiment needs to standardize the data of the same type into the same format, which helps to ensure the accuracy of data alignment processing based on timestamps.

[0076] S231. When the time format, numerical format or text format is inconsistent, the data format information corresponding to the buried point data, system collected data and task video information is standardized.

[0077] Check whether the timestamp formats in the tracking data, system collection data, and task video information are consistent. If there are differences, convert all timestamps to a unified format; for numerical data in the tracking data and system collection data, such as resource consumption data, data processing result values, etc., determine a unified numerical representation format and precision; for text data in the tracking data and system collection data, such as node names, error information descriptions, business process names, etc., unify the character encoding format to achieve the purpose of standardized processing.

[0078] S240, establish time indexes for the buried point data, system collection data, task video information and system monitoring log respectively, and obtain the buried point time data, collection time data, video time data and monitoring time log.

[0079] The method of establishing a time index for the buried data to obtain the buried time data can be: for each buried data record, extract its timestamp field, and then use the timestamp as the key and the corresponding buried data record as the value. In this way, the buried data at a specific time point can be quickly found according to the timestamp. For example, if the buried data record contains information such as the start execution time, end execution time, resource consumption, etc. of the executor at a certain node, all relevant buried data during the execution of the node can be quickly located through the time index.

[0080] The method of establishing a time index for the system collected data to obtain the collected time data may be: extracting the timestamp information from the system collected data, which may indicate the time point of data collection, and automatically sorting the data in the order of the timestamps to facilitate subsequent time order traversal. For example, the database connection status information and server load information in the system collected data can be viewed in time order through the time index to view the changes in system resources and connection status during the task execution process.

[0081] The task video information consists of a series of video frames, each of which needs to be timestamped. During or after video recording, use the video processing library to add millisecond timestamps to video frames. Then create a data structure to store the video time index, so that when you need to find a video frame based on time, you can quickly locate the corresponding element in the list and obtain the corresponding video frame data to display the video screen of the task execution at a specific time point.

[0082] System monitoring logs are usually in text format, and each line contains a timestamp and related system events or status information. Parse the log file, extract the timestamp, and use it as the key, and the corresponding log line content as the value, and store it in a suitable data structure to establish a time index. When you need to view system monitoring events at a specific time point, you can efficiently obtain relevant log records from the time index.

[0083] S241. Determine an initial alignment point based on timestamps from the burial time data, the acquisition time data, the video time data, and the monitoring time log.

[0084] Traverse all timestamps in the tracking time data, collection time data, video time data, and monitoring time logs. Compare these timestamps, find the earliest timestamp, and determine the earliest timestamp as the initial alignment point.

[0085] S242. Align and store the buried time data, the collected time data, the video time data and the monitoring time log based on the initial alignment point and the preset data storage structure to obtain a set of accident scene data.

[0086] In this embodiment, a multi-layer nested storage result can be created. The outermost layer uses the task identifier as the key, and the value is a container containing multiple sub-dictionaries or objects, which are used to store point-of-sale data, system acquisition data, video data, and monitoring log data. In each sub-dictionary or object, the time sequence is used as the index to store the corresponding specific data records after time alignment, so as to obtain the accident scene data set. For example, for the point-of-sale data sub-dictionary, the point-of-sale data records for each time point starting from the initial alignment point are stored in chronological order; for video data, the video frame information or video clip references of the corresponding time point can be stored, so that the video content can be quickly retrieved and played according to time later.

[0087] This embodiment can closely associate actuator buried point data, system acquisition data, task video information and system monitoring log by establishing time index for multi-source data and aligning data based on timestamp. This allows to examine the task execution process from multiple angles at the same time when performing abnormal analysis; by establishing time index and aligning and storing multi-source data, it helps to perform efficient retrieval, shorten data search time when performing abnormal analysis, and improve operation and analysis efficiency.

[0088] S250, displaying the accident scene data set based on a multi-dimensional display method to obtain a multi-dimensional display result, so as to perform an abnormal analysis on the task execution status of the executor according to the multi-dimensional display result.

[0089] In one implementation, the multi-dimensional display method provided in this embodiment at least includes a timeline display diagram, a data table display diagram, a node flow diagram, and a resource occupancy diagram.

[0090] Among them, the timeline display diagram is based on the horizontal timeline in the visual interface, and the time-related data in the actuator point data, real-time system collection data and system monitoring log are marked and drawn on the timeline in chronological order; for example, the start time of the task, the execution time point of each node (based on the actuator point data), the change curve of system resource usage (such as CPU usage, memory usage, etc. from real-time system collection data), and the occurrence time of system events (such as network connection changes, database operation events, etc. from system monitoring logs) are marked on the timeline. Through the timeline view, analysts can intuitively see the change trend of different data over time during the task execution process, and quickly locate the approximate time range of the abnormality. For the task video information, set the video playback progress bar on the timeline to correspond to the time point on the timeline. When the analyst clicks a time point on the timeline, the video automatically plays to the screen near the time point, showing the operation interface of the actuator at that time, providing an intuitive visual basis for abnormality analysis. For example, if an abnormal peak in system resources is found at a certain point in time on the timeline, by playing the corresponding video screen, you can check whether the executor's operation at that time has any abnormal behavior, such as whether there is any freeze, error prompt pop-up window, etc.

[0091] The data table display diagram presents the detailed data in the actuator tracking data, real-time system collection data and system monitoring log in a tabular form. The columns of the table can include fields such as data type (such as actuator node information, resource consumption information, system event information, etc.), timestamp, specific data value, etc. For example, for the actuator tracking data column, the name, input parameters, output results, execution status and other information of each node can be listed in detail; for the real-time system collection data column, the CPU usage, memory usage, network bandwidth usage and other values ​​at different time points are displayed; for the system monitoring log column, the type, description, related parameters and other contents of the system event are recorded. Analysts can view the detailed information of a specific time point or a specific data type through filtering, sorting and searching operations on the table data, which is convenient for discovering abnormal patterns or regularities in the data.

[0092] The node flow chart is a node flow chart of the task drawn according to the task logic information of the executor, and the execution status, execution time and related input and output parameter information of each node are marked on the chart. When a node is abnormal, the node will be highlighted on the flow chart. The operation and maintenance personnel can view the node's previous and subsequent node relationships, input and output data, and the associated information with the timeline view and data table view to deeply analyze the position of the abnormality in the entire task process and possible influencing factors.

[0093] The resource usage graph is used to display the resource usage in the real-time system collection data. The dashboard can include multiple dials, which represent key resource indicators such as CPU usage, memory usage, network bandwidth usage, etc. For example, when the CPU usage exceeds 80%, the dial pointer enters the red warning area and flashes, reminding analysts to pay attention to resource bottlenecks. Through the resource usage dashboard view, analysts can quickly understand the overall usage of system resources during task execution and determine whether there are abnormal situations caused by insufficient resources.

[0094] Preferably, the above step S250 can perform abnormal analysis in the following manner:

[0095] Obtain the abnormal time point based on the timeline display diagram; determine the abnormal node data from the data table display diagram according to the abnormal time point, and determine the abnormal data flow corresponding to the abnormal node data from the node flow chart; perform abnormal analysis on the task execution of the executor based on the abnormal data flow direction and the corresponding resource occupancy diagram when the abnormal node data is generated.

[0096] Through the timeline display diagram, the excessively long or short execution time of a node can be identified as an abnormal time point; in the data table display diagram, find the executor buried point data record corresponding to the time point. For example, if the abnormal time point falls within the execution time period of a data processing node, check the data table record of the node, focusing on whether the input parameters are correct, whether the output results meet expectations, and whether there are error information or abnormal codes. At the same time, check the relevant information in the system acquisition data column, such as the database connection status and server load at the time point, to determine whether there are external system factors affecting the node execution; according to the abnormal node data corresponding to the abnormal time point, locate the abnormal node in the node flow chart. Then, analyze the input data source and output data destination of the node. After determining the abnormal time point, abnormal node data, and abnormal data flow, check the resource usage of the resource occupancy diagram near the abnormal time point. For example, if a node is found to be abnormal and the CPU usage is too high at the time point, analyze whether it is because the algorithm calculation amount of the node is too large, resulting in CPU resource exhaustion, thereby affecting the normal execution and data output of the node. If the memory usage is abnormal, check whether there is a memory leak, especially whether a large amount of data is unreasonably occupying memory space and not released in time during the execution of the abnormal node. For network bandwidth usage, determine whether data transmission is delayed or failed due to network congestion, which in turn affects the execution of nodes that rely on network data transmission.

[0097] This embodiment realizes cross-validation of multi-dimensional information through the collaborative work of the timeline display diagram, the data table display diagram, the node flow chart and the resource occupancy diagram. The interactive analysis of multi-dimensional information can avoid the limitations of single-dimensional information analysis and locate the root cause of the anomaly more accurately. The multi-dimensional display method provides a visual guide for anomaly analysis, allowing operation and maintenance personnel to perform analysis in a certain logical order. Starting from the timeline display diagram, quickly locate the abnormal time point, and then obtain relevant information in the data table display diagram and the node flow chart based on the time point, and finally conduct a comprehensive analysis in combination with the resource occupancy diagram. This visual guidance reduces the time of blind search and analysis of operation and maintenance personnel in massive data, and improves the efficiency of anomaly analysis.

[0098] Another preferred embodiment, the solution provided in this embodiment can also obtain the corresponding abnormal video from the task video information according to the abnormal time point, and mark the abnormal video as abnormal. In this embodiment, the task video information adds an accurate timestamp to each video frame during the recording process. After the abnormal time point is determined, first search and match in the video time index to obtain the abnormal video; and mark the abnormality according to the abnormal type. For example, the abnormal type may include interface freezes, error prompt pop-ups, abnormal operation behaviors, etc. Combined with the method of extracting abnormal videos from the task video information, the abnormal situation during the task execution process is displayed in an intuitive visual form, and by marking the abnormal video, the key details of the abnormality are further highlighted. This intuitive way of presenting abnormal details improves the visual understanding of abnormal situations, allowing relevant personnel to locate abnormal information points more quickly and accurately.

[0099] The abnormal analysis method provided in this embodiment collects the buried point data, system acquisition data and task video information generated when the executor performs the task, and establishes time indexes for the buried point data, system acquisition data, task video information and system monitoring logs respectively, so that when performing abnormal analysis, the task execution process can be examined from multiple angles at the same time; by establishing a time index and aligning and storing multi-source data, it is helpful to perform efficient retrieval, and when performing abnormal analysis, the data search time can be shortened, and the operation and maintenance and analysis efficiency can be improved; finally, the multi-dimensional display of the accident scene data set through the timeline display diagram, data table display diagram, node flow chart and resource occupancy diagram provides a visual guide for abnormal analysis, so that the operation and maintenance personnel can analyze in a certain logical order. Starting from the timeline display diagram, the abnormal time point is quickly located, and then the relevant information is obtained in the data table display diagram and the node flow chart according to the time point, and finally a comprehensive analysis is performed in combination with the resource occupancy diagram. This visual guidance reduces the time of blind search and analysis of operation and maintenance personnel in massive data, and improves the efficiency of abnormal analysis.

[0100] Figure 3Schematic diagram of a structure of an abnormality analysis device provided in an embodiment of the present application, which is suitable for executing the abnormality analysis method provided in an embodiment of the present application. Figure 3 As shown, the device may specifically include: a file acquisition module 310, a task execution module 320, a data processing module 330 and a multi-dimensional display module 340, wherein:

[0101] The file acquisition module 310 is used to acquire a monitoring configuration file, wherein the monitoring configuration file includes task information corresponding to each executor; the task information includes at least a task identifier, node parameters, and logic information between nodes;

[0102] The task execution module 320 is used to control each of the executors to execute the task information according to the corresponding node parameters and the logic information between the nodes, and obtain the corresponding point data, system acquisition data and task video information of each executor;

[0103] The data processing module 330 is used to align the buried point data, system collected data, task video information and system monitoring log corresponding to each actuator according to the task identifier and timestamp when an abnormal prompt is generated in the system monitoring log, so as to obtain an accident scene data set;

[0104] The multi-dimensional display module 340 is used to display the accident scene data set based on a multi-dimensional display method to obtain a multi-dimensional display result, so as to perform an abnormal analysis on the task execution status of the executor according to the multi-dimensional display result.

[0105] The abnormal analysis device provided in this embodiment, when the system monitoring log generates an abnormal prompt, aligns the buried point data, system acquisition data, task video information and system monitoring log corresponding to each actuator according to the task identifier and timestamp, and obtains the accident scene data set, which can comprehensively present detailed information in the task execution process from multiple dimensions. In this solution, through automated data collection, integration and alignment processing, and multi-dimensional display of the accident scene data set based on timestamps, operation and maintenance personnel can intuitively and clearly obtain all relevant information when the actuator performs the task; and combined with the task video information display method, it can assist in quickly locating abnormal points, achieving the beneficial effect of improving the accuracy and efficiency of abnormal analysis.

[0106] In one embodiment, the task execution module 320 includes a data acquisition unit, a data collection unit and a data acquisition unit, wherein:

[0107] A data acquisition unit, configured to acquire, for a current node, node data generated when the current node executes the node parameters, wherein the node data at least includes running node information, resource consumption data corresponding to the current node, and node execution information;

[0108] A data collection unit, configured to collect system operation data corresponding to the operation of the current node based on the monitoring interface after obtaining the node data, wherein the system operation data at least includes a database connection status and system load information;

[0109] A data acquisition unit, configured to acquire the buried point data according to the running node information corresponding to each of the nodes, the resource consumption data corresponding to the current node, and the node execution information when each node finishes executing based on the node parameters and the logic information between the nodes;

[0110] The data acquisition unit is further used to obtain the system collection data according to the database connection status and the system load information corresponding to each of the nodes.

[0111] In one embodiment, the task execution module 320 further includes a parameter acquisition unit and a video recording unit, wherein:

[0112] A parameter acquisition unit, used to acquire recording trigger parameters corresponding to each of the actuators;

[0113] The video recording unit is used to record the task execution process of the executor according to the recording trigger parameters in the process in which each executor executes the task information according to the corresponding node parameters and the logical information between the nodes, so as to obtain the task video information.

[0114] In one embodiment, the device further comprises a standardization processing module, wherein:

[0115] A standardization processing module is used to obtain data format information corresponding to the buried point data, the system collected data and the task video information respectively, and the data format information includes a time format, a numerical format and a text format; when the time format, the numerical format or the text format is inconsistent, the data format information corresponding to the buried point data, the system collected data and the task video information is standardized.

[0116] In one embodiment, the data processing module 330 includes an index creation unit, an alignment point determination unit, and a data storage unit, wherein:

[0117] An index establishment unit, used to establish time indexes for the buried point data, the system collected data, the task video information and the system monitoring log, respectively, to obtain buried point time data, collection time data, video time data and monitoring time log;

[0118] An alignment point determination unit, configured to determine an initial alignment point based on a timestamp from the embedding time data, the acquisition time data, the video time data, and the monitoring time log;

[0119] A data storage unit is used to align and store the burial time data, the acquisition time data, the video time data and the monitoring time log based on the initial alignment point and the preset data storage structure to obtain the accident scene data set.

[0120] In one embodiment, the multi-dimensional display method at least includes a timeline display diagram, a data table display diagram, a node flow diagram and a resource occupancy diagram.

[0121] The multi-dimensional display module 340 includes an abnormal point acquisition unit, an abnormal data determination unit and an abnormal analysis unit, wherein:

[0122] An abnormal point acquisition unit, used to acquire abnormal time points based on the timeline display diagram;

[0123] An abnormal data determination unit, configured to determine abnormal node data from the data table display diagram according to the abnormal time point, and determine the abnormal data flow direction corresponding to the abnormal node data from the node flow diagram;

[0124] The abnormality analysis unit is used to perform abnormality analysis on the task execution status of the executor according to the abnormal data flow direction and the corresponding resource occupancy diagram when the abnormal node data is generated.

[0125] In one embodiment, the multi-dimensional display module 340 further includes an abnormality labeling unit, wherein:

[0126] The abnormality marking unit is used to obtain the corresponding abnormal video from the task video information according to the abnormal time point, and mark the abnormal video as abnormal.

[0127] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0128] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data comply with relevant laws, regulations and standards in relevant regions.

[0129] An embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the anomaly analysis method described in any embodiment of the present application.

[0130] An embodiment of the present application further provides a computer-readable medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the anomaly analysis method described in any embodiment of the present application when executed.

[0131] Reference below Figure 4 , Figure 4 1 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, and shows a schematic diagram of the structure of a computer system 500 suitable for implementing the electronic device in an embodiment of the present application. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0132] like Figure 4 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0133] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0134] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present application are executed.

[0135] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, etc., or any suitable combination of the above.

[0136] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0137] The modules and / or units involved in the embodiments described in the present application may be implemented by software or hardware. The modules and / or units described may also be arranged in a processor, for example, it may be described as: a processor includes a file acquisition module, a task execution module, a data processing module and a multi-dimensional display module. The names of these modules do not constitute limitations on the modules themselves in some cases.

[0138] As another aspect, the present application also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: obtaining a monitoring configuration file, the monitoring configuration file includes task information corresponding to each executor; the task information includes at least a task identifier, a node parameter, and logic information between each node; controlling each of the executors to execute the task information according to the corresponding node parameters and logic information between each node, and obtaining the buried point data, system acquisition data, and task video information corresponding to each of the executors; when the system monitoring log generates an abnormal prompt, aligning the buried point data, system acquisition data, task video information, and system monitoring log corresponding to each of the executors according to the task identifier and timestamp to obtain an accident scene data set; displaying the accident scene data set based on a multi-dimensional display method to obtain a multi-dimensional display result, so as to perform an abnormal analysis on the task execution of the executor according to the multi-dimensional display result.

[0139] According to the technical solution of this embodiment, through automated data collection, integration and alignment processing, and multi-dimensional display of accident scene data sets based on timestamps, operation and maintenance personnel can intuitively and clearly obtain all relevant information when the actuator performs a task; and combined with the task video information display method, it can assist in quickly locating abnormal points, achieving the beneficial effect of improving the accuracy and efficiency of abnormality analysis.

[0140] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. An abnormality analysis method, characterized in that: include: Obtain a monitoring configuration file, wherein the monitoring configuration file includes task information corresponding to each executor; The task information at least includes a task identifier, node parameters and logic information between nodes; Control each of the actuators to execute the task information according to the corresponding node parameters and the logic information between the nodes, and obtain the corresponding point data, system acquisition data and task video information of each actuator; When the system monitoring log generates an abnormal prompt, the buried point data, system collected data, task video information and the system monitoring log corresponding to each actuator are aligned according to the task identifier and timestamp to obtain an accident scene data set; The accident scene data set is displayed based on a multi-dimensional display method to obtain a multi-dimensional display result, so as to perform an abnormal analysis on the task execution status of the actuator according to the multi-dimensional display result.

2. The abnormality analysis method according to claim 1, characterized in that: Obtaining the buried point data and system collected data corresponding to each of the actuators, including: For the current node, obtaining node data generated when the current node executes the node parameters, the node data at least including running node information, resource consumption data corresponding to the current node, and node execution information; After obtaining the node data, collecting system operation data corresponding to the operation of the current node based on the monitoring interface, the system operation data at least including database connection status and system load information; When each node finishes executing based on the node parameters and the logic information between the nodes, the buried point data is obtained according to the running node information corresponding to each node, the resource consumption data corresponding to the current node, and the node execution information; The system acquisition data is obtained according to the database connection status and the system load information respectively corresponding to each of the nodes.

3. The abnormality analysis method according to claim 1, characterized in that: The controlling each of the executors to execute the task information according to the corresponding node parameters and the logic information between the nodes, and obtaining the task video information corresponding to each of the executors, includes: Obtaining recording trigger parameters corresponding to each of the executors; In the process where each of the executors executes the task information according to the corresponding node parameters and the logic information between the nodes, the task execution process of the executor is recorded according to the recording trigger parameters to obtain the task video information.

4. The abnormality analysis method according to claim 1, characterized in that: Before obtaining the accident scene data collection, it also includes: Obtain data format information corresponding to the buried point data, the system collected data and the task video information respectively, wherein the data format information includes a time format, a numerical format and a text format; When the time format, the numerical format or the text format is inconsistent, the data format information corresponding to the buried point data, the system collected data and the task video information is standardized.

5. The abnormality analysis method according to claim 1, characterized in that: The step of aligning the buried point data, the system collected data, the task video information and the system monitoring log corresponding to each actuator according to the task identifier and the timestamp to obtain the accident scene data set includes: Establish time indexes for the buried point data, the system collected data, the task video information and the system monitoring log respectively, and obtain buried point time data, collection time data, video time data and monitoring time log; Determine an initial alignment point based on a timestamp from the embedding time data, the acquisition time data, the video time data, and the monitoring time log; The buried point time data, the acquisition time data, the video time data and the monitoring time log are aligned and stored based on the initial alignment point and the preset data storage structure to obtain the accident scene data set.

6. The abnormality analysis method according to claim 1, characterized in that: The multi-dimensional display method at least includes a timeline display diagram, a data table display diagram, a node flow diagram and a resource occupancy diagram; The presenting of the accident scene data set based on a multi-dimensional presentation method to obtain a multi-dimensional presentation result, and performing an abnormal analysis on the task execution of the actuator according to the multi-dimensional presentation result, includes: Obtaining the abnormal time point based on the timeline display diagram; Determine abnormal node data from the data table display diagram according to the abnormal time point, and determine the abnormal data flow direction corresponding to the abnormal node data from the node flow chart; An abnormality analysis is performed on the task execution status of the executor according to the abnormal data flow direction and the corresponding resource occupancy diagram when the abnormal node data is generated.

7. The abnormality analysis method according to claim 6, characterized in that: The method further comprises: The corresponding abnormal video is obtained from the task video information according to the abnormal time point, and the abnormal video is marked as abnormal.

8. An abnormality analysis device, characterized in that: include: A file acquisition module, used to acquire a monitoring configuration file, wherein the monitoring configuration file includes task information corresponding to each executor; The task information at least includes a task identifier, node parameters and logic information between nodes; A task execution module is used to control each of the executors to execute the task information according to the corresponding node parameters and the logic information between the nodes, and obtain the corresponding point data, system acquisition data and task video information of each executor; A data processing module, used for aligning the buried point data, system collected data, task video information and system monitoring log corresponding to each actuator according to the task identifier and timestamp when an abnormal prompt is generated in the system monitoring log, so as to obtain an accident scene data set; The multi-dimensional display module is used to display the accident scene data set based on a multi-dimensional display method to obtain a multi-dimensional display result, so as to perform an abnormal analysis on the task execution status of the executor according to the multi-dimensional display result.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the abnormality analysis method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the anomaly analysis method according to any one of claims 1 to 7.

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