Robot cluster management method and device, equipment, storage medium and program product

Abstract

The invention discloses a robot cluster management method, device and equipment, a storage medium and a program product, and relates to the technical field of cloud computing, the method is applied to a conch robot automation process platform, and the method comprises the steps of obtaining cluster database change log data; performing anomaly monitoring on the cluster database change log data based on a pre-generated monitoring early warning strategy to obtain an anomaly monitoring result; and performing statistical analysis according to the abnormal monitoring result, generating an abnormal statistical chart, and obtaining a cluster management result according to the abnormal statistical chart. Through the conch robot automatic process platform, a set of monitoring and early warning system is provided, the monitoring and early warning system comprises various task state circulation monitoring, business detail processing monitoring and the like, problems are found in time in the monitoring process, early warning is given to users through multiple channels, the technical problem that the management capacity of a large-scale robot cluster is poor is solved, and the management efficiency of the large-scale robot cluster is improved. The state of each robot can be reflected in real time, and the overall working efficiency of the cluster is optimized.

Description

Technical Field

[0001] The present application relates to the field of cloud computing technology, and in particular to a robot cluster management method, device, equipment, storage medium and program product. Background Art

[0002] Although current RPA (Robotic Process Automation) software performs well in automating a single workflow or task, its monitoring and visualization capabilities are often limited to individual RPA scenarios. This is not enough when faced with complex, large-scale automation needs, especially when enterprises need to handle not just one or a few tasks, but hundreds or thousands of concurrent automation processes. This challenge is particularly prominent in large enterprise environments.

[0003] For example, consider a financial institution that automatically compiles a large number of financial statements at the end of the month, or an e-commerce company that automatically processes countless orders during peak shopping season. Such scenarios require not just one RPA robot, but a whole "team" of robots that work together to achieve efficient and error-free processing. Cluster-scale RPA can allocate tasks and flexibly schedule resources to ensure system stability and responsiveness under high load. However, many existing RPA solutions do not fully take this complexity into account, lack effective management and monitoring tools for large-scale robot clusters, and cannot reflect the status of each robot in real time to optimize overall work efficiency.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a robot cluster management method, device, equipment, storage medium and program product, aiming to solve the technical problem of poor management capabilities of large-scale robot clusters.

[0006] To achieve the above objectives, the present application proposes a robot cluster management method, which is applied to the Conch Robotics Automation Process Platform and includes:

[0007] Get cluster database change log data;

[0008] Based on the pre-generated monitoring and early warning strategy, abnormal monitoring is performed on the cluster database change log data to obtain abnormal monitoring results;

[0009] Statistical analysis is performed based on the abnormal monitoring results to generate abnormal statistical charts, and cluster management results are obtained based on the abnormal statistical charts.

[0010] In one embodiment, the step of obtaining cluster database change log data includes:

[0011] Collect the real-time heartbeat information of several robots and obtain the robot heartbeat data;

[0012] When it is detected that the robot performs a local task, the execution process of the local task is recorded to obtain task record data;

[0013] Generate cluster database change log data based on the robot heartbeat data and task record data.

[0014] In one embodiment, when the robot is detected to perform a local task, the execution process of the local task is recorded, and the step of obtaining task record data includes:

[0015] When it is detected that the robot is performing a local task, the scene operation monitoring is performed on the automation scene corresponding to the local task to obtain scene operation data;

[0016] Receive an action report of a component node corresponding to the local task, and obtain component node operation log data;

[0017] The task record data is obtained according to the scenario operation data and component node operation log data.

[0018] In one embodiment, after the step of generating cluster database change log data according to the robot heartbeat data and task record data, the step further includes:

[0019] When the preset robot has local recording permission, the execution process of the local task is screen recorded to obtain process execution video data;

[0020] Generate scene backtracking data based on the process execution video data, robot heartbeat data, task record data, and cluster database change log data;

[0021] After the step of performing abnormal monitoring on the cluster database change log data based on the pre-generated monitoring and early warning strategy and obtaining the abnormal monitoring result, the method further includes:

[0022] In response to a preset scene backtracking request, the scene operation backtracking is performed according to the scene backtracking data to provide on-site restoration for the staff.

[0023] In one embodiment, the step of performing abnormal monitoring on the cluster database change log data based on a pre-generated monitoring and early warning strategy and obtaining abnormal monitoring results further includes:

[0024] Receiving user layer monitoring parameters, wherein the user layer monitoring parameters include component node monitoring parameters, robot monitoring parameters, and task execution status monitoring parameters;

[0025] Obtaining a user-level personalized monitoring strategy according to the user-level monitoring parameters;

[0026] The monitoring and early warning strategy is generated based on the preset basic monitoring strategy and the user-level personalized monitoring strategy.

[0027] In one embodiment, the abnormal monitoring results include component node abnormal monitoring results, resource pool abnormal monitoring results and robot abnormal monitoring results, the abnormal statistical chart includes a task abnormal coding flow chart, a resource pool utilization abnormal statistical chart and a robot performance abnormal statistical chart, and the steps of performing statistical analysis according to the abnormal monitoring results, generating an abnormal statistical chart, and obtaining cluster management results according to the abnormal statistical chart include:

[0028] Generate the task exception coding flow chart according to the component node exception monitoring result;

[0029] Generate a resource pool utilization abnormality statistics chart based on the resource pool abnormality monitoring result;

[0030] Generate a statistical chart of the robot performance abnormality according to the robot abnormality monitoring result;

[0031] The cluster management result is obtained based on the task exception coding flow chart, the resource pool utilization exception statistical chart and the robot performance exception statistical chart.

[0032] In addition, to achieve the above purpose, the present application also proposes a robot cluster management device, the robot cluster management device comprising:

[0033] Data acquisition module, used to obtain cluster database change log data;

[0034] An abnormal monitoring module is used to perform abnormal monitoring on the cluster database change log data based on a pre-generated monitoring and early warning strategy to obtain abnormal monitoring results;

[0035] The cluster management module is used to perform statistical analysis based on the abnormal monitoring results, generate abnormal statistical charts, and obtain cluster management results based on the abnormal statistical charts.

[0036] In addition, to achieve the above-mentioned objectives, the present application also proposes a robot cluster management device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the robot cluster management method described above.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the robot cluster management method described above are implemented.

[0038] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the robot cluster management method as described above.

[0039] The present application provides a robot cluster management method, which provides a monitoring and early warning system through the Conch Robotics automated process platform, including various task status flow monitoring, business detail processing monitoring, task waiting timeout monitoring, task execution timeout monitoring, etc., which can promptly detect problems during monitoring and warn users through multiple channels, solving the technical problem of poor management capabilities of large-scale robot clusters in the prior art, and can reflect the status of each robot in real time, optimizing the overall work efficiency of the cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 A flowchart of the first embodiment of the robot cluster management method of the present application is provided;

[0043] Figure 2 A flowchart of the second embodiment of the robot cluster management method of the present application is provided;

[0044] Figure 3 A schematic diagram of a brief process of the robot cluster management method provided in Embodiment 1 or 2 of the present application;

[0045] Figure 4 A schematic diagram of real-time indicator monitoring of the first part of the robot cluster management method provided in Embodiment 1 or 2 of the present application;

[0046] Figure 5 A schematic diagram of real-time indicator monitoring of the second part of the robot cluster management method provided in Embodiment 1 or 2 of the present application;

[0047] Figure 6 This is a schematic diagram of the module structure of the robot cluster management device according to an embodiment of the present application;

[0048] Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the robot cluster management method in the embodiment of the present application.

[0049] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0051] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0052] The main solutions of the embodiments of this application are:

[0053] Get cluster database change log data;

[0054] Based on the pre-generated monitoring and early warning strategy, abnormal monitoring is performed on the cluster database change log data to obtain abnormal monitoring results;

[0055] Statistical analysis is performed based on the abnormal monitoring results to generate abnormal statistical charts, and cluster management results are obtained based on the abnormal statistical charts.

[0056] Although current RPA (Robotic Process Automation) software excels at automating a single workflow or task, its monitoring and visualization capabilities are often limited to individual RPA scenarios. This is not enough when faced with complex, large-scale automation needs, especially when enterprises need to handle not just one or a few tasks, but hundreds or thousands of concurrent automation processes. This challenge is particularly prominent in large enterprise environments.

[0057] For example, consider a financial institution that automatically compiles a large number of financial statements at the end of the month, or an e-commerce company that automatically processes countless orders during peak shopping season. Such scenarios require not just one RPA robot, but a whole "team" of robots that work together to achieve efficient and error-free processing. Cluster-scale RPA can allocate tasks and flexibly schedule resources to ensure system stability and responsiveness under high load. However, many existing RPA solutions do not fully take this complexity into account, lack effective management and monitoring tools for large-scale robot clusters, and cannot reflect the status of each robot in real time to optimize overall work efficiency.

[0058] Therefore, developing an RPA platform that can meet this challenge and achieve seamless monitoring and visualization of large-scale robot clusters is an important direction for the current development of RPA technology and a key requirement for large enterprises to achieve automation transformation. Such a platform should have functions such as intelligent scheduling, automatic fault recovery, and performance analysis to ensure that the potential of RPA can be fully unleashed in a complex enterprise environment.

[0059] The present application provides a solution, which provides a monitoring and early warning system through the Conch Robotics automated process platform, including various task status flow monitoring, business detail processing monitoring, task waiting timeout monitoring, task execution timeout monitoring, etc., which can promptly detect problems during monitoring and warn users through multiple channels, solving the technical problem of poor management capabilities of large-scale robot clusters in the prior art, and can reflect the status of each robot in real time, thereby optimizing the overall work efficiency of the cluster.

[0060] It should be noted that the execution entity of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device that can realize the above functions, a Conch Robot automation process platform, etc.

[0061] As a high-performance automation solution, the Conch Robotics Automation Process Platform faces a difficult and critical task: ensuring stable monitoring and integration of real-time heartbeat information from more than 15,000 active robots. These robots work around the clock in various business environments, and the "heartbeat" data of each robot, that is, their operating status and performance indicators, is a key indicator for evaluating system health and optimizing efficiency. Every day, the platform needs to process not only these massive heartbeat data, but also capture and analyze the detailed operating data of tens of thousands of automation scenarios during execution, which reveals the execution efficiency, error rate and optimization potential of each task. More significantly, the Conch Robotics Automation Process Platform receives and organizes more than 180 million separate action reports every day. These actions range from basic data input to complex logical decisions. Each action is like a tiny brick, which together builds the magnificent building of the automation process. Through efficient data processing mechanisms, the platform conducts deep learning and intelligent analysis of these massive action data to achieve refined management and continuous improvement of automation processes. At the same time, considering the importance of visual monitoring, the platform also needs to integrate the robot's execution video records. These videos intuitively show the execution process of each step, providing an intuitive basis for troubleshooting, effect verification and optimization suggestions. On the premise of sparing no effort to ensure data security and privacy, the Conch Robotics Automation Process Platform, with its powerful data processing capabilities, carries and analyzes the massive amount of information in this automated world, driving the intelligent transformation of enterprise operations.

[0062] The following uses the Conch Robotics automated process platform as an example to illustrate this embodiment and the following embodiments.

[0063] Based on this, the present application embodiment provides a robot cluster management method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the robot cluster management method of the present application.

[0064] In this embodiment, the robot cluster management method includes steps S10 to S30:

[0065] Step S10, obtaining cluster database change log data;

[0066] The Conch Robotics Automation Process Platform obtains cluster database change log data.

[0067] It should be noted that the cluster database is a system composed of multiple database instances, which achieves high availability and high performance through coordination and load balancing. The change log data records all modification operations in the database, including timestamp, operation type and modified content.

[0068] Step S20, based on the pre-generated monitoring and early warning strategy, perform abnormal monitoring on the cluster database change log data to obtain abnormal monitoring results;

[0069] The Conch Robotics automated process platform uses pre-set monitoring and early warning strategies to analyze the acquired change log data to detect whether there are abnormal operations or behaviors and obtain abnormal monitoring results, which usually involves pattern recognition, statistical analysis and threshold comparison of log data.

[0070] In a feasible implementation manner, before step S20, steps S401 to S403 may also be included:

[0071] Step S401, receiving user layer monitoring parameters, wherein the user layer monitoring parameters include component node monitoring parameters, robot monitoring parameters and task execution status monitoring parameters;

[0072] Step S402, obtaining a user-level personalized monitoring strategy according to the user-level monitoring parameters;

[0073] Step S403: generating the monitoring and early warning strategy based on the preset basic monitoring strategy and the user-level personalized monitoring strategy.

[0074] The Conch Robot Automation Process Platform receives user-level monitoring parameters, which include component node monitoring parameters, robot monitoring parameters, and task execution status monitoring parameters. By analyzing the received component node monitoring parameters, robot monitoring parameters, and task execution status monitoring parameters, key indicators and potential risk points are identified. Based on the analysis results, targeted monitoring strategies are formulated, including: Threshold setting: reasonable alarm thresholds are set for each key parameter to ensure that early warnings can be issued in time under abnormal circumstances; Monitoring frequency: the monitoring frequency is adjusted according to the nature of the task and the complexity of the system to ensure the real-time nature of the data; a predefined rule engine is used to automatically generate monitoring strategies to obtain user-level personalized monitoring strategies; finally, the user-level personalized monitoring strategies are integrated with the basic monitoring strategies to ensure that specific user needs and environmental characteristics are fully considered. The specific steps include: comparing and analyzing the similarities and differences between the two to find effective points of integration; determining which personalized strategies can cover or enhance the content of the basic strategy, and generating the monitoring early warning strategy based on the integrated monitoring strategy.

[0075] It should be noted that the component node monitoring parameters are performance indicators for each component node in the cluster (such as servers, storage, etc.); the robot monitoring parameters are specifically for the robot's status, task execution efficiency and other related information; the task execution node monitoring parameters are about the monitoring of task execution, including task delays, failure rates, etc. The user-level personalized monitoring strategy is a monitoring solution tailored to the user's specific situation, which can more effectively capture abnormal situations.

[0076] In addition, it should be noted that the basic monitoring strategy usually covers the general monitoring needs of the system, such as: common system performance indicator monitoring (such as CPU, memory, network, etc.), preset fault detection rules and response measures.

[0077] In this implementation, the Conch Robotics automated process platform can more accurately adapt to user needs, thereby improving the flexibility and responsiveness of monitoring and ensuring the stable execution of clusters and tasks. At the same time, personalized monitoring strategies will help users discover potential problems in a timely manner and avoid system failures.

[0078] Step S30, performing statistical analysis based on the abnormal monitoring results, generating abnormal statistical charts, and obtaining cluster management results based on the abnormal statistical charts.

[0079] The Conch Robot Automation Process Platform performs statistical analysis based on the abnormal monitoring results obtained in step S20, generates abnormal statistical charts, and performs analysis and evaluation based on the abnormal statistical charts to obtain cluster management results in order to formulate corresponding robot cluster management measures. This includes responding to abnormal situations, analyzing the root causes, and making necessary adjustments to ensure the stability and security of the robot cluster, and handling detected abnormalities through timely management measures to prevent the expansion of potential problems.

[0080] In a feasible implementation manner, the abnormal monitoring results include component node abnormal monitoring results, resource pool abnormal monitoring results and robot abnormal monitoring results, the abnormal statistical chart includes a task abnormal coding flow chart, a resource pool utilization abnormal statistical chart and a robot performance abnormal statistical chart, and step S30 may include steps S301 to S304:

[0081] Step S301, generating the task exception coding flow chart according to the component node exception monitoring result;

[0082] Step S302, generating a resource pool utilization abnormality statistics chart according to the resource pool abnormality monitoring result;

[0083] Step S303, generating a statistical chart of the robot performance abnormality according to the robot abnormality monitoring result;

[0084] Step S304, obtaining the cluster management result based on the task exception coding flowchart, the resource pool utilization exception statistical chart and the robot performance exception statistical chart.

[0085] Based on the abnormal monitoring results of component nodes, the Conch Robot Automation Process Platform analyzes the performance of each node in task execution, generates a flowchart, and displays the abnormal situations that may occur in task execution and their corresponding codes, so as to quickly locate the problem. Based on the abnormal monitoring results of the resource pool, it analyzes the usage of resources, identifies the time periods and nodes with abnormal utilization, creates statistical charts, and intuitively displays the utilization changes of the resource pool to help managers make adjustments. Based on the abnormal monitoring results of the robot, it analyzes the performance indicators of each robot in the process of executing the task, generates statistical charts, and displays the frequency and type of performance abnormalities to facilitate the identification of potential maintenance needs. It conducts a comprehensive analysis of the various charts and flowcharts generated by steps S301, S302 and S303. Based on the analysis results, it forms a cluster management result to provide a basis for subsequent optimization and adjustment.

[0086] For example, real-time monitoring plays a vital role in the RPA (Robotic Process Automation) environment. It is like a transparent window that allows users to have a clear insight into the running status of the automated process. Data visualization is the key to this process. It converts complex data into easy-to-understand graphics and charts, like a vivid picture scroll, showing the dynamic process of the RPA scenario, the utilization of the resource pool, and the work efficiency of the robot. In the monitoring of the RPA scenario, each process node, task execution status, and abnormal situation are presented in the form of clear charts. Users can intuitively see the progress of the automated task, understand the time and effect of each step, and discover potential problems in time. For example, through color-coded flowcharts, green may represent smooth operation, and red may warn of failures or delays, making problem location quick and intuitive. Resource pool monitoring reveals the allocation and use of hardware and software resources. This may include CPU utilization, memory consumption, etc., displayed in the form of bar charts, pie charts, etc., allowing users to determine whether resource allocation needs to be adjusted to optimize overall performance. When resources are close to saturation or abnormalities occur, the early warning system will immediately issue a notification to prevent operation interruption. As for robot monitoring, this involves key indicators such as the operating status, task completion, and error rate of each robot. Users can track the activities of individual robots, such as their online time, execution speed, and error handling capabilities, and compare and analyze them through real-time charts to optimize performance or troubleshoot. The early warning function will be triggered when the robot status is abnormal or the task execution fails, warning of possible impacts in advance to ensure the continuity and stability of the service. In summary, this three-dimensional monitoring (scenario, resource pool, robot) provides a comprehensive perspective, which not only enhances users' understanding and control of the RPA system, but also provides a powerful tool for problem prevention and decision support.

[0087] In addition, the platform supports robot heartbeat timeout alarms, scenario business volume abnormality alarms, business success rate alarms, transaction volume forecast alarms, and resource pool utilization risk warnings; at the same time, based on the quasi-real-time collection of underlying data, it can easily expand the alarm capabilities of new dimensions.

[0088] In this implementation, the Conch Robotics automation process platform provides a comprehensive exception monitoring and analysis framework, which helps to continuously improve system performance and reliability, realizes visual analysis of various exceptions, helps the team quickly locate problems, and improves the efficiency of robot cluster management. This process provides data support for resource optimization and task adjustment, ensuring efficient operation of the system.

[0089] This embodiment provides a robot cluster management method. Through the Conch Robotics automated process platform, a monitoring and early warning system is provided, including various task status flow monitoring, business detail processing monitoring, task waiting timeout monitoring, task execution timeout monitoring, etc. Problems can be discovered in a timely manner during monitoring and users can be warned through multiple channels, thus solving the technical problem of poor management capabilities of large-scale robot clusters in the prior art, reflecting the status of each robot in real time, and optimizing the overall work efficiency of the cluster.

[0090] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 , step S10 may further include steps S101 to S103:

[0091] Step S101, collecting real-time heartbeat information of several robots to obtain robot heartbeat data;

[0092] Step S102, when it is detected that the robot performs a local task, the execution process of the local task is recorded to obtain task record data;

[0093] Step S103: Generate cluster database change log data according to the robot heartbeat data and task record data.

[0094] The Conch Robot Automation Process Platform sets up scheduled tasks to regularly collect heartbeat information of multiple robots. The heartbeat information includes key indicators such as the robot's online status, CPU usage, and memory utilization. The robot's heartbeat data is obtained. When it is detected that the robot starts to execute a local task, the recording function is started to record the execution process of the local task. The recorded information includes the task start time, execution steps, error log, and completion time. The task record data is obtained. Finally, the collected heartbeat data and task record data are integrated to generate cluster database change log data. The log includes timestamp, robot ID, task status, heartbeat status and other information.

[0095] It should be noted that real-time heartbeat information is status data sent periodically by the robot, reflecting its health status, and robot heartbeat data is a collection of collected heartbeat information, including various performance indicators.

[0096] In a feasible implementation, step S102 may include steps S1021 to S1023:

[0097] Step S1021, when it is detected that the robot performs a local task, scene operation monitoring is performed on the automation scene corresponding to the local task to obtain scene operation data;

[0098] Step S1022, receiving an action report of a component node corresponding to the local task, and obtaining component node operation log data;

[0099] Step S1023, obtaining the task record data according to the scenario operation data and component node operation log data.

[0100] When the robot is detected to perform a local task, the Conch Robot Automation Process Platform starts the scene monitoring module to monitor the scene operation of the automation scene corresponding to the local task, collect various operation data in the scene, such as environmental parameters, state changes, etc., obtain scene operation data, and then receive the action report of the component node corresponding to the local task, including the timestamp, execution status and results of each action, obtain the component node operation log data, integrate the scene operation data with the component node log, and generate complete task record data, ensuring that it includes timestamp, task ID, robot ID and other information.

[0101] In this implementation, the problem of information dispersion is solved by integrating scene and component data. The real-time monitoring and feedback mechanism improves the stability and accuracy of task execution. Comprehensive data analysis can identify bottlenecks in the process and optimize task execution strategies. It can significantly improve the effectiveness and reliability of the robot in performing local tasks and ensure its efficient operation in complex environments.

[0102] In another feasible implementation manner, after step S103, steps S501 to S502 may also be included:

[0103] Step S501, when the preset robot has local recording permission, screen recording is performed on the execution process of the local task to obtain process execution video data;

[0104] Step S502, generating scene backtracking data according to the process execution video data, robot heartbeat data, task record data and cluster database change log data;

[0105] After step S20, step S503 may also be included:

[0106] Step S503, in response to a preset scene backtracking request, scene operation backtracking is performed according to the scene backtracking data, so that the staff can perform on-site restoration.

[0107] When the preset robot has local recording permissions, the Conch Robotics Automation Process Platform begins screen recording of the task execution process. The generated video data can fully reflect every detail of the task, including the operation interface, key decision points, and feedback information. The process execution video data, robot heartbeat data, task record data, and cluster database change log data are integrated to form a comprehensive backtracking data set. The process execution video data is obtained to support subsequent analysis and on-site restoration. In response to the preset scene backtracking request, the scene operation backtracking is performed according to the scene backtracking data for the staff to perform on-site restoration.

[0108] It should be noted that when a scene backtracking request is requested by a worker to restore a specific scene, the system will analyze it based on the scene backtracking data. Scene operation backtracking uses integrated backtracking data to reproduce each step and state of task execution in detail, so that the worker can accurately understand and restore the on-site situation. As a result, the Conch Robotics Automation Process Platform can provide greater flexibility and responsiveness when executing tasks, ultimately improving the robot's work efficiency and decision-making quality.

[0109] Exemplarily, a feasible implementation of step S501 is as follows:

[0110] 1) Before executing a local task, the executor asks the robot center whether it has local recording permission;

[0111] 2) If you have local recording permission, start the independent recorder process to record the desktop video;

[0112] 3) Start the execution process;

[0113] 4) After the process is completed, encrypt and save the video.

[0114] Users can apply for their own scenes. After the approval, they can record the image information of the scene project operation on the actuator, and can directly view it on the platform through the on-site restoration function. The focus is on unattended related machines.

[0115] 5) You need to set the appropriate frame rate, bit rate, and quality, which should not affect the performance of the entire system, but also take into account the video quality. You can choose h264 or mp4 format;

[0116] 6) Use ffmpeg for video encoding, the development language is C++, and call the Microsoft ffmpeg library;

[0117] 7) Use D3C, Dxgi, and Duplication to capture the screen change area. The development language is C++ and the D3D library is called;

[0118] 8) Add watermark to the video;

[0119] 9) Encrypt video files.

[0120] Exemplarily, a feasible implementation of steps S502 and S503 is as follows:

[0121] 1) On-site restoration is also called five-in-one broadcast, which specifically refers to video, log, scene flow chart, CPU, and memory usage.

[0122] 2) Videos are uploaded to ECS, and a lifecycle is set for the video storage bucket to periodically delete video data in batches to prevent the ECS load from reaching the upper limit. To prevent a single video file from being too large and affecting the upload rate, the client is agreed upon on the size limit and format limit for a single video file. In addition, Mp4parser is introduced to obtain the length of the video and other information for processing. For video playback, user permissions are also controlled, the ECS direct link is encapsulated, and the "Range" field is added to the request header to set the read byte range. Multiple requests are made to read the video in segments, which speeds up the response time when playing the video.

[0123] 3) The action log is refreshed regularly according to the video playback time. The data is obtained from ES. After the log message is written to Kafka, it is consumed by ES.

[0124] 4CPU / memory usage is the same as above. It is preloaded in advance according to the video playback time and added according to the time.

[0125] The scene flowchart is parsed, and the currently running process block or sub-process is displayed along with the video timeline. The execution flowchart information comes from the process json file in the scene version package. The multiple nodes in the flowchart can be understood as a multi-branch tree as a whole, and the in-order traversal is performed to complete the tracing of all the entry nodes. Finally, the parsed node coordinates and edge information are integrated to obtain a complete flowchart.

[0126] In this implementation, through screen recording and data integration, each operation process can be traced, which improves the transparency of task execution. Combined with the backtracking data, the root cause of the problem can be quickly identified, targeted improvement plans can be proposed, the task execution site can be accurately restored, and staff can conduct more in-depth analysis and training.

[0127] It can reduce the occurrence of failures, record the task execution process, provide a clear execution track, facilitate auditing and optimization, and timely discover potential problems and intervene through data analysis to avoid greater losses. The change log of the cluster database supports subsequent data analysis and decision-making. The Conch Robot Automation Process Platform ensures a comprehensive grasp of the robot task execution through real-time monitoring and recording, thereby providing support for subsequent data analysis and optimization. This can identify potential problems in a timely manner and improve the efficiency and reliability of robot cluster management.

[0128] For example, in order to help understand the implementation process of the robot cluster management method obtained by combining the first embodiment, the second embodiment and the third embodiment, please refer to Figure 3 , Figure 4 and Figure 5 , Figure 3 A brief flowchart of the robot cluster management method provided in Embodiment 1 or 2 of the present application is provided. Figure 4 A schematic diagram of real-time indicator monitoring of the first part of the robot cluster management method provided in Embodiment 1 or 2 of the present application, Figure 5 A schematic diagram of real-time indicator monitoring of the second part of the robot cluster management method provided in Embodiment 1 or 2 of the present application, specifically:

[0129] First, collect information from the data generation points in dimensions such as scenes, robots, and component nodes to achieve quasi-real-time information reporting. Without affecting business performance, use Canel to analyze database logs for monitoring. Canal is a tool based on incremental database log analysis, providing a framework for incremental data subscription and consumption. The analysis service analyzes data in multiple dimensions and writes it to ElasticSearch (ES for short, a real-time search and analysis engine based on Lucene) in real time by listening to the topic (subscription topic) of the relevant kafka (message queue);

[0130] The monitoring and early warning service monitors and scans the underlying raw data 24 hours a day for the platform's basic monitoring strategy and the user-level personalized monitoring strategy. Once an abnormality is found, the relevant personnel will be notified immediately through the alarm system. This method ensures that there is no delay in the entire process from data generation to processing and anomaly detection, greatly improving the efficiency of data processing and the speed of problem response. Canal's lightweight design enables it to maintain low latency and high stability when processing a large number of database change events, providing a solid foundation for real-time data analysis.

[0131] The distributed nature of ElasticSearch makes data storage and retrieval highly scalable and fault-tolerant, ensuring fast and accurate queries regardless of how the amount of data grows. Through the aggregation analysis function of ES, you can gain in-depth insights into the behavior patterns of various dimensions such as scenarios, robots, and component nodes, providing strong data support for business optimization and decision-making.

[0132] At the same time, the monitoring and early warning service is like the guardian of the platform. Through intelligent algorithm models, it monitors user behavior, system performance, service stability and other aspects around the clock. Whether it is resource usage at the system level or abnormal operations at the user level, they can be captured at the first time, thereby preventing possible problems in advance and ensuring the robustness of the system and user experience.

[0133] In addition, Logstash is used for log collection and processing, and logs from different sources are managed in a unified manner, forming a complete data pipeline with Canal and ES. This end-to-end data monitoring system not only improves data analysis capabilities, but also increases the speed of troubleshooting and problem repair, and can always maintain agility and efficiency in the ever-changing digital environment.

[0134] Compared with the prior art, the robot cluster management method described in this embodiment has the following advantages:

[0135] 1. The monitoring and early warning mechanism of the Conch Robotics Automation Process Platform demonstrates its advanced nature in the field of intelligent operations. It continuously monitors and comprehensively analyzes each step of the RPA process. The system has sophisticated task status tracking capabilities to ensure the seamless operation of the process, while deeply analyzing business details to effectively identify potential anomalies. It is worth noting that it integrates an intelligent timeout monitoring function. Whether it is the waiting or execution stage, once the preset time limit is exceeded, the system immediately triggers an early warning and quickly notifies the user through multiple communication channels (such as email, SMS and application notifications) to promote immediate response and resolution of problems.

[0136] Second, in terms of RPA management and monitoring, the Conch Robotics Automation Process Platform achieves efficient and centralized operations. Users can remotely and uniformly monitor scenes and robots scattered across the country, forming a virtual operation command center, and real-time data updates ensure clear visibility of process status. Whether it is the robot's operating status or the scene execution effect, information can be obtained in real time, significantly improving management efficiency.

[0137] 3. Conch Robotics' automated process platform allows users to customize early warning strategies to meet different business needs. Users can set alarm trigger conditions according to specific needs, such as adjusting alarm thresholds and selecting specific recipients or groups. This customized design enables the early warning system to better match various complex business environments.

[0138] 4. At the data analysis level, the Conch Robotics Automation Process Platform provides a comprehensive multi-dimensional view. Users can trace the historical operation records of RPA scenarios, just like analyzing video frames one by one, to accurately locate problems. In addition, the platform's RPA post-audit function ensures process compliance, and each step of the operation is recorded in detail, which enhances business transparency and trust, and builds a solid backing for the company's process management.

[0139] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the robot cluster management method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0140] This application also provides a robot cluster management device, please refer to Figure 6 , the robot cluster management device comprises:

[0141] The data acquisition module 10 is used to acquire cluster database change log data;

[0142] The abnormal monitoring module 20 is used to perform abnormal monitoring on the cluster database change log data based on a pre-generated monitoring and early warning strategy to obtain abnormal monitoring results;

[0143] The cluster management module 30 performs statistical analysis based on the abnormal monitoring results, generates abnormal statistical charts, and obtains cluster management results based on the abnormal statistical charts.

[0144] Optionally, the data acquisition module 10 is further used for:

[0145] Collect the real-time heartbeat information of several robots and obtain the robot heartbeat data;

[0146] When it is detected that the robot performs a local task, the execution process of the local task is recorded to obtain task record data;

[0147] Generate cluster database change log data based on the robot heartbeat data and task record data.

[0148] Optionally, the data acquisition module 10 is further used for:

[0149] When it is detected that the robot is performing a local task, the scene operation monitoring is performed on the automation scene corresponding to the local task to obtain scene operation data;

[0150] Receive an action report of a component node corresponding to the local task, and obtain component node operation log data;

[0151] The task record data is obtained according to the scenario operation data and component node operation log data.

[0152] Optionally, the data acquisition module 10 is further used for:

[0153] When the preset robot has local recording permission, the execution process of the local task is screen recorded to obtain process execution video data;

[0154] Generate scene backtracking data based on the process execution video data, robot heartbeat data, task record data, and cluster database change log data;

[0155] The abnormality monitoring module 20 is also used for:

[0156] In response to a preset scene backtracking request, the scene operation backtracking is performed according to the scene backtracking data to provide on-site restoration for the staff.

[0157] Optionally, the abnormality monitoring module 20 is further used for:

[0158] Receiving user layer monitoring parameters, wherein the user layer monitoring parameters include component node monitoring parameters, robot monitoring parameters, and task execution status monitoring parameters;

[0159] Obtaining a user-level personalized monitoring strategy according to the user-level monitoring parameters;

[0160] The monitoring and early warning strategy is generated based on the preset basic monitoring strategy and the user-level personalized monitoring strategy.

[0161] Optionally, the abnormal monitoring results include component node abnormal monitoring results, resource pool abnormal monitoring results and robot abnormal monitoring results, the abnormal statistical chart includes a task abnormal coding flow chart, a resource pool utilization abnormal statistical chart and a robot performance abnormal statistical chart, and the cluster management module 30 is further used to:

[0162] Generate the task exception coding flow chart according to the component node exception monitoring result;

[0163] Generate a resource pool utilization abnormality statistics chart based on the resource pool abnormality monitoring result;

[0164] Generate a statistical chart of the robot performance abnormality according to the robot abnormality monitoring result;

[0165] The cluster management result is obtained based on the task exception coding flow chart, the resource pool utilization exception statistical chart and the robot performance exception statistical chart.

[0166] The robot cluster management device provided by the present application adopts the robot cluster management method in the above embodiment, which can solve the technical problem of poor management ability of large-scale robot clusters. Compared with the prior art, the beneficial effects of the robot cluster management device provided by the present application are the same as the beneficial effects of the robot cluster management method provided by the above embodiment, and other technical features in the robot cluster management device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0167] The present application provides a robot cluster management device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the robot cluster management method in the above-mentioned embodiment one.

[0168] Reference below Figure 7 , which shows a schematic diagram of the structure of a robot cluster management device suitable for implementing the embodiment of the present application. The robot cluster management device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The robot cluster management 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.

[0169] like Figure 7As shown, the robot cluster management device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the robot cluster management device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the robot cluster management device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a robot cluster management device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0170] 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 a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0171] The robot cluster management device provided by the present application adopts the robot cluster management method in the above embodiment, which can solve the technical problem of poor management ability of large-scale robot clusters. Compared with the prior art, the beneficial effects of the robot cluster management device provided by the present application are the same as the beneficial effects of the robot cluster management method provided by the above embodiment, and the other technical features in the robot cluster management device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0172] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0173] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0174] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the robot cluster management method in the above-mentioned embodiment.

[0175] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, 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 this embodiment, the 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, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0176] The computer-readable storage medium may be included in the robot cluster management device; or may exist independently without being assembled into the robot cluster management device.

[0177] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the robot cluster management device, the robot cluster management device:

[0178] Get cluster database change log data;

[0179] Based on the pre-generated monitoring and early warning strategy, abnormal monitoring is performed on the cluster database change log data to obtain abnormal monitoring results;

[0180] Statistical analysis is performed based on the abnormal monitoring results to generate abnormal statistical charts, and cluster management results are obtained based on the abnormal statistical charts.

[0181] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0182] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the 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 and / or flowchart, and the combination of boxes in the block diagram and / or flowchart 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.

[0183] The modules involved in the embodiments of the present application may be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.

[0184] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned robot cluster management method, and can solve the technical problem of poor management capabilities of large-scale robot clusters. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the robot cluster management method provided by the above-mentioned embodiment, and are not described in detail here.

[0185] The present application also provides a computer program product, including a computer program, which implements the steps of the robot cluster management method as described above when executed by a processor.

[0186] The computer program product provided by this application can solve the technical problem of poor management ability of large-scale robot clusters. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the robot cluster management method provided by the above embodiment, which will not be repeated here.

[0187] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A robot cluster management method, characterized in that: The method is applied to the Conch Robotics automated process platform, and the method comprises: Get cluster database change log data; Based on the pre-generated monitoring and early warning strategy, abnormal monitoring is performed on the cluster database change log data to obtain abnormal monitoring results; Statistical analysis is performed based on the abnormal monitoring results to generate abnormal statistical charts, and cluster management results are obtained based on the abnormal statistical charts.

2. The method according to claim 1, characterized in that The step of obtaining cluster database change log data includes: Collect the real-time heartbeat information of several robots and obtain the robot heartbeat data; When it is detected that the robot performs a local task, the execution process of the local task is recorded to obtain task record data; Generate cluster database change log data based on the robot heartbeat data and task record data.

3. The method according to claim 2, characterized in that When the robot is detected to perform a local task, the step of recording the execution process of the local task and obtaining task record data includes: When it is detected that the robot is performing a local task, the scene operation monitoring is performed on the automation scene corresponding to the local task to obtain scene operation data; Receive an action report of a component node corresponding to the local task, and obtain component node operation log data; The task record data is obtained according to the scenario operation data and component node operation log data.

4. The method according to claim 2, characterized in that After the step of generating cluster database change log data according to the robot heartbeat data and task record data, the method further includes: When the preset robot has local recording permission, the execution process of the local task is screen recorded to obtain process execution video data; Generate scene backtracking data based on the process execution video data, robot heartbeat data, task record data, and cluster database change log data; After the step of performing abnormal monitoring on the cluster database change log data based on the pre-generated monitoring and early warning strategy and obtaining the abnormal monitoring result, the method further includes: In response to a preset scene backtracking request, the scene operation backtracking is performed according to the scene backtracking data to provide on-site restoration for the staff.

5. The method according to claim 1, characterized in that Before the step of performing abnormal monitoring on the cluster database change log data based on the pre-generated monitoring and early warning strategy and obtaining the abnormal monitoring result, the following steps are further included: Receiving user layer monitoring parameters, wherein the user layer monitoring parameters include component node monitoring parameters, robot monitoring parameters, and task execution status monitoring parameters; Obtaining a user-level personalized monitoring strategy according to the user-level monitoring parameters; The monitoring and early warning strategy is generated based on the preset basic monitoring strategy and the user-level personalized monitoring strategy.

6. The method according to claim 1, characterized in that The abnormal monitoring results include component node abnormal monitoring results, resource pool abnormal monitoring results and robot abnormal monitoring results, the abnormal statistical chart includes task abnormal coding flow chart, resource pool utilization abnormal statistical chart and robot performance abnormal statistical chart, and the steps of performing statistical analysis according to the abnormal monitoring results, generating abnormal statistical charts, and obtaining cluster management results according to the abnormal statistical charts include: Generate the task exception coding flow chart according to the component node exception monitoring result; Generate a resource pool utilization abnormality statistics chart based on the resource pool abnormality monitoring result; Generate a statistical chart of the robot performance abnormality according to the robot abnormality monitoring result; The cluster management result is obtained based on the task exception coding flow chart, the resource pool utilization exception statistical chart and the robot performance exception statistical chart.

7. A robot cluster management device, characterized in that: The device comprises: Data acquisition module, used to obtain cluster database change log data; An abnormal monitoring module is used to perform abnormal monitoring on the cluster database change log data based on a pre-generated monitoring and early warning strategy to obtain abnormal monitoring results; The cluster management module is used to perform statistical analysis based on the abnormal monitoring results, generate abnormal statistical charts, and obtain cluster management results based on the abnormal statistical charts.

8. A robot cluster management device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the robot cluster management method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the robot cluster management method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the robot cluster management method according to any one of claims 1 to 6 are implemented.

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