Robot data acquisition, storage and visualization method

Abstract

The invention discloses a robot data acquisition, storage and visualization method, which comprises a data acquisition program, a time sequence database and a data visualization tool, and is characterized in that the data acquisition program is deployed on a robot and is used for acquiring robot operation data; the time sequence database records robot operation data through a data acquisition program; and the data visualization tool is connected with the time sequence database. According to the robot data acquisition, storage and visualization method, in the acquisition stage, the acquisition speed and stability can be improved, the data quality can be ensured, the method adapts to the subsequent process, and the processing time and cost are reduced; in terms of storage, the storage efficiency can be improved, the cost can be reduced, query can be quickly responded, and guarantee is provided for data processing and analysis; on the monitoring level, the defects of analysis and early warning capacity in the prior art can be overcome, potential faults can be found in time, the risks of production interruption and equipment damage are reduced, and stable operation of the robot and production continuity of enterprises are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of software and information technology, and in particular to a robot data collection, storage and visualization method. Background Art

[0002] With the rapid development of robotics technology, robots have been widely used in many fields such as industrial production, logistics and warehousing, and medical services. During the operation of robots, it is crucial to efficiently collect, store, and monitor their operating data. This not only helps to understand the working status of the robot in a timely manner and ensure its stable operation, but also provides data support for subsequent optimization and upgrades. In the early days, robot data processing technology was relatively simple. With the complexity of application scenarios and the growth of data volume, the demand for more advanced data processing architectures and methods has become increasingly urgent.

[0003] Existing solutions address data collection. Traditional approaches not only limit data transmission rates, but also often use relational databases for storage, which are inefficient for storing massive amounts of time-series data. In the monitoring field, data is typically displayed through simple interfaces, lacking in-depth data analysis and real-time fault warning capabilities. While some solutions offer some visual presentation, they struggle to meet the demand for comprehensive and accurate monitoring of robot operating status in complex scenarios. However, existing technologies face the following challenges: First, single-interface data collection methods cannot meet the ever-increasing data demands of robots and are prone to data transmission bottlenecks. Second, using relational databases to store time-series data results in low query efficiency and an inability to quickly respond to real-time data analysis requests. Furthermore, the simplistic data visualization and lack of effective warning mechanisms make it difficult to promptly detect potential faults in robot operation, potentially leading to serious consequences such as production interruptions and equipment damage. Overall, existing technologies lack efficiency in collecting, storing, and monitoring robot operating data, failing to provide reliable and comprehensive technical support for robot anomaly diagnosis. Therefore, we propose a method for collecting, storing, and visualizing robot data. Summary of the Invention

[0004] The main purpose of the present invention is to provide a robot data collection, storage and visualization method, which can effectively solve the problems in the background technology.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for collecting, storing, and visualizing robot data includes a data acquisition program, a time series database, and a data visualization tool. The data acquisition program is deployed on the robot to collect robot operation data; the time series database inputs the robot operation data through the data acquisition program; and the data visualization tool connects to the time series database to create an intuitive and interactive dashboard to display the time series data.

[0007] The data acquisition program consists of a USB serial port module, a data processing module, and a database operation module. Specifically, the USB serial port module is responsible for collecting data during the robot's operation. The collected data is processed by the data processing module and then stored in the time series database by the database operation module.

[0008] The robot data collection, storage and visualization method includes the following process:

[0009] Deploy the data acquisition program on the robot device, install the time series database InfluxDB, and configure the database password, data acquisition program connection parameters, and data sampling parameters. The data sampling parameters refer to the parameters of the data sampling module in the data acquisition program, which are used to dynamically adjust the data sampling frequency to balance data granularity and storage efficiency.

[0010] Install the visualization tool Grafana on the server and configure the connection configuration of the time series database InfluxDB;

[0011] The data acquisition program will use the USB serial port module to collect robot motion data and pass the acquired robot motion data to the data processing module in the data acquisition program; the data processing module in the data acquisition program will perform data sampling and data format normalization based on the robot data collected by the USB serial port module to achieve sampling frequency limit and data format regularization; the data format fields include: joint radian, joint speed, joint acceleration, joint torque, Cartesian coordinates, motor current, motor temperature and other robot parameters; the database operation module in the data acquisition program stores the data processed by the data processing module into the time series database InfluxDB to achieve data persistence; the InfluxDB used in the time series database will parse the data and organize and classify the data according to the time series based on the timestamp information of the data. Subsequently, using its efficient storage engine, the data is stored in a specific file structure for fast reading and writing; the data visualization tool Grafana mainly connects to InfluxDB through data source configuration, and the data source configuration includes the InfluxDB address, port number, database name and authentication credentials required for access. Once configured, Grafana will communicate with InfluxDB using HTTP, sending specially formatted query requests to retrieve the required data. These queries are based on InfluxDB's Flux query language, a functional data processing scripting language designed to unify querying, processing, analyzing, and manipulating data into a single syntax, enabling precise location and filtering of data that meets specific criteria.

[0012] The robot data collection, storage, and visualization method described above supports multi-machine data collection. Data collection programs must be deployed on each robot in equal quantities, depending on the number of robots. It also supports the configuration of multiple time-series databases to distribute the performance and space pressures required for data storage. For data visualization, a data visualization tool is typically deployed, which has the advantage of enabling monitoring and evaluation of data from multiple robots through a single web page.

[0013] In the technical solution of this patent, an interactive dashboard created based on Grafana supports multiple user operations, including: time range selection, data screening and filtering, linkage interaction and warning functions;

[0014] The time range selection function is a function for selecting a time range on the web chart interface. Users can flexibly select different time ranges according to their needs. Through this function, users can focus on the robot's operating data within a specific time period, thereby deeply analyzing the robot's performance at different time scales;

[0015] Data screening and filtering allows users to set various filtering conditions on the web, such as specific joint numbers, speed thresholds, torque ranges, etc., allowing users to quickly filter data subsets of interest. This feature allows users to analyze the operation of specific parts of the robot or under specific working conditions in a targeted manner. By accurately filtering data, it can avoid interference from large amounts of irrelevant data, improve the efficiency and accuracy of data analysis, and provide strong support for robot performance evaluation in specific scenarios.

[0016] The linkage interaction refers to the linkage between multiple charts related to different robot parameters. Specifically, when a user clicks a data point on a joint position chart, the related speed, torque, and current charts will be synchronized to the data at the same time. This linkage interaction feature can help users comprehensively analyze the correlation between different parameters.

[0017] The warning function allows users to set thresholds for robot joints, speed, torque, current, and other data in Grafana based on the normal operating range of each robot parameter. If the data transmitted by InfluxDB shows that a parameter exceeds or falls below the set threshold, Grafana will immediately trigger a warning mechanism. Optional warning mechanisms include chart color, flashing icons, and email.

[0018] The technical solution involved in this patent can support users in obtaining offline historical robot motion data, specifically including the following two methods: first, users can download CSV files through Grafana's web interface to obtain relevant data; second, users can also use third-party InfluxDB query tools such as InfluxDBStudio to obtain offline historical robot motion data.

[0019] Preferably, the time series database uses InfluxDB to achieve persistent storage of data, and the data visualization tool uses Grafana tool.

[0020] Preferably, the data processing module in the data acquisition program includes two key functions: data sampling and data format normalization. Among them, data sampling can dynamically adjust the data sampling frequency to achieve a balance between data granularity and storage efficiency; data format normalization is to standardize the original data according to a unified format, thereby facilitating subsequent data storage and analysis.

[0021] Preferably, the InfluxDB is an open-source time-series database that is specifically optimized for time-series data. This database offers both efficient storage and fast query capabilities, enabling it to process massive amounts of data that dynamically changes over time with exceptional performance. Grafana is a powerful open-source visualization tool that supports the creation of intuitive, interactive dashboards, presenting complex data in a variety of visual formats, such as charts and graphs. In the technical solution of this patent, Grafana can intuitively display robot operation-related data, enabling R&D personnel and decision makers to quickly gain insight into the information behind the data.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] In the present invention, during the acquisition phase, data is acquired through the serial port, and format normalization and sampling frequency adjustment are integrated to improve acquisition speed and stability, ensure data quality, adapt to subsequent processes, and reduce processing time and costs. For storage, the InfluxDB time series database is used to overcome the shortcomings of traditional databases, improve storage efficiency, reduce costs, and quickly respond to queries, providing guarantees for data processing and analysis. At the monitoring level, the Grafana platform is combined for visualization and historical data analysis, and the anomaly detection module is used to realize fault analysis and early warning, making up for the insufficient analysis and early warning capabilities of existing technologies, timely discovering potential faults, reducing the risks of production interruptions and equipment damage, and ensuring the stable operation of robots and the continuity of enterprise production. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a module diagram of a robot data collection, storage and visualization method of the present invention.

[0025] Figure 2 A data acquisition program module diagram of a robot data acquisition, storage and visualization method of the present invention;

[0026] Figure 3 This is a data acquisition, storage and visualization flow chart of a robot data acquisition, storage and visualization method of the present invention;

[0027] Figure 4 This is a multi-machine data acquisition structure diagram of a robot data acquisition, storage and visualization method of the present invention;

[0028] Figure 5 This is a flowchart of obtaining historical data in a robot data collection, storage and visualization method of the present invention. DETAILED DESCRIPTION

[0029] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0030] like Figure 1-5 As shown in the figure, a robot data collection, storage and visualization method, a data collection program, a time series database and a data visualization tool are provided, wherein the data collection program is deployed on the robot to collect the robot operation data; the time series database enters the robot operation data through the data collection program; the data visualization tool displays the time series data by connecting to the time series database and creating an intuitive and interactive dashboard. The time series database uses InfluxDB to achieve persistent storage of data, and the data visualization tool uses Grafana tool; the data processing module in the data collection program includes two key functions: data sampling and data format normalization. Among them, data sampling can dynamically adjust the data sampling frequency to achieve a balance between data granularity and storage efficiency; data format normalization is to standardize the original data according to a unified format, thereby facilitating subsequent data storage and analysis; InfluxDB is an open source time series database that is specially optimized for time series data. This database features both efficient storage and fast query speeds, enabling it to process massive amounts of dynamically changing data with exceptional performance. Grafana is a powerful open-source visualization tool that supports the creation of intuitive, interactive dashboards, presenting complex data in a variety of visual formats, including charts and graphs. In this patented solution, Grafana can visually display robot operation-related data, enabling R&D personnel and decision makers to quickly gain insight into the underlying information.

[0031] The data acquisition program consists of a USB serial port module, a data processing module, and a database operation module. Specifically, the USB serial port module is responsible for collecting data during the robot's operation. The collected data is processed by the data processing module and then stored in the time series database by the database operation module.

[0032] The robot data collection, storage and visualization method includes the following processes:

[0033] Deploy the data acquisition program on the robot device, install the time series database InfluxDB, and configure the database password, data acquisition program connection parameters, and data sampling parameters. Data sampling parameters refer to the parameters of the data sampling module in the data acquisition program, which are used to dynamically adjust the data sampling frequency to balance data granularity and storage efficiency.

[0034] Install the visualization tool Grafana on the server and configure the connection configuration of the time series database InfluxDB;

[0035] The data acquisition program uses the USB serial port module to collect robot motion data and passes the acquired robot motion data to the data processing module in the data acquisition program. The data processing module in the data acquisition program will perform data sampling and data format normalization based on the robot data collected by the USB serial port module to achieve sampling frequency restrictions and regular data format. The data format fields include: joint arc, joint velocity, joint acceleration, joint torque, Cartesian coordinates, motor current, motor temperature and other robot parameters. The database operation module in the data acquisition program stores the data processed by the data processing module in the time series database InfluxDB to achieve data persistence. The time series database used by InfluxDB will parse the data and organize and classify the data according to the time series information based on the data's inherent timestamp information. Subsequently, using its efficient storage engine, the data is stored in a specific file structure for fast reading and writing. The data visualization tool used by Grafana mainly connects to InfluxDB through data source configuration. The data source configuration includes the InfluxDB address, port number, database name, and the authentication credentials required for access. Once configured, Grafana will communicate with InfluxDB using HTTP, sending specially formatted query requests to retrieve the required data. These queries are based on InfluxDB's Flux query language, a functional data processing scripting language designed to unify querying, processing, analyzing, and manipulating data into a single syntax, enabling precise location and filtering of data that meets specific criteria.

[0036] Robotic data collection, storage, and visualization methods support multi-machine data collection. Data collection programs must be deployed on each robot in equal quantities, based on the number of robots. Multiple time-series databases can also be configured to distribute the performance and space pressures required for data storage. For data visualization, a data visualization tool is typically deployed, which offers the advantage of enabling monitoring and evaluation of data from multiple robots through a single web page.

[0037] In the technical solution of this patent, an interactive dashboard created based on Grafana supports multiple user operations, including: time range selection, data screening and filtering, linkage interaction and warning functions;

[0038] Time range selection is a feature that allows users to select a time range on the web chart interface. Users can flexibly select different time ranges based on their needs. This feature allows users to focus on the robot's operating data within a specific time period, allowing for in-depth analysis of the robot's performance at different time scales.

[0039] Data screening and filtering allows users to set various filtering conditions on the web, such as specific joint numbers, speed thresholds, torque ranges, etc., allowing users to quickly filter data subsets of interest. This feature allows users to analyze the operation of specific parts of the robot or under specific working conditions in a targeted manner. By accurately filtering data, it can avoid the interference of large amounts of irrelevant data, improve the efficiency and accuracy of data analysis, and provide strong support for the performance evaluation of robots in specific scenarios.

[0040] Linked interaction refers to the linkage between multiple graphs related to different robot parameters. Specifically, when a user clicks a data point on a joint position graph, the related speed, torque, and current graphs will be synchronized to the data at the same time. This linked interaction feature can help users comprehensively analyze the relationship between different parameters.

[0041] The warning function allows users to set thresholds for robot joints, speed, torque, current, and other data in Grafana based on the normal operating range of each robot parameter. If the data transmitted by InfluxDB shows that a parameter exceeds or falls below the set threshold, Grafana will immediately trigger a warning mechanism. Optional warning mechanisms include chart color, flashing icons, and email.

[0042] The technical solution involved in this patent supports users in obtaining offline historical robot motion data. Specifically, there are two methods: first, users can download CSV files through the Grafana web interface to obtain relevant data; second, users can also use third-party InfluxDB query tools such as InfluxDBStudio to obtain offline historical robot motion data;

[0043] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for collecting, storing and visualizing robot data, characterized by: include: Data acquisition program, time series database, and data visualization tool. The data acquisition program is deployed on the robot to collect robot operation data; the time series database enters the robot operation data through the data acquisition program; the data visualization tool connects to the time series database to create an intuitive and interactive dashboard to display the time series data; The data acquisition program consists of a USB serial port module, a data processing module, and a database operation module. Specifically, the USB serial port module is responsible for collecting data during the robot's operation. The collected data is processed by the data processing module and then stored in the time series database by the database operation module. The robot data collection, storage and visualization method includes the following process: Deploy the data acquisition program on the robot device, install the time series database InfluxDB, and configure the database password, data acquisition program connection parameters, and data sampling parameters. The data sampling parameters refer to the parameters of the data sampling module in the data acquisition program, which are used to dynamically adjust the data sampling frequency to balance data granularity and storage efficiency. Install the visualization tool Grafana on the server and configure the connection configuration of the time series database InfluxDB; The data acquisition program will use the USB serial port module to collect robot motion data and pass the acquired robot motion data to the data processing module in the data acquisition program; the data processing module in the data acquisition program will perform data sampling and data format normalization based on the robot data collected by the USB serial port module to achieve sampling frequency limit and data format regularization; the data format fields include: joint radian, joint speed, joint acceleration, joint torque, Cartesian coordinates, motor current, motor temperature and other robot parameters; the database operation module in the data acquisition program stores the data processed by the data processing module into the time series database InfluxDB to achieve data persistence; the InfluxDB used in the time series database will parse the data and organize and classify the data according to the time series based on the timestamp information of the data. Subsequently, using its efficient storage engine, the data is stored in a specific file structure for fast reading and writing; the data visualization tool Grafana mainly connects to InfluxDB through data source configuration, and the data source configuration includes the InfluxDB address, port number, database name and authentication credentials required for access. Once configured, Grafana will communicate with InfluxDB using HTTP, sending specially formatted query requests to retrieve the required data. These queries are based on InfluxDB's Flux query language, a functional data processing scripting language designed to unify querying, processing, analyzing, and manipulating data into a single syntax, enabling precise location and filtering of data that meets specific criteria. The robot data collection, storage, and visualization method described above supports multi-machine data collection. Data collection programs must be deployed on each robot in equal quantities, depending on the number of robots. It also supports the configuration of multiple time-series databases to distribute the performance and space pressures required for data storage. For data visualization, a data visualization tool is typically deployed, which has the advantage of enabling monitoring and evaluation of data from multiple robots through a single web page. In the technical solution of this patent, an interactive dashboard created based on Grafana supports multiple user operations, including: time range selection, data screening and filtering, linkage interaction and warning functions; The time range selection function is a function for selecting a time range on the web chart interface. Users can flexibly select different time ranges according to their needs. Through this function, users can focus on the robot's operating data within a specific time period, thereby deeply analyzing the robot's performance at different time scales; Data screening and filtering allows users to set various filtering conditions on the web, such as specific joint numbers, speed thresholds, torque ranges, etc., allowing users to quickly filter data subsets of interest. This feature allows users to analyze the operation of specific parts of the robot or under specific working conditions in a targeted manner. By accurately filtering data, it can avoid interference from large amounts of irrelevant data, improve the efficiency and accuracy of data analysis, and provide strong support for robot performance evaluation in specific scenarios. The linkage interaction refers to the linkage between multiple charts related to different robot parameters. Specifically, when a user clicks a data point on a joint position chart, the related speed, torque, and current charts will be synchronized to the data at the same time. This linkage interaction feature can help users comprehensively analyze the correlation between different parameters. The warning function allows users to set thresholds for robot joints, speed, torque, current, and other data in Grafana based on the normal operating range of each robot parameter. If the data transmitted by InfluxDB shows that a parameter exceeds or falls below the set threshold, Grafana will immediately trigger a warning mechanism. Optional warning mechanisms include chart color, flashing icons, and email. The technical solution involved in this patent can support users in obtaining offline historical robot motion data, specifically including the following two methods: first, users can download CSV files through Grafana's web interface to obtain relevant data; second, users can also use third-party InfluxDB query tools such as InfluxDBStudio to obtain offline historical robot motion data.

2. A robot data collection, storage and visualization method according to claim 1, characterized in that: The time series database uses InfluxDB to achieve persistent storage of data, and the data visualization tool uses Grafana.

3. The method for collecting, storing and visualizing robot data according to claim 1, characterized in that: The data processing module in the data acquisition program includes two key functions: data sampling and data format normalization. Among them, data sampling can dynamically adjust the data sampling frequency to achieve a balance between data granularity and storage efficiency; data format normalization is to standardize the original data according to a unified format, thereby facilitating subsequent data storage and analysis.

4. The method for collecting, storing and visualizing robot data according to claim 2, characterized in that: InfluxDB is an open-source time-series database specifically optimized for time-series data. It boasts efficient storage and fast query capabilities, enabling it to process massive amounts of dynamically changing data with exceptional performance. Grafana is a powerful open-source visualization tool that supports the creation of intuitive, interactive dashboards, presenting complex data in a variety of visual formats, such as charts and graphs. In this patented technology, Grafana can intuitively display robot operation-related data, enabling R&D personnel and decision makers to quickly gain insight into the underlying information.

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