A method for maintaining and calibrating precision during operation of a robot

Abstract

The present invention discloses a method for maintaining and calibrating the accuracy of a manipulator during operation, and relates to the technical field of maintaining and calibrating the accuracy of a manipulator, and specifically includes the following steps: when a manipulator installed on a high-speed assembly line is performing high-speed movement, the working state information of each joint and the end effector is obtained through a sensor; the urgency information of each data in the working state information of each joint and the end effector is extracted; the urgency information of each data is analyzed, the urgency of each data is evaluated, and each data is divided into urgent information and non-urgent information according to the evaluation result; a closed-loop control system prioritizes the urgent information, and a module to be processed is constructed, and non-urgent information is stored in the module to be processed. The present invention realizes the real-time response and high-precision stable operation of the manipulator on the high-speed assembly line by giving priority to the urgent information and dynamically managing the non-urgent information through the closed-loop control system.

Description

Technical Field

[0001] The present invention relates to the technical field of precision maintenance and calibration of manipulators, and in particular to a method for precision maintenance and calibration of manipulators during operation. Background Art

[0002] A robot is an automated device that usually has the functions of a human arm and is used to perform highly repetitive, dangerous or delicate tasks such as assembly, welding, handling and precision machining. The accuracy maintenance and calibration of the robot at work refers to the use of various technical means and methods to ensure that the robot can always maintain high accuracy and consistency when performing tasks, and regularly adjust and calibrate to correct any factors that may cause errors. This includes the use of high-precision sensors and closed-loop control systems for real-time monitoring and adjustment, optimizing mechanical structure design to reduce the impact of deformation and vibration, applying error compensation technology, and performing regular maintenance and calibration. The purpose of this is to ensure that the robot can complete tasks stably and efficiently, ensure the quality and efficiency of the production process, reduce failures and production losses caused by errors, and thus improve the reliability and productivity of the entire automation system.

[0003] The specific working process of the existing technology for maintaining and calibrating the accuracy of manipulators during operation includes the following steps: first, install high-precision position and force sensors to monitor the position, posture and force of each joint and end effector of the manipulator in real time; second, use a closed-loop control system to adjust the motion trajectory and speed of the manipulator in real time according to the data fed back by the sensors to ensure precise positioning; then, optimize the mechanical structure design and material selection of the manipulator to reduce errors caused by vibration and deformation; at the same time, implement a temperature compensation algorithm to reduce the impact of ambient temperature changes on the accuracy of the manipulator; in addition, through error compensation technology, establish an error model and correct the system error; finally, perform regular calibration maintenance, including recalibrating the sensors and manipulator joint positions, checking and replacing worn parts, and ensuring the normal working condition and accuracy of each component. This series of steps work together to ensure that the manipulator maintains high accuracy and reliability during long-term operation.

[0004] The prior art has the following deficiencies:

[0005] On high-speed assembly lines, the robot needs to use a closed-loop control system to adjust the motion trajectory and speed in real time according to the data fed back by the sensors to ensure precise positioning. However, when the robot moves at high speed, each joint and end effector needs to be updated and adjusted at a high frequency, and the amount of data fed back by the sensors in real time is large and the frequency is high. The computing power and response speed of existing control systems are limited, and it is difficult to complete the reception, processing and feedback of data in a short time, resulting in system response delays. This delay makes it impossible for the robot to adjust the motion trajectory quickly and accurately, causing the overall rhythm of the production line to be disrupted and reducing production efficiency. In addition, the hardware and software architecture of the existing technology shows obvious bottlenecks when dealing with such high-frequency and high-precision requirements, and cannot meet the extremely high requirements of high-speed assembly lines for real-time and precision, thereby further exacerbating the decline in production efficiency.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method for maintaining and calibrating the accuracy of a robot during operation, so as to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for maintaining and calibrating the accuracy of a manipulator during operation, specifically comprising the following steps:

[0009] When the robot installed on the high-speed assembly line is moving at high speed, it obtains the working status information of each joint and end effector through sensors;

[0010] Extract the urgent information of each data in the working status information of each joint and end effector;

[0011] Analyze the urgency information of each data, evaluate the urgency of each data, and classify each data into urgent information and non-urgent information according to the evaluation results;

[0012] The closed-loop control system prioritizes urgent information and builds a waiting-for-processing module, storing non-urgent information in the waiting-for-processing module;

[0013] Continuously monitor the changes in the closed-loop control system's processing volume of urgent information and the response status of the closed-loop control system, evaluate whether the closed-loop control system needs to process non-urgent information, and decide whether to process the non-urgent information based on the evaluation results.

[0014] Preferably, the urgency information of each data is analyzed, the urgency of each data is evaluated, and each data is divided into urgent information and non-urgent information according to the evaluation result, which specifically includes the following steps:

[0015] Preprocess the urgent information of each data in the working status information of each joint and end effector;

[0016] Extracting data value information and time information from the urgent information of each preprocessed data;

[0017] Comprehensively analyze the data value information and time information of each data, and generate the change rate coefficient and deviation degree index of each data respectively;

[0018] The change rate coefficient and deviation index of each data generated are used to construct an urgency assessment model to generate the urgency coefficient of each data;

[0019] The generated urgency coefficient of each data is compared with a preset urgency coefficient threshold, the urgency of each data is evaluated, and each data is divided into urgent information and non-urgent information according to the evaluation result.

[0020] Preferably, the acquisition logic of the change rate coefficient and the deviation index of each data is as follows:

[0021] Get the data values ​​and corresponding time points of each data obtained by the sensor at different times within a period of time, and use the function to X the data values ​​of each data obtained by the sensor at different times within a period of time according to the time series i (t) indicates that t indicates the corresponding time point, and the time period is defined as [t 1 , t 2 ], X i (t) represents the data value of the i-th data acquired by the sensor at time t within a period of time, i = 1, 2, 3, ..., g, g is a positive integer;

[0022] For the data value of the i-th data acquired by the sensor at time t within a period of time, calculate its data change rate relative to the previous time point According to the formula:

[0023]

[0024] Where Δt is the time interval;

[0025] Calculate the change rate coefficient of each data. The specific calculation formula is as follows:

[0026]

[0027] In the formula, RCC i is the change rate coefficient of the i-th data;

[0028] Obtain the preset data values ​​of each data obtained by the sensor when the manipulator is in high-speed motion. After obtaining, calibrate the preset data values ​​of each data obtained by the sensor when the manipulator is in high-speed motion as represents the preset data value of the i-th data acquired by the sensor when the manipulator is performing high-speed motion;

[0029] For the data value of the i-th data acquired by the sensor at time t within a period of time, calculate the deviation between it and the preset data value according to the formula:

[0030]

[0031] In the formula, is the deviation between the data value of the i-th data obtained by the sensor at time t within a period of time and the preset data value;

[0032] Calculate the deviation index of each data. The specific calculation formula is as follows:

[0033]

[0034] Where, DDI i is the deviation index of the i-th data.

[0035] Preferably, the rate of change coefficient RCC of each generated data i and Deviation Degree Index (DDI) i Construct an urgency assessment model and generate the urgency coefficient JP of each data through weighted summation i , JP i Represents the urgency coefficient of the i-th data.

[0036] Preferably, the urgency coefficient JP of each data generated i The preset urgency factor threshold JP yuzhi Compare and evaluate the urgency of each data, and divide each data into urgent information and non-urgent information according to the evaluation results. The specific division process is as follows:

[0037] If JP i ≤JP yuzhi , the urgency of the data is low, and the data is classified as non-urgent information;

[0038] If JP i >JP yuzhi , the urgency of the data is high, and the data is classified as urgent information.

[0039] Preferably, after evaluating the urgency of each data, the closed-loop control system processes and responds to the data evaluated as urgent information in real time to ensure the high precision and stability of the manipulator; at the same time, a module to be processed is constructed to store the data evaluated as non-urgent information in the module to be processed.

[0040] Preferably, continuously monitoring the changes in the amount of processing of urgent information by the closed-loop control system and the response state of the closed-loop control system, evaluating whether the closed-loop control system needs to process non-urgent information, and deciding whether to process the non-urgent information based on the evaluation result, specifically includes the following steps:

[0041] Collect two types of data, one of which is the change in the amount of urgent information processing, and the other is the response time of the closed-loop control system;

[0042] Conduct real-time data analysis on the first type of data to generate urgent information processing efficiency;

[0043] Perform real-time data analysis on the second type of data to generate a closed-loop control system response index;

[0044] Determine the threshold of urgent information processing efficiency and closed-loop control system response index;

[0045] The urgent information processing efficiency and the closed-loop control system response index are compared with the urgent information processing efficiency threshold and the closed-loop control system response index threshold respectively to determine whether the non-urgent information needs to be processed. The specific determination is as follows:

[0046] If the urgent information processing efficiency is greater than the urgent information processing efficiency threshold and the closed-loop control system response index is less than the closed-loop control system response index threshold, the non-urgent information is processed;

[0047] If the urgent information processing efficiency is greater than the urgent information processing efficiency threshold and the closed-loop control system response index is less than the closed-loop control system response index threshold, the non-urgent information is not processed.

[0048] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0049] 1. The present invention processes urgent information with priority through a closed-loop control system, and constructs a module to be processed, in which non-urgent information is stored, thereby solving the computational bottleneck of existing control systems when processing high-frequency and high-precision requirements. By real-time acquisition and analysis of the working status information of each joint and end effector, the system can quickly identify and process the urgent information that has the greatest impact on the operating accuracy of the manipulator, avoid motion trajectory deviations caused by response delays, and thus ensure the precise positioning and stable operation of the manipulator on the high-speed assembly line. This advantage significantly improves the real-time response capability of the system and reduces the confusion in production rhythm and the decline in efficiency caused by delays.

[0050] 2. By continuously monitoring the changes in the processing volume and response status of the closed-loop control system to urgent information, the system can dynamically evaluate whether it is necessary to process the non-urgent information stored in the module to be processed. When the efficiency of urgent information processing is high and the system response time is within a reasonable range, the system can automatically schedule resources to process non-urgent information to ensure that all data is processed in a timely manner. This method effectively optimizes the use and scheduling of resources, avoids resource waste and processing bottlenecks, and improves the overall efficiency and stability of the system. Through such resource management, the system can still maintain a good operating state under high load conditions.

[0051] 3. The present invention ensures the accuracy and reliability of data processing by preprocessing the urgency information of each data, extracting data value information and time information, generating a change rate coefficient and a deviation index, and calculating the urgency coefficient through an urgency assessment model. By comparing the generated urgency coefficient with the preset threshold, the system can accurately distinguish between urgent information and non-urgent information, and realize accurate monitoring and adjustment of the working status of the manipulator. This method not only improves the accuracy of data processing, but also enhances the system's ability to detect and respond to abnormal situations, ensuring the stability and reliability of the manipulator in high-speed motion, thereby meeting the extremely high requirements of high-speed assembly lines for real-time performance and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0053] Figure 1 The present invention is a flowchart of a method for maintaining and calibrating precision during operation of a manipulator. DETAILED DESCRIPTION

[0054] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0055] The present invention provides Figure 1 A method for maintaining and calibrating the working accuracy of a manipulator shown in the figure specifically comprises the following steps:

[0056] When the robot installed on the high-speed assembly line is moving at high speed, it obtains the working status information of each joint and end effector through sensors. This information includes but is not limited to position, speed, acceleration and force.

[0057] To judge whether the manipulator installed on the high-speed assembly line is performing high-speed movement, it can be achieved by monitoring its movement speed and acceleration in real time. Specifically, the system can set a speed threshold and an acceleration threshold. When the movement speed of the manipulator exceeds the preset speed threshold and the acceleration is also within a higher range, it can be judged that the manipulator is performing high-speed movement. This judgment can be achieved through the motion monitoring module in the manipulator control system, which collects and analyzes the movement data of the manipulator in real time to ensure the accuracy of judging the movement status of the manipulator.

[0058] The working status information of each joint and end effector of the manipulator is obtained through sensors, which mainly includes the following steps: First, high-precision position sensors and force sensors are installed on each joint and end effector. The position sensor is used to detect the displacement, angle and speed of the joint and actuator, while the force sensor is used to detect the force and torque applied to the manipulator during operation. The sensor transmits real-time data to the central control system through a high-speed data acquisition module. The data processing module in the control system filters and preprocesses the collected raw data to remove noise and errors to ensure the accuracy and real-time performance of the data. Ultimately, these processed data will be used to monitor and adjust the motion status of the manipulator in real time.

[0059] The working status information of each joint and end effector refers to the real-time physical and dynamic status of each joint and end effector during the operation of the manipulator. This status information includes but is not limited to: position (including displacement and angle), speed, acceleration, force conditions (including force and torque), temperature and current, etc. Specifically, the position information refers to the spatial coordinates and angles of the joints and end effectors, the speed information refers to the speed of their movement, the acceleration information refers to the speed of their speed changes, the force conditions include the external forces and torques on the joints and end effectors, the temperature information refers to the operating temperature of the sensors and actuators, and the current information refers to the current of the drive motor. The comprehensive analysis of this information can accurately reflect the real-time working status of the manipulator and help ensure its accuracy and stability in high-speed movement.

[0060] Extract the urgent information of each data in the working status information of each joint and end effector. The urgent information refers to the data that has a direct impact on the accuracy and stability of the manipulator;

[0061] Extracting urgent information from the working status information of each joint and end effector can be achieved through data processing algorithms and priority screening mechanisms. The specific steps are as follows: First, the working status information collected in real time is preprocessed by the data processing module, such as filtering and denoising, to ensure the accuracy of the data. Then, the system will analyze the preprocessed data and extract key indicators, such as displacement deviation, velocity change rate, acceleration mutation, abnormal values ​​of force and torque, etc. These key indicators reflect the working status of each part of the manipulator and possible problems. Then, based on the predetermined urgency evaluation criteria, the system prioritizes these key indicators and marks the indicators that have the greatest impact on the working accuracy and safety of the manipulator as urgent information. This process can be implemented through software, using machine learning algorithms or rule engines to automate data screening and marking to ensure that urgent information can be extracted quickly and accurately.

[0062] Urgent information refers to data in the working status data of the manipulator that has a direct and significant impact on its accuracy, stability and safety. Urgent information is characterized by large changes, fast changes or exceeding the set safety threshold. For example, the displacement deviation of a joint exceeds the allowable tolerance range, or the force condition of a certain end effector undergoes an abnormal mutation, which are all urgent information. The analysis of urgent information needs to consider its impact on the overall performance of the manipulator. Therefore, when processing data, the system will evaluate the urgency of the urgent information. The evaluation criteria include the change amplitude, change rate and the degree of impact on the current operation task. According to the evaluation results, the system divides each data into urgent information and non-urgent information, and the urgent information will be processed first to ensure the working accuracy and safety of the manipulator. Non-urgent information will be stored in the pending module and wait for processing when the system load is low. In this way, the system can efficiently manage and respond to various working status information to ensure the stable operation of the manipulator on the high-speed assembly line.

[0063] Analyze the urgency information of each data, evaluate the urgency of each data, and classify each data into urgent information and non-urgent information according to the evaluation results;

[0064] In this embodiment, the urgency information of each data is analyzed, the urgency of each data is evaluated, and each data is divided into urgent information and non-urgent information according to the evaluation result, which specifically includes the following steps:

[0065] Preprocess the urgent information of each data in the working status information of each joint and end effector;

[0066] Preprocessing the urgent information of each data in the working status information of each joint and end effector can be achieved through data cleaning and filtering algorithms. The specific steps include: first, using data cleaning methods to remove outliers and noise in the working status information, such as using median filtering or mean filtering to remove mutation points and random noise in sensor data; second, filtering the cleaned data, using Kalman filtering or low-pass filtering and other technologies to smooth the data curve and ensure the stability and continuity of the data. The purpose of preprocessing is to ensure the accuracy of subsequent urgent information extraction and analysis, reduce errors and noise interference in sensor data, and thus improve the accurate assessment of the working status of the manipulator. This process can be achieved through software, using existing data processing libraries and algorithms to perform preprocessing immediately after data acquisition to ensure real-time and high efficiency.

[0067] Extracting data value information and time information from the urgent information of each preprocessed data;

[0068] Extracting data value information and time information from the urgent information of each preprocessed data can be achieved through data parsing and marking methods. The specific steps include: first, identifying the numerical information and timestamp of each data point in the preprocessed data stream. Using the data parsing algorithm, the numerical information (such as displacement, speed, force, etc.) and the corresponding timestamp of each data point are extracted and marked. Secondly, using data structures such as time series or key-value pairs with timestamps, these data value information and time information are organized into an ordered list or array for subsequent processing. The purpose of extraction is to ensure that each data point has a clear time reference during the analysis process, so that the change rate coefficient and deviation index can be accurately calculated. This process can be implemented through software, using existing data parsing and time series processing libraries, and extracting and marking immediately after data preprocessing to ensure that the timing and values ​​of the data are accurate.

[0069] Comprehensively analyze the data value information and time information of each data, and generate the change rate coefficient and deviation degree index of each data respectively;

[0070] The change rate coefficient and deviation index of each data generated are used to construct an urgency assessment model to generate the urgency coefficient of each data;

[0071] The generated urgency coefficient of each data is compared with a preset urgency coefficient threshold, the urgency of each data is evaluated, and each data is divided into urgent information and non-urgent information according to the evaluation result.

[0072] The preset threshold of the urgency coefficient can be determined by data analysis and statistical learning methods. The specific steps include: first, collect a large number of historical data of the manipulator under normal working conditions and data samples under emergency conditions, and analyze the distribution of the urgency coefficient generated in these data samples. Through statistical methods, such as mean and standard deviation calculation, determine the average value and range of the urgency coefficient under normal working conditions. Secondly, apply machine learning techniques, such as cluster analysis or classification algorithms, to model historical data and identify the range of urgency coefficients corresponding to normal and abnormal working conditions. Finally, set a reasonable urgency coefficient threshold based on the analysis results. The threshold should be able to effectively distinguish between normal and abnormal situations, ensuring that the system can respond and process in a timely manner when urgent information is detected. The entire process can be implemented through software, using data analysis and machine learning libraries to automatically complete data collection, analysis, and threshold setting to ensure the scientificity and reliability of the threshold.

[0073] In this embodiment, the logic for obtaining the change rate coefficient and deviation index of each data is as follows:

[0074] Get the data values ​​and corresponding time points of each data obtained by the sensor at different times within a period of time, and use the function to X the data values ​​of each data obtained by the sensor at different times within a period of time according to the time series i (t) indicates that t indicates the corresponding time point, and the time period is defined as [t 1 , t 2 ], X i (t) represents the data value of the i-th data acquired by the sensor at time t within a period of time, i = 1, 2, 3, ..., g, g is a positive integer;

[0075] The data values ​​and corresponding time points of each data acquired by the sensor at different times within a period of time can be obtained through continuous data acquisition and timestamp recording. The specific acquisition method includes: installing high-precision sensors on each joint and end effector of the manipulator, and performing continuous data acquisition through a real-time data acquisition module. The data acquisition module records the numerical information of the sensor at a preset time interval (for example, every millisecond), and adds a corresponding timestamp to each data point. Timestamp recording can use the system clock or synchronized clock to ensure that the time information of each data point is accurate. These data are transmitted to the central control system through the data bus, and the data processing module in the central control system stores and manages the received raw data for subsequent analysis and processing. This process can be implemented through software, using the existing data acquisition library and time synchronization technology to automatically add time information while collecting data to ensure the timing and accuracy of the data.

[0076] For the data value of the i-th data acquired by the sensor at time t within a period of time, calculate its data change rate relative to the previous time point According to the formula:

[0077]

[0078] Where Δt is the time interval;

[0079] Calculate the change rate coefficient of each data. The specific calculation formula is as follows:

[0080]

[0081] In the formula, RCC i is the change rate coefficient of the i-th data;

[0082] This formula means that in the time period [t 1 , t 2 ], the change rate coefficient of the i-th data. The specific calculation steps include: first, continuously collect data X through the sensor i (t), calculate the rate of change of data at different times t That is, the speed at which the data changes at each time point. Then, the absolute values ​​of these change rates are integrated to obtain the total change over the entire time period and divided by the length of the time period t 2 -t 1 , find the average value of the rate of change. The rate of change coefficient RCC i It reflects the overall speed and magnitude of data changes within a given time period and is highly dynamic and sensitive.

[0083] The change rate coefficient RCC of the i-th data i The size directly reflects the speed of change of data in a unit of time. The larger the rate of change, the more drastic the data change. For evaluating the urgency of each data, a larger change rate coefficient usually means that the data has changed significantly in a short period of time, which may have a greater impact on the accuracy and stability of the robot. Therefore, in the urgency assessment, the change rate coefficient is a key indicator used to identify which data requires the most attention and processing in the current operation. By comparing the change rate coefficient with the preset urgency coefficient threshold through software, urgent information and non-urgent information can be effectively distinguished, ensuring that the system prioritizes the urgent data that has the greatest impact on the performance of the robot, and improving the response speed and stability of the overall system.

[0084] Obtain the preset data values ​​of each data obtained by the sensor when the manipulator is in high-speed motion. After obtaining, calibrate the preset data values ​​of each data obtained by the sensor when the manipulator is in high-speed motion as represents the preset data value of the i-th data acquired by the sensor when the manipulator is performing high-speed motion;

[0085] Obtaining the preset data values ​​of various data acquired by the sensors when the manipulator is in high-speed motion can be achieved through historical data analysis and model prediction methods. The specific acquisition method includes: first, collecting the sensor data of the manipulator in a normal high-speed motion state to establish a historical data set. Then, using data analysis technology, extract the average value and standard deviation of each data from the historical data set as the basis for the preset data value. Furthermore, a machine learning algorithm (such as regression analysis or neural network) can be used to train a prediction model to predict the preset data values ​​of the manipulator in high-speed motion in real time based on the current working conditions and environmental parameters. These preset data values ​​will be stored in the control system and compared with the real-time collected data during operation. The entire process can be implemented through software, using data analysis and machine learning libraries to automatically process and predict preset data values ​​to ensure the accuracy and stability of the manipulator in high-speed motion.

[0086] For the data value of the i-th data acquired by the sensor at time t within a period of time, calculate the deviation between it and the preset data value according to the formula:

[0087]

[0088] In the formula, is the deviation between the data value of the i-th data obtained by the sensor at time t within a period of time and the preset data value;

[0089] Calculate the deviation index of each data. The specific calculation formula is as follows:

[0090]

[0091] Where, DDI i is the deviation index of the i-th data;

[0092] This formula means that in the time period [t 1 , t 2 ], the deviation index of the i-th data. The specific calculation steps include: first, continuously collect data X through the sensor i (t), and obtain the preset data value in the corresponding time period Then, calculate the data X at each time t i (t) and the preset data value The deviation between Next, the absolute values ​​of these deviations are calculated over the time period [t 1 , t 2 ] to obtain the total deviation and divide it by the length of the time period t 2 -t 1 , and the average deviation is obtained, that is, the deviation degree index DDI i The deviation index reflects the overall degree and stability of the data's deviation from the preset value within a given time period.

[0093] The deviation index DDI of the i-th data i The size of directly reflects the degree of deviation of the data from the preset value. The greater the deviation, the greater the deviation of the data from the normal working state. For evaluating the urgency of each data, a larger deviation index usually means that the data has a significant abnormality in the current operation, which may have a significant impact on the accuracy and stability of the robot. Therefore, in the urgency assessment, the deviation index is a key indicator used to identify which data has obvious deviations and requires priority attention and processing. By comparing the deviation index with the preset urgency coefficient threshold through software, urgent information and non-urgent information can be effectively distinguished, ensuring that the system can respond and process abnormal data that have a greater impact on the performance of the robot in a timely manner, thereby maintaining the efficient and stable operation of the robot on the high-speed assembly line.

[0094] In this embodiment, the change rate coefficient RCC of each data generated is i and Deviation Degree Index (DDI) i Construct an urgency assessment model and generate the urgency coefficient JP of each data through weighted summation i , JP i Represents the urgency coefficient of the i-th data.

[0095] The rate of change coefficient RCC of each generated data i and Deviation Degree Index (DDI) i Construct an urgency assessment model and generate the urgency coefficient JP of each data through weighted summation i The specific method is: First, calculate the RCC of each data point acquired by the sensor i , reflecting the speed of data changes, and DDI i , reflecting the degree of deviation of the data from the preset value; then, in order to balance the influence of the two parameters in the urgency assessment, weights α and β are assigned, where α represents RCC i The importance of β represents DDI i The importance of these weights can be set through historical data analysis or experience; finally, through the formula JP i =α*RCC i +β*DDI i Perform weighted summation to generate the urgency coefficient JP for each data i , which is used to comprehensively evaluate the urgency of data. This process can be automated in the software through data collection, preprocessing, weight setting and urgency coefficient calculation to ensure accurate and real-time urgency evaluation.

[0096] In this embodiment, the urgency coefficient JP of each data generated is i The preset urgency factor threshold JP yuzhi Compare and evaluate the urgency of each data, and divide each data into urgent information and non-urgent information according to the evaluation results. The specific division process is as follows:

[0097] If JP i ≤JP yuzhi , the urgency of the data is low, and the data is classified as non-urgent information;

[0098] When the calculated urgency factor JP i Less than or equal to the preset urgency coefficient threshold JP yuzhiWhen the data is , it means that the change rate and deviation of the data are within the acceptable range, its dynamic change and deviation from the preset value are small, the operation state of the robot is relatively stable, and it will not have a significant impact on the accuracy and safety of the overall system. In this case, the data is evaluated as low-urgency information and is classified as non-urgent information. This process can be achieved in the software through the threshold comparison module, and the system automatically compares the JP i With JP yuzhi A comparison is performed and the data that meets the conditions is marked as non-urgent information and stored in the pending module for processing when the system load is lower, ensuring that urgent information is processed first.

[0099] If JP i >JP yuzhi , the urgency of the data is high, and the data is classified as urgent information;

[0100] When the calculated urgency factor JP i Greater than the preset urgency coefficient threshold JP yuzhi When the data is , it means that the rate of change and the degree of deviation of the data are beyond the acceptable range, indicating that the dynamic change of the robot at this data point is drastic or the deviation is significant, which may have a great impact on the accuracy and safety of the system. In this case, the data is evaluated as high-urgency information and is classified as urgent information. The software system automatically compares the JP i With JP yuzhi The system compares the data and marks the data that meets the conditions as urgent information, which is immediately passed to the closed-loop control system for priority processing. In this way, the system can respond and adjust the operation status of the robot in a timely manner to ensure high precision and stability.

[0101] The closed-loop control system prioritizes urgent information and builds a waiting-for-processing module, storing non-urgent information in the waiting-for-processing module;

[0102] In this embodiment, after evaluating the urgency of each data, the closed-loop control system processes and responds to the data evaluated as urgent information in real time to ensure the high precision and stability of the manipulator; at the same time, a to-be-processed module is constructed to store the data evaluated as non-urgent information in the to-be-processed module, and the non-urgent information is processed when the system load is low or the urgent information is processed, thereby realizing the classified processing of urgent information and non-urgent information.

[0103] In this method, the closed-loop control system prioritizes urgent information and constructs a module to be processed, storing non-urgent information in the module to be processed. Specifically, after evaluating the urgency of each data, the system processes and responds to the data evaluated as urgent information in real time to ensure the high precision and stability of the manipulator; at the same time, the data evaluated as non-urgent information is stored in the module to be processed, and the non-urgent information is processed when the system load is low or the urgent information is processed, thereby achieving classified processing and efficient management of urgent information and non-urgent information.

[0104] The specific steps for the closed-loop control system to process and respond to data evaluated as urgent information in real time include: First, when the system detects that the urgency coefficient of a certain data exceeds the preset threshold, the data is marked as urgent information. The system immediately triggers the control algorithm and dynamically adjusts the robot's motion trajectory, speed, torque and other parameters according to the urgent information collected in real time to correct its deviations and anomalies. The closed-loop control system uses a fast feedback loop to ensure that urgent information is responded to and processed in the shortest time through real-time monitoring and calculation to avoid major impacts on the production process. The entire process is implemented through software, and the optimization of the control algorithm and feedback mechanism ensures the efficiency and accuracy of the system.

[0105] The specific method of constructing a to-be-processed module and storing data evaluated as non-urgent information in the to-be-processed module includes: first, after evaluating the urgency of the data, the system marks the data that does not exceed the urgency coefficient threshold as non-urgent information. Next, a to-be-processed module is created in the software system, which consists of a data storage queue or buffer for temporarily storing non-urgent information. The data storage queue can use a first-in-first-out (FIFO) queue structure to ensure that the data is processed in sequence in the order in which it is received. The system automatically stores the data marked as non-urgent information in the to-be-processed module through data management software, and attaches a timestamp or priority tag for subsequent processing. When the system load is low or the urgent information is processed, the scheduler extracts the non-urgent information from the to-be-processed module in sequence for processing, ensuring that the system can gradually process all the data without affecting the overall performance. This process is achieved through the task scheduling and queue management functions of the software, ensuring the orderliness and efficiency of data storage and processing, and solving the challenge of the robot arm needing to process a large amount of data at the same time during high-speed operation.

[0106] Continuously monitor the changes in the closed-loop control system's processing volume of urgent information and the response status of the closed-loop control system, evaluate whether the closed-loop control system needs to process non-urgent information, and decide whether to process the non-urgent information based on the evaluation results.

[0107] In this embodiment, the processing amount change of the closed-loop control system for urgent information and the response state of the closed-loop control system are continuously monitored, whether the closed-loop control system needs to process the non-urgent information is evaluated, and whether to process the non-urgent information is determined according to the evaluation result, specifically including the following steps:

[0108] Collect two types of data, one of which is the change in the amount of urgent information processing, and the other is the response time of the closed-loop control system;

[0109] Collecting two types of data, one of which is the change in the amount of urgent information processed and the other is the response time of the closed-loop control system, can be achieved in the following ways: First, using the system log and data monitoring module, the amount of urgent information processed is recorded and tracked in real time, including the number of urgent information processed in each time period and the time it takes to complete the processing. These data are automatically collected through sensors and logging functions and stored in the database. Secondly, the response time of the closed-loop control system is measured and recorded through the system performance monitoring tool, including the time interval from the receipt of the urgent information to the completion of the processing. This can be done by setting a timestamp in the system to mark the time when each urgent information is received and processed, and then calculating the time difference. All collected data will be transmitted to the central data processing module in real time for aggregation and analysis to generate the urgent information processing efficiency and system response index. This process is achieved through the software's automated data collection and monitoring functions to ensure the accuracy and real-time nature of the data.

[0110] Conduct real-time data analysis on the first type of data to generate urgent information processing efficiency;

[0111] The specific method of performing real-time data analysis on the first type of data and generating the urgent information processing efficiency includes: first, extracting the number of urgent information processed in each time period and the corresponding processing time from the collected urgent information processing volume change data. Then, the processing speed in each time period, that is, the number of urgent information processed per unit time, is calculated, and the formula is: urgent information processing efficiency = number of urgent information processed / processing time. Then, through the data analysis module in the software, these processing speed data are analyzed in real time to generate a time series of urgent information processing efficiency. In order to improve accuracy, the moving average or weighted average method can be used to smooth the processing efficiency data to eliminate the impact of short-term fluctuations. Finally, the system will update the urgent information processing efficiency in real time and store it in the database for comparison with the preset efficiency threshold to ensure that the system can promptly identify and respond to changes in processing efficiency. This process is achieved through the automated data processing and analysis functions of the software to ensure the real-time and accuracy of the calculation and monitoring of processing efficiency.

[0112] Perform real-time data analysis on the second type of data to generate a closed-loop control system response index;

[0113] The specific method of performing real-time data analysis on the second type of data and generating a closed-loop control system response index includes: first, extracting the response time for each processing of urgent information from the collected closed-loop control system response time data, that is, the time interval from receiving the urgent information to completing the processing. Then, the average response time in each time period is calculated to measure the overall response speed of the system. The specific steps are: statistically analyzing the response time data in each time period, calculating the average value and standard deviation, and the formula is: average response time = total response time / number of responses. Then, through the data analysis module in the software, these average response time data are analyzed in real time to generate a closed-loop control system response index. In order to enhance the stability of the analysis, moving average or exponential smoothing methods can be used to process the data to reduce the impact of short-term fluctuations on the index. Finally, the system will update the response index in real time and store it in the database for comparison with the preset response index threshold to ensure that the system can promptly identify and respond to changes in response time. This process is achieved through the automated data processing and analysis functions of the software to ensure the real-time and accuracy of the calculation and monitoring of the response index.

[0114] Determine the threshold of urgent information processing efficiency and closed-loop control system response index;

[0115] Determining the threshold of urgent information processing efficiency and the threshold of closed-loop control system response index can be achieved through historical data analysis and statistical methods. The specific steps include: first, collecting and organizing the historical data of the manipulator under normal operating conditions, including records of urgent information processing efficiency and closed-loop control system response time. Through data analysis software, these historical data are statistically analyzed to calculate the average and standard deviation of urgent information processing efficiency and response time. Then, based on these statistical results, a reasonable threshold range is determined, such as setting the threshold to the average plus or minus two times the standard deviation to cover the fluctuation range under most normal operating conditions. Then, these thresholds are further optimized through machine learning algorithms. A supervised learning model can be used to train the model using historical data marked as normal and abnormal, and the thresholds are automatically adjusted and optimized to improve the accuracy and applicability of the thresholds. Finally, the system stores the determined thresholds in a database and uses these thresholds for comparison and judgment during real-time monitoring and evaluation. Through the automated data analysis and machine learning functions of the software, the threshold of urgent information processing efficiency and the threshold of closed-loop control system response index can be accurately determined to ensure that the system can identify and handle abnormal situations in a timely manner while operating efficiently.

[0116] The urgent information processing efficiency and the closed-loop control system response index are compared with the urgent information processing efficiency threshold and the closed-loop control system response index threshold respectively to determine whether the non-urgent information needs to be processed. The specific determination is as follows:

[0117] If the urgent information processing efficiency is greater than the urgent information processing efficiency threshold and the closed-loop control system response index is less than the closed-loop control system response index threshold, the non-urgent information is processed;

[0118] When the urgent information processing efficiency is greater than the preset urgent information processing efficiency threshold and the closed-loop control system response index is less than the preset response index threshold, it means that the system performs well in processing urgent information, and can efficiently process a large amount of urgent information and complete the processing task within a reasonable time. This means that the system load is low and the processing resources are sufficient, which can free up time and capacity to process non-urgent information. At this time, the software system will extract the stored non-urgent information from the module to be processed and process them one by one in sequence. This processing includes analysis, calculation and necessary operation adjustments of non-urgent information to ensure that non-urgent information is processed in a timely manner without affecting the overall performance of the system. In this way, the system can gradually digest the backlog of non-urgent information while efficiently processing urgent information, ensuring that all data receives proper attention and processing.

[0119] If the urgent information processing efficiency is greater than the urgent information processing efficiency threshold and the closed-loop control system response index is less than the closed-loop control system response index threshold, the non-urgent information is not processed.

[0120] When the efficiency of urgent information processing fails to exceed the preset threshold of urgent information processing efficiency or the closed-loop control system response index exceeds the preset threshold of response index, it means that there is a bottleneck in the system when processing urgent information, which may be caused by excessive load or long response time, resulting in reduced processing efficiency. This means that the system resources are tight and cannot efficiently process urgent information and non-urgent information at the same time. In this case, the software system will give priority to the processing of urgent information, and continue to store non-urgent information in the pending module without processing it for the time being. Non-urgent information in the pending module will be queued in order and wait for the system load to be reduced or the processing efficiency to be improved before processing. The purpose of this is to ensure that the system can focus on processing urgent information that has the greatest impact on accuracy and stability, avoid overall performance degradation due to resource dispersion, and thus ensure the efficient operation of the robot on the high-speed assembly line.

[0121] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0122] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0123] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0124] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0128] 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.

Claims

1. A method for maintaining and calibrating the accuracy of a robot during operation, characterized in that: The specific steps include: When the robot installed on the high-speed assembly line is moving at high speed, it obtains the working status information of each joint and end effector through sensors; Extract various data from the working status information of each joint and end effector; Analyze each piece of data, evaluate the urgency of each piece of data, and classify each piece of data into urgent information and non-urgent information according to the evaluation results; Analyze each piece of data, evaluate the urgency of each piece of data, and classify each piece of data into urgent information and non-urgent information according to the evaluation results, specifically including the following steps: Preprocessing each data in the working status information of each joint and end effector; Extracting data value information and time information from each preprocessed data; Comprehensively analyze the data value information and time information of each data, and generate the change rate coefficient and deviation degree index of each data respectively; The change rate coefficient and deviation index of each generated data are used to construct an urgency assessment model, and the urgency coefficient of each data is generated by weighted summation; Compare the generated urgency coefficient of each data with the preset urgency coefficient threshold, evaluate the urgency of each data, and classify each data into urgent information and non-urgent information according to the evaluation result; The closed-loop control system prioritizes urgent information and builds a waiting-for-processing module, storing non-urgent information in the waiting-for-processing module; Continuously monitor the changes in the closed-loop control system's processing volume of urgent information and the response status of the closed-loop control system, evaluate whether the closed-loop control system needs to process non-urgent information, and decide whether to process the non-urgent information based on the evaluation results.

2. A method for maintaining and calibrating the accuracy of a robot during operation according to claim 1, characterized in that: The logic for obtaining the change rate coefficient and deviation index of each data is as follows: Get the data values ​​and corresponding time points of each data obtained by the sensor at different times within a period of time, and use functions to convert the data values ​​of each data obtained by the sensor at different times within a period of time into time series. express, Indicates the corresponding time point, and defines the time period as , Indicates that the sensor is within a period of time The moment obtained The data value of the data, , is a positive integer; For the sensor over a period of time The moment obtained The data value of a data point is calculated to calculate its data change rate relative to the previous time point , according to the formula: , where is the time interval; Calculate the change rate coefficient of each data. The specific calculation formula is as follows: , where For the The change rate coefficient of each data; Obtain the preset data values ​​of each data obtained by the sensor when the manipulator is in high-speed motion. After obtaining, calibrate the preset data values ​​of each data obtained by the sensor when the manipulator is in high-speed motion as , Indicates the first The preset data value of each data; For the sensor over a period of time The moment obtained The data value of each data is calculated, and the deviation between it and the preset data value is calculated according to the formula: , where For the sensor over a period of time The moment obtained The deviation between the data value of a data and the preset data value; Calculate the deviation index of each data. The specific calculation formula is as follows: , In the formula, For the The deviation index of the data.

3. A method for maintaining and calibrating the accuracy of a robot during operation according to claim 2, characterized in that: The urgency coefficient of each data generated With the pre-set urgency factor threshold Compare and evaluate the urgency of each data, and divide each data into urgent information and non-urgent information according to the evaluation results. Indicates The urgency coefficient of each data is divided into the following steps: like , the urgency of the data is low, and the data is classified as non-urgent information; like , the urgency of the data is high, and the data is classified as urgent information.

4. A method for maintaining and calibrating the accuracy of a robot during operation according to claim 3, characterized in that: After evaluating the urgency of each data, the closed-loop control system processes and responds to the data evaluated as urgent information in real time to ensure the high precision and stability of the robot; at the same time, a waiting-to-be-processed module is constructed to store the data evaluated as non-urgent information in the waiting-to-be-processed module.

5. A method for maintaining and calibrating the accuracy of a robot during operation according to claim 4, characterized in that: Continuously monitor the changes in the processing volume of urgent information by the closed-loop control system and the response status of the closed-loop control system, evaluate whether the closed-loop control system needs to process non-urgent information, and decide whether to process the non-urgent information based on the evaluation results, specifically including the following steps: Collect two types of data, one of which is the change in the amount of urgent information processing, and the other is the response time of the closed-loop control system; Conduct real-time data analysis on the first type of data to generate urgent information processing efficiency; Perform real-time data analysis on the second type of data to generate a closed-loop control system response index; Determine the threshold of urgent information processing efficiency and closed-loop control system response index; The urgent information processing efficiency and the closed-loop control system response index are compared with the urgent information processing efficiency threshold and the closed-loop control system response index threshold respectively to determine whether the non-urgent information needs to be processed. The specific determination is as follows: If the urgent information processing efficiency is greater than the urgent information processing efficiency threshold and the closed-loop control system response index is less than the closed-loop control system response index threshold, the non-urgent information is processed; If the urgent information processing efficiency is greater than the urgent information processing efficiency threshold and the closed-loop control system response index is less than the closed-loop control system response index threshold, the non-urgent information is not processed.

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