Robot data processing method and system based on industrial Internet of Things

Abstract

The invention relates to the technical field of industrial automation, in particular to a robot data processing method and system based on the industrial Internet of Things, and the method comprises the following steps: collecting the angle, speed and acceleration data of a plurality of joints of a robot in a running state in real time based on the running data of the robot; and by analyzing the difference between the actual motion trail and the ideal motion trail of the robot, the control parameters of the robot are adjusted in real time, and a control parameter adjustment record is generated. According to the invention, through real-time acquisition and processing of operation data of the robot, real-time adjustment of control parameters and calibration of a sensor are realized, the response speed and adaptability of the robot are improved, the adaptability of the robot to a dynamic production environment is optimized, overload and task delay conditions are effectively predicted through analysis of environment variables and workloads, and the reliability of the robot is improved. The task priority is effectively adjusted, the continuity and efficiency of a production line are guaranteed, and the safety of the production process is guaranteed by combining real-time detection and abnormal behavior recognition.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation technology, and in particular to a robot data processing method and system based on industrial Internet of Things. Background Art

[0002] The field of industrial automation technology involves the automated control of various operations and tasks in the industrial production process through mechanization, electrification, informatization and intelligent means. By introducing a variety of automation equipment, control systems, and robotics technologies, it improves production efficiency, reduces human intervention, improves product quality, reduces costs, ensures the accuracy and safety of the production process, and realizes the design of automated production lines, robot control, application of sensors and actuators, and integration of the Industrial Internet of Things. Combined with artificial intelligence, machine learning, and edge computing, it achieves highly integrated and intelligent industrial production.

[0003] Among them, the robot data processing method aims to achieve real-time monitoring, data collection and intelligent processing of the robot's working status through a variety of technologies. By integrating sensors and communication modules in the robot system, using the industrial Internet of Things network to collect and transmit data in real time, and combining the central processing system to analyze, process and feedback the data, the intelligence level of the robot system can be improved, accurate and efficient decisions can be made in a dynamically changing production environment, production efficiency can be optimized, the flexibility and responsiveness of the automated production line can be improved, and an efficient, safe and intelligent manufacturing process can be achieved.

[0004] Traditional robot data processing methods are not capable of processing real-time data and quickly adapting to changes in the production environment. They cannot respond quickly to emergencies. Failure to adjust robot operating parameters or sensor calibration in a timely manner can lead to decreased production efficiency, product quality problems, and safety accidents. They rely on outdated data to predict robot overloads and task delays and lack an effective prediction mechanism, making production line adjustments inflexible and unable to maximize resource utilization efficiency, affecting the overall performance and competitiveness of automated production lines in high-speed and dynamic production environments. Summary of the invention

[0005] In order to solve the technical problem of poor production efficiency in the prior art, an embodiment of the present invention provides a robot data processing method and system based on the industrial Internet of Things. The technical solution is as follows:

[0006] On the one hand, a robot data processing method based on industrial Internet of Things is provided, the method comprising:

[0007] S1: Based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the robot in operation are collected in real time. By analyzing the difference between the actual motion trajectory and the ideal motion trajectory of the robot, the control parameters of the robot are adjusted in real time, and a control parameter adjustment record is generated;

[0008] S2: Based on the control parameter adjustment record, the sensor is calibrated by analyzing the influence of environmental conditions and load conditions on the readings of various sensors, and various operating data are collected to generate sensor calibration results;

[0009] S3: Based on the sensor calibration result, by real-time monitoring of the robot's working status and production line material information, analyzing the currently executed tasks and the robot's task list, evaluating the robot's operating environment and workload, predicting robot overload and task delay, and generating working condition prediction information;

[0010] S4: According to the working condition prediction information, the task load of the robot is evaluated in real time, the priorities of multiple execution tasks are adjusted, and a task list adjustment record is generated;

[0011] S5: Use the task list to adjust the records, continuously monitor the robot behavior in real time, detect abnormal behavior and perform real-time analysis, identify and record the cause of the abnormality, and generate abnormal behavior analysis results.

[0012] As a further solution of the present invention, the control parameter adjustment record specifically includes joint angle correction value, speed correction parameter, and acceleration compensation index; the sensor calibration result includes temperature adjustment factor, voltage stability coefficient, and current calibration index; the working condition prediction information specifically refers to robot load level, task delay risk, and overload warning index; the task list adjustment record includes task priority index, task execution path planning information, and resource allocation ratio information; the abnormal behavior analysis result specifically includes abnormal type identification information, abnormal cause diagnosis result, and abnormal event response record.

[0013] As a further solution of the present invention, based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the robot in operation are collected in real time, and the control parameters of the robot are adjusted in real time by analyzing the difference between the actual motion trajectory and the ideal motion trajectory of the robot. The steps of generating the control parameter adjustment record are specifically as follows:

[0014] S101: Based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the running robot are collected in real time to generate a robot motion data set;

[0015] S102: Analyzing the deviation between the actual motion trajectory of the robot and the preset ideal trajectory based on the robot motion data set, and generating motion deviation detection data;

[0016] S103: Based on the motion deviation detection data, adjusting various motion control parameters, including calculating angular velocity and acceleration control parameters of joints, and generating control parameter adjustment records.

[0017] As a further solution of the present invention, the specific formula for analyzing the deviation between the actual motion trajectory of the robot and the preset ideal trajectory is:

[0018]

[0019] Among them, P i represents the ideal position data of the i-th joint, A i represents the actual position data of the i-th joint, W i represents the weight coefficient of the i-th joint, i represents the index number of the joint, and D represents the total deviation.

[0020] As a further solution of the present invention, based on the control parameter adjustment record, by analyzing the influence of environmental conditions and load conditions on the readings of various sensors, calibrating the sensors, and collecting various operating data, the steps of generating sensor calibration results are specifically as follows:

[0021] S201: Based on the control parameter adjustment record, analyzing the influence of environmental conditions and machine load on sensor readings, identifying multiple influencing factors and sensor types that need to be calibrated, including temperature fluctuations and electromagnetic interference, and generating sensor influencing factor analysis data;

[0022] S202: Analyzing the sensor influencing factor data, calculating compensation parameters, calibrating multiple sensors, and generating calibration parameter calculation results;

[0023] S203: Based on the calibration parameter calculation result, collect various operating data, identify and eliminate background noise caused by environmental and load changes, and generate a sensor calibration result.

[0024] As a further solution of the present invention, based on the sensor calibration result, by real-time monitoring of the robot's working status and production line material information, analyzing the currently executed tasks and the robot's task list, evaluating the robot's operating environment and workload, predicting robot overload and task delay, the steps of generating working condition prediction information are specifically as follows:

[0025] S301: Based on the sensor calibration result, monitor and record various operating status data of the robot in real time, including speed, load, and power consumption, and generate robot operating data;

[0026] S302: Based on the robot operation data, combined with the type, size and weight information of the production line materials, analyze the task list, evaluate the operation environment and workload, and generate task execution analysis data;

[0027] S303: Based on the task execution analysis data, predict the risk of robot overload and task delay, and generate working condition prediction information.

[0028] As a further solution of the present invention, the steps of evaluating the robot's task load in real time according to the working condition prediction information, adjusting the priorities of multiple execution tasks, and generating a task list adjustment record are specifically as follows:

[0029] S401: Based on the working condition prediction information, evaluate the current task load of the robot in real time, record the urgency and resource requirements of multiple tasks, and generate task load evaluation data;

[0030] S402: Based on the task load evaluation data and in combination with the real-time status information of the robot, calculating and adjusting the execution priorities of the multiple tasks, and generating a priority adjustment list;

[0031] S403: Based on the priority adjustment list, adjust the robot's work queue, analyze the efficiency of task execution and adjustment effect after adjustment, and generate a task list adjustment record.

[0032] As a further solution of the present invention, the specific formula for calculating and adjusting the execution priorities of multiple tasks is:

[0033]

[0034] Among them, P i represents the priority of the i-th task, i represents the task number, L i represents the load evaluation value of the i-th task, S i represents the urgency score of the i-th task, T i represents the waiting time of task i so far, w 1 is the weight parameter of the task load, w 2 is the weight parameter of task urgency, w 3 is the weight parameter of the waiting time.

[0035] As a further solution of the present invention, the steps of using the task list adjustment record to continuously monitor the robot behavior in real time, detect abnormal behavior and perform real-time analysis, identify and record the abnormal cause, and generate abnormal behavior analysis results are specifically as follows:

[0036] S501: Based on the task list adjustment record, the movement and operation behavior of the robot are monitored in real time, the movement data and operation log are recorded, and a real-time monitoring log is generated;

[0037] S502: Analyze the robot's motion data based on the real-time monitoring log, detect and identify abnormal behaviors, and generate behavior data detection records;

[0038] S503: Based on the behavior data detection record, analyze and identify the causes of abnormal behavior, including robot overload, failure, external interference, record abnormal events, and generate abnormal behavior analysis results.

[0039] On the other hand, a robot data processing system based on industrial Internet of Things is provided, which is applied to a robot data processing method based on industrial Internet of Things, and the system includes:

[0040] The motion data acquisition module collects the angle, velocity and acceleration data of multiple joints of the robot in real time based on the robot operation data, analyzes the motion trajectory and identifies the difference from the ideal motion trajectory, and generates joint motion difference data;

[0041] The motion trajectory adjustment module adjusts the control parameters of the robot based on the joint motion difference data, including the joint angle correction value, the speed adjustment parameter and the acceleration compensation value, and generates a control parameter adjustment record;

[0042] The sensor calibration module analyzes the effects of environmental conditions and load conditions on sensor readings based on the control parameter adjustment records, calculates calibration parameters and collects various operating data, including temperature, voltage, current, and pressure, to generate sensor calibration results;

[0043] The real-time working condition analysis module analyzes the task list, evaluates the operating environment and workload, predicts overload and task delay, and generates working condition prediction information based on the sensor calibration results, combined with the robot's real-time working status data and the type, size, and weight information of the production line materials;

[0044] The priority adjustment module evaluates the task load and adjusts the priorities of multiple tasks based on the working condition prediction information, and generates a task list adjustment record;

[0045] The real-time behavior monitoring module adjusts the records based on the task list, monitors the robot behavior in real time, detects abnormal behavior and analyzes the cause of the abnormality, and generates abnormal behavior analysis results.

[0046] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0047] By collecting and processing the robot's operating data in real time, the control parameters can be adjusted instantly and the sensors can be calibrated. This improves the robot's response speed and adaptability, and optimizes the robot's adaptability to dynamic production environments. By analyzing environmental variables and workloads, overloads and task delays can be effectively predicted, and task priorities can be effectively adjusted to ensure the continuity and efficiency of the production line. In combination with real-time detection and identification of abnormal behavior, the safety of the production process can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0059] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] The embodiment of the present invention provides a robot data processing method based on industrial Internet of Things, such as Figure 1 The flowchart of the robot data processing method based on the industrial Internet of Things is shown, and the processing flow of the method may include the following steps:

[0062] S1: Based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the robot in operation are collected in real time. By analyzing the difference between the actual motion trajectory and the ideal motion trajectory of the robot, the control parameters of the robot are adjusted in real time, and a control parameter adjustment record is generated;

[0063] S2: Based on the control parameter adjustment record, the sensor is calibrated by analyzing the impact of environmental conditions and load conditions on the readings of various sensors, and various operating data are collected to generate sensor calibration results;

[0064] S3: Based on the sensor calibration results, by real-time monitoring of the robot's working status and production line material information, analyzing the currently executed tasks and the robot's task list, evaluating the robot's operating environment and workload, predicting robot overload and task delays, and generating working condition prediction information;

[0065] S4: Based on the working condition prediction information, the robot's task load is evaluated in real time, the priorities of multiple execution tasks are adjusted, and a task list adjustment record is generated;

[0066] S5: Use the task list to adjust the records, continuously monitor the robot behavior in real time, detect abnormal behavior and perform real-time analysis, identify and record the causes of abnormalities, and generate abnormal behavior analysis results.

[0067] The control parameter adjustment records specifically include the joint angle correction value, speed correction parameter, and acceleration compensation index. The sensor calibration results include the temperature adjustment factor, voltage stability coefficient, and current calibration index. The working condition prediction information specifically refers to the robot load level, task delay risk, and overload warning index. The task list adjustment records include the task priority index, task execution path planning information, and resource allocation ratio information. The abnormal behavior analysis results specifically include abnormal type identification information, abnormal cause diagnosis results, and abnormal event response records.

[0068] See also Figure 2 Based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the robot in operation are collected in real time. By analyzing the difference between the actual motion trajectory and the ideal motion trajectory of the robot, the control parameters of the robot are adjusted in real time. The specific steps for generating the control parameter adjustment record are as follows:

[0069] S101: Based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the running robot are collected in real time to generate a robot motion data set;

[0070] In the data collection sub-step, based on the real-time robot motion data, sensors are deployed at multiple joints and moving parts of the robot to monitor and record the angle, speed and acceleration data of the robot joints in real time. The data is sent to the central processing unit through wireless transmission technology. The data undergoes preliminary formatting, including noise removal, data normalization and timestamp marking. Target processing is performed through a real-time data stream processing platform such as Apache Kafka to ensure the real-time and accuracy of the data. The central processing unit uses a data buffer to temporarily store the target real-time data to provide support for subsequent data analysis and parameter adjustment. The generated result is a detailed robot motion data set.

[0071] S102: Analyze the deviation between the actual motion trajectory of the robot and the preset ideal trajectory based on the robot motion data set, and generate motion deviation detection data;

[0072] The specific formula for analyzing the deviation between the actual motion trajectory of the robot and the preset ideal trajectory is:

[0073]

[0074] Among them, P i represents the ideal position data of the i-th joint, A i represents the actual position data of the i-th joint, W i represents the weight coefficient of the i-th joint, i represents the index number of the joint, and D represents the total deviation.

[0075] formula:

[0076]

[0077] Detailed explanation of the formula and the process of formula calculation and derivation:

[0078] The formula is used to calculate the robot motion deviation, and the results are used to evaluate the need for adjustment of the robot motion control system;

[0079] Parameter meaning and setting value:

[0080] P i is the preset position data of the i-th joint. Assume that the preset position of the target joint is P 1 =45 ° ;

[0081] A i is the actual position data of the i-th joint. Assume that the actual position of the target joint obtained by real-time monitoring is A 1 =43 ° ;

[0082] W i is the weight coefficient of the i-th joint, reflecting the influence of different joints on the overall robot performance. Assume that the weight of the target joint is W 1 =1.5;

[0083] Substitute the parameters into the formula for calculation:

[0084]

[0085] The result D=2.45 shows that the current robot motion control system has a certain deviation in the control accuracy of the target joint. The result is used to evaluate the accuracy of the robot's motion control system and adjust the control parameters to optimize the motion accuracy and reduce the deviation.

[0086] S103: adjusting various motion control parameters based on the motion deviation detection data, including calculating the angular velocity and acceleration control parameters of the joints, and generating a control parameter adjustment record;

[0087] In the control parameter adjustment sub-step, based on the aforementioned motion deviation detection data, a sensitivity analysis is performed on the key motion control parameters to identify which parameters have the greatest impact on the deviation. Linear regression analysis is used to determine the adjustment range of the target key parameters, and a fuzzy logic controller is used to adjust the target parameters. The fuzzy logic controller evaluates the deviation between the current value and the target value of each parameter based on the set rule base, and calculates the adjustment value, which improves the flexibility and adaptability of the parameter adjustment, allowing the system to accurately control the movement of the robot under various working conditions. The generated control parameter adjustment record records the parameter adjustment process and results of each joint and component, ensuring the high consistency and accuracy of the robot's movement.

[0088] See also Figure 3 Based on the control parameter adjustment record, the sensor is calibrated by analyzing the impact of environmental conditions and load conditions on the readings of various sensors, and various operating data are collected. The specific steps for generating the sensor calibration results are as follows:

[0089] S201: Based on the control parameter adjustment record, analyze the impact of environmental conditions and machine load on sensor readings, identify multiple influencing factors and sensor types that need to be calibrated, including temperature fluctuations and electromagnetic interference, and generate sensor influencing factor analysis data;

[0090] In the environmental impact analysis sub-step, based on the control parameter adjustment records, the impact of external environmental factors such as temperature fluctuations and electromagnetic interference on the robot sensors was evaluated. Statistical regression analysis was used to determine the relationship between key environmental variables and sensor errors. The analysis involved collecting a large amount of environmental and sensor data. After eliminating outliers through data cleaning, the Pearson correlation coefficient analysis method was used to identify the most influential environmental factors. The decision tree algorithm was used to classify the degree of influence of various influencing factors on different types of sensors and the calibration requirements, ensuring the pertinence and effectiveness of the calibration measures. The generated sensor influencing factor analysis data recorded in detail how and to what extent each factor affects the sensor readings, providing a scientific basis for subsequent sensor calibration.

[0091] S202: Analyzing data based on sensor influencing factors, calculating compensation parameters, calibrating multiple sensors, and generating calibration parameter calculation results;

[0092] In the above content, based on the analysis data of sensor influencing factors, a multivariate linear regression model is used to calculate the compensation parameters according to the influence of temperature fluctuation and electromagnetic interference on sensor readings. According to the formula S adjusted =S raw +β 1 ·T+β 2 E, calculates the compensated sensor readings and generates calibration parameter calculations;

[0093] In the formula, S adjusted Represents the adjusted sensor reading, S raw represents the original sensor reading, T represents the effect of temperature fluctuation on the sensor reading, E represents the effect of electromagnetic interference on the sensor reading, and β 1 and β 2 is the compensation coefficient for temperature and electromagnetic interference,

[0094] Detailed explanation of the formula and the process of formula calculation and derivation:

[0095] Assume that the influence coefficient of ambient temperature fluctuation T is 0.05, the influence coefficient of electromagnetic interference E is 0.03, and the original sensor reading Sraw is 100 units, temperature fluctuation T = 10, electromagnetic interference E = 20;

[0096] Calculate S adjusted :

[0097] S a dj uste d =100+0.05·10+0.03·20=100+0.5+0.6=101.1;

[0098] The results show that after considering environmental factors, the calibration reading of the sensor is 101.1. The calculation process is used to compensate for sensor errors caused by temperature fluctuations and electromagnetic interference, ensuring the accuracy and reliability of sensor data.

[0099] S203: Based on the calibration parameter calculation result, collect various operation data, identify and eliminate background noise caused by environmental and load changes, and generate sensor calibration results;

[0100] In the sensor calibration completion sub-step, sensor data collection and calibration are performed based on the calibration parameter calculation results. Digital filtering technology such as the Kalman filter is used to process the real-time data stream, effectively separating the background noise caused by environmental and load changes and retaining useful signals. The Kalman filter dynamically adjusts the filtering parameters based on the previously set error model and real-time input observation data, optimizes data quality, and improves data reliability through iterative correction. The generated sensor calibration results clearly mark the accuracy of the adjusted data of each sensor, ensuring the authenticity and validity of the data in subsequent applications.

[0101] See also Figure 4 Based on the sensor calibration results, by real-time monitoring of the robot's working status and production line material information, analyzing the currently executed tasks and the robot's task list, evaluating the robot's operating environment and workload, and predicting robot overload and task delay, the steps for generating working condition prediction information are as follows:

[0102] S301: Based on the sensor calibration results, various operating status data of the robot are monitored and recorded in real time, including speed, load, and power consumption, to generate robot operating data;

[0103] In the robot status monitoring sub-step, based on the sensor calibration results, the system is configured with high-performance sensors to monitor and record the robot's key operating states in real time. The target states include speed, load and power consumption. A real-time data acquisition system is used to automatically collect data by integrating the inputs of various sensors, such as speedometers, load sensors and power consumption monitors, and send the data to the central database through wireless communication technology. After the data is received, data preprocessing algorithms such as outlier detection and data smoothing are used to ensure the accuracy and consistency of the data. Data warehouse technology is used to store historical data to support long-term analysis and trend prediction of data. The generated robot operation data includes the results of time series analysis, which provides a basis for data analysis.

[0104] S302: Based on the robot operation data, combined with the type, size and weight information of the production line materials, the task list is analyzed, the operation environment and workload are evaluated, and task execution analysis data is generated;

[0105] In the task execution analysis sub-step, based on the robot operation data, multivariate analysis methods are used to conduct in-depth analysis considering information such as the type, size, and weight of the production line materials. Association rule learning algorithms, such as the Apriori algorithm, are used in the process to analyze the relationship between different task types and material requirements, the time and spatial dependencies of task execution, identify material demand patterns, and optimize material supply chain management. Resource optimization models, such as linear programming, are applied to evaluate the robot's operating environment and workload, optimize task allocation and scheduling strategies, and the generated task execution analysis data records the resource matching and environmental adaptability of each task, providing decision support.

[0106] S303: Based on the task execution analysis data, predict the risk of robot overload and task delay, and generate working condition prediction information;

[0107] In the working condition prediction sub-step, based on the task execution analysis data, machine learning technology, decision tree and neural network models are used to predict the robot's overload and task delay risks. According to historical data training, the key factors that lead to overload and delay are identified, such as task complexity, robot status and production line changes. By real-time monitoring of data streams, the system dynamically updates the prediction model to ensure the real-time and accuracy of the prediction. The generated working condition prediction information provides a forward-looking risk assessment for production management, allowing production scheduling to flexibly respond to production challenges, thereby reducing downtime and improving production efficiency.

[0108] See also Figure 5 ,According to the working condition prediction information, the robot's task load is evaluated in real time, the priorities of multiple execution tasks are adjusted, and the steps of generating task list adjustment records are as follows:

[0109] S401: Based on the working condition prediction information, the current task load of the robot is evaluated in real time, the urgency and resource requirements of multiple tasks are recorded, and task load evaluation data is generated;

[0110] In the task load assessment sub-step, based on the working condition prediction information, real-time data analysis technology is used to continuously monitor the current status of each robot task, including the urgency of the task and resource requirements. A dynamic resource assessment algorithm is used to dynamically calculate the task urgency index and resource consumption index according to the scheduled deadline of the task and the required material resources. The robot's operating status data, such as energy consumption and current load conditions, is integrated. Through data weighting and normalization processing, the accuracy and practicality of the assessment data are ensured. The data visualization component displays the assessment results in real time, allowing the operator to intuitively understand the robot's workload. The generated task load assessment data provides a scientific basis for the subsequent task priority adjustment.

[0111] S402: Based on the task load evaluation data and in combination with the real-time status information of the robot, the execution priorities of the multiple tasks are calculated and adjusted to generate a priority adjustment list;

[0112] The specific formula for calculating and adjusting the execution priority of multiple tasks is:

[0113]

[0114] Among them, P i represents the priority of the i-th task, i represents the task number, L i represents the load evaluation value of the i-th task, S i represents the urgency score of the i-th task, T i represents the waiting time of task i so far, w 1 is the weight parameter of the task load, w 2 is the weight parameter of task urgency, w 3 is the weight parameter of the waiting time.

[0115] formula:

[0116]

[0117] Detailed explanation of the formula and the process of formula calculation and derivation:

[0118] The formula is used to calculate the priority of each task, and the result is used to determine the order in which the tasks are executed on the production line;

[0119] Parameter meaning and setting value:

[0120] L i is the load evaluation value of the ith task, assumed to be 0.8, representing the ratio of the current task to the robot's full power operation;

[0121] S i Score the urgency of the i-th task, assuming it is 0.9;

[0122] T i is the waiting time of task i so far, assuming it is 2 hours, which reflects the stagnation time of the task in the list;

[0123] w 1 、w 2 、w 3 are the weight parameters of task load, urgency and waiting time respectively. Assume that w 1 =0.5,w 2 =0.3,w 3 =0.2, reflecting the importance of different task features in priority calculation;

[0124] Substitute the parameters into the formula for calculation:

[0125]

[0126] The result 0.479 shows that the comprehensive priority of the task is relatively low, indicating that in the current production environment, based on the comprehensive evaluation of its load, urgency and waiting time, the task can be executed later. The result is used to reasonably arrange the order of task execution and optimize resource allocation and the operating efficiency of the production line.

[0127] S403: Based on the priority adjustment list, adjust the robot's work queue, analyze the efficiency of task execution after adjustment and the adjustment effect, and generate a task list adjustment record;

[0128] In the task queue adjustment sub-step, the robot's work queue is configured based on the priority adjustment list, and a task scheduling algorithm, such as a genetic algorithm, is used to optimize the execution order of tasks and resource allocation. By simulating natural selection and genetic mechanisms, the optimal task sequence is found to maximize production efficiency and minimize delays. The execution of the adjusted tasks is monitored in real time, the speed and quality of task completion are evaluated, and the effectiveness of the adjustment measures is ensured. Through comprehensive analysis of the adjusted task execution efficiency and resource utilization, a task list adjustment record is generated. The target record helps the management team evaluate the effectiveness of the adjustment measures and provide experience for task scheduling.

[0129] See also Figure 6 , use the task list to adjust the record, monitor the robot behavior in real time, detect abnormal behavior and perform real-time analysis, identify and record the cause of the abnormality, and generate the abnormal behavior analysis results in the following steps:

[0130] S501: Based on the task list adjustment record, the robot's movement and operation behavior are monitored in real time, the movement data and operation log are recorded, and a real-time monitoring log is generated;

[0131] In the robot behavior monitoring sub-step, records are adjusted based on the task list, and a comprehensive monitoring framework is implemented. Sampling sensors and cameras are used to monitor each movement and operation of the robot. Through the real-time data acquisition system, the target data is synchronously transmitted to the central monitoring server. The motion data includes the robot's position, speed and acceleration. The operation log includes the task execution status, timestamp and operation results. In order to process a large amount of target data, the system applies stream data processing technology such as Apache Storm to ensure real-time updating of monitoring data. Through real-time database management systems such as InfluxDB, data storage and access are performed, which optimizes data query and analysis efficiency. The generated real-time monitoring log provides detailed basic data for subsequent data analysis and anomaly detection.

[0132] S502: Analyze the robot's motion data based on the real-time monitoring log, detect and identify abnormal behaviors, and generate behavior data detection records;

[0133] In the behavior data detection sub-step, based on the real-time monitoring log, the random forest algorithm is used to conduct in-depth analysis of the robot's motion data. By establishing multiple decision trees, the data features are learned and classified to identify abnormal patterns in the robot's behavior. The algorithm extracts features from the motion data set, including the changing trend of motion speed, abnormal fluctuations in acceleration, etc. The relationship between the target features and abnormal behaviors is identified through model training. Cross-validation technology is implemented to ensure the accuracy and generalization ability of the model. The generated behavior data detection records list in detail all identified abnormal behaviors and their corresponding data features.

[0134] S503: Based on the behavior data detection record, analyze and identify the causes of abnormal behavior, including robot overload, failure, external interference, record abnormal events, and generate abnormal behavior analysis results;

[0135] In the abnormal behavior analysis sub-step, the root cause of the abnormal behavior is analyzed based on the behavior data detection records. The causal reasoning model is applied, combined with statistical analysis and machine learning techniques, to evaluate the probabilities of multiple potential causes, including robot overload, mechanical failure, and external interference. Through historical data comparison and pattern recognition, the source of the problem is accurately located, and the specific impact on the robot operation is evaluated. The fault tree analysis technology is used to analyze and record the occurrence path and probability of each abnormal event, which provides a basis for formulating preventive measures and maintenance plans. The generated abnormal behavior analysis results include a detailed cause analysis report and recommended response measures, which provide support for improving the stability and reliability of the robot system.

[0136] See also Figure 7 , a robot data processing system based on industrial Internet of Things, the robot data processing system based on industrial Internet of Things is used to execute the above-mentioned robot data processing method based on industrial Internet of Things, the system includes:

[0137] The motion data acquisition module collects the angle, velocity and acceleration data of multiple joints of the robot in real time based on the robot operation data, analyzes the motion trajectory and identifies the difference from the ideal motion trajectory, and generates joint motion difference data;

[0138] The motion trajectory adjustment module adjusts the robot's control parameters based on the joint motion difference data, including joint angle correction values, speed adjustment parameters and acceleration compensation values, and generates a control parameter adjustment record;

[0139] The sensor calibration module analyzes the impact of environmental conditions and load conditions on sensor readings based on control parameter adjustment records, calculates calibration parameters and collects a variety of operating data, including temperature, voltage, current, and pressure, to generate sensor calibration results;

[0140] The real-time working condition analysis module analyzes the task list, evaluates the operating environment and workload, predicts overload and task delay, and generates working condition prediction information based on the sensor calibration results, combined with the robot's real-time working status data and the type, size, and weight information of the production line materials;

[0141] The priority adjustment module evaluates the task load and adjusts the priorities of multiple tasks based on the working condition prediction information, and generates task list adjustment records;

[0142] The real-time behavior monitoring module adjusts the records based on the task list, monitors the robot behavior in real time, detects abnormal behavior and analyzes the causes of abnormality, and generates abnormal behavior analysis results.

[0143] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by 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 invention 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, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0144] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0145] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0146] It should be understood that in various embodiments of the present invention, 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 invention.

[0147] Those skilled 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 the present invention.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device 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 device, 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 through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] 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.

[0151] In addition, each functional unit in each embodiment of the present invention 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.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0153] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A robot data processing method based on industrial Internet of Things, characterized in that: The method comprises: Based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the robot in operation are collected in real time. By analyzing the difference between the actual motion trajectory and the ideal motion trajectory of the robot, the control parameters of the robot are adjusted in real time, and a control parameter adjustment record is generated; Based on the control parameter adjustment record, the sensor is calibrated by analyzing the influence of environmental conditions and load conditions on the readings of various sensors, and various operating data are collected to generate sensor calibration results; Based on the sensor calibration results, by real-time monitoring of the robot's working status and production line material information, analyzing the currently executed tasks and the robot's task list, evaluating the robot's operating environment and workload, predicting robot overload and task delay, and generating working condition prediction information; According to the working condition prediction information, the task load of the robot is evaluated in real time, the priorities of multiple execution tasks are adjusted, and a task list adjustment record is generated; Use the task list to adjust the records, monitor the robot behavior in real time and continuously, detect abnormal behavior and perform real-time analysis, identify and record the cause of the abnormality, and generate abnormal behavior analysis results.

2. The robot data processing method based on industrial Internet of Things according to claim 1 is characterized in that: The control parameter adjustment record specifically includes the joint angle correction value, speed correction parameter, and acceleration compensation index; the sensor calibration result includes the temperature adjustment factor, voltage stability coefficient, and current calibration index; the working condition prediction information specifically refers to the robot load level, task delay risk, and overload warning index; the task list adjustment record includes the task priority index, task execution path planning information, and resource allocation ratio information; the abnormal behavior analysis result specifically includes abnormal type identification information, abnormal cause diagnosis result, and abnormal event response record.

3. The robot data processing method based on industrial Internet of Things according to claim 1 is characterized in that: Based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the robot in operation are collected in real time. By analyzing the difference between the actual motion trajectory and the ideal motion trajectory of the robot, the control parameters of the robot are adjusted in real time. The specific steps for generating the control parameter adjustment record are as follows: Based on the robot operation data, the angle, speed, and acceleration data of multiple joints of the running robot are collected in real time to generate a robot motion data set; Based on the robot motion data set, analyzing the deviation between the actual motion trajectory of the robot and the preset ideal trajectory, and generating motion deviation detection data; Based on the motion deviation detection data, various motion control parameters are adjusted, including calculating the angular velocity and acceleration control parameters of the joints, and generating a control parameter adjustment record.

4. The robot data processing method based on industrial Internet of Things according to claim 3 is characterized in that: The specific formula for analyzing the deviation between the actual motion trajectory of the robot and the preset ideal trajectory is: Among them, P i represents the ideal position data of the i-th joint, A i represents the actual position data of the i-th joint, W i represents the weight coefficient of the i-th joint, i represents the index number of the joint, and D represents the total deviation.

5. The robot data processing method based on industrial Internet of Things according to claim 1 is characterized in that: Based on the control parameter adjustment record, the sensor is calibrated by analyzing the influence of environmental conditions and load conditions on the readings of various sensors, and various operating data are collected to generate the sensor calibration results in the following steps: Based on the control parameter adjustment record, analyzing the impact of environmental conditions and machine load on sensor readings, identifying multiple influencing factors and sensor types that need to be calibrated, including temperature fluctuations and electromagnetic interference, and generating sensor influencing factor analysis data; Analyze the data based on the sensor influencing factors, calculate compensation parameters, calibrate multiple sensors, and generate calibration parameter calculation results; Based on the calibration parameter calculation results, various operating data are collected, background noise caused by environmental and load changes is identified and eliminated, and a sensor calibration result is generated.

6. The robot data processing method based on industrial Internet of Things according to claim 1 is characterized in that: Based on the sensor calibration results, by real-time monitoring of the robot's working status and production line material information, analyzing the currently executed tasks and the robot's task list, evaluating the robot's operating environment and workload, predicting robot overload and task delay, the steps of generating working condition prediction information are as follows: Based on the sensor calibration results, various operating status data of the robot, including speed, load, and power consumption, are monitored and recorded in real time to generate robot operating data; Based on the robot operation data, combined with the type, size and weight information of the production line materials, the task list is analyzed, the operation environment and workload are evaluated, and task execution analysis data is generated; Based on the task execution analysis data, the risk of robot overload and task delay is predicted, and working condition prediction information is generated.

7. The robot data processing method based on industrial Internet of Things according to claim 1 is characterized in that: According to the working condition prediction information, the task load of the robot is evaluated in real time, the priorities of multiple execution tasks are adjusted, and the steps of generating a task list adjustment record are specifically as follows: Based on the working condition prediction information, the current task load of the robot is evaluated in real time, the urgency and resource requirements of multiple tasks are recorded, and task load evaluation data is generated; Based on the task load evaluation data and in combination with the real-time status information of the robot, the execution priorities of the multiple tasks are calculated and adjusted to generate a priority adjustment list; Based on the priority adjustment list, the robot's work queue is adjusted, the efficiency of the adjusted task execution and the adjustment effect are analyzed, and a task list adjustment record is generated.

8. The method for processing robot data based on industrial Internet of Things according to claim 7, characterized in that: The specific formula for calculating and adjusting the execution priorities of multiple tasks is: Among them, P i represents the priority of the i-th task, i represents the task number, L i represents the load evaluation value of the i-th task, S i represents the urgency score of the i-th task, T i represents the waiting time of task i so far, w1 is the weight parameter of task load, w2 is the weight parameter of task urgency, and w3 is the weight parameter of waiting time.

9. The robot data processing method based on industrial Internet of Things according to claim 1 is characterized in that: The steps of using the task list to adjust the records, continuously monitoring the robot behavior in real time, detecting abnormal behavior and performing real-time analysis, identifying and recording the abnormal causes, and generating abnormal behavior analysis results are as follows: Based on the task list adjustment record, the robot's movement and operation behavior are monitored in real time, the movement data and operation log are recorded, and a real-time monitoring log is generated; Based on the real-time monitoring log, the robot's motion data is analyzed, abnormal behaviors are detected and identified, and behavior data detection records are generated; Based on the behavior data detection records, analyze and identify the causes of abnormal behavior, including robot overload, failure, external interference, record abnormal events, and generate abnormal behavior analysis results.

10. A robot data processing system based on industrial Internet of Things, characterized in that: According to any one of claims 1 to 9, the robot data processing method based on the industrial Internet of Things comprises: The motion data acquisition module collects the angle, velocity and acceleration data of multiple joints of the robot in real time based on the robot operation data, analyzes the motion trajectory and identifies the difference from the ideal motion trajectory, and generates joint motion difference data; The motion trajectory adjustment module adjusts the control parameters of the robot based on the joint motion difference data, including joint angle correction values, speed adjustment parameters and acceleration compensation values, and generates a control parameter adjustment record; The sensor calibration module analyzes the effects of environmental conditions and load conditions on sensor readings based on the control parameter adjustment records, calculates calibration parameters and collects various operating data, including temperature, voltage, current, and pressure, to generate sensor calibration results; The real-time working condition analysis module analyzes the task list, evaluates the operating environment and workload, predicts overload and task delay, and generates working condition prediction information based on the sensor calibration results, combined with the robot's real-time working status data and the type, size, and weight information of the production line materials; The priority adjustment module evaluates the task load and adjusts the priorities of multiple tasks based on the working condition prediction information, and generates a task list adjustment record; The real-time behavior monitoring module adjusts the records based on the task list, monitors the robot behavior in real time, detects abnormal behavior and analyzes the cause of the abnormality, and generates abnormal behavior analysis results.

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