Industrial mechanical arm tool changing operation monitoring system based on digital twinning

Abstract

The invention discloses an industrial mechanical arm tool changing operation monitoring system based on digital twinning, and relates to the technical field of digital twinning. The system establishes a complete digital twinning model by integrating a floating platform model, a mechanical arm model and a tool changing device model; and a sensing sensor group is arranged to acquire real physical world data of the platform, the mechanical arm and the tool changing device in real time. The data are transmitted to a digital twin model in real time for processing and simulation, so that the virtual world state and the physical world state can be bidirectionally synchronized. According to the highly integrated monitoring mode, the resonance risk between the platform and the mechanical arm and the change of the tool locking trend can be recognized in time. Through an effective risk early warning and adjusting mechanism, the system can ensure the accuracy and safety of tool changing operation, and tool changing failure or equipment damage caused by resonance or abnormal locking is avoided, so that the production efficiency is improved, and the downtime is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and specifically to an industrial robotic arm tool change operation monitoring system based on digital twins. Background Art

[0002] With the continuous development of technology, intelligent manufacturing has gradually become the mainstream in global industrial production, covering multiple directions such as robotics, sensor technology, and artificial intelligence. Specifically in the field of robots, industrial robotic arms have been widely used in manufacturing, assembly, inspection, packaging and other links, especially in industries such as automotive, electronics, and aerospace. Further refined, the tool change operation of the robotic arm is an important link in its operation, especially in fields such as numerical control machine tools and precision machining. The accuracy and efficiency of tool change directly affect production quality and cost. The smooth progress of the tool change operation requires the robotic arm to be able to complete tool replacement efficiently and accurately. However, factors such as vibration and resonance involved often lead to operation failures or instability.

[0003] At present, although many robotic arm systems have achieved automatic tool change functions, they still face multiple problems, especially the resonance problem that occurs during the tool change process. Due to the frequency coupling and resonance phenomenon between the floating platform and the robotic arm, the vibration of the end effector of the robotic arm is often too large, which affects the locking accuracy of the tool and may even lead to tool locking failure. This situation is particularly obvious under the action of floating platform structures and environmental factors such as wind speed and temperature changes. Most traditional monitoring systems only consider the vibration characteristics of the robotic arm itself and ignore the influence of platform frequency, and cannot effectively monitor and adjust the resonance coupling problem between the two. This deficiency in design makes the system prone to serious problems such as tool locking and alignment deviation when encountering high-frequency superposition and unstable environmental factors, affecting production efficiency and product quality. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an industrial robotic arm tool change operation monitoring system based on digital twins, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An industrial robotic arm tool change operation monitoring system based on digital twins, including

[0006] A model integration module: By integrating the floating platform model, the robotic arm model, and the tool change device model, a digital twin model is created, and a set of sensing sensors is set to collect real-time data and transmit it to the digital twin model;

[0007] Digital Twin Synchronization Module: Based on real data, execute a two-way synchronization mechanism in the digital twin model to synchronize the states of the real physical world and the virtual physical world, extract perception data, and then perform preprocessing to obtain a standard perception data set;

[0008] Resonance Coupling Simulation Module: By extracting a standard resonance data set, calculate and output the resonance risk factor R, and based on the resonance risk factor R, calculate and output the tool locking trend index S. At the same time, set a risk threshold Sth for preliminary comparative evaluation;

[0009] Environmental Adaptation Adjustment Module: When triggering environmental adaptation adjustment, extract a standard environmental perception data set, calculate and output the environmental impact coefficient N, and based on the environmental impact coefficient N, calculate and output the adjustment amount of the micro-vibration frequency at the end of the robotic arm △Fm;

[0010] Tool Change Operation Risk Analysis Module: By integrating the adjusted resonance risk factor R' and the adjusted tool locking trend index S', comprehensively calculate and output the comprehensive risk index Palarm, and set a tool change warning threshold Pth for secondary comparative evaluation.

[0011] Preferably, the model integration module includes an integration unit and a real data acquisition unit;

[0012] The integration unit creates a digital twin model by obtaining a floating platform model, a robotic arm model, and a tool change device model and integrating them;

[0013] The floating platform model is modeled for the floating platform structure using a finite element modeling tool. The floating platform structure includes platform dimensions and connection relationships. By using structural dynamics analysis, extract the simulation frequency, damping coefficient, and vibration transfer path. Summarize the platform structure and structural dynamics to obtain the floating platform model and input it into the digital twin engine as technical modal response characteristics;

[0014] The robotic arm model is constructed for the physical model of the robotic arm using ROS and GAzebo / Webots. The physical model includes a multi-joint structure and degrees of freedom. By using Lagrange for dynamic modeling, establish the angular velocity, angular acceleration, and end effector trajectory of each key of the robotic arm;

[0015] The tool change device model is constructed by using the discrete element modeling method DEM to simulate the action trajectories of the three stages of tool installation, alignment, and locking between tools, and using a time-domain and frequency-domain hybrid model to describe the behavior deviation under re-vibration;

[0016] The real data acquisition unit collects the real data perceived from the real physical world of the floating platform, robotic arm, and tool changing device in real time by setting up a sensor group, and transmits the real data to the constructed digital twin model through wireless communication;

[0017] The sensor group includes an IMU inertial detection unit, a laser interferometer, a joint position encoder, a temperature sensor, a humidity sensor, a wind speed sensor, an industrial camera, and a visual recognition system;

[0018] The real data includes the three-axis acceleration and angular velocity of the floating platform and the robotic arm, high-frequency micro-vibration, the real-time posture of the robotic arm, temperature, humidity, wind speed, and the tool changing state.

[0019] Preferably, the digital twin synchronization module includes a data processing input unit, a synchronization update and communication central unit, and a perception data extraction unit;

[0020] The data processing input unit receives the real data in real time in the digital twin model and standardizes the data format of the real data. The data format standardization includes timestamp synchronization and multi-source heterogeneous data fusion, and drives the digital twin model based on the real data after data format standardization. Among them, the driving methods of the digital twin model include data driving and model prediction;

[0021] The data driving drives the state of the virtual model in the digital twin model through real-time real data;

[0022] The model prediction predicts the physical real state 2 seconds later through the state of the virtual model;

[0023] The timestamp synchronization synchronizes the timestamps of all parameters in the real data to the same time by using ROS time;

[0024] The multi-source heterogeneous data fusion fuses the real data from different sources by adopting a multi-modal fusion algorithm;

[0025] The synchronization update and communication central unit uses the DDS / MQTT protocol as the main communication central framework and executes a two-way synchronization mechanism to synchronize the real physical world state and the virtual physical world state;

[0026] The two-way synchronization mechanism simulates the state of the virtual model by obtaining real data from the physical world in real time, and then predicts the physical world state 2 seconds later through the simulation of the state of the virtual model, and feeds back the control instructions to the robotic arm control system.

[0027] Preferably, the perception data extraction unit performs virtual-real synchronization and prediction feedback based on the created digital twin model, extracts perception data in real time, and preprocesses the perception data to obtain a standard perception data set. The preprocessing includes dimensionless standardization processing to eliminate the dimensional influence of all parameters in the perception data. The standard perception data set includes an environmental perception data set and a resonance perception data set;

[0028] The environmental perception data set includes the temperature Wd(t) at time t, the humidity Sd(t) at time t, the wind speed Fs(t) at time t, and the wind speed frequency Fw(t) at time t;

[0029] The resonance perception data set includes the resonance frequency Fp(t) of the floating platform at time t, the micro-vibration frequency Fm(t) of the end of the robotic arm at time t, and the vibration amplitude Ap(t) of the floating platform at time t.

[0030] Preferably, the resonance coupling simulation module includes a resonance risk analysis unit, a tool locking trend analysis unit, and a locking evaluation unit;

[0031] The resonance risk analysis unit constructs a resonance risk factor calculation formula in the digital twin model, extracts the resonance perception data set and inputs it into the resonance risk factor calculation formula for calculation to output the resonance risk factor R, and analyzes the resonance risk between the floating platform and the robotic arm;

[0032] The resonance risk factor R is calculated and output through the following resonance risk factor calculation formula;

[0033]

[0034] In the formula, R(t) represents the resonance risk factor at time t, cos represents the cosine function, φ(t) represents the phase difference of frequency coupling at time t, γ represents the resonance sensitivity coefficient, e represents the exponential function, β represents the adjustment factor, and Ath represents the vibration threshold of the floating platform, with a dimensionless value.

[0035] Preferably, the tool locking trend analysis unit calculates and outputs the tool locking trend index S by extracting the resonance risk factor R(t) at the current time t in combination with the environmental temperature, analyzes the cumulative risk of tool locking, and performs dynamic simulation feedback based on the resonance risk assessment between the floating platform and the robotic arm and environmental changes;

[0036] The tool locking trend index S is calculated and output through the following algorithm formula;

[0037]

[0038] Wherein, S(t) represents the tool locking trend index at time t, Wdmax represents the upper limit value of the safe temperature, d represents the differential variable, and dt represents the time differential variable.

[0039] Preferably, the locking evaluation unit obtains the risk threshold Sth through a back-calculation formula by extracting the time points of tool locking failure under different combinations of frequency, temperature, and floating platform acceleration, and preliminarily compares and evaluates the tool locking trend index S(t) at time t with the risk threshold Sth to analyze the cumulative risk status of the current tool. The specific evaluation content is as follows;

[0040] When the tool locking trend index S(t) at time t < the risk threshold Sth, it indicates that the locking trend is within the controllable range, and the current tool change is safe and no adjustment is required;

[0041] When the tool locking trend index S(t) at time t ≥ the risk threshold Sth, it indicates that the cumulative locking trend is abnormal. At this time, the tool change operation is paused, and the environmental adaptation adjustment is triggered.

[0042] Preferably, the environmental adaptation adjustment module includes an environmental impact analysis unit and a perception adjustment unit;

[0043] After the environmental adaptation adjustment is triggered through the preliminary comparison and evaluation, the environmental impact analysis unit extracts the current environmental perception data set and calculates and outputs the environmental impact coefficient N;

[0044] The environmental impact coefficient N is calculated and output through the following algorithm formula:

[0045] N(t) = (a1·Wd(t)) + (a2·Sd(t)) + (a3·Fs(t));

[0046] Wherein, N(t) represents the environmental impact coefficient at time t, a1, a2, and a3 respectively represent the preset weight values of temperature Wd, humidity Sd, and wind speed Fs, and a1 + a2 + a3 = 1, and their specific values are set by the user;

[0047] The perception adjustment unit calculates and outputs the micro-vibration frequency adjustment amount △Fm at the end of the robotic arm based on the environmental impact coefficient N(t) at time t, and dynamically adjusts the operation frequency of the robotic arm;

[0048] The micro-vibration frequency adjustment amount △Fm at the end of the robotic arm is calculated and output through the following algorithm formula;

[0049]

[0050] Wherein, △Fm(t) represents the micro-vibration frequency adjustment amount at the end of the robotic arm at time t, and Fwind represents the wind speed frequency influence threshold, and the value is dimensionless.

[0051] Preferably, the tool change operation risk analysis module includes a comprehensive tool change operation risk analysis unit and a tool change operation risk assessment unit;

[0052] After environmental adaptation adjustment, the comprehensive tool change operation risk analysis unit combines the real-time feedback of the digital twin model and operation adjustment to extract and integrate the adjusted resonance risk factor R' and the adjusted tool locking trend index S', and performs comprehensive calculation to output the comprehensive risk index Palarm;

[0053] The comprehensive risk index Palarm is calculated and output through the following algorithm formula;

[0054]

[0055] In the formula, Palarm(t) represents the comprehensive risk index at time t, Tmax represents the upper limit value of the tool change cycle, Rmax represents the upper limit value of the resonance risk, dS(t)' represents the differential variable of the adjusted tool locking trend index at time t, dR(t) represents the differential variable of the adjusted resonance risk factor at time t, d represents the differential variable, R(t)' represents the adjusted resonance risk factor at time t, and S(t)' represents the adjusted tool locking trend index at time t.

[0056] Preferably, when the user determines that there is an abnormality in the tool change operation based on the historical comprehensive risk index Palarm, the tool change operation risk assessment unit sets the tool change warning threshold Pth, and then performs a secondary comparison and evaluation between the tool change warning threshold Pth and the comprehensive risk index Palarm(t) at time t to analyze the tool change risk situation after environmental adaptation adjustment, and generates relevant warning prompts according to the evaluation results. The specific evaluation content is as follows;

[0057] When the comprehensive risk index Palarm(t) at time t > the tool change warning threshold Pth, it indicates that after environmental adaptation adjustment, the manipulator tool change operation is in an abnormal state. At this time, an emergency warning mechanism is started, and a warning message is sent to the operator through the digital twin model, and the tool change operation is continued to be paused;

[0058] When the comprehensive risk index Palarm(t) at time t ≤ the tool change warning threshold Pth, it indicates that after environmental adaptation adjustment, the manipulator tool change operation is in a normal state. At this time, no warning needs to be generated, and the tool change operation is started.

[0059] Beneficial effects

[0060] The present invention provides an industrial manipulator tool change operation monitoring system based on digital twins, which has the following beneficial effects:

[0061] (1) By integrating the floating platform model, robotic arm model, and tool changing device model, the system creates a complete digital twin model and sets up a group of sensing sensors to collect real physical world data of the platform, robotic arm, and tool changing device in real time. These data are transmitted to the digital twin model for processing and simulation in real time, enabling two-way synchronization of the virtual and physical world states. This highly integrated monitoring method can promptly identify the resonance risk between the platform and the robotic arm and changes in the tool locking trend. Through an effective risk warning and adjustment mechanism, the system can ensure the accuracy and safety of the tool changing operation, avoid tool changing failures or equipment damage caused by resonance or abnormal locking, thereby improving production efficiency and reducing downtime.

[0062] (2) By triggering the environmental adaptation adjustment module, the system collects environmental perception data such as temperature, humidity, and wind speed in real time during the tool changing operation. By calculating the environmental impact coefficient N(t), it dynamically adjusts the operation frequency ΔFm(t) of the robotic arm to avoid resonance or misoperation caused by environmental changes. Especially in an environment with strong wind speed or drastic changes in temperature and humidity, the system will automatically adjust the micro-vibration frequency of the robotic arm, thereby reducing the instability or resonance of the robotic arm caused by wind speed or other environmental factors. This environmental self-adaptation adjustment not only improves the adaptability of the system under various environmental conditions but also enhances its stability and operation accuracy, ensuring that the tool changing operation can be successfully completed under different environmental conditions.

[0063] (3) Through the tool changing operation risk analysis module, the system integrates the adjusted resonance risk factor R' and the adjusted tool locking trend index S’, and calculates the comprehensive risk index Palarm. This index comprehensively considers changes in the tool locking trend, resonance risk, and environmental adaptation adjustment. When the comprehensive risk index Palarm exceeds the preset tool changing warning threshold Pth, the system will automatically trigger an emergency warning mechanism, suspend the tool changing operation, and send an alarm message through the digital twin model. Through this mechanism, the system can identify potential risks in real time during the tool changing task, ensuring that operators can take necessary measures in a timely manner to avoid equipment damage or production stagnation caused by abnormal risks, thereby improving the safety and reliability of the tool changing operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the modules of the industrial robotic arm tool changing operation monitoring system based on digital twin of the present invention;

[0065] Figure 2 It is an evaluation flowchart of the industrial robotic arm tool changing operation monitoring system based on digital twin of the present invention;

[0066] Figure 3 It is a trend chart of the tool locking trend index S. DETAILED IMPLEMENTATION MANNER

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] Embodiment 1

[0069] The present invention provides a tool change operation monitoring system for an industrial robotic arm based on digital twin. Please refer to Figure 1 、 Figure 2 and Figure 3 , including

[0070] Model integration module: By integrating the floating platform model, robotic arm model, and tool change device model, a digital twin model is created, and a set of sensing sensors is set to collect real-time data and transmit it to the digital twin model;

[0071] Digital twin synchronization module: Based on the real data, a two-way synchronization mechanism is executed in the digital twin model to synchronize the real physical world state and the virtual physical world state, extract the sensing data, and then perform preprocessing to obtain a standard sensing data set;

[0072] Resonance coupling simulation module: By extracting the standard resonance data set, calculate and output the resonance risk factor R, and calculate and output the tool locking trend index S based on the resonance risk factor R. At the same time, set a risk threshold Sth for preliminary comparison and evaluation;

[0073] Environment adaptation adjustment module: When triggering the environment adaptation adjustment, extract the standard environment sensing data set, calculate and output the environment impact coefficient N, and calculate and output the micro-vibration frequency adjustment amount ΔFm of the robotic arm end based on the environment impact coefficient N;

[0074] Tool change operation risk analysis module: By integrating the adjusted resonance risk factor R' and the adjusted tool locking trend index S', comprehensively calculate and output the comprehensive risk index Palarm, and set a tool change warning threshold Pth for secondary comparison and evaluation.

[0075] In this embodiment, the system integrates the floating platform model, the robotic arm model, and the tool changing device model through the model integration module, constructs a high-precision digital twin model using finite element, Lagrange modeling, and DEM methods, and collects real physical world data through the sensor group to achieve the integration of model-driven and data-driven. Secondly, the digital twin synchronization module uses timestamp synchronization, multimodal fusion, and DDS / MQTT communication protocols to construct a two-way synchronization mechanism for virtual and real states, and extracts a standard perception data group for subsequent analysis. Then, through the resonance coupling simulation module, based on the resonance perception data group, the system calculates and outputs the resonance risk factor R, further combines the environmental temperature to evaluate the tool locking trend index S, and compares it with the locking risk threshold Sth for evaluation to identify potential locking risks in advance. After the preliminary evaluation of the resonance risk exceeds the threshold, the system automatically triggers the environmental adaptation adjustment module, extracts parameters such as temperature, humidity, and wind speed from the standard environmental perception data group, calculates the environmental impact coefficient N, and outputs the adjustment amount ΔFm of the micro-vibration frequency at the end of the robotic arm to dynamically optimize the operating parameters and enhance the system's adaptive ability to environmental disturbances. Finally, the tool changing operation risk analysis module integrates the adjusted resonance risk factor R' and the adjusted tool locking trend index S', calculates and outputs the comprehensive risk index Palarm, and conducts a secondary evaluation with the tool changing warning threshold Pth to determine whether the current tool changing task is in an abnormal state, thereby realizing the whole-process risk control and intelligent decision-making. Compared with the passive monitoring mode of traditional tool changing systems that ignore the platform frequency domain characteristics and environmental dynamic changes and only make static judgments based on the robotic arm itself, this system realizes the dynamic simulation prediction of frequency coupling and phase synchronization between the floating platform and the robotic arm by introducing the "floating platform structure and frequency domain resonance coupling parameters", and integrates environmental perception and frequency self-adjustment capabilities to achieve a higher-precision and stronger-robustness tool self-locking anomaly identification and risk avoidance mechanism.

[0076] Embodiment 2

[0077] Please refer to Figure 1 , specifically: The model integration module includes an integration unit and a real data acquisition unit;

[0078] The integration unit creates a digital twin model by integrating the floating platform model, the robotic arm model, and the tool changing device model obtained;

[0079] The floating platform model is modeled for the floating platform structure using a finite element modeling tool. The floating platform structure includes platform dimensions and connection relationships. Using structural dynamics analysis, the simulated frequency, damping coefficient, and vibration transmission path are extracted. The platform structure and structural dynamics are summarized to obtain the floating platform model, which is input into the digital twin engine as the technical modal response characteristics;

[0080] The robotic arm model constructs the physical model of the robotic arm by using ROS and GAzebo / Webots. The physical model includes a multi-joint structure and degrees of freedom. Lagrange is used for dynamic modeling to establish the angular velocity, angular acceleration of each key part of the robotic arm, and the trajectory of the end effector.

[0081] The tool change device model uses the discrete element modeling method DEM to simulate the action trajectories of the three stages of tool installation, alignment, and locking between tools, and uses a hybrid time-domain and frequency-domain model to describe the behavior deviation under re-vibration to construct the tool change device model.

[0082] The real data acquisition unit sets up a sensor group to collect real data sensed from the real physical world of the floating platform, robotic arm, and tool change device in real time, and transmits the real data to the constructed digital twin model through wireless communication.

[0083] The sensor group includes an IMU inertial detection unit, a laser interferometer, a joint position encoder, a temperature sensor, a humidity sensor, a wind speed sensor, an industrial camera, and a visual recognition system.

[0084] The real data includes the three-axis acceleration and angular velocity of the floating platform and the robotic arm, high-frequency micro-vibration, the real-time attitude of the robotic arm, temperature, humidity, wind speed, and the tool change state.

[0085] In this embodiment, the system collaborates with the real data acquisition unit through the integration unit to complete the high-fidelity modeling and multi-dimensional data-driven integration of the three core systems of the floating platform, industrial robotic arm, and tool change device. The integration unit uses the finite element method to construct the structural model of the floating platform, and extracts modal response characteristics such as its simulated frequency, damping coefficient, and vibration transfer path through structural dynamics; at the same time, based on ROS and GAzebo / Webots, it constructs the multi-joint physical model of the robotic arm, and through Lagrange dynamic modeling, it realizes the accurate description of the angular velocity, angular acceleration, and end trajectory of each joint of the robotic arm; in addition, the DEM discrete element method is used to construct the tool change device model, covering the whole process of tool installation, alignment, and locking, and integrating the time-domain and frequency-domain hybrid modeling to truly simulate the action deviation behavior under the influence of vibration. The real data acquisition unit configures multi-source sensing devices such as IMUs, laser interferometers, encoders, temperature and humidity sensors, wind speed sensors, and industrial cameras to realize the real-time perception and wireless transmission of the full-chain state parameters of the robotic arm, platform, and tool change device, such as three-axis acceleration, angular velocity, high-frequency micro-vibration, attitude, and change state, and constructs a data basis for two-way virtual-real linkage.

[0086] Embodiment 3

[0087] Please refer to Figure 1, specifically: the digital twin synchronization module includes a data processing input unit, a synchronization update and communication central unit, and a sensing data extraction unit;

[0088] The data processing input unit receives real-time data in the digital twin model and standardizes the data format of the real data. The data format standardization includes timestamp synchronization and multi-source heterogeneous data fusion, and drives the digital twin model based on the real data after data format standardization. Among them, the driving methods of the digital twin model include data-driven and model prediction;

[0089] Data-driven drives the state of the virtual model in the digital twin model through real-time real data;

[0090] Model prediction predicts the physical real state 2 seconds later through the virtual model state;

[0091] Timestamp synchronization synchronizes the timestamps of all parameters in the real data to the same time by using ROS time;

[0092] Multi-source heterogeneous data fusion fuses real data from different sources by adopting a multimodal fusion algorithm;

[0093] The synchronization update and communication central unit uses the DDS / MQTT protocol as the main communication central framework to achieve low-latency high-frequency data stream synchronization and execute a two-way synchronization mechanism to synchronize the real physical world state and the virtual physical world state;

[0094] The two-way synchronization mechanism simulates the virtual model state by obtaining real data from the physical world in real time, and then predicts the physical world state 2 seconds later through the virtual model state simulation, and feeds back the control instructions to the robotic arm control system.

[0095] The sensing data extraction unit performs virtual-real synchronization and prediction feedback based on the created digital twin model, extracts sensing data in real time, and preprocesses the sensing data to obtain a standard sensing data set. The preprocessing includes dimensionless standardization processing to eliminate the dimensional influence of all parameters in the sensing data. The standard sensing data set includes an environmental sensing data set and a resonance sensing data set;

[0096] The environmental sensing data set includes the temperature Wd(t) at time t, the humidity Sd(t) at time t, the wind speed Fs(t) at time t, and the wind speed frequency Fw(t) at time t;

[0097] The resonance sensing data set includes the resonance frequency Fp(t) of the floating platform at time t, the micro-vibration frequency Fm(t) of the robotic arm end at time t, and the vibration amplitude Ap(t) of the floating platform at time t.

[0098] In this embodiment, the digital twin synchronization module of the system realizes high-precision, low-latency two-way synchronization and closed-loop drive control between the virtual model and the real physical system through the collaborative operation of the data processing input unit, the synchronization update and communication central unit, and the sensing data extraction unit. First, the data processing input unit performs timestamp synchronization and multi-modal fusion processing on the multi-source real data received, unifies the data timing, fuses heterogeneous data sources, and forms a standardized input format for real-time driving the digital twin model and realizing the dual-mechanism linkage of data-driven and model prediction. This not only ensures a high degree of consistency in the current state but also can predict and feedback on the physical state within the next 2 seconds, providing a priori basis for early warning and control. The DDS / MQTT low-latency communication framework constructed by the synchronization update and communication central unit ensures the stable transmission of high-frequency data streams and the real-time update of physical-virtual two-way states, ensuring the closed-loop response efficiency of system commands. The sensing data extraction unit then extracts two core sensing data of environment and resonance from the synchronized model in real time, and through dimensionless standardization processing, eliminates the physical dimension differences, and forms a standard sensing data set that can be used for risk calculation and dynamic adjustment. The deployment of this module completes key tasks such as dynamic driving, prediction feedback, data standardization, and sensing synchronization of the digital twin model, and constructs the core central mechanism in the digital twin system from "data acquisition" to "model driving" and then to "feedback control". Compared with the current situation of the traditional robotic arm tool change system where the processing of sensing data is scattered, the timing is asynchronous, and the feedback is lagged.

[0099] Embodiment 4

[0100] Please refer to Figure 1 、 Figure 2 and Figure 3 , specifically: The resonance coupling simulation module includes a resonance risk analysis unit, a tool locking trend analysis unit, and a locking evaluation unit;

[0101] The resonance risk analysis unit constructs a resonance risk factor calculation formula in the digital twin model, extracts the resonance sensing data set and inputs it into the resonance risk factor calculation formula, calculates and outputs the resonance risk factor R, and analyzes the resonance risk between the floating platform and the robotic arm;

[0102] The resonance risk factor R is calculated and output through the following resonance risk factor calculation formula;

[0103]

[0104] In the formula, \(R(t)\) represents the resonance risk factor at time \(t\), \(\cos\) represents the cosine function, \(\varphi(t)\) represents the phase difference of frequency coupling at time \(t\), that is, the difference between the angular displacement of the end of the robotic arm changing with time and the angular displacement of the floating platform changing with time, \(\gamma\) represents the resonance sensitivity coefficient, \(e\) represents the exponential function, \(\beta\) represents the adjustment factor used to control the sensitivity of the influence of the floating platform vibration, \(A_{th}\) represents the floating platform vibration threshold, and the value is dimensionless. That is, when the platform vibration exceeds this floating platform vibration threshold, the resonance risk increases sharply;

[0105] represents the ratio of the frequency difference between the platform and the robotic arm. This term quantifies the frequency difference between the platform and the robotic arm. When the frequencies of the platform and the robotic arm are close;

[0106] (\(\cos(\varphi(t))\cdot\gamma\)) represents the influence of the phase difference on the resonance risk. This term represents the influence of the phase difference between the platform and the robotic arm. If the phase difference \(\varphi(t)\), that is, the vibrations of the two are basically synchronized, the resonance risk is greater;

[0107] represents the non - linear enhancement of the resonance risk when the vibration amplitude of the platform exceeds the threshold, and is used to adjust the influence of the platform vibration on the resonance risk. When the vibration amplitude \(A_p(t)\) of the platform approaches or exceeds \(A_{th}\), the output of the function will increase sharply, which indicates a significant increase in the resonance risk.

[0108] The tool locking trend analysis unit calculates and outputs the tool locking trend index \(S\) by extracting the resonance risk factor \(R(t)\) at the current time \(t\) and combining it with the environmental temperature, analyzes the cumulative risk of tool locking, and performs dynamic simulation feedback based on the resonance risk assessment of the floating platform and the robotic arm and environmental changes;

[0109] The tool locking trend index \(S\) is calculated and output through the following algorithm formula;

[0110]

[0111] In the formula, \(S(t)\) represents the tool locking trend index at time \(t\), \(W_{dmax}\) represents the upper limit value of the safe temperature, \(d\) represents the differential variable, and \(dt\) represents the time differential variable.

[0112] The locking evaluation unit obtains the risk threshold \(S_{th}\) through the inverse - deduction formula by extracting the time points of tool locking failure under different combinations of frequencies, temperatures, and floating platform accelerations. The inverse - deduction formula is \(S_{th}=\max(S(t)|1)\), where 1 represents that the tool change process is completed safely, and preliminarily compares and evaluates the tool locking trend index \(S(t)\) at time \(t\) with the risk threshold \(S_{th}\) to analyze the cumulative risk state of the current tool. The specific evaluation content is as follows;

[0113] When the tool locking trend index S(t) at time t < the risk threshold Sth, it indicates that the locking trend is within the controllable range, and the current tool change is safe without adjustment;

[0114] When the tool locking trend index S(t) at time t ≥ the risk threshold Sth, it indicates that the locking trend accumulates abnormally, and there are risks of the tool being clamped, locked, and misaligned by resonance. At this time, the tool change operation is suspended, and the environment adaptation adjustment is triggered.

[0115] In this embodiment, the resonance coupling simulation module of the system realizes the accurate modeling of the vibration coupling mechanism between the robotic arm and the floating platform and the dynamic monitoring of the risk evolution process through the hierarchical cooperation of the resonance risk analysis unit, the tool locking trend analysis unit, and the locking evaluation unit. First, the resonance risk analysis unit in the module calls the resonance perception data set in the digital twin model and constructs a resonance risk factor calculation formula R based on key parameters such as the coupling frequency difference, phase difference, and platform vibration amplitude to realize the quantitative analysis of the potential resonance state between the floating platform and the robotic arm. Further, the tool locking trend analysis unit jointly models the real-time resonance risk factor R with external disturbance factors such as environmental temperature, and calculates the tool locking trend index S, thereby reflecting the cumulative change trend of the tool locking risk under a specific vibration and temperature environment. Finally, the locking evaluation unit inversely obtains the safety locking threshold Sth of the tool change task based on the successful sample data in the historical tool change process, and compares the current locking trend index S with it to realize the dynamic evaluation of the current tool locking state and automatically trigger the environment adaptation adjustment process when the threshold is exceeded. The core value of this module lies in the first establishment of a physical interaction modeling path dominated by resonance coupling among the robotic arm-platform-tool, completing three major tasks: resonance risk identification, locking trend evolution tracking, and tool change safety evaluation, truly realizing the computability, predictability, and intervention of potential self-locking abnormalities of the tool. Compared with the traditional system that only makes static judgments based on a single vibration threshold and is difficult to cope with the situation of multi-source disturbances and complex working conditions, this module is supported by digital twin, integrates frequency-domain resonance analysis, nonlinear coupling modeling, and dynamic temperature-vibration factor coupling feedback mechanism, significantly improving the safety of the tool change process and the system's adaptive ability.

[0116] Embodiment 5

[0117] Please refer to Figure 1 , specifically: The environment adaptation adjustment module includes an environmental impact analysis unit and a perception adjustment unit;

[0118] After the preliminary comparison and evaluation trigger the environment adaptation adjustment, the environmental impact analysis unit extracts the current environmental perception data set and calculates and outputs the environmental impact coefficient N;

[0119] The environmental impact coefficient N is calculated and output through the following algorithm formula:

[0120] N(t) = (a1·Wd(t)) + (a2·Sd(t)) + (a3·Fs(t));

[0121] In the formula, N(t) represents the environmental impact coefficient at time t, a1, a2, and a3 respectively represent the preset weight values of temperature Wd, humidity Sd, and wind speed Fs, and a1 + a2 + a3 = 1, and their specific values are set by the user;

[0122] The sensing and adjustment unit calculates and outputs the adjustment amount △Fm of the micro-vibration frequency at the end of the robotic arm based on the environmental impact coefficient N(t) at time t, and dynamically adjusts the operating frequency of the robotic arm to avoid misoperations or resonances caused by environmental changes;

[0123] The adjustment amount △Fm of the micro-vibration frequency at the end of the robotic arm is calculated and output through the following algorithm formula;

[0124]

[0125] In the formula, △Fm(t) represents the adjustment amount of the micro-vibration frequency at the end of the robotic arm at time t, Fwind represents the wind speed frequency influence threshold, which is the critical value of the influence of the wind speed on the robotic arm, and the value is dimensionless;

[0126] This ratio quantifies the frequency difference between the platform and the robotic arm. When the frequencies of the platform and the robotic arm are close, resonance is likely to occur, resulting in violent vibrations and unstable operations. Therefore, by adjusting the operating frequency of the robotic arm to reduce the frequency difference, the resonance risk can be avoided;

[0127] It means that the influence of the wind speed on the operation of the robotic arm increases with the increase of the wind speed. This term compares the current wind speed Fw(t) with the set wind speed threshold Fwind. When the wind speed exceeds the critical value, the frequency adjustment amount is increased to ensure that the robotic arm can adapt to the strong wind environment;

[0128] This formula adjusts the micro-vibration frequency of the robotic arm in real time according to environmental factors such as wind speed and humidity, and avoids resonances or misoperations caused by environmental changes.

[0129] In this embodiment, the environmental adaptation and adjustment module of the system consists of an environmental impact analysis unit and a perception and adjustment unit, aiming to achieve the perception, evaluation, and adaptive regulation of external environmental disturbance factors, and ensure the stable operation of the robotic arm and the safety of tool changing operations under complex environmental conditions. First, through the environmental impact analysis unit, after the resonance risk or abnormal tool locking trend is triggered, the module extracts the environmental perception data set in real time, constructs a calculation formula for the environmental impact coefficient, and outputs the environmental impact coefficient N at the current moment to quantify the interference degree of the external environment on the dynamic performance of the system. Subsequently, with the environmental impact coefficient N as the core adjustment basis, the perception and adjustment unit combines the current frequency difference ratio and wind speed influence ratio between the robotic arm and the platform, calculates and outputs the micro-vibration frequency adjustment amount ΔFm(t) at the end of the robotic arm, and realizes the dynamic adjustment of the operation frequency of the robotic arm, thereby avoiding the risks of system resonance and misoperation induced by frequency coupling or environmental disturbances. The deployment of this module completes three key tasks: quantitative perception, dynamic response, and feedback control of environmental factor changes, making up for the shortcoming of the traditional tool changing system lacking a real-time environmental disturbance suppression mechanism in complex operating environments. Traditional systems mostly rely on static set parameters and cannot respond in real time to drastic fluctuations in external conditions such as wind speed, humidity, and temperature, which easily cause problems such as unstable operation of the robotic arm, misalignment of the tool, or abnormal resonance.

[0130] Embodiment 6

[0131] Please refer to Figure 1 and Figure 3 , specifically: The tool changing operation risk analysis module includes a comprehensive tool changing operation risk analysis unit and a tool changing operation risk assessment unit;

[0132] After environmental adaptation and adjustment, the comprehensive tool changing operation risk analysis unit combines the real-time feedback of the digital twin model and the operation adjustment to extract and integrate the adjusted resonance risk factor R' and the adjusted tool locking trend index S', and performs comprehensive calculation to output the comprehensive risk index Palarm;

[0133] The comprehensive risk index Palarm is calculated and output through the following algorithm formula;

[0134]

[0135] In the formula, Palarm(t) represents the comprehensive risk index at time t, Tmax represents the upper limit value of the tool changing cycle, Rmax represents the upper limit value of the resonance risk, dS(t)' represents the differential variable of the adjusted tool locking trend index at time t, dR(t) represents the differential variable of the adjusted resonance risk factor at time t, d represents the differential variable, R(t)' represents the adjusted resonance risk factor at time t, and S(t)' represents the adjusted tool locking trend index at time t.

[0136] When the user determines that there are abnormalities in the tool change operation based on the historical comprehensive risk index Palarm, for example, assuming that the system finds in historical data that when the comprehensive risk index Palarm exceeds 0.8, the failure probability of the tool change task is relatively high, which may lead to tool locking or operation failures. Therefore, the tool change warning threshold Pth = 0.8 can be set. For the tool change warning threshold Pth, a secondary comparison and evaluation is carried out with the comprehensive risk index Palarm(t) at time t to analyze the tool change risk situation after environmental adaptation adjustment, and relevant warning prompts are generated according to the evaluation results. The specific evaluation content is as follows;

[0137] When the comprehensive risk index Palarm(t) at time t > the tool change warning threshold Pth, it indicates that after environmental adaptation adjustment, the robotic arm tool change operation is in an abnormal state. At this time, the emergency warning mechanism is activated, and a warning message is sent to the operator through the digital twin model, while the tool change operation is continued to be paused;

[0138] When the comprehensive risk index Palarm(t) at time t ≤ the tool change warning threshold Pth, it indicates that after environmental adaptation adjustment, the robotic arm tool change operation is in a normal state. At this time, no warning needs to be generated, and the tool change operation is started.

[0139] In this embodiment, the tool change operation risk analysis module of the system consists of a comprehensive tool change operation risk analysis unit and a tool change operation risk assessment unit, which is the core module for realizing system-level tool change safety judgment and operation decision control. After completing the environmental adaptation adjustment, based on the real-time feedback of the digital twin model, this module extracts and integrates the adjusted resonance risk factor R' and the adjusted tool locking trend index S', and constructs a comprehensive risk index calculation formula by introducing the risk change rate, that is, the differential term, to output the dynamic tool change risk comprehensive criterion Palarm. This index not only considers the current risk state of the system, but also incorporates its change trend, achieving a leap from static assessment to dynamic prediction. Subsequently, the risk assessment unit sets the tool change warning threshold Pth by comparing with historical data, and conducts a real-time secondary comparison on the tool change risk comprehensive criterion Palarm to accurately judge whether the current tool change task is in an abnormal state. Once the threshold is exceeded, the system will immediately trigger the emergency warning mechanism in the digital twin model, send warning messages to the control system and operators simultaneously, and automatically pause the tool change operation to prevent the risk from spreading further. This module effectively completes key tasks such as multi-factor comprehensive determination of tool change risk, perception of dynamic change trends, comparison with historical experience thresholds, and triggering of automated responses. Compared with the traditional means of relying on a single static index for extensive risk judgment, this module constructs a more robust and forward-looking comprehensive discrimination mechanism by integrating three types of risk factors: resonance-locking-environment. At the same time, by introducing the risk differential term to capture the risk mutation trend and combining the historical data to customize the threshold strategy, the warning system is made more targeted and adaptable.

[0140] Specific example: Assume that at a certain moment t:

[0141] The current tool locking trend index S(t) = 0.75 (the current tool locking risk is relatively high);

[0142] The current resonance risk factor R(t) = 0.8 (the resonance risk between the platform and the robotic arm is relatively high);

[0143] The current resonance risk upper limit Rmax = 1.0;

[0144] The current tool change cycle upper limit Tmax = 10 seconds;

[0145] The current resonance risk differential value dR(t) = 0.02, the risk change rate;

[0146] The current tool locking trend differential value dS(t) = 0.03, the locking trend change rate;

[0147] Calculation steps

[0148] Calculate the comprehensive risk index Palarm according to the formula:

[0149]

[0150] Assume the weight coefficient α = 1, then substitute the above data:

[0151] Palarm(t) = 0.75 / 10 + 0.8 / 1.0 + 1·(0.03 + 0.02) = 0.925

[0152] Determine whether to trigger an alarm

[0153] Assume the tool change warning threshold Pth = 0.8;

[0154] According to the calculation result Palarm = 0.925, since Palarm(t) > Pth, the system determines that the risk of the current tool change task is relatively high and has exceeded the preset warning threshold. Therefore, the system will trigger an emergency warning mechanism, suspend the tool change operation, and issue an alarm to the operator.

[0155] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it is understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An industrial robotic arm tool change operation monitoring system based on digital twin, characterized in that: including Model integration module: By integrating the floating platform model, the robotic arm model, and the tool changing device model, a digital twin model is created, and a set of sensing sensors is set to collect real-time data and transmit it into the digital twin model; Digital twin synchronization module: Based on real data, a two-way synchronization mechanism is executed in the digital twin model to synchronize the real physical world state and the virtual physical world state, extract sensing data, and then perform preprocessing to obtain a standard sensing data set; Resonance coupling simulation module: By extracting the standard resonance data set, calculate and output the resonance risk factor R, and calculate and output the tool locking trend index S based on the resonance risk factor R. At the same time, set the risk threshold Sth for preliminary comparison and evaluation; Environment adaptation adjustment module: When triggering environment adaptation adjustment, extract the standard environmental sensing data set, calculate and output the environmental impact coefficient N, and calculate and output the adjustment amount of the micro-vibration frequency at the end of the robotic arm △Fm based on the environmental impact coefficient N; Tool changing operation risk analysis module: By integrating the adjusted resonance risk factor R' and the adjusted tool locking trend index S', comprehensively calculate and output the comprehensive risk index Palarm, and set the tool changing warning threshold Pth for secondary comparison and evaluation.

2. The tool changing operation monitoring system for industrial robotic arms based on digital twin according to claim 1, wherein: The model integration module includes an integration unit and a real data acquisition unit; The integration unit creates a digital twin model by integrating the floating platform model, the robotic arm model, and the tool changing device model; The floating platform model is modeled by using a finite element modeling tool for the floating platform structure. The floating platform structure includes platform dimensions and connection relationships. By using structural dynamics analysis, extract the simulation frequency, damping coefficient, and vibration transmission path, and summarize the platform structure and structural dynamics to obtain the floating platform model, which is input into the digital twin engine as the technical modal response characteristics; The robotic arm model is constructed by using ROS and GAzebo / Webots for the physical model of the robotic arm. The physical model includes a multi-joint structure and degrees of freedom. By using Lagrange for dynamic modeling, establish the angular velocity, angular acceleration, and end effector trajectory of each key of the robotic arm; The tool changing device model is constructed by using the discrete element modeling method DEM to simulate the action trajectories of the three stages of tool installation, alignment, and locking between tools, and use a time-domain and frequency-domain hybrid model to describe the behavior deviation under re-vibration; The real data acquisition unit collects real data sensed from the real physical world of the floating platform, the robotic arm, and the tool changing device in real time by setting a set of sensors, and transmits the real data into the constructed digital twin model through wireless communication; The set of sensors includes an IMU inertial detection unit, a laser interferometer, a joint position encoder, a temperature sensor, a humidity sensor, a wind speed sensor, an industrial camera, and a visual recognition system; The real data includes the three-axis acceleration and angular velocity of the floating platform and the robotic arm, high-frequency micro-vibration, the real-time posture of the robotic arm, temperature, humidity, wind speed, and the tool changing state.

3. The tool changing operation monitoring system for an industrial robotic arm based on digital twin according to claim 2, wherein: The digital twin synchronization module includes a data processing input unit, a synchronization update and communication central unit, and a perception data extraction unit; The data processing input unit receives real-time real data in the digital twin model and standardizes the data format of the real data. The data format standardization includes timestamp synchronization and multi-source heterogeneous data fusion, and drives the digital twin model based on the real data after data format standardization. Among them, the driving methods of the digital twin model include data-driven and model prediction; The data-driven drives the virtual model state in the digital twin model through real-time real data; The model prediction predicts the physical real state 2 seconds later through the virtual model state; The timestamp synchronization synchronizes the timestamps of all parameters in the real data to the same time by using ROS time; The multi-source heterogeneous data fusion fuses real data from different sources by adopting a multi-modal fusion algorithm; The synchronization update and communication central unit uses the DDS / MQTT protocol as the main communication central framework and executes a two-way synchronization mechanism to synchronize the real physical world state and the virtual physical world state; The two-way synchronization mechanism simulates the virtual model state by obtaining real data from the physical world in real time, and then predicts the physical world state 2 seconds later through the virtual model state simulation, and feeds back the control instructions to the robotic arm control system.

4. The tool changing operation monitoring system for industrial robotic arms based on digital twin according to claim 3, characterized in that: The perception data extraction unit performs virtual-real synchronization and prediction feedback based on the created digital twin model, extracts perception data in real time, and preprocesses the perception data to obtain a standard perception data set. The preprocessing includes dimensionless standardization processing to eliminate the dimensional influence of all parameters in the perception data. The standard perception data set includes an environmental perception data set and a resonance perception data set; The environmental perception data set includes the temperature Wd(t) at time t, the humidity Sd(t) at time t, the wind speed Fs(t) at time t, and the wind speed frequency Fw(t) at time t; The resonance perception data set includes the resonance frequency Fp(t) of the floating platform at time t, the micro-vibration frequency Fm(t) at the end of the robotic arm at time t, and the vibration amplitude Ap(t) of the floating platform at time t.

5. The tool changing operation monitoring system for industrial robotic arms based on digital twin according to claim 4, wherein: The resonance coupling simulation module includes a resonance risk analysis unit, a tool locking trend analysis unit, and a locking evaluation unit; The resonance risk analysis unit constructs a resonance risk factor calculation formula in the digital twin model, extracts the resonance perception data set and inputs it into the resonance risk factor calculation formula, calculates and outputs the resonance risk factor R, and analyzes the resonance risk between the floating platform and the robotic arm; The resonance risk factor R is calculated and output through the following resonance risk factor calculation formula; In the formula, R(t) represents the resonance risk factor at time t, cos represents the cosine function, φ(t) represents the phase difference of frequency coupling at time t, γ represents the resonance sensitivity coefficient, e represents the exponential function, β represents the adjustment factor, and Ath represents the vibration threshold of the floating platform, with a dimensionless value.

6. The tool changing operation monitoring system of an industrial robotic arm based on digital twin according to claim 5, characterized in that: The tool locking trend analysis unit calculates and outputs the tool locking trend index S by extracting the resonance risk factor R(t) at the current time t and combining the ambient temperature, analyzes the cumulative risk of tool locking, and performs dynamic simulation feedback based on the resonance risk assessment of the floating platform and the robotic arm and environmental changes; The tool locking trend index S is calculated and output through the following algorithm formula; In the formula, S(t) represents the tool locking trend index at time t, Wdmax represents the upper limit value of the safe temperature, d represents the differential variable, and dt represents the time differential variable.

7. The tool changing operation monitoring system for an industrial robotic arm based on digital twin according to claim 6, characterized in that: The locking evaluation unit extracts the time points of tool locking failure under different combinations of frequencies, temperatures, and floating platform accelerations, obtains the risk threshold Sth through the inverse formula, and makes a preliminary comparison and evaluation between the tool locking trend index S(t) at time t and the risk threshold Sth to analyze the cumulative risk status of the current tool. The specific evaluation content is as follows; When the tool locking trend index S(t) at time t < the risk threshold Sth, it indicates that the locking trend is within the controllable range, and the current tool change is safe without adjustment; When the tool locking trend index S(t) at time t ≥ the risk threshold Sth, it indicates that the cumulative locking trend is abnormal. At this time, the tool change operation is paused, and the environmental adaptation adjustment is triggered.

8. The tool changing operation monitoring system of an industrial robotic arm based on digital twin according to claim 7, characterized in that: The environmental adaptation adjustment module includes an environmental impact analysis unit and a perception adjustment unit; The environmental impact analysis unit extracts the current environmental perception data set after the environmental adaptation adjustment is triggered by the preliminary comparison and evaluation, and calculates and outputs the environmental impact coefficient N; The environmental impact coefficient N is calculated and output through the following algorithm formula: N(t) = (a1·Wd(t)) + (a2·Sd(t)) + (a3·Fs(t)); In the formula, N(t) represents the environmental impact coefficient at time t, a1, a2, and a3 respectively represent the preset weight values of temperature Wd, humidity Sd, and wind speed Fs, and a1 + a2 + a3 = 1. The specific values are set by the user; The perception adjustment unit calculates and outputs the adjustment amount △Fm of the micro-vibration frequency at the end of the robotic arm based on the environmental impact coefficient N(t) at time t, and dynamically adjusts the operation frequency of the robotic arm; The adjustment amount △Fm of the micro-vibration frequency at the end of the robotic arm is calculated and output through the following algorithm formula; In the formula, △Fm(t) represents the adjustment amount of the micro-vibration frequency at the end of the robotic arm at time t, and Fwind represents the influence threshold of the wind speed frequency, with a dimensionless value.

9. The tool changing operation monitoring system for industrial robotic arms based on digital twin according to claim 8, characterized in that: The tool change operation risk analysis module includes a comprehensive tool change operation risk analysis unit and a tool change operation risk assessment unit; The comprehensive tool change operation risk analysis unit combines the real-time feedback of the digital twin model and the operation adjustment after the environmental adaptation adjustment, extracts and integrates the adjusted resonance risk factor R' and the adjusted tool locking trend index S', and calculates and outputs the comprehensive risk index Palarm; The comprehensive risk index Palarm is calculated and output through the following algorithm formula; Where, Palarm(t) represents the comprehensive risk index at time t, Tmax represents the upper limit value of the tool change cycle, Rmax represents the upper limit value of the resonance risk, dS(t)' represents the differential variable of the adjusted tool locking trend index at time t, dR(t) represents the differential variable of the adjusted resonance risk factor at time t, d represents the differential variable, R(t)' represents the adjusted resonance risk factor at time t, and S(t)' represents the adjusted tool locking trend index at time t.

10. The tool changing operation monitoring system for industrial robotic arms based on digital twin according to claim 9, characterized in that: When the user determines that there is an abnormality in the tool change operation based on the historical comprehensive risk index Palarm, the tool change warning threshold Pth is set by the tool change operation risk assessment unit. Then, the tool change warning threshold Pth is compared with the comprehensive risk index Palarm(t) at time t for a secondary evaluation to analyze the tool change risk situation after environmental adaptation adjustment, and relevant warning prompts are generated according to the evaluation results. The specific evaluation content is as follows: When the comprehensive risk index Palarm(t) at time t > the tool change warning threshold Pth, it indicates that after environmental adaptation adjustment, the robotic arm tool change operation is in an abnormal state. At this time, the emergency warning mechanism is activated to send a warning message to the operator through the digital twin model, and the tool change operation is continued to be paused simultaneously. When the comprehensive risk index Palarm(t) at time t ≤ the tool change warning threshold Pth, it indicates that after environmental adaptation adjustment, the robotic arm tool change operation is in a normal state. At this time, no warning needs to be generated, and the tool change operation is started.

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