Improved industrial robot energy consumption control method

Abstract

The invention relates to a robot energy consumption control technology, in particular to an improved industrial robot energy consumption control method, which comprises the following steps: acquiring energy consumption data of a robot during working in real time, and establishing an energy consumption monitoring system; based on historical data and real-time monitoring data, energy consumption characteristics of the robot are analyzed, and main factors and change rules influencing energy consumption are identified; based on the energy consumption characteristics, selecting energy-saving optimization schemes in different scenes; parameters of the robot are dynamically adjusted based on an energy-saving strategy; path planning and a motion control algorithm of the robot are optimized, an energy-saving strategy is combined, the motion path and posture of the robot are adjusted, and the motion track of the robot is optimized; feeding back and adjusting an energy-saving strategy according to the real-time monitoring data and the energy consumption model; and performance evaluation and optimization are carried out on the energy-saving strategy and the control algorithm regularly. Through energy consumption prediction and real-time data updating, energy consumption distribution of the robot can be more reasonable, and a large amount of energy consumption is saved.

Description

Technical Field

[0001] The invention belongs to the technical field of robot energy consumption control, and in particular relates to an improved industrial robot energy consumption control method. Background Art

[0002] Industrial robots are high-energy consumption equipment with extremely low energy efficiency. They are widely used in handling, welding, spraying and other occasions. The actual demand for energy conservation and emission reduction makes reducing the energy consumption of robots another important task in addition to improving control accuracy. The energy consumption of industrial robots is multi-source, complex and dynamic; the existing robot energy consumption control technology mainly controls from the perspective of predicting energy consumption. The energy consumption prediction of industrial robots is relatively complex and needs to be considered from multiple aspects, resulting in poor prediction results. Many studies on optimizing robot trajectories remain in the simulation stage, and it is impossible to know the actual effect of energy consumption optimization. In order to effectively reduce the energy consumption of industrial robots during operation, it is necessary to establish a detailed power consumption model of the robot system and conduct actual tests on the optimized trajectory on the robot so that the system can be updated in real time. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an improved industrial robot energy consumption control method, which has the advantages of being able to effectively save energy consumption, accurately predict and test energy consumption, and perform real-time updates.

[0004] The technical solution adopted by the present invention to solve the above technical problems is: an improved industrial robot energy consumption control method, the energy consumption control method comprising: real-time collection of energy consumption data of the robot when it is working, and establishment of an energy consumption monitoring system; based on historical data and real-time monitoring data, analyzing the energy consumption characteristics of the robot, and identifying the main factors affecting energy consumption and the changing laws; based on the energy consumption characteristics, combined with the working state, environmental conditions and task requirements of the robot, selecting energy-saving optimization solutions under different scenarios; based on energy-saving strategies, dynamically adjusting the parameters of the robot; optimizing the robot's path planning and motion control algorithms, combining energy-saving strategies, adjusting the robot's motion path and posture, and optimizing the robot's motion trajectory; real-time monitoring of the robot's energy consumption, and feedback adjustment of the energy-saving strategy based on the real-time monitoring data and energy consumption model; regular performance evaluation and optimization of energy-saving strategies and control algorithms.

[0005] Preferably, the specific steps of S1 are:

[0006] Arrange sensors according to the structure and working characteristics of the robot. The sensors can accurately monitor the energy consumption parameters of the robot and install the sensors at the positions of the robot's motor and controller.

[0007] Connect the sensor to the data acquisition device and use a signal amplifier or converter to convert the sensor signal into a digital signal for subsequent processing and analysis;

[0008] Configure data acquisition equipment, establish a data acquisition system, ensure that the robot's energy consumption data can be collected in real time, select appropriate data acquisition software or programming language, and write programs to achieve real-time data collection and storage;

[0009] Process and analyze the collected energy consumption data, including data cleaning, denoising, calibration and other operations to ensure the accuracy and reliability of the data;

[0010] Use data analysis tools or programming languages ​​to conduct statistical analysis and trend analysis on energy consumption data to find out the patterns and changing trends of energy consumption;

[0011] The processed and analyzed energy consumption data is visualized, such as drawing energy consumption curves and bar charts, so as to intuitively understand the energy consumption of the robot.

[0012] Preferably, the specific steps of S2 are:

[0013] Use statistical methods to extract average energy consumption, peak energy consumption and energy consumption fluctuation characteristics from the data;

[0014] Use machine learning technology to build an energy consumption model, associate the robot's energy consumption with environmental parameters, operating conditions and other factors, and then make energy consumption predictions, estimating parameters β0 and β1 by minimizing the difference between the observed value and the model's predicted value;

[0015] The machine learning technique formula is:

[0016] [G fh =β0+β1F bj +ε]

[0017] Among them, G fh is the energy consumption, F bj is the environmental parameter, β0 is the offset of the model, β1 is the influence of the independent variable on the dependent variable, and ε is the random error;

[0018] The energy consumption prediction formula is:

[0019]

[0020] Use historical data to train the established model to learn the relationship between energy consumption and various factors and evaluate the performance of the model;

[0021] Model verification: Use real-time monitoring data to verify the trained model, evaluate the model's generalization ability on new data, and check whether the model can accurately predict the robot's energy consumption;

[0022] Analysis results: Analyze the output results of the model, explore the main influencing factors and changing patterns of the robot's energy consumption, identify the factors that have the greatest impact on energy consumption, and the correlation between them.

[0023] Preferably, the specific steps of S3 are:

[0024] Classify the robot's working scenarios and analyze the robot's task requirements and performance requirements in each scenario, as well as the characteristics of environmental conditions, including temperature, humidity and light;

[0025] Design targeted energy-saving optimization strategies based on energy consumption characteristics and scenario requirements, including:

[0026] Dynamically adjust the robot's working mode and select the appropriate energy consumption mode according to real-time working requirements and environmental conditions;

[0027] Optimize the mobile path planning algorithm to reduce the robot's energy consumption during movement;

[0028] Control the robot's movement speed and acceleration to reduce energy consumption and ensure task execution efficiency;

[0029] Adjust the sensor usage strategy and select the appropriate sensor type and working mode according to needs to reduce energy consumption;

[0030] Formulate a robot standby strategy to avoid unnecessary energy waste;

[0031] Use smart charging and energy recovery technology to manage the robot's energy intelligently and improve energy efficiency;

[0032] Use simulation tools or simulation software to simulate and test the designed energy-saving optimization plan. Through simulation, the effects of different strategies can be evaluated to find the optimal energy-saving plan.

[0033] Preferably, the specific steps of S4 are:

[0034] According to the real-time monitoring data and the preset adjustment strategy, the decision of parameter adjustment is made. When the robot is under low load, its movement speed is reduced to reduce energy consumption; when the ambient temperature is high, the sampling frequency of the sensor is reduced to reduce power consumption;

[0035] Dynamically adjust the robot's parameters based on the decision results. This can be achieved through control software or hardware interfaces, such as adjusting the robot's control instructions, modifying the sensor's working mode, adjusting the charging strategy, etc.

[0036] After parameter tuning is performed, the robot’s performance and energy consumption are evaluated.

[0037] Preferably, the specific steps of S5 are:

[0038] Establish an evaluation model for robot motion energy consumption, consider the energy consumption characteristics of the robot in different motion states, including the impact of speed, acceleration and turning radius on energy consumption, and design an energy-saving path planning strategy based on the energy consumption evaluation model. During the path planning process, adjust the robot's motion path according to real-time work requirements and environmental conditions, with the goal of minimizing energy consumption, reducing unnecessary turns and repeated movements, and selecting the optimal route to reduce energy consumption;

[0039] Design a motion control algorithm for energy-saving path planning. The motion control algorithm can control the robot's motion parameters such as speed, acceleration, turning radius, etc. to achieve energy-saving goals, use a smooth speed curve to reduce energy consumption, avoid energy waste caused by sudden acceleration or deceleration, and dynamically adjust the robot's motion trajectory based on real-time environmental monitoring results. According to the energy-saving path planning design strategy, adjust the robot's motion path and posture to adapt to real-time work requirements and environmental conditions.

[0040] Preferably, the specific steps of S6 are:

[0041] During the operation of the robot, real-time energy consumption data is continuously collected, including the robot's motion status, energy consumption, and working environment;

[0042] Use the collected real-time energy consumption data to update the energy consumption model, which can accurately reflect the energy consumption characteristics of the robot under different working conditions, including the impact of speed, load, and acceleration on energy consumption. Correct and adjust the model according to the actual data to improve accuracy and reliability.

[0043] By analyzing and processing the collected energy consumption data, data analysis techniques and algorithms can be used to identify the main factors affecting energy consumption, discover potential problems and optimization space for energy consumption, and determine the adjustment direction and optimization goals of energy-saving strategies by analyzing energy consumption data;

[0044] Adjust energy-saving strategies based on the results of energy consumption data analysis, and adjust the robot's movement speed, path selection, and work route based on energy consumption models and real-time monitoring data to reduce energy consumption and improve efficiency;

[0045] Feedback the adjusted energy-saving strategy to the robot's control system in real time, and control the robot's movement in real time. This can be achieved through the real-time communication interface of the robot control system, ensuring that the adjustment of the energy-saving strategy can be applied to the actual operation of the robot in a timely and effective manner;

[0046] After the energy-saving strategy is adjusted, the performance and energy consumption of the robot are continuously monitored and evaluated, and the energy-saving strategy is further optimized and improved based on the actual effects and feedback results. This can be evaluated and optimized by comparing the actual energy consumption with the expected energy consumption, the robot's work efficiency and other indicators.

[0047] Preferably, the specific steps of S7 are: regularly collect performance data during the operation of the robot, including energy consumption, running time, motion path and load conditions, formulate evaluation indicators, determine key indicators for evaluating the performance of energy-saving strategies and control algorithms, analyze the collected data, identify potential performance problems and optimization space, use data analysis techniques and tools to discover the correlation and regularity between data, so as to deeply understand the influencing factors of energy-saving strategies and control algorithms, evaluate the performance of energy-saving strategies and control algorithms according to the formulated evaluation indicators, compare actual performance with expected performance, identify performance gaps and determine the focus and direction of improvement, and formulate optimization strategies and improvement measures based on the results of performance evaluation, involving adjusting the parameters of energy-saving strategies, improving the design of control algorithms, optimizing the robot's motion planning and path selection, etc., to ensure that the optimization strategy is targeted and feasible, and can effectively improve the performance of the system.

[0048] Preferably, the optimization strategies and improvement measures are implemented into the robot system, including updating and modifying the control software, algorithm code and robot hardware, to ensure that the implementation process of the improvement measures is safe and reliable and will not affect the stability and reliability of the system.

[0049] Preferably, monitor the implementation effect of improvement measures, collect feedback information in a timely manner, continuously monitor the performance changes of the robot system, and compare them with previous data. According to the monitoring feedback, timely adjust and optimize the improvement measures to ensure that the system performance continues to improve steadily, establish a mechanism and process for continuous improvement, ensure regular performance evaluation and optimization of energy-saving strategies and control algorithms, continuously accumulate experience and improvement measures, and improve the performance and energy efficiency of the robot system.

[0050] The beneficial effects of the present invention are: through the prediction of energy consumption and real-time updating of data, the energy consumption distribution of the robot can be made more reasonable, a large amount of energy consumption can be saved, the energy consumption can be accurately predicted and tested, and real-time updating and real-time adjustment can be performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of the industrial robot energy consumption control method of the present invention. DETAILED DESCRIPTION

[0052] The present invention will now be described in further detail in conjunction with the accompanying drawings and preferred embodiments. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0053] An improved industrial robot energy consumption control method, the energy consumption control method comprising:

[0054] S1: Real-time collection of energy consumption data of the robot during operation and establishment of an energy consumption monitoring system;

[0055] S2: Based on historical data and real-time monitoring data, analyze the energy consumption characteristics of the robot and identify the main factors affecting energy consumption and their changing patterns;

[0056] S3: Based on the energy consumption characteristics, combined with the robot's working status, environmental conditions and task requirements, select energy-saving optimization solutions in different scenarios;

[0057] S4: Dynamically adjust robot parameters based on energy-saving strategies;

[0058] S5: Optimize the robot's path planning and motion control algorithms, combine energy-saving strategies, adjust the robot's motion path and posture, and optimize the robot's motion trajectory;

[0059] S6: Monitor the energy consumption of the robot in real time, and adjust the energy-saving strategy based on the real-time monitoring data and energy consumption model;

[0060] S7: Regularly evaluate and optimize the performance of energy-saving strategies and control algorithms

[0061] Among them, the specific steps of S1 are:

[0062] Arrange sensors according to the structure and working characteristics of the robot. The sensors can accurately monitor the energy consumption parameters of the robot and install the sensors at the positions of the robot's motor and controller.

[0063] Connect the sensor to the data acquisition device and use a signal amplifier or converter to convert the sensor signal into a digital signal for subsequent processing and analysis;

[0064] Configure data acquisition equipment, establish a data acquisition system, ensure that the robot's energy consumption data can be collected in real time, select appropriate data acquisition software or programming language, and write programs to achieve real-time data collection and storage;

[0065] Process and analyze the collected energy consumption data, including data cleaning, denoising, calibration and other operations to ensure the accuracy and reliability of the data;

[0066] Use data analysis tools or programming languages ​​to conduct statistical analysis and trend analysis on energy consumption data to find out the patterns and changing trends of energy consumption;

[0067] The processed and analyzed energy consumption data is visualized, such as drawing energy consumption curves and bar charts, so as to intuitively understand the energy consumption of the robot.

[0068] Furthermore, the specific steps of S2 are:

[0069] Use statistical methods to extract average energy consumption, peak energy consumption and energy consumption fluctuation characteristics from the data;

[0070] Use machine learning technology to build an energy consumption model, associate the robot's energy consumption with environmental parameters, operating conditions and other factors, and then make energy consumption predictions, estimating parameters β0 and β1 by minimizing the difference between the observed value and the model's predicted value;

[0071] The machine learning technique formula is:

[0072]

[0073] Among them, G fh is the energy consumption, F bj is the environmental parameter, β0 is the offset of the model, β1 is the influence of the independent variable on the dependent variable, and ε is the random error;

[0074] The energy consumption prediction formula is:

[0075]

[0076] Use historical data to train the established model to learn the relationship between energy consumption and various factors and evaluate the performance of the model;

[0077] Model verification: Use real-time monitoring data to verify the trained model, evaluate the model's generalization ability on new data, and check whether the model can accurately predict the robot's energy consumption;

[0078] Analysis results: Analyze the output results of the model, explore the main influencing factors and changing patterns of the robot's energy consumption, identify the factors that have the greatest impact on energy consumption, and the correlation between them.

[0079] Furthermore, the specific steps of S3 are:

[0080] Classify the robot's working scenarios and analyze the robot's task requirements and performance requirements in each scenario, as well as the characteristics of environmental conditions, including temperature, humidity and light;

[0081] Design targeted energy-saving optimization strategies based on energy consumption characteristics and scenario requirements, including:

[0082] Dynamically adjust the robot's working mode and select the appropriate energy consumption mode according to real-time working requirements and environmental conditions;

[0083] Optimize the mobile path planning algorithm to reduce the robot's energy consumption during movement;

[0084] Control the robot's movement speed and acceleration to reduce energy consumption and ensure task execution efficiency;

[0085] Adjust the sensor usage strategy and select the appropriate sensor type and working mode according to needs to reduce energy consumption;

[0086] Formulate a robot standby strategy to avoid unnecessary energy waste;

[0087] Use smart charging and energy recovery technology to manage the robot's energy intelligently and improve energy efficiency;

[0088] Use simulation tools or simulation software to simulate and test the designed energy-saving optimization plan. Through simulation, the effects of different strategies can be evaluated to find the optimal energy-saving plan.

[0089] Furthermore, the specific steps of S4 are:

[0090] According to the real-time monitoring data and the preset adjustment strategy, the decision of parameter adjustment is made. When the robot is under low load, its movement speed is reduced to reduce energy consumption; when the ambient temperature is high, the sampling frequency of the sensor is reduced to reduce power consumption;

[0091] Dynamically adjust the robot's parameters based on the decision results. This can be achieved through control software or hardware interfaces, such as adjusting the robot's control instructions, modifying the sensor's working mode, adjusting the charging strategy, etc.

[0092] After parameter tuning is performed, the robot’s performance and energy consumption are evaluated.

[0093] Furthermore, the specific steps of S5 are:

[0094] Establish an evaluation model for robot motion energy consumption, consider the energy consumption characteristics of the robot in different motion states, including the impact of speed, acceleration and turning radius on energy consumption, and design an energy-saving path planning strategy based on the energy consumption evaluation model. During the path planning process, adjust the robot's motion path according to real-time work requirements and environmental conditions, with the goal of minimizing energy consumption, reducing unnecessary turns and repeated movements, and selecting the optimal route to reduce energy consumption;

[0095] Furthermore, the specific steps of S6 are:

[0096] During the operation of the robot, real-time energy consumption data is continuously collected, including the robot's motion status, energy consumption, and working environment;

[0097] Use the collected real-time energy consumption data to update the energy consumption model, which can accurately reflect the energy consumption characteristics of the robot under different working conditions, including the impact of speed, load, and acceleration on energy consumption. Correct and adjust the model according to the actual data to improve accuracy and reliability.

[0098] By analyzing and processing the collected energy consumption data, data analysis techniques and algorithms can be used to identify the main factors affecting energy consumption, discover potential problems and optimization space for energy consumption, and determine the adjustment direction and optimization goals of energy-saving strategies by analyzing energy consumption data;

[0099] Adjust energy-saving strategies based on the results of energy consumption data analysis, and adjust the robot's movement speed, path selection, and work route based on energy consumption models and real-time monitoring data to reduce energy consumption and improve efficiency;

[0100] Feedback the adjusted energy-saving strategy to the robot's control system in real time, and control the robot's movement in real time. This can be achieved through the real-time communication interface of the robot control system, ensuring that the adjustment of the energy-saving strategy can be applied to the actual operation of the robot in a timely and effective manner;

[0101] After the energy-saving strategy is adjusted, the performance and energy consumption of the robot are continuously monitored and evaluated, and the energy-saving strategy is further optimized and improved based on the actual effects and feedback results. This can be evaluated and optimized by comparing the actual energy consumption with the expected energy consumption, the robot's work efficiency and other indicators.

[0102] Furthermore, the specific steps of S7 are: regularly collect performance data during the operation of the robot, including energy consumption, operating time, motion path and load conditions to formulate evaluation indicators, determine key indicators for evaluating the performance of energy-saving strategies and control algorithms, analyze the collected data, identify potential performance problems and optimization space, use data analysis techniques and tools to discover the correlation and regularity between data, so as to deeply understand the influencing factors of energy-saving strategies and control algorithms, evaluate the performance of energy-saving strategies and control algorithms according to the formulated evaluation indicators, compare actual performance with expected performance, identify performance gaps and determine the focus and direction of improvement, and formulate optimization strategies and improvement measures based on the results of performance evaluation, involving adjusting the parameters of energy-saving strategies, improving the design of control algorithms, optimizing the robot's motion planning and path selection, etc., to ensure that the optimization strategy is targeted and feasible and can effectively improve the performance of the system.

[0103] Design a motion control algorithm for energy-saving path planning. The motion control algorithm can control the robot's motion parameters such as speed, acceleration, turning radius, etc. to achieve energy-saving goals, use a smooth speed curve to reduce energy consumption, avoid energy waste caused by sudden acceleration or deceleration, and dynamically adjust the robot's motion trajectory based on real-time environmental monitoring results. According to the energy-saving path planning design strategy, adjust the robot's motion path and posture to adapt to real-time work requirements and environmental conditions.

[0104] Furthermore, the optimization strategies and improvement measures will be implemented into the robot system, including the update and modification of the control software, algorithm code and robot hardware, to ensure that the implementation process of the improvement measures is safe and reliable and will not affect the stability and reliability of the system.

[0105] Furthermore, monitor the implementation effect of improvement measures, collect feedback information in a timely manner, continuously monitor the performance changes of the robot system, and compare them with previous data. According to the monitoring feedback, timely adjust and optimize improvement measures to ensure that the system performance continues to improve steadily, establish a mechanism and process for continuous improvement, ensure regular performance evaluation and optimization of energy-saving strategies and control algorithms, continuously accumulate experience and improvement measures, and improve the performance and energy efficiency of the robot system.

[0106] The above description only describes the specific implementation mode of the present invention. Various examples do not limit the essential content of the present invention. After reading the description, ordinary technicians in the relevant technical field can modify or deform the specific implementation mode described above without departing from the essence and scope of the invention.

Claims

1. An improved industrial robot energy consumption control method, characterized in that: Energy consumption control methods include: S1: Real-time collection of energy consumption data of the robot during operation and establishment of an energy consumption monitoring system; S2: Based on historical data and real-time monitoring data, analyze the energy consumption characteristics of the robot and identify the main factors affecting energy consumption and their changing patterns; S3: Based on the energy consumption characteristics, combined with the robot's working status, environmental conditions and task requirements, select energy-saving optimization solutions in different scenarios; S4: Dynamically adjust robot parameters based on energy-saving strategies; S5: Optimize the robot's path planning and motion control algorithms, combine energy-saving strategies, adjust the robot's motion path and posture, and optimize the robot's motion trajectory; S6: Monitor the energy consumption of the robot in real time, and adjust the energy-saving strategy based on the real-time monitoring data and energy consumption model; S7: Regularly evaluate and optimize the performance of energy-saving strategies and control algorithms.

2. The improved industrial robot energy consumption control method according to claim 1, characterized in that: The specific steps of S1 are: Arrange sensors according to the structure and working characteristics of the robot and install them at the positions of the robot's motor and controller; Connect the sensor to the data acquisition device and use a signal amplifier or converter to convert the sensor signal into a digital signal; Configure data acquisition equipment, establish a data acquisition system, and collect and store data in real time; Clean, denoise and calibrate the collected energy consumption data; Use data analysis tools or programming languages ​​to conduct statistical analysis and trend analysis on energy consumption data to find out the patterns and changing trends of energy consumption; The processed and analyzed energy consumption data is visualized.

3. The improved industrial robot energy consumption control method according to claim 1, characterized in that: The specific steps of S2 are: Use statistical methods to extract average energy consumption, peak energy consumption and energy consumption fluctuation characteristics from the data; Use machine learning technology to build an energy consumption model, associate the robot's energy consumption with environmental parameters, operating conditions and other factors, and then make energy consumption predictions, estimating parameters β0 and β1 by minimizing the difference between the observed value and the model's predicted value; The machine learning technique formula is: Among them, G fh is the energy consumption, F bj is the environmental parameter, β0 is the offset of the model, β1 is the influence of the independent variable on the dependent variable, and ε is the random error; The energy consumption prediction formula is: Use historical data to train the established model to learn the relationship between energy consumption and various factors and evaluate the performance of the model; Model verification: Use real-time monitoring data to verify the trained model, evaluate the model's generalization ability on new data, and check whether the model can accurately predict the robot's energy consumption; Analysis results: Analyze the output results of the model, explore the main influencing factors and changing patterns of the robot's energy consumption, identify the factors that have the greatest impact on energy consumption, and the correlation between them.

4. The improved industrial robot energy consumption control method according to claim 1, characterized in that: The specific steps of S3 are: Classify the robot's working scenarios and analyze the robot's task requirements and performance requirements in each scenario, as well as the characteristics of environmental conditions, including temperature, humidity and light; Design targeted energy-saving optimization strategies based on energy consumption characteristics and scenario requirements; Dynamically adjust the robot's working mode, optimize the mobile path planning algorithm, reduce the robot's energy consumption during movement, control the robot's movement speed and acceleration to reduce energy consumption and ensure task execution efficiency, adjust the sensor usage strategy, select the appropriate sensor type and working mode according to needs, and formulate the robot's standby strategy; Use simulation tools or simulation software to simulate and test the designed energy-saving optimization plan, evaluate the effects of different strategies through simulation, and select the optimal energy-saving plan.

5. The improved industrial robot energy consumption control method according to claim 1, characterized in that: The specific steps of S4 are: According to the real-time monitoring data and the preset adjustment strategy, the decision of parameter adjustment is made. When the robot is under low load, its movement speed is reduced to reduce energy consumption. When the ambient temperature is high, the sampling frequency of the sensor is reduced to reduce power consumption. According to the decision results, the robot parameters are dynamically adjusted, and after the parameter adjustment is executed, the robot's performance and energy consumption are evaluated.

6. The improved industrial robot energy consumption control method according to claim 1, characterized in that: The specific steps of S5 are: Establish an evaluation model for robot motion energy consumption, consider the energy consumption characteristics of the robot under different motion states, and design an energy-saving path planning strategy in combination with the energy consumption evaluation model. During the path planning process, adjust the robot's motion path according to real-time work requirements and environmental conditions, with the goal of minimizing energy consumption, reducing unnecessary turns and repeated movements, and selecting the optimal route to reduce energy consumption.

7. The improved industrial robot energy consumption control method according to claim 1, characterized in that: The specific steps of S6 are: During the operation of the robot, real-time energy consumption data is continuously collected, including the robot's motion status, energy consumption, and working environment; Use the collected real-time energy consumption data to update the energy consumption model; Analyze and process the collected energy consumption data, identify the main factors affecting energy consumption, find potential problems and optimization space for energy consumption, and determine the adjustment direction and optimization goals of energy-saving strategies by analyzing energy consumption data; Feedback the adjusted energy-saving strategy to the robot's control system in real time, and control the robot's movement in real time; After the energy-saving strategy is adjusted, the performance and energy consumption of the robot will be continuously monitored and evaluated, and the energy-saving strategy will be further optimized and improved based on the actual effects and feedback results.

8. The improved industrial robot energy consumption control method according to claim 1, characterized in that: The specific steps of S7 are: regularly collect performance data during robot operation, including energy consumption, running time, motion path and load conditions to formulate evaluation indicators, identify potential performance problems and optimization space, evaluate the performance of energy-saving strategies and control algorithms according to the formulated evaluation indicators, compare actual performance with expected performance, identify performance gaps and determine the focus and direction of improvement, and formulate optimization strategies and improvement measures based on the results of performance evaluation.

9. The improved industrial robot energy consumption control method according to claim 1, characterized in that: Implement optimization strategies and improvements into the robotic system, including updates and modifications to control software, algorithm code, and robotic hardware.

10. The improved industrial robot energy consumption control method according to claim 1, characterized in that: Monitor the implementation effect of improvement measures, collect feedback information in a timely manner, continuously monitor the performance changes of the robot system, and compare them with previous data. According to the monitoring feedback, timely adjust and optimize the improvement measures.

Images