Automatic response control equipment and system based on artificial intelligence

Abstract

The invention relates to the technical field of automatic control, and particularly discloses an automatic response control device and system based on artificial intelligence, and the system comprises a data collection module which is used for collecting the operation state data of a mechanical arm of an automatic robot in the part grabbing process, the data calculation module is combined with the operation state data of the mechanical arm after cleaning in the part grabbing process of the automatic robot, and the data can reflect the load condition of the mechanical arm in each grabbing operation process; due to the fact that the load condition of the mechanical arm can reflect the adaptation condition of the mechanical arm to inertia force generated during grabbing, artificial intelligence can make a decision on whether the grabbing speed of the mechanical arm needs to be adjusted or not on the basis of the data, and therefore the grabbing speed of the mechanical arm is automatically adjusted. And the situation that the service life of the mechanical arm is shortened due to the fact that the mechanical arm generates microcracks under the high-load condition is avoided.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, specifically to automated response control devices and systems based on artificial intelligence. Background Technology

[0002] Artificial intelligence-based automated response control systems are advanced systems that integrate artificial intelligence theory and automated control technology. They are commonly used in the field of industrial robot control and aim to simulate human intelligence to achieve autonomous decision-making, optimize response, and dynamically adjust control strategies in complex environments.

[0003] As an important piece of equipment in industrial production, automated robots are typically used to grip materials for screening and sorting operations. In order to ensure the long-term use of automated robots, traditional automated response control systems will set an appropriate gripping speed for the robotic arm before the material gripping operation, based on the physical data of the material, such as weight and volume. This not only ensures the rapid gripping of materials, but also avoids the situation where the robotic arm's lifespan is affected by excessive inertial impact due to excessive gripping speed, thereby ensuring production efficiency.

[0004] In existing technologies, traditional automated response control systems set a suitable gripping speed for the robotic arm by combining physical data of the material, such as weight and volume. However, due to fatigue after prolonged use, i.e., as the load on the robotic arm increases, its adaptability to the inertial forces generated during gripping gradually decreases. If the gripping speed is not adjusted in time, micro-cracks may develop in the robotic arm, leading to a significant reduction in its service life. Summary of the Invention

[0005] The purpose of this invention is to provide an automated response control device and system based on artificial intelligence, and to solve the following technical problems: How to dynamically adjust the gripping speed of a robotic arm to improve its service life.

[0006] The objective of this invention can be achieved through the following technical solutions: An automated response control device and system based on artificial intelligence, the system comprising: The data acquisition module is used to collect the operating status data of the robotic arm during the process of the automated robot grasping parts; The data cleaning module uses artificial intelligence to clean the operational status data of the robotic arm collected by the data acquisition module. The data calculation module is used to calculate the mechanical load index of the robotic arm in each grasping operation by combining the running status data of the robotic arm after cleaning during the part grasping process of the automated robot. The load analysis module is used to compare the mechanical load index of the robotic arm in each grasping operation with the preset mechanical load index threshold, and decide whether to adjust the robotic arm grasping speed based on the comparison results. The intelligent control module is used to adjust the gripping speed of the robotic arm when the decision requires adjustment. It establishes an algorithm model based on artificial intelligence and the mechanical load index of the robotic arm in each gripping operation, and controls the gripping speed of the robotic arm in the next gripping operation.

[0007] Furthermore, the data collected by the data acquisition module includes: The joint temperature, noise level, total energy consumption, and output power of the robotic arm during each part grabbing process.

[0008] Furthermore, the data cleaning module's cleaning process includes: S1: Based on artificial intelligence and the correlation coefficient calculation formula, the correlation coefficient between the output power and the total energy consumption is calculated from the start of the robotic arm's grasping operation to any grasping operation. S2: By comparing and analyzing the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of the grasping operation to any grasping operation with the preset correlation coefficient, the correlation between the output power of the robotic arm and the total energy consumption is determined based on the comparison results. S3: By combining the correlation analysis results between the output power of the robotic arm and the total energy consumption, the total energy consumption data of the robotic arm in any grasping operation is corrected.

[0009] Furthermore, the calculation process in S1 includes: By combining artificial intelligence with the output power and total energy consumption of the robotic arm during each grasping operation after it begins to grasp, and based on the correlation coefficient formula, the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of the grasping operation to any grasping operation is calculated.

[0010] Furthermore, the analysis process in S2 includes: The correlation coefficient between the output power of the robotic arm and the total energy consumption from the start of the grasping operation to any grasping operation is compared with a preset correlation coefficient threshold. If the correlation coefficient between the output power of the robotic arm and the total energy consumption is greater than the preset correlation coefficient threshold after the robotic arm starts grasping operations until any grasping operation, the system determines that the output power of the robotic arm and the total energy consumption are positively correlated. Conversely, the system determines that the output power of the robotic arm is unrelated to the total energy consumption.

[0011] Furthermore, the correction process in S3 includes: By combining the correlation coefficient between the output power of the robotic arm and the total energy consumption from the start of the grasping operation to any grasping operation, the total energy consumption data of the robotic arm during any grasping operation is corrected.

[0012] Furthermore, the calculation process of the data calculation module includes: By combining the total energy consumption data, temperature change data, and noise decibel dispersion coefficient during any single grasping operation of the robotic arm, the mechanical load index during any single grasping operation of the robotic arm is calculated.

[0013] Furthermore, the analysis process of the load analysis module includes: By comparing the mechanical load index during any single grasping operation of the robotic arm with a preset mechanical load index threshold; If the mechanical load index during any grasping operation of the robotic arm is greater than or equal to the preset mechanical load index threshold, it indicates that the mechanical load of the robotic arm is serious and the grasping speed of the robotic arm needs to be reduced. Conversely, if the system determines that the mechanical load on the robotic arm is minor, there is no need to reduce the robotic arm's gripping speed.

[0014] Furthermore, the control process of the intelligent control module includes: When it is determined that the mechanical load is severe during any single grasping operation of the robotic arm; By combining the mechanical load index of any grasping operation of the robotic arm with the preset grasping speed of the same grasping operation using artificial intelligence, the adjusted grasping speed of the robotic arm for the next grasping operation can be calculated.

[0015] An AI-based automated response control device includes an automated robot, which is equipped with a data acquisition module, a data cleaning module, a data calculation module, a load analysis module, and an intelligent control module.

[0016] The beneficial effects of this invention are: (1) The present invention combines the data calculation module with the running status data of the robotic arm after cleaning during the part grasping process of the automated robot. This data can reflect the load of the robotic arm during each grasping operation. Based on this, since the load of the robotic arm can reflect the adaptation of the robotic arm to the inertial force generated during grasping, artificial intelligence can make a decision on whether the grasping speed of the robotic arm needs to be adjusted based on this data, thereby realizing the automatic adjustment of the grasping speed of the robotic arm and avoiding the occurrence of micro-cracks in the robotic arm under high load, which would reduce its service life.

[0017] (2) The present invention uses the correlation coefficient between the output power of the robotic arm and the total energy consumption from the start of the grasping operation to the a-th grasping operation. Correlation coefficient threshold with preset By comparing the two, we can accurately determine whether there is a correlation between the output power of the robotic arm and the total energy consumption. Based on this comparison, we can further determine whether changes in the output power of the robotic arm will affect the total energy consumption data. Based on this analysis, we can decide whether the total energy consumption parameter needs to be corrected and provide data support to ensure the reliability and accuracy of the correction results.

[0018] (3) The present invention uses the mechanical load index during the a-th grasping operation of the robotic arm. Compared with the preset mechanical load index threshold By comparing the data, an accurate judgment can be made on the severity of the mechanical load during the robotic arm's first grasping operation. Since this data is calculated based on the total energy consumption after cleaning, its reliability and accuracy are high. Based on this, the rationality and accuracy of the judgment results can be improved. This not only allows for appropriate decisions on whether to adjust the grasping speed, but also provides reliable data support for subsequent adjustments to the grasping speed.

[0019] (4) This invention can use artificial intelligence to determine the mechanical load index during the a-th grasping operation of the robotic arm. Compared with the preset mechanical load index threshold The comparison results, and the mechanical load index during the a-th grasping operation of the robotic arm. The gripping speed of the robotic arm is automatically adjusted for the next gripping operation after the completion of the first gripping operation. Based on this, the adjusted gripping speed can significantly reduce the acceleration of the robotic arm during gripping, thereby reducing inertial impact force and preventing the formation of micro-cracks in the robotic arm under high load conditions, which would reduce its service life. Attached Figure Description

[0020] The invention will now be further described with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic block diagram of the AI-based automated response control system in this invention; Figure 2 This is a flowchart of the data cleaning module in this invention. Figure 3 This is a structural diagram of the AI-based automated response control device in this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 As shown, in one embodiment, this application provides an automated response control device and system based on artificial intelligence, the system comprising: The data acquisition module is used to collect the operating status data of the robotic arm during the process of the automated robot grasping parts; The data cleaning module uses artificial intelligence to clean the operational status data of the robotic arm collected by the data acquisition module. The data calculation module is used to calculate the mechanical load index of the robotic arm in each grasping operation by combining the running status data of the robotic arm after cleaning during the part grasping process of the automated robot. The load analysis module is used to compare the mechanical load index of the robotic arm in each grasping operation with the preset mechanical load index threshold, and decide whether to adjust the robotic arm grasping speed based on the comparison results. The intelligent control module is used to adjust the gripping speed of the robotic arm when the decision requires adjustment of the gripping speed. It is based on artificial intelligence and combined with the mechanical load index of the robotic arm in each gripping operation to establish an algorithm model to control the gripping speed of the robotic arm in the next gripping operation. Through the above technical solution, this example provides a data acquisition module for collecting the operating status data of the robotic arm during the part-grabbing process of the automated robot. When the system is in use, the data cleaning module first cleans the operating status data of the robotic arm collected by the data acquisition module based on artificial intelligence. Then, the data calculation module combines the cleaned operating status data of the robotic arm during the part-grabbing process to calculate the mechanical load index of the robotic arm in each grasping operation. The load analysis module compares the mechanical load index of the robotic arm in each grasping operation with the preset mechanical load index threshold and decides whether to adjust the grasping speed of the robotic arm based on the comparison results. When it is determined that the grasping speed of the robotic arm needs to be adjusted, the intelligent control module establishes an algorithm model based on artificial intelligence and the mechanical load index of the robotic arm in each grasping operation to control the grasping speed of the robotic arm in the next grasping operation. With this setup, the data calculation module combines the robotic arm's operational status data after cleaning during the part-grabbing process with the data calculation module. This data reflects the load on the robotic arm during each grasping operation. Based on this, since the load on the robotic arm reflects its adaptation to the inertial force generated during grasping, artificial intelligence can make decisions based on this data to determine whether the robotic arm's grasping speed needs to be adjusted. This enables automated adjustment of the robotic arm's grasping speed, preventing the formation of micro-cracks under high load conditions that could reduce its service life.

[0024] The data collected by the data acquisition module includes: The joint temperature, noise level, total energy consumption, and output power of the robotic arm during each part grasping process; Through the above technical solution, this embodiment provides data collected by the data acquisition module, including the joint temperature, noise level, total energy consumption, and output power of the robotic arm during each part grasping process. With this setting, the data collected by the data acquisition module can reflect the load of the robotic arm during each part grasping process. Based on this, reliable data support can be provided for the automated control of the robotic arm's grasping speed to ensure the rationality of the automated control.

[0025] Please see Figure 2 As shown, the data cleaning process of the data cleaning module includes: S1: Based on artificial intelligence and the correlation coefficient calculation formula, the correlation coefficient between the output power and the total energy consumption is calculated from the start of the robotic arm's grasping operation to any grasping operation. S2: By comparing and analyzing the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of the grasping operation to any grasping operation with the preset correlation coefficient, the correlation between the output power of the robotic arm and the total energy consumption is determined based on the comparison results. S3: By combining the correlation analysis results between the output power of the robotic arm and the total energy consumption, the total energy consumption data of the robotic arm in any grasping operation is corrected. Through the above technical solution, this embodiment provides a data cleaning module cleaning process. First, based on artificial intelligence and the correlation coefficient calculation formula, the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of the grasping operation to any grasping operation is calculated. Then, the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of the grasping operation to any grasping operation is compared and analyzed with a preset correlation coefficient. Based on the comparison results, the correlation between the output power of the robotic arm and the total energy consumption is determined. Finally, by combining the correlation analysis results between the output power of the robotic arm and the total energy consumption, the total energy consumption data of the robotic arm in any grasping operation is corrected. By setting it up in this way, the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of the grasping operation to any grasping operation is calculated. This data can reflect the relationship between the output power and total energy consumption of the robotic arm during use, that is, the impact of changes in the output power of the robotic arm on the total energy consumption. Based on this, when the correlation between the two sets of data is high, it means that if the output power of the robotic arm changes during a grasping operation, it will cause the total energy consumption data for that grasping operation to be artificially high or low. In other words, the total energy consumption data cannot truly reflect the load status of the robotic arm. Based on this, by combining the output power data to correct the total energy consumption data, a more accurate total energy consumption parameter can be obtained, thereby improving the reliability of subsequent robotic arm load status analysis results.

[0026] The calculation process in S1 includes: By combining artificial intelligence with the output power and total energy consumption of the robotic arm during each grasping operation after the robotic arm starts grasping, and based on the correlation coefficient formula, the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of grasping to any grasping operation is calculated. Specifically, through the formula Calculate the correlation coefficient between the robotic arm's output power and total energy consumption from the start of the gripping operation to the a-th gripping operation. ; Where 'a' represents any grasping operation performed by the robotic arm after it begins working. Let a be the total number of grasping operations performed by the robotic arm during the a-th grasping operation. Let be the average output power of the robotic arm during the a-th grasping operation. For all The average value, Let a be the total energy consumption during the a-th grasping operation of the robotic arm. For all The average value; Through the above technical solution, this example provides the correlation coefficient between the robotic arm's output power and total energy consumption from the start of the grasping operation to the a-th grasping operation. It can be done through the formula The calculated data reflects the relationship between the output power and total energy consumption of the robotic arm during use. Based on this, the correlation coefficient between the robotic arm's output power and total energy consumption is calculated from the start of the grasping operation to the a-th grasping operation. A higher value indicates a change in the output power of the robotic arm during a single grasping process. This can lead to inaccurate total energy consumption data for that grasping process, meaning the total energy consumption data cannot accurately reflect the robotic arm's load status. Based on this, this calculation method can provide data support for subsequent correction of the total energy consumption data, thereby obtaining more accurate total energy consumption parameters and improving the reliability of subsequent robotic arm load status analysis results.

[0027] The analysis process in S2 includes: The correlation coefficient between the output power of the robotic arm and the total energy consumption from the start of the grasping operation to any grasping operation is compared with a preset correlation coefficient threshold. If the correlation coefficient between the output power of the robotic arm and the total energy consumption is greater than the preset correlation coefficient threshold after the robotic arm starts grasping operations until any grasping operation, the system determines that the output power of the robotic arm and the total energy consumption are positively correlated. Conversely, the system determines that the robotic arm's output power is unrelated to its total energy consumption; Specifically, the correlation coefficient between the robotic arm's output power and total energy consumption from the start of the grasping operation to the a-th grasping operation is used. Correlation coefficient threshold with preset Perform a comparison; like The system determines that the output power of the robotic arm is positively correlated with the total energy consumption, meaning that a change in the output power of the robotic arm will cause a synchronous change in the total energy consumption. like The system determines that the output power of the robotic arm is not related to the total energy consumption, that is, changes in the output power of the robotic arm have no effect on changes in the total energy consumption. Through the above technical solution, this embodiment uses the correlation coefficient between the output power of the robotic arm and the total energy consumption from the start of the grasping operation to the a-th grasping operation. Correlation coefficient threshold with preset By comparing the two, we can accurately determine whether there is a correlation between the output power of the robotic arm and the total energy consumption. Based on this comparison, we can further determine whether changes in the output power of the robotic arm will affect the total energy consumption data. Based on this analysis, we can decide whether the total energy consumption parameter needs to be corrected and provide data support to ensure the reliability and accuracy of the correction results.

[0028] The correction process in S3 includes: By combining the correlation coefficient between the output power of the robotic arm and the total energy consumption from the start of the grasping operation to any grasping operation, the total energy consumption data of the robotic arm during any grasping operation is corrected. Specifically, through the formula Calculate the corrected total energy consumption during the a-th grasping operation of the robotic arm. ; in, The output power correction function is based on empirical data. The impact of the range of numerical values ​​on the total energy consumption of the robotic arm during a single grasping operation was obtained based on testing. Using the above technical solution, this example provides the corrected total energy consumption during the a-th grasping operation of the robotic arm. It can be done through the formula This calculation method allows for the determination of more accurate total energy consumption parameters, thereby improving the reliability of subsequent robotic arm load status analysis results.

[0029] The calculation process of the data calculation module includes: By combining the total energy consumption data, temperature change data, and noise decibel dispersion coefficient during any single grasping operation of the robotic arm, the mechanical load index during any single grasping operation of the robotic arm is calculated. Specifically, by using the data acquisition module to collect real-time joint temperature data of the robotic arm during a single part grasping process, a joint temperature change curve is established. ; And through the formula

[0030] Calculate the mechanical load index during the a-th grasping operation of the robotic arm. ; in, Let a be the start time of the robotic arm's a-th grasping operation. Let be the end time of the robotic arm's a-th grasping operation, and let i be a data acquisition interval at fixed time intervals. Let be the total number of data acquisitions during the a-th grasping operation of the robotic arm. Let be the noise decibel value during the i-th data acquisition in the a-th grasping operation of the robotic arm. For all The average value, The total energy consumption preset for the robotic arm in one grasping operation; Using the above technical solution, this example provides the mechanical load index during the a-th grasping operation of the robotic arm. It can be done through the formula The calculation yields the result, where the formula is... The average temperature change during the process from the start time to the end of the robotic arm's a-th grasping operation can be calculated using the formula. The noise distribution dispersion coefficient can be calculated for the period from the start to the end of the robotic arm's a-th grasping operation. Clearly, the higher the average temperature change and the noise distribution dispersion coefficient during the a-th grasping operation, the higher the corrected total energy consumption during the a-th grasping operation. The higher the index, the higher the mechanical load index during the a-th grasping operation of the robotic arm. The higher the value, the more severe the load on the robotic arm during the a-th grasping operation. Specifically, a higher average temperature change during the a-th grasping operation indicates a continuous temperature rise, meaning increased heat generation from the motor and accelerator, directly reflecting an increased load on the robotic arm. Conversely, a higher noise distribution coefficient during the a-th grasping operation suggests mechanical resonance or gear meshing impact, indicating wear on the reducer gears leading to increased load on the robotic arm. Finally, the corrected total energy consumption during the a-th grasping operation... This directly indicates that the load on the robotic arm has increased. Based on this, artificial intelligence can dynamically analyze the mechanical load status of the robotic arm during the a-th grasping operation based on the calculation result, providing reliable data support for subsequent decisions on whether to adjust the grasping speed.

[0031] The analysis process of the load analysis module includes: By comparing the mechanical load index during any single grasping operation of the robotic arm with a preset mechanical load index threshold; If the mechanical load index during any grasping operation of the robotic arm is greater than or equal to the preset mechanical load index threshold, it indicates that the mechanical load of the robotic arm is serious and the grasping speed of the robotic arm needs to be reduced. Conversely, if the system determines that the mechanical load on the robotic arm is slight, there is no need to reduce the robotic arm's grasping speed; Specifically, by measuring the mechanical load index during the a-th grasping operation of the robotic arm... Compared with the preset mechanical load index threshold Perform a comparison; like The system determines that the mechanical load on the robotic arm is too high during the a-th grasping operation, and the grasping speed of the robotic arm needs to be reduced. like The system determines that the mechanical load during the robotic arm's first grasping operation is slight and there is no need to reduce the robotic arm's grasping speed. Using the above technical solution, this example demonstrates the mechanical load index during the a-th grasping operation of the robotic arm. Compared with the preset mechanical load index threshold By comparing the data, an accurate judgment can be made on the severity of the mechanical load during the robotic arm's first grasping operation. Since this data is calculated based on the total energy consumption after cleaning, its reliability and accuracy are high. Based on this, the rationality and accuracy of the judgment results can be improved. This not only allows for appropriate decisions on whether to adjust the grasping speed, but also provides reliable data support for subsequent adjustments to the grasping speed.

[0032] The control process of the intelligent control module includes: When it is determined that the mechanical load is severe during any single grasping operation of the robotic arm; By combining the mechanical load index of any grasping operation of the robotic arm with the preset grasping speed of the same grasping operation using artificial intelligence, the adjusted grasping speed of the robotic arm for the next grasping operation can be calculated. Specifically, when it is determined that the mechanical load during the robotic arm's a-th grasping operation is severe; Through formula Calculate the adjusted gripping speed of the robotic arm for the (a+1)th gripping operation. ; Where a+1 represents the next grasping operation after the robotic arm completes the a-th grasping operation. To adjust the coefficient lookup table function, based on empirical data... The impact of the numerical value range on the robotic arm's grasping speed was obtained based on deep learning model training; Through the above technical solution, this example provides the adjusted grasping speed for the (a+1)th grasping operation of the robotic arm. It can be done through the formula Based on the calculations, artificial intelligence can then determine the mechanical load index during the robotic arm's a-th grasping operation. Compared with the preset mechanical load index threshold The comparison results, and the mechanical load index during the a-th grasping operation of the robotic arm. The gripping speed of the robotic arm is automatically adjusted for the next gripping operation after the completion of the first gripping operation. Based on this, the adjusted gripping speed can significantly reduce the acceleration of the robotic arm during gripping, thereby reducing inertial impact force and preventing the formation of micro-cracks in the robotic arm under high load conditions, which would reduce its service life.

[0033] Please see Figure 3 As shown, an automated response control device based on artificial intelligence includes an automated robot, which is equipped with a data acquisition module, a data cleaning module, a data calculation module, a load analysis module, and an intelligent control module. Through the above technical solution, this example provides an automated robot, which is equipped with a data acquisition module, a data cleaning module, a data calculation module, a load analysis module, and an intelligent control module. Based on these modules, the gripping speed of the robotic arm can be automatically adjusted, avoiding the occurrence of micro-cracks in the robotic arm due to failure to adjust the gripping speed in time, which would lead to a significant reduction in the service life of the robotic arm.

[0034] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An automated response control system based on artificial intelligence, characterized in that, The system includes: The data acquisition module is used to collect the operating status data of the robotic arm during the process of the automated robot grasping parts; The data cleaning module uses artificial intelligence to clean the operational status data of the robotic arm collected by the data acquisition module. The data calculation module is used to calculate the mechanical load index of the robotic arm in each grasping operation by combining the running status data of the robotic arm after cleaning during the part grasping process of the automated robot. The load analysis module is used to compare the mechanical load index of the robotic arm in each grasping operation with the preset mechanical load index threshold, and decide whether to adjust the robotic arm grasping speed based on the comparison results. The intelligent control module is used to adjust the gripping speed of the robotic arm when the decision requires adjustment. It establishes an algorithm model based on artificial intelligence and the mechanical load index of the robotic arm in each gripping operation, and controls the gripping speed of the robotic arm in the next gripping operation.

2. The AI-based automated response control system according to claim 1, characterized in that, The data collected by the data acquisition module includes: The joint temperature, noise level, total energy consumption, and output power of the robotic arm during each part grabbing process.

3. The AI-based automated response control system according to claim 1, characterized in that, The data cleaning module's cleaning process includes: S1: Based on artificial intelligence and the correlation coefficient calculation formula, the correlation coefficient between the output power and the total energy consumption is calculated from the start of the robotic arm's grasping operation to any grasping operation. S2: By comparing and analyzing the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of the grasping operation to any grasping operation with the preset correlation coefficient, the correlation between the output power of the robotic arm and the total energy consumption is determined based on the comparison results. S3: By combining the correlation analysis results between the output power of the robotic arm and the total energy consumption, the total energy consumption data of the robotic arm in any grasping operation is corrected.

4. The AI-based automated response control system according to claim 3, characterized in that, The calculation process in S1 includes: By combining artificial intelligence with the output power and total energy consumption of the robotic arm during each grasping operation after it begins to grasp, and based on the correlation coefficient formula, the correlation coefficient between the output power and total energy consumption of the robotic arm from the start of the grasping operation to any grasping operation is calculated.

5. The AI-based automated response control system according to claim 4, characterized in that, The analysis process in S2 includes: The correlation coefficient between the output power of the robotic arm and the total energy consumption from the start of the grasping operation to any grasping operation is compared with a preset correlation coefficient threshold. If the correlation coefficient between the output power of the robotic arm and the total energy consumption is greater than the preset correlation coefficient threshold after the robotic arm starts grasping operations until any grasping operation, the system determines that the output power of the robotic arm and the total energy consumption are positively correlated. Conversely, the system determines that the output power of the robotic arm is unrelated to the total energy consumption.

6. The AI-based automated response control system according to claim 5, characterized in that, The correction process in S3 includes: By combining the correlation coefficient between the output power of the robotic arm and the total energy consumption from the start of the grasping operation to any grasping operation, the total energy consumption data of the robotic arm during any grasping operation is corrected.

7. The AI-based automated response control system according to claim 6, characterized in that, The calculation process of the data calculation module includes: By combining the total energy consumption data, temperature change data, and noise decibel dispersion coefficient during any single grasping operation of the robotic arm, the mechanical load index during any single grasping operation of the robotic arm is calculated.

8. The AI-based automated response control system according to claim 7, characterized in that, The analysis process of the load analysis module includes: By comparing the mechanical load index during any single grasping operation of the robotic arm with a preset mechanical load index threshold; If the mechanical load index during any grasping operation of the robotic arm is greater than or equal to the preset mechanical load index threshold, it indicates that the mechanical load of the robotic arm is serious and the grasping speed of the robotic arm needs to be reduced. Conversely, if the system determines that the mechanical load on the robotic arm is minor, there is no need to reduce the robotic arm's gripping speed.

9. The AI-based automated response control system according to claim 8, characterized in that, The control process of the intelligent control module includes: When it is determined that the mechanical load is severe during any single grasping operation of the robotic arm; By combining the mechanical load index of any grasping operation of the robotic arm with the preset grasping speed of the same grasping operation using artificial intelligence, the adjusted grasping speed of the robotic arm for the next grasping operation can be calculated.

10. An AI-based automated response control device, applicable to any of the AI-based automated response control devices described in claims 1-9, characterized in that, The device includes an automated robot, which is equipped with a data acquisition module, a data cleaning module, a data calculation module, a load analysis module, and an intelligent control module.

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