Intelligent control method and system for man-machine interaction cooperation type industrial robot and medium

Abstract

The invention provides a man-machine interaction cooperation type industrial robot intelligent control method and system and a medium, and belongs to the technical field of industrial robots and big data. The method comprises the following steps: acquiring environment perception information, acquiring movement planning data and carrying out simulation movement, acquiring robot operation monitoring information according to a simulation result, extracting positioning error data and movement duration data, respectively judging whether a robot positioning error and movement duration meet requirements or not, and according to the environment perception information and the robot operation monitoring information, determining whether the robot positioning error and movement duration meet requirements or not. And obtaining an obstacle avoidance capability index, performing correction to obtain an obstacle avoidance capability correction index, and judging whether the obstacle avoidance capability correction index meets requirements or not. The environment where the robot is located can be comprehensively known, time and resources are saved through movement simulation, the movement performance of the robot is comprehensively evaluated and optimized, the safety and operation efficiency of the robot in the complex environment are improved, and potential risks caused by inaccurate positioning or insufficient obstacle avoidance capacity are reduced.

Description

Technical Field

[0001] The present application relates to the field of industrial robots and big data technology, and more specifically, to an intelligent control method, system and medium for a human-machine interactive collaborative industrial robot. Background Art

[0002] Industrial robot technology, also known as manufacturing robot technology, originated in the early 1960s to meet the automotive manufacturing industry's demand for large-scale, high-precision, repetitive work automation. With the continuous advancement of science and technology, industrial robot technology has been widely used in various tasks such as assembly, welding, handling, packaging, etc. on production lines, greatly improving production efficiency and reducing labor intensity, while also promoting the modernization and lean production of the manufacturing industry.

[0003] Currently, there is no targeted monitoring and obstacle avoidance capability assessment for industrial robots, especially in terms of their ability to avoid obstacles. There is a lack of an intelligent control technology method for industrial robots that can comprehensively monitor key parameters and assess their obstacle avoidance capabilities.

[0004] In view of the above problems, effective technical solutions are currently awaited. Summary of the invention

[0005] The purpose of this application is to provide a human-machine interactive collaborative industrial robot intelligent control method, system and medium, which can obtain environmental perception information, extract obstacle coordinate data, obstacle contour data and target point coordinate data, obtain mobile planning data and simulate movement, obtain robot operation monitoring information according to the results of simulated movement, extract positioning error data and movement duration data, respectively judge whether the robot positioning error and movement duration meet the requirements, process according to environmental perception information combined with robot operation monitoring information, obtain obstacle avoidance ability index, correct the obstacle avoidance ability correction index and compare it with the preset obstacle avoidance ability correction index threshold, and judge whether the robot's obstacle avoidance ability meets the requirements. This application can fully understand the robot's environment, save time and resources through simulated movement, comprehensively evaluate and optimize the robot's mobile performance, improve its safety and operation efficiency in complex environments, and reduce potential risks caused by inaccurate positioning or insufficient obstacle avoidance ability.

[0006] The present application provides a human-machine interactive collaborative industrial robot intelligent control method, comprising the following steps: Obtain environmental perception information within the preset space, extract obstacle coordinate data, obstacle outline data and target point coordinate data, obtain movement planning data and perform simulated movement; Acquire robot operation monitoring information according to the result of the simulated movement, and extract positioning error data and movement duration data; Comparing the positioning error data with a preset error threshold, determining whether the robot positioning error meets the requirements; Comparing the movement duration data with a preset duration threshold, determining whether the movement duration of the robot meets the requirement; Processing the environment perception information in combination with the robot operation monitoring information to obtain an obstacle avoidance capability index; Correction processing is performed according to the obstacle avoidance capability index to obtain an obstacle avoidance capability correction index and compare it with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements.

[0007] Among them, in the human-machine interactive collaborative industrial robot intelligent control method described in the present application, the environmental perception information in the preset space is obtained, the obstacle coordinate data, obstacle contour data and target point coordinate data are extracted, the movement planning data is obtained and the simulated movement is performed, specifically: Obtain environmental perception information within the preset space, and extract obstacle coordinate data, obstacle outline data, and target point coordinate data; The obstacle contour data includes contour size data and point set boundary data; According to the obstacle coordinate data, obstacle contour data and target point coordinate data, movement planning data is obtained through a preset digital twin model and simulated movement is performed.

[0008] Among them, in the human-machine interactive collaborative industrial robot intelligent control method described in the present application, the positioning error data is compared with a preset error threshold to determine whether the robot positioning error meets the requirements, specifically: Comparing the positioning error data with a preset error threshold; If the positioning error data is less than the error threshold, the robot positioning error meets the requirements; If the positioning error data is greater than or equal to the error threshold, the robot positioning error does not meet the requirements.

[0009] Among them, in the human-machine interactive collaborative industrial robot intelligent control method described in the present application, the comparison between the movement duration data and the preset duration threshold is used to determine whether the robot movement duration meets the requirements, specifically: Comparing the moving duration data with a preset duration threshold to obtain a duration deviation rate; Comparing the duration deviation rate with a preset duration deviation rate threshold; If the duration deviation rate is less than the duration deviation rate threshold, the robot's movement duration meets the requirement; If the duration deviation rate is greater than or equal to the duration deviation rate threshold, the robot's movement duration does not meet the requirement.

[0010] Among them, in the human-machine interactive collaborative industrial robot intelligent control method described in the present application, the obstacle avoidance capability index is obtained by processing the environmental perception information in combination with the robot operation monitoring information, specifically: Extracting movement speed data according to the robot operation monitoring information; The obstacle avoidance capability index is obtained by processing the obstacle coordinate data, obstacle contour data, positioning error data and moving duration data in combination with the moving speed data through a preset obstacle avoidance capability evaluation model.

[0011] Among them, in the human-machine interactive collaborative industrial robot intelligent control method described in the present application, the correction processing is performed according to the obstacle avoidance ability index, the obstacle avoidance ability correction index is obtained and compared with the preset obstacle avoidance ability correction index threshold, and whether the robot's obstacle avoidance ability meets the requirements is judged, specifically: Obtaining obstacle avoidance capability indexes of multiple different spaces and calculating an average obstacle avoidance capability index, and correcting the obstacle avoidance capability index using a preset obstacle avoidance capability correction model to obtain an obstacle avoidance capability correction index; Obtaining an obstacle avoidance deviation rate by comparing the obstacle avoidance capability correction index with a preset obstacle avoidance capability correction index threshold; comparing the obstacle avoidance deviation rate with a preset obstacle avoidance deviation rate threshold; If the obstacle avoidance deviation rate is less than the obstacle avoidance deviation rate threshold, the robot's obstacle avoidance capability meets the requirements; If the obstacle avoidance deviation rate is greater than or equal to the obstacle avoidance deviation rate threshold, the robot's obstacle avoidance capability does not meet the requirements.

[0012] In a second aspect, the present application provides a human-machine interactive collaborative industrial robot intelligent control system, the system comprising: a memory and a processor, the memory comprising a program of a human-machine interactive collaborative industrial robot intelligent control method, and the program of the human-machine interactive collaborative industrial robot intelligent control method is executed by the processor to implement the following steps: Obtain environmental perception information within the preset space, extract obstacle coordinate data, obstacle outline data and target point coordinate data, obtain movement planning data and perform simulated movement; Acquire robot operation monitoring information according to the result of the simulated movement, and extract positioning error data and movement duration data; Comparing the positioning error data with a preset error threshold, determining whether the robot positioning error meets the requirements; Comparing the movement duration data with a preset duration threshold, determining whether the movement duration of the robot meets the requirement; Processing the environment perception information in combination with the robot operation monitoring information to obtain an obstacle avoidance capability index; Correction processing is performed according to the obstacle avoidance capability index to obtain an obstacle avoidance capability correction index and compare it with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements.

[0013] Among them, in the human-machine interactive collaborative industrial robot intelligent control system described in the present application, the environmental perception information in the preset space is obtained, the obstacle coordinate data, obstacle contour data and target point coordinate data are extracted, the movement planning data is obtained and the simulated movement is performed, specifically: Obtain environmental perception information within the preset space, and extract obstacle coordinate data, obstacle outline data, and target point coordinate data; The obstacle contour data includes contour size data and point set boundary data; According to the obstacle coordinate data, obstacle contour data and target point coordinate data, movement planning data is obtained through a preset digital twin model and simulated movement is performed.

[0014] Among them, in the human-machine interactive collaborative industrial robot intelligent control system described in the present application, the positioning error data is compared with the preset error threshold to determine whether the robot positioning error meets the requirements, specifically: Comparing the positioning error data with a preset error threshold; If the positioning error data is less than the error threshold, the robot positioning error meets the requirements; If the positioning error data is greater than or equal to the error threshold, the robot positioning error does not meet the requirements.

[0015] In a third aspect, the present application also provides a computer-readable storage medium, which includes a human-machine interactive collaborative industrial robot intelligent control method program. When the human-machine interactive collaborative industrial robot intelligent control method program is executed by a processor, the steps of the human-machine interactive collaborative industrial robot intelligent control method as described in any one of the above items are implemented.

[0016] As can be seen from the above, the human-machine interactive collaborative industrial robot intelligent control method, system and medium provided in the embodiments of the present application obtain environmental perception information in a preset space, extract obstacle coordinate data, obstacle contour data and target point coordinate data, obtain movement planning data and simulate movement through a preset digital twin model, obtain robot operation monitoring information according to the result of the simulated movement, extract positioning error data and movement time data, compare the positioning error data with a preset error threshold, and judge whether the robot positioning error meets the requirements, compare the movement time data with a preset time threshold, and judge whether the robot movement time meets the requirements, and process the obstacle coordinate data, obstacle contour data, positioning error data and movement time data in combination with the movement speed data to obtain an obstacle avoidance ability index; obtain the obstacle avoidance ability index of multiple different spaces and calculate the average obstacle avoidance ability index, correct the obstacle avoidance ability index through a preset obstacle avoidance ability correction model, obtain the obstacle avoidance ability correction index and compare it with the preset obstacle avoidance ability correction index threshold to judge whether the robot obstacle avoidance ability meets the requirements. This application can fully understand the spatial environment in which the robot is located, provide an accurate data basis for subsequent mobile planning, save time and resources by simulating movement, and then by calculating the obstacle avoidance capability index, it can quantitatively evaluate the robot's ability to cope with obstacles when encountering obstacles, making the evaluation results more specific and reliable. This application comprehensively evaluates and optimizes the robot's mobility performance, which can improve its safety and operating efficiency in complex environments, and reduce potential risks caused by inaccurate positioning or insufficient obstacle avoidance capabilities.

[0017] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flowchart of the intelligent control method of a human-machine interactive collaborative industrial robot provided in an embodiment of the present application; Figure 2 A flowchart of obtaining movement planning data and performing simulated movement in the intelligent control method of a human-machine interactive collaborative industrial robot provided in an embodiment of the present application; Figure 3 A flow chart of the method for intelligently controlling a human-machine interactive collaborative industrial robot provided in an embodiment of the present application for determining whether the robot positioning error meets the requirements; Figure 4 A flow chart of the intelligent control method for a human-machine interactive collaborative industrial robot provided in an embodiment of the present application for determining whether the robot's movement time meets the requirements; Figure 5 A flow chart of obtaining an obstacle avoidance capability index for the intelligent control method of a human-machine interactive collaborative industrial robot provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0021] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0022] Please refer to Figure 1 , Figure 1 1 is a flow chart of a human-machine interactive collaborative industrial robot intelligent control method in some embodiments of the present application. The human-machine interactive collaborative industrial robot intelligent control method is used in a terminal device, such as a computer, a mobile phone terminal, etc. The human-machine interactive collaborative industrial robot intelligent control method comprises the following steps: S101, obtaining environmental perception information in a preset space, extracting obstacle coordinate data, obstacle contour data and target point coordinate data, obtaining movement planning data and performing simulated movement; S102, obtaining robot operation monitoring information according to the result of the simulated movement, and extracting positioning error data and movement duration data; S103, comparing the positioning error data with a preset error threshold to determine whether the robot positioning error meets the requirements; S104, comparing the movement duration data with a preset duration threshold to determine whether the movement duration of the robot meets the requirement; S105, processing the environment perception information in combination with the robot operation monitoring information to obtain an obstacle avoidance capability index; S106, performing correction processing according to the obstacle avoidance capability index, obtaining an obstacle avoidance capability correction index and comparing it with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements.

[0023] Among them, the present application obtains environmental perception information in a preset space, extracts obstacle coordinate data, obstacle contour data and target point coordinate data, obtains movement planning data and simulates movement through a preset digital twin model, obtains robot operation monitoring information according to the results of the simulated movement, extracts positioning error data and movement duration data, compares the positioning error data with a preset error threshold, and judges whether the robot positioning error meets the requirements, compares the movement duration data with a preset duration threshold, and judges whether the robot movement duration meets the requirements, and processes the obstacle coordinate data, obstacle contour data, positioning error data and movement duration data in combination with the movement speed data to obtain an obstacle avoidance ability index; obtains obstacle avoidance ability indexes in multiple different spaces and calculates an average obstacle avoidance ability index, corrects the obstacle avoidance ability index through a preset obstacle avoidance ability correction model, obtains an obstacle avoidance ability correction index and compares it with a preset obstacle avoidance ability correction index threshold to judge whether the robot obstacle avoidance ability meets the requirements. This application can fully understand the spatial environment in which the robot is located, provide an accurate data basis for subsequent mobile planning, save time and resources by simulating movement, and then by calculating the obstacle avoidance capability index, it can quantitatively evaluate the robot's ability to cope with obstacles when encountering obstacles, making the evaluation results more specific and reliable. This application comprehensively evaluates and optimizes the robot's mobility performance, which can improve its safety and operating efficiency in complex environments, and reduce potential risks caused by inaccurate positioning or insufficient obstacle avoidance capabilities.

[0024] Please refer to Figure 2 , Figure 2 The flowchart of the method for intelligent control of a human-machine interactive collaborative industrial robot in some embodiments of the present application is to obtain movement planning data and perform simulated movement. According to an embodiment of the present invention, the environmental perception information in the preset space is obtained, obstacle coordinate data, obstacle contour data and target point coordinate data are extracted, and movement planning data is obtained and simulated movement is performed, specifically: S201, obtaining environmental perception information in a preset space, and extracting obstacle coordinate data, obstacle contour data, and target point coordinate data; S202, the obstacle contour data includes contour size data and point set boundary data; S203: obtaining movement planning data through a preset digital twin model according to the obstacle coordinate data, obstacle contour data and target point coordinate data, and performing simulated movement.

[0025] Among them, in order to obtain the mobile planning data and simulate the movement, the environmental perception information in the preset space is obtained, and the obstacle coordinate data, obstacle contour data and target point coordinate data are extracted. The obstacle coordinate data refers to the location information of all obstacles in the environment. The obstacle contour data includes contour size data and point set boundary data. The contour size data describes the size of the obstacle, such as width, height, depth, etc. The point set boundary data describes the precise shape of the obstacle contour, which is usually a collection of a series of points. These points define the boundary of the obstacle. The target point coordinate data is the location information of the mobile target and is also the destination of the mobile planning. According to the obstacle coordinate data, obstacle contour data and target point coordinate data, the mobile planning data is obtained through the preset digital twin model and simulated movement is performed. After obtaining the mobile planning data, simulated movement can be performed in the preset digital twin model to verify the effectiveness and safety of the planning. The simulated movement can include simulating the motion trajectory, speed change, and interaction with other objects of the mobile entity. Through simulated movement, potential problems such as path conflict, undetected obstacles, etc. can be discovered and solved in time, thereby optimizing the mobile planning before the actual movement.

[0026] Please refer to Figure 3 , Figure 3 The flowchart of the method for intelligent control of a human-machine interactive collaborative industrial robot in some embodiments of the present application is to determine whether the robot positioning error meets the requirements. According to an embodiment of the present invention, the comparison between the positioning error data and a preset error threshold is used to determine whether the robot positioning error meets the requirements, specifically: S301, comparing the positioning error data with a preset error threshold; S302: If the positioning error data is less than the error threshold, the robot positioning error meets the requirement; S303: If the positioning error data is greater than or equal to the error threshold, the robot positioning error does not meet the requirement.

[0027] Among them, in order to determine whether the robot positioning error meets the requirements, the positioning error data is compared with the preset error threshold. If the positioning error data is less than the error threshold, it means that the robot's positioning error is within an acceptable range, and therefore meets the positioning error requirements. If the positioning error data is greater than or equal to the error threshold, it means that the robot's positioning error exceeds the acceptable range, and therefore does not meet the positioning error requirements.

[0028] Please refer to Figure 4 , Figure 4 The flowchart of the intelligent control method of the human-machine interactive collaborative industrial robot in some embodiments of the present application is to determine whether the robot movement time meets the requirements. According to the embodiment of the present invention, the comparison between the movement time data and the preset time threshold is used to determine whether the robot movement time meets the requirements, specifically: S401, comparing the moving duration data with a preset duration threshold to obtain a duration deviation rate; S402, comparing the duration deviation rate with a preset duration deviation rate threshold; S403: If the duration deviation rate is less than the duration deviation rate threshold, the robot movement duration meets the requirement; S404: If the duration deviation rate is greater than or equal to the duration deviation rate threshold, the robot movement duration does not meet the requirement.

[0029] Among them, in order to determine whether the robot's movement duration meets the requirements, the movement duration data is compared with the preset duration threshold to obtain the duration deviation rate, which is then compared with the preset duration deviation rate threshold. If the duration deviation rate is less than the duration deviation rate threshold, it indicates that the robot's movement duration is within an acceptable range and meets the requirements. If the duration deviation rate is greater than or equal to the duration deviation rate threshold, it indicates that the robot's movement duration exceeds the acceptable range and does not meet the requirements.

[0030] Please refer to Figure 5 , Figure 5 The flowchart of the method for intelligent control of a human-machine interactive collaborative industrial robot in some embodiments of the present application is as follows. According to an embodiment of the present invention, the obstacle avoidance capability index is obtained by processing the environmental perception information in combination with the robot operation monitoring information, specifically: S501, extracting movement speed data according to the robot operation monitoring information; S502: Obstacle avoidance capability index is obtained by processing the obstacle coordinate data, obstacle contour data, positioning error data and moving duration data in combination with the moving speed data through a preset obstacle avoidance capability evaluation model.

[0031] Among them, in order to further accurately reflect the robot's obstacle avoidance ability and obtain the obstacle avoidance ability index, the movement speed data is extracted according to the robot operation monitoring information, including the instantaneous speed at different time points and the average speed on the path. According to the obstacle coordinate data, obstacle contour data, positioning error data and movement duration data, the movement speed data is combined with the preset obstacle avoidance ability evaluation model for processing to obtain the obstacle avoidance ability index. The higher the obstacle avoidance ability index, the stronger the robot's obstacle avoidance ability is, and it can identify and avoid obstacles more quickly and accurately. Conversely, the lower the index, the relatively weaker the robot's obstacle avoidance ability is, and further optimization or improvement may be required; The calculation formula of the obstacle avoidance capability evaluation model is: ; in, is the obstacle avoidance ability index, They are obstacle coordinate data, obstacle contour data, positioning error data, moving duration data and moving speed data. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset robot obstacle avoidance database).

[0032] According to an embodiment of the present invention, the obstacle avoidance capability index is corrected and processed to obtain the obstacle avoidance capability correction index and compared with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements, specifically: Obtaining obstacle avoidance capability indexes of multiple different spaces and calculating an average obstacle avoidance capability index, and correcting the obstacle avoidance capability index using a preset obstacle avoidance capability correction model to obtain an obstacle avoidance capability correction index; Obtaining an obstacle avoidance deviation rate by comparing the obstacle avoidance capability correction index with a preset obstacle avoidance capability correction index threshold; comparing the obstacle avoidance deviation rate with a preset obstacle avoidance deviation rate threshold; If the obstacle avoidance deviation rate is less than the obstacle avoidance deviation rate threshold, the robot's obstacle avoidance capability meets the requirements; If the obstacle avoidance deviation rate is greater than or equal to the obstacle avoidance deviation rate threshold, the robot's obstacle avoidance capability does not meet the requirements.

[0033] Among them, in order to obtain the obstacle avoidance capability correction index and determine whether the robot's obstacle avoidance capability meets the requirements, first obtain the obstacle avoidance capability indexes of multiple different spaces and calculate the average obstacle avoidance capability index, correct the obstacle avoidance capability index through the preset obstacle avoidance capability correction model, obtain the obstacle avoidance capability correction index, and then compare it with the preset obstacle avoidance capability correction index threshold to obtain the obstacle avoidance deviation rate. Compare the obstacle avoidance deviation rate with the preset obstacle avoidance deviation rate threshold. If the obstacle avoidance deviation rate is less than the obstacle avoidance deviation rate threshold, it indicates that the robot's obstacle avoidance capability is consistent with expectations in actual applications and meets the requirements. If the obstacle avoidance deviation rate is greater than or equal to the obstacle avoidance deviation rate threshold, it indicates that the robot's obstacle avoidance capability is significantly different from expectations in actual applications and does not meet the requirements. The calculation formula of the obstacle avoidance capability correction model is: ; in, To correct the obstacle avoidance capability index, is the obstacle avoidance ability index, is the average index of obstacle avoidance ability, is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset robot obstacle avoidance database).

[0034] According to an embodiment of the present invention, it also includes: Extracting angular velocity data according to the robot operation monitoring information; Comparing the angular velocity data with a preset first angular velocity threshold and a preset second angular velocity threshold, respectively, the first angular velocity threshold being smaller than the second angular velocity threshold; If the angular velocity data is less than the first angular velocity threshold, the angular velocity of the robot is too low; If the angular velocity data is greater than or equal to the first angular velocity threshold and less than or equal to the second angular velocity threshold, the angular velocity of the robot meets the requirement; If the angular velocity data is greater than the second angular velocity threshold, the angular velocity of the robot is too high.

[0035] Among them, in order to judge and control the angular velocity of the robot, angular velocity data is extracted according to the robot operation monitoring information, and the angular velocity data is compared with the preset first angular velocity threshold and the second angular velocity threshold respectively, the first angular velocity threshold is less than the second angular velocity threshold, the first angular velocity threshold is set to a lower value, indicating the lower limit of the angular velocity of the robot, and the second angular velocity threshold is set to a higher value, indicating the upper limit of the angular velocity of the robot. If the angular velocity data is less than the first angular velocity threshold, it indicates that the angular velocity of the robot is too low and may not meet the requirements of fast turning or flexible movement. If the angular velocity data is greater than or equal to the first angular velocity threshold and less than or equal to the second angular velocity threshold, it indicates that the angular velocity of the robot is within an appropriate range and meets the design requirements and application scenarios. If the angular velocity data is greater than the second angular velocity threshold, it indicates that the angular velocity of the robot is too high, which may cause safety problems or mechanical wear and other problems.

[0036] According to an embodiment of the present invention, it also includes: Extracting tracking speed data according to the robot operation monitoring information; Comparing the tracking speed data with a preset tracking speed threshold; If the tracking speed data is greater than the tracking speed threshold, a tracking speed too high message is sent and timing is performed to obtain tracking speed duration data; comparing the tracking speed duration data with a preset duration threshold; If the tracking speed duration data is greater than or equal to the duration threshold, the robot's tracking speed is too high and needs to be adjusted.

[0037] Among them, in order to judge and control the speed of the robot tracking the target, the tracking speed data is extracted according to the robot operation monitoring information. The tracking speed refers to the speed of the robot's movement when following the target or path. The tracking speed data is compared with the preset tracking speed threshold. If the tracking speed data is greater than the tracking speed threshold, a tracking speed too high message is sent and timing is performed to obtain the tracking speed duration data. The tracking speed duration data is compared with the preset duration threshold. If the tracking speed duration data is greater than or equal to the duration threshold, it indicates that the robot's tracking speed is too high for too long and needs to be adjusted.

[0038] According to an embodiment of the present invention, it also includes: Obtain human-computer interaction information in a preset time period and extract interaction frequency data of multiple interaction types; Obtaining first interaction frequency data according to the interaction frequency data of the plurality of interaction types; comparing the first interaction frequency data with a preset interaction frequency threshold; If the first interaction frequency data is greater than or equal to the interaction frequency threshold, user preference marking is performed.

[0039] Among them, in order to understand the interaction methods that users are more accustomed to, obtain the human-computer interaction information of the preset time period, extract the interaction frequency data of multiple interaction types, obtain the first interaction frequency data, and compare it with the preset interaction frequency threshold. If the first interaction frequency data is greater than or equal to the interaction frequency threshold, then perform user preference marking. By analyzing the human-computer interaction information, we can gain an in-depth understanding of the user's behavior patterns and preferences in a specific time period, which is crucial for product design, user experience optimization, and market strategy formulation. Based on user preference marking, we can provide users with more personalized content or service recommendations, improve user satisfaction and loyalty, and user preference marking is an important part of building user portraits, which helps companies understand users more comprehensively and provide more accurate services and marketing.

[0040] The present invention also discloses a human-machine interactive collaborative industrial robot intelligent control system, comprising a memory and a processor, wherein the memory comprises a human-machine interactive collaborative industrial robot intelligent control method program, and when the human-machine interactive collaborative industrial robot intelligent control method program is executed by the processor, the following steps are implemented: Obtain environmental perception information within the preset space, extract obstacle coordinate data, obstacle outline data and target point coordinate data, obtain movement planning data and perform simulated movement; Acquire robot operation monitoring information according to the result of the simulated movement, and extract positioning error data and movement duration data; Comparing the positioning error data with a preset error threshold, determining whether the robot positioning error meets the requirements; Comparing the movement duration data with a preset duration threshold, determining whether the movement duration of the robot meets the requirement; Processing the environment perception information in combination with the robot operation monitoring information to obtain an obstacle avoidance capability index; Correction processing is performed according to the obstacle avoidance capability index to obtain an obstacle avoidance capability correction index and compare it with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements.

[0041] Among them, the present application obtains environmental perception information in a preset space, extracts obstacle coordinate data, obstacle contour data and target point coordinate data, obtains movement planning data and simulates movement through a preset digital twin model, obtains robot operation monitoring information according to the results of the simulated movement, extracts positioning error data and movement duration data, compares the positioning error data with a preset error threshold, and judges whether the robot positioning error meets the requirements, compares the movement duration data with a preset duration threshold, and judges whether the robot movement duration meets the requirements, and processes the obstacle coordinate data, obstacle contour data, positioning error data and movement duration data in combination with the movement speed data to obtain an obstacle avoidance ability index; obtains obstacle avoidance ability indexes in multiple different spaces and calculates an average obstacle avoidance ability index, corrects the obstacle avoidance ability index through a preset obstacle avoidance ability correction model, obtains an obstacle avoidance ability correction index and compares it with a preset obstacle avoidance ability correction index threshold to judge whether the robot obstacle avoidance ability meets the requirements. This application can fully understand the spatial environment in which the robot is located, provide an accurate data basis for subsequent mobile planning, save time and resources by simulating movement, and then by calculating the obstacle avoidance capability index, it can quantitatively evaluate the robot's ability to cope with obstacles when encountering obstacles, making the evaluation results more specific and reliable. This application comprehensively evaluates and optimizes the robot's mobility performance, which can improve its safety and operating efficiency in complex environments, and reduce potential risks caused by inaccurate positioning or insufficient obstacle avoidance capabilities.

[0042] According to an embodiment of the present invention, the environmental perception information in the preset space is obtained, obstacle coordinate data, obstacle contour data and target point coordinate data are extracted, movement planning data is obtained and simulated movement is performed, specifically: Obtain environmental perception information within the preset space, and extract obstacle coordinate data, obstacle outline data, and target point coordinate data; The obstacle contour data includes contour size data and point set boundary data; According to the obstacle coordinate data, obstacle contour data and target point coordinate data, movement planning data is obtained through a preset digital twin model and simulated movement is performed.

[0043] Among them, in order to obtain the mobile planning data and simulate the movement, the environmental perception information in the preset space is obtained, and the obstacle coordinate data, obstacle contour data and target point coordinate data are extracted. The obstacle coordinate data refers to the location information of all obstacles in the environment. The obstacle contour data includes contour size data and point set boundary data. The contour size data describes the size of the obstacle, such as width, height, depth, etc. The point set boundary data describes the precise shape of the obstacle contour, which is usually a collection of a series of points. These points define the boundary of the obstacle. The target point coordinate data is the location information of the mobile target and is also the destination of the mobile planning. According to the obstacle coordinate data, obstacle contour data and target point coordinate data, the mobile planning data is obtained through the preset digital twin model and simulated movement is performed. After obtaining the mobile planning data, simulated movement can be performed in the preset digital twin model to verify the effectiveness and safety of the planning. The simulated movement can include simulating the motion trajectory, speed change, and interaction with other objects of the mobile entity. Through simulated movement, potential problems such as path conflict, undetected obstacles, etc. can be discovered and solved in time, thereby optimizing the mobile planning before the actual movement.

[0044] According to an embodiment of the present invention, comparing the positioning error data with a preset error threshold to determine whether the robot positioning error meets the requirement is specifically as follows: Comparing the positioning error data with a preset error threshold; If the positioning error data is less than the error threshold, the robot positioning error meets the requirements; If the positioning error data is greater than or equal to the error threshold, the robot positioning error does not meet the requirements.

[0045] Among them, in order to determine whether the robot positioning error meets the requirements, the positioning error data is compared with the preset error threshold. If the positioning error data is less than the error threshold, it means that the robot's positioning error is within an acceptable range, and therefore meets the positioning error requirements. If the positioning error data is greater than or equal to the error threshold, it means that the robot's positioning error exceeds the acceptable range, and therefore does not meet the positioning error requirements.

[0046] According to an embodiment of the present invention, the comparison between the movement duration data and a preset duration threshold is used to determine whether the movement duration of the robot meets the requirement, specifically: Comparing the moving duration data with a preset duration threshold to obtain a duration deviation rate; Comparing the duration deviation rate with a preset duration deviation rate threshold; If the duration deviation rate is less than the duration deviation rate threshold, the robot's movement duration meets the requirement; If the duration deviation rate is greater than or equal to the duration deviation rate threshold, the robot's movement duration does not meet the requirement.

[0047] Among them, in order to determine whether the robot's movement duration meets the requirements, the movement duration data is compared with the preset duration threshold to obtain the duration deviation rate, which is then compared with the preset duration deviation rate threshold. If the duration deviation rate is less than the duration deviation rate threshold, it indicates that the robot's movement duration is within an acceptable range and meets the requirements. If the duration deviation rate is greater than or equal to the duration deviation rate threshold, it indicates that the robot's movement duration exceeds the acceptable range and does not meet the requirements.

[0048] According to an embodiment of the present invention, the obstacle avoidance capability index is obtained by processing the environmental perception information in combination with the robot operation monitoring information, specifically: Extracting movement speed data according to the robot operation monitoring information; The obstacle avoidance capability index is obtained by processing the obstacle coordinate data, obstacle contour data, positioning error data and moving duration data in combination with the moving speed data through a preset obstacle avoidance capability evaluation model.

[0049] Among them, in order to further accurately reflect the robot's obstacle avoidance ability and obtain the obstacle avoidance ability index, the movement speed data is extracted according to the robot operation monitoring information, including the instantaneous speed at different time points and the average speed on the path. According to the obstacle coordinate data, obstacle contour data, positioning error data and movement duration data, the movement speed data is combined with the preset obstacle avoidance ability evaluation model for processing to obtain the obstacle avoidance ability index. The higher the obstacle avoidance ability index, the stronger the robot's obstacle avoidance ability is, and it can identify and avoid obstacles more quickly and accurately. Conversely, the lower the index, the relatively weaker the robot's obstacle avoidance ability is, and further optimization or improvement may be required; The calculation formula of the obstacle avoidance capability evaluation model is: ; in, is the obstacle avoidance ability index, They are obstacle coordinate data, obstacle contour data, positioning error data, moving duration data and moving speed data. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset robot obstacle avoidance database).

[0050] According to an embodiment of the present invention, the obstacle avoidance capability index is corrected and processed to obtain the obstacle avoidance capability correction index and compared with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements, specifically: Obtaining obstacle avoidance capability indexes of multiple different spaces and calculating an average obstacle avoidance capability index, and correcting the obstacle avoidance capability index using a preset obstacle avoidance capability correction model to obtain an obstacle avoidance capability correction index; Obtaining an obstacle avoidance deviation rate by comparing the obstacle avoidance capability correction index with a preset obstacle avoidance capability correction index threshold; comparing the obstacle avoidance deviation rate with a preset obstacle avoidance deviation rate threshold; If the obstacle avoidance deviation rate is less than the obstacle avoidance deviation rate threshold, the robot's obstacle avoidance capability meets the requirements; If the obstacle avoidance deviation rate is greater than or equal to the obstacle avoidance deviation rate threshold, the robot's obstacle avoidance capability does not meet the requirements.

[0051] Among them, in order to obtain the obstacle avoidance capability correction index and determine whether the robot's obstacle avoidance capability meets the requirements, first obtain the obstacle avoidance capability indexes of multiple different spaces and calculate the average obstacle avoidance capability index, correct the obstacle avoidance capability index through the preset obstacle avoidance capability correction model, obtain the obstacle avoidance capability correction index, and then compare it with the preset obstacle avoidance capability correction index threshold to obtain the obstacle avoidance deviation rate. Compare the obstacle avoidance deviation rate with the preset obstacle avoidance deviation rate threshold. If the obstacle avoidance deviation rate is less than the obstacle avoidance deviation rate threshold, it indicates that the robot's obstacle avoidance capability is consistent with expectations in actual applications and meets the requirements. If the obstacle avoidance deviation rate is greater than or equal to the obstacle avoidance deviation rate threshold, it indicates that the robot's obstacle avoidance capability is significantly different from expectations in actual applications and does not meet the requirements. The calculation formula of the obstacle avoidance capability correction model is: ; in, To correct the obstacle avoidance capability index, is the obstacle avoidance ability index, is the average index of obstacle avoidance ability, is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset robot obstacle avoidance database).

[0052] According to an embodiment of the present invention, it also includes: Extracting angular velocity data according to the robot operation monitoring information; Comparing the angular velocity data with a preset first angular velocity threshold and a preset second angular velocity threshold, respectively, the first angular velocity threshold being smaller than the second angular velocity threshold; If the angular velocity data is less than the first angular velocity threshold, the angular velocity of the robot is too low; If the angular velocity data is greater than or equal to the first angular velocity threshold and less than or equal to the second angular velocity threshold, the angular velocity of the robot meets the requirement; If the angular velocity data is greater than the second angular velocity threshold, the angular velocity of the robot is too high.

[0053] Among them, in order to judge and control the angular velocity of the robot, angular velocity data is extracted according to the robot operation monitoring information, and the angular velocity data is compared with the preset first angular velocity threshold and the second angular velocity threshold respectively, the first angular velocity threshold is less than the second angular velocity threshold, the first angular velocity threshold is set to a lower value, indicating the lower limit of the angular velocity of the robot, and the second angular velocity threshold is set to a higher value, indicating the upper limit of the angular velocity of the robot. If the angular velocity data is less than the first angular velocity threshold, it indicates that the angular velocity of the robot is too low and may not meet the requirements of fast turning or flexible movement. If the angular velocity data is greater than or equal to the first angular velocity threshold and less than or equal to the second angular velocity threshold, it indicates that the angular velocity of the robot is within an appropriate range and meets the design requirements and application scenarios. If the angular velocity data is greater than the second angular velocity threshold, it indicates that the angular velocity of the robot is too high, which may cause safety problems or mechanical wear and other problems.

[0054] According to an embodiment of the present invention, it also includes: Extracting tracking speed data according to the robot operation monitoring information; Comparing the tracking speed data with a preset tracking speed threshold; If the tracking speed data is greater than the tracking speed threshold, a tracking speed too high message is sent and timing is performed to obtain tracking speed duration data; comparing the tracking speed duration data with a preset duration threshold; If the tracking speed duration data is greater than or equal to the duration threshold, the robot's tracking speed is too high and needs to be adjusted.

[0055] Among them, in order to judge and control the speed of the robot tracking the target, the tracking speed data is extracted according to the robot operation monitoring information. The tracking speed refers to the speed of the robot's movement when following the target or path. The tracking speed data is compared with the preset tracking speed threshold. If the tracking speed data is greater than the tracking speed threshold, a tracking speed too high message is sent and timing is performed to obtain the tracking speed duration data. The tracking speed duration data is compared with the preset duration threshold. If the tracking speed duration data is greater than or equal to the duration threshold, it indicates that the robot's tracking speed is too high for too long and needs to be adjusted.

[0056] According to an embodiment of the present invention, it also includes: Obtain human-computer interaction information in a preset time period and extract interaction frequency data of multiple interaction types; Obtaining first interaction frequency data according to the interaction frequency data of the plurality of interaction types; comparing the first interaction frequency data with a preset interaction frequency threshold; If the first interaction frequency data is greater than or equal to the interaction frequency threshold, user preference marking is performed.

[0057] Among them, in order to understand the interaction methods that users are more accustomed to, obtain the human-computer interaction information of the preset time period, extract the interaction frequency data of multiple interaction types, obtain the first interaction frequency data, and compare it with the preset interaction frequency threshold. If the first interaction frequency data is greater than or equal to the interaction frequency threshold, then perform user preference marking. By analyzing the human-computer interaction information, we can gain an in-depth understanding of the user's behavior patterns and preferences in a specific time period, which is crucial for product design, user experience optimization, and market strategy formulation. Based on user preference marking, we can provide users with more personalized content or service recommendations, improve user satisfaction and loyalty, and user preference marking is an important part of building user portraits, which helps companies understand users more comprehensively and provide more accurate services and marketing.

[0058] The third aspect of the present invention provides a computer-readable storage medium, which includes a human-machine interactive collaborative industrial robot intelligent control method program. When the human-machine interactive collaborative industrial robot intelligent control method program is executed by a processor, the steps of the human-machine interactive collaborative industrial robot intelligent control method as described in any one of the above items are implemented.

[0059] The human-machine interactive collaborative industrial robot intelligent control method, system and medium disclosed in the present invention obtain environmental perception information in a preset space, extract obstacle coordinate data, obstacle contour data and target point coordinate data, obtain movement planning data and simulate movement through a preset digital twin model, obtain robot operation monitoring information according to the result of the simulated movement, extract positioning error data and movement duration data, compare the positioning error data with a preset error threshold to determine whether the robot positioning error meets the requirement, compare the movement duration data with a preset duration threshold to determine whether the robot movement duration meets the requirement, process the obstacle coordinate data, obstacle contour data, positioning error data and movement duration data in combination with the movement speed data to obtain an obstacle avoidance ability index; obtain the obstacle avoidance ability indexes of multiple different spaces and calculate to obtain an average obstacle avoidance ability index, correct the obstacle avoidance ability index through a preset obstacle avoidance ability correction model, obtain the obstacle avoidance ability correction index and compare it with a preset obstacle avoidance ability correction index threshold to determine whether the robot obstacle avoidance ability meets the requirement. This application can fully understand the spatial environment in which the robot is located, provide an accurate data basis for subsequent mobile planning, save time and resources by simulating movement, and then by calculating the obstacle avoidance capability index, it can quantitatively evaluate the robot's ability to cope with obstacles when encountering obstacles, making the evaluation results more specific and reliable. This application comprehensively evaluates and optimizes the robot's mobility performance, which can improve its safety and operating efficiency in complex environments, and reduce potential risks caused by inaccurate positioning or insufficient obstacle avoidance capabilities.

[0060] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0061] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0062] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0063] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0064] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A human-machine interactive collaborative industrial robot intelligent control method, characterized in that: The following steps are involved: Obtain environmental perception information within the preset space, extract obstacle coordinate data, obstacle outline data and target point coordinate data, obtain movement planning data and perform simulated movement; Acquire robot operation monitoring information according to the result of the simulated movement, and extract positioning error data and movement duration data; Comparing the positioning error data with a preset error threshold, determining whether the robot positioning error meets the requirements; Comparing the movement duration data with a preset duration threshold, determining whether the movement duration of the robot meets the requirement; Processing the environment perception information in combination with the robot operation monitoring information to obtain an obstacle avoidance capability index; Correction processing is performed according to the obstacle avoidance capability index to obtain an obstacle avoidance capability correction index and compare it with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements.

2. The intelligent control method of a human-machine interactive collaborative industrial robot according to claim 1, characterized in that: The step of acquiring environmental perception information in a preset space, extracting obstacle coordinate data, obstacle outline data and target point coordinate data, obtaining movement planning data and performing simulated movement is specifically as follows: Obtain environmental perception information within the preset space, and extract obstacle coordinate data, obstacle outline data, and target point coordinate data; The obstacle contour data includes contour size data and point set boundary data; According to the obstacle coordinate data, obstacle contour data and target point coordinate data, movement planning data is obtained through a preset digital twin model and simulated movement is performed.

3. The intelligent control method of a human-machine interactive collaborative industrial robot according to claim 2, characterized in that: The comparison between the positioning error data and the preset error threshold is used to determine whether the robot positioning error meets the requirements, specifically: Comparing the positioning error data with a preset error threshold; If the positioning error data is less than the error threshold, the robot positioning error meets the requirements; If the positioning error data is greater than or equal to the error threshold, the robot positioning error does not meet the requirements.

4. The intelligent control method of a human-machine interactive collaborative industrial robot according to claim 3, characterized in that: The comparison between the movement duration data and the preset duration threshold is used to determine whether the movement duration of the robot meets the requirement, specifically: Comparing the moving duration data with a preset duration threshold to obtain a duration deviation rate; Comparing the duration deviation rate with a preset duration deviation rate threshold; If the duration deviation rate is less than the duration deviation rate threshold, the robot's movement duration meets the requirement; If the duration deviation rate is greater than or equal to the duration deviation rate threshold, the robot's movement duration does not meet the requirement.

5. The intelligent control method of a human-machine interactive collaborative industrial robot according to claim 4, characterized in that: The obstacle avoidance capability index is obtained by processing the environment perception information in combination with the robot operation monitoring information, specifically: Extracting movement speed data according to the robot operation monitoring information; The obstacle avoidance capability index is obtained by processing the obstacle coordinate data, obstacle contour data, positioning error data and moving duration data in combination with the moving speed data through a preset obstacle avoidance capability evaluation model.

6. The intelligent control method of a human-machine interactive collaborative industrial robot according to claim 5, characterized in that: The obstacle avoidance capability correction index is obtained by performing correction processing according to the obstacle avoidance capability index and compared with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements, specifically: Obtaining obstacle avoidance capability indexes of multiple different spaces and calculating an average obstacle avoidance capability index, and correcting the obstacle avoidance capability index using a preset obstacle avoidance capability correction model to obtain an obstacle avoidance capability correction index; Obtaining an obstacle avoidance deviation rate by comparing the obstacle avoidance capability correction index with a preset obstacle avoidance capability correction index threshold; comparing the obstacle avoidance deviation rate with a preset obstacle avoidance deviation rate threshold; If the obstacle avoidance deviation rate is less than the obstacle avoidance deviation rate threshold, the robot's obstacle avoidance capability meets the requirements; If the obstacle avoidance deviation rate is greater than or equal to the obstacle avoidance deviation rate threshold, the robot's obstacle avoidance capability does not meet the requirements.

7. Human-machine interactive collaborative industrial robot intelligent control system, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a human-machine interactive collaborative industrial robot intelligent control method program, and when the human-machine interactive collaborative industrial robot intelligent control method program is executed by the processor, the following steps are implemented: Obtain environmental perception information within the preset space, extract obstacle coordinate data, obstacle outline data and target point coordinate data, obtain movement planning data and perform simulated movement; Acquire robot operation monitoring information according to the result of the simulated movement, and extract positioning error data and movement duration data; Comparing the positioning error data with a preset error threshold, determining whether the robot positioning error meets the requirements; Comparing the movement duration data with a preset duration threshold, determining whether the movement duration of the robot meets the requirement; Processing the environment perception information in combination with the robot operation monitoring information to obtain an obstacle avoidance capability index; Correction processing is performed according to the obstacle avoidance capability index to obtain an obstacle avoidance capability correction index and compare it with a preset obstacle avoidance capability correction index threshold to determine whether the robot's obstacle avoidance capability meets the requirements.

8. The human-machine interactive collaborative industrial robot intelligent control system according to claim 7, characterized in that: The step of acquiring environmental perception information in a preset space, extracting obstacle coordinate data, obstacle outline data and target point coordinate data, obtaining movement planning data and performing simulated movement is specifically as follows: Obtain environmental perception information within the preset space, and extract obstacle coordinate data, obstacle outline data, and target point coordinate data; The obstacle contour data includes contour size data and point set boundary data; According to the obstacle coordinate data, obstacle contour data and target point coordinate data, movement planning data is obtained through a preset digital twin model and simulated movement is performed.

9. The human-machine interactive collaborative industrial robot intelligent control system according to claim 8, characterized in that: The comparison between the positioning error data and the preset error threshold is used to determine whether the robot positioning error meets the requirements, specifically: Comparing the positioning error data with a preset error threshold; If the positioning error data is less than the error threshold, the robot positioning error meets the requirements; If the positioning error data is greater than or equal to the error threshold, the robot positioning error does not meet the requirements.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a human-machine interactive collaborative industrial robot intelligent control method program. When the human-machine interactive collaborative industrial robot intelligent control method program is executed by a processor, the steps of the human-machine interactive collaborative industrial robot intelligent control method according to any one of claims 1 to 6 are implemented.

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