Device control method and system based on visual recognition and network authentication

Abstract

The application provides a device control method and system based on visual recognition and network authentication, collects and analyzes internal environment images of a workshop site, obtains running state information of all robot devices inside the workshop site, and performs real-time visual motion recognition on the robot devices; further based on the motion state information, the robot devices that have motion abnormal events are identified, and the network communication channels currently accessed by the robot devices are determined, so as to facilitate comprehensive screening of motion control instructions for the robot devices; the motion control instruction receiving logs of the robot devices that have motion abnormal events are further analyzed, abnormal motion control instructions received by the robot devices are identified, accurate and comprehensive identification of motion control instructions for the robot devices is realized, and based on the abnormal motion control instructions, motion control correction processing is performed on the robot devices, so as to ensure the motion control accuracy, motion safety and reliability of the robot devices.

Description

Technical Field

[0001] This invention relates to the field of equipment control, and more particularly to equipment control methods and systems based on visual recognition and network authentication. Background Technology

[0002] Automated workshops contain multiple robotic devices, each capable of independent wireless control. Upon receiving motion control commands, each robot parses and processes these commands to execute the corresponding actions. However, with a large number of robots in an automated workshop, relying solely on wireless communication may not be sufficient for accurate motion control of all devices. Furthermore, crosstalk or reception delays between different robots can prevent them from receiving the correct motion control commands accurately and quickly, potentially leading to collisions and other accidents during movement. This reduces the safety and reliability of robot operations within the workshop. Summary of the Invention

[0003] The purpose of this invention is to provide a device control method and system based on visual recognition and network authentication. This method collects and analyzes images of the internal environment of a workshop to obtain the operating status information of all robotic devices within the workshop, and performs real-time visual motion recognition on the robotic devices. Based on the motion status information, it identifies robotic devices experiencing abnormal motion events and determines their current network communication channels, facilitating a comprehensive screening of motion control commands for the robotic devices. Furthermore, it analyzes the motion control command reception logs of the robotic devices experiencing abnormal motion events to identify the abnormal motion control commands received by the robotic devices, achieving accurate and comprehensive identification of the motion control commands. Based on these abnormal motion control commands, it performs motion control correction processing on the robotic devices, ensuring the accuracy, safety, and reliability of the robot's motion control.

[0004] This invention is achieved through the following technical solution:

[0005] Device control methods based on visual recognition and network authentication include:

[0006] Visual data is captured of the workshop area to obtain an image of the internal environment; the image is then analyzed to obtain motion state information of all robotic devices within the workshop area.

[0007] Based on the motion state information, identify the robot equipment that has experienced an abnormal motion event within the workshop area; perform communication network identification processing on the robot equipment that has experienced the abnormal motion event to obtain the network communication channel currently accessed by the robot equipment that has experienced the abnormal motion event;

[0008] Based on the network communication channel, obtain the motion control command reception log of the robot device that experienced the motion abnormality event; analyze the motion control command reception log to identify the abnormal motion control command received by the robot device that experienced the motion abnormality event;

[0009] Based on the abnormal motion control command, the robot device is subjected to motion control correction processing.

[0010] Optionally, visual data is acquired from the workshop area to obtain an image of the workshop's internal environment; the internal environment image is then analyzed to obtain motion state information for all robotic devices within the workshop area, including:

[0011] The workshop area is dynamically scanned and visually captured to obtain dynamic images of the internal environment; the dynamic images of the internal environment are then processed into frames to obtain several internal environment image frames; pixel contour recognition processing is performed on all internal environment image frames to obtain pixel contour feature information for each internal environment image frame; based on the pixel contour feature information, the motion trajectory information and motion heading angle change information of all robot devices inside the workshop area are obtained.

[0012] Optionally, visual acquisition of the workshop premises to obtain internal environmental images of the workshop premises includes: visual acquisition of the workshop premises using multiple rotatable cameras evenly arranged inside the workshop premises, specifically including:

[0013] Step S1: Using formula (1) below, based on the floor area and height of the workshop and the number of rotatable cameras, obtain the final minimum rotation radius of the rotatable cameras.

[0014]

[0015] In the above formula (1), R min The minimum rotation radius of the rotating camera is represented by S; the floor area of ​​the workshop is represented by H; the height of the workshop is represented by l; the length of the rotating camera is represented by N; and the number of rotating cameras is represented by N.

[0016] The rotatable camera starts shooting from vertically downwards. Each time, the rotation radius of the rotatable camera lens in its width and length directions is increased to perform rotational shooting. When the rotation radius is first greater than or equal to the final minimum rotation radius of the rotatable camera, the rotation radius is shortened to perform rotational shooting until the rotatable camera returns to vertically downwards. This cycle is repeated.

[0017] Step S2: Using formula (2) below, determine the rotational angular velocity of the rotatable camera based on its final minimum rotation radius and the number of all robotic devices within the workshop area.

[0018]

[0019] In the above formula (2), ω represents the rotational angular velocity of the rotatable camera; D represents the number of all robotic devices inside the workshop; V min This indicates the minimum linear rotation speed of the preset rotatable camera; int() indicates rounding the value within the parentheses; Indicates the request The maximum value among 1 and 2;

[0020] Step S3: Using the formula (3) below, control the shooting frequency of the rotatable camera according to the movement speed of all robot equipment inside the workshop.

[0021]

[0022] In the above formula (3), f represents the shooting frequency of the rotatable camera; v(a) represents the movement speed of the a-th robot device inside the workshop; max a∈[1,D] [v(a)] represents the maximum speed of all robotic devices within the workshop area.

[0023] Optionally, based on the motion state information, identify the robot equipment experiencing an abnormal motion event within the workshop area; perform communication network identification processing on the robot equipment experiencing the abnormal motion event to obtain the network communication channel currently accessed by the robot equipment experiencing the abnormal motion event, including:

[0024] The motion trajectory information contained in the motion state information is analyzed to obtain the minimum offset distance between the actual motion trajectory and the desired motion path of the robot device; the motion heading angle change information contained in the motion state information is analyzed to obtain the rate of change of the motion heading angle of the robot device.

[0025] If the minimum offset distance is greater than a preset distance threshold, or the rate of change of the heading angle is greater than a preset rate threshold, then the corresponding robot device is determined to be a robot device that has experienced a motion abnormality event; otherwise, the corresponding robot is determined to be a robot device that has not experienced a motion abnormality event.

[0026] The wireless communication network where the robot device experiencing the motion abnormality event is located is identified to obtain the wireless communication gateway to which the robot device experiencing the motion abnormality event is connected; and based on the identity information of the data transmission destination terminals of all network communication channels currently connected to the wireless communication gateway, the network communication channel currently connected to the robot device experiencing the motion abnormality event is determined.

[0027] Optionally, based on the network communication channel, the motion control command reception log of the robot device that experienced the motion anomaly event is obtained; the motion control command reception log is analyzed to identify the abnormal motion control commands received by the robot device that experienced the motion anomaly event, including:

[0028] Based on the channel link of the network communication channel, the operation control command reception log of the robot device that experienced the motion abnormality event is obtained, and the motion control command reception log is analyzed to obtain the instruction code content and reception time of each motion control command received by the robot device that experienced the motion abnormality event.

[0029] Based on the instruction code content, determine whether the corresponding motion control instruction contains interference code components; based on the reception time, determine whether the corresponding motion control instruction experiences a reception delay; if the corresponding motion control instruction contains interference code components or experiences a reception delay, then the corresponding motion control instruction is identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event; otherwise, the corresponding motion control instruction is not identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event.

[0030] Optionally, based on the abnormal motion control command, motion control correction processing is performed on the robot device, including:

[0031] The abnormal motion control command is parsed to determine the location of all interference code components contained in the abnormal motion control command and / or the reception delay time of the abnormal motion control command; based on the location of all interference code components, interference code correction processing is performed on the abnormal motion control command; based on the reception delay time, the command execution progress of the abnormal motion control command on the corresponding robot device is advanced.

[0032] The device control system based on visual recognition and network authentication includes:

[0033] The visual acquisition and analysis module is used to acquire visual images of the workshop area to obtain internal environmental images of the workshop area; and to analyze the internal environmental images to obtain motion state information of all robotic devices inside the workshop area.

[0034] A robot equipment identification module is used to identify robot equipment that has experienced abnormal motion events within the workshop area based on the motion state information.

[0035] The network communication channel determination module is used to perform communication network identification processing on the robot device that has experienced a motion abnormality event, and to obtain the network communication channel currently accessed by the robot device that has experienced a motion abnormality event.

[0036] The motion control command recognition module is used to acquire the motion control command reception log of the robot device that has experienced a motion abnormality event based on the network communication channel; analyze the motion control command reception log to identify the abnormal motion control command received by the robot device that has experienced the motion abnormality event;

[0037] The device motion control correction module is used to perform motion control correction processing on the robot device based on the abnormal motion control command.

[0038] Optionally, the visual acquisition and analysis module is used to visually acquire images of the workshop environment to obtain internal environmental images of the workshop; and to analyze the internal environmental images to obtain motion state information of all robotic devices within the workshop, including:

[0039] The workshop area is dynamically scanned and visually captured to obtain dynamic images of the internal environment; the dynamic images of the internal environment are then processed into frames to obtain several internal environment image frames; pixel contour recognition processing is performed on all internal environment image frames to obtain pixel contour feature information for each internal environment image frame; based on the pixel contour feature information, the motion trajectory information and motion heading angle change information of all robot devices inside the workshop area are obtained.

[0040] Optionally, the robot device identification module is used to identify robot devices that have experienced abnormal motion events within the workshop based on the motion state information, including:

[0041] The motion trajectory information contained in the motion state information is analyzed to obtain the minimum offset distance between the actual motion trajectory and the desired motion path of the robot device; the motion heading angle change information contained in the motion state information is analyzed to obtain the rate of change of the motion heading angle of the robot device.

[0042] If the minimum offset distance is greater than a preset distance threshold, or the rate of change of the heading angle is greater than a preset rate threshold, then the corresponding robot device is determined to be a robot device that has experienced a motion abnormality event; otherwise, the corresponding robot is determined to be a robot device that has not experienced a motion abnormality event.

[0043] The network communication channel determination module is used to perform communication network identification processing on the robot device that has experienced a motion abnormality event, and to obtain the network communication channel currently accessed by the robot device that has experienced the motion abnormality event, including:

[0044] The wireless communication network where the robot device experiencing the motion abnormality event is located is identified to obtain the wireless communication gateway to which the robot device experiencing the motion abnormality event is connected; and based on the identity information of the data transmission destination terminals of all network communication channels currently connected to the wireless communication gateway, the network communication channel currently connected to the robot device experiencing the motion abnormality event is determined.

[0045] Optionally, the motion control command recognition module is used to acquire the motion control command reception log of the robot device that experienced the motion abnormality event based on the network communication channel; analyze the motion control command reception log to identify the abnormal motion control commands received by the robot device that experienced the motion abnormality event, including:

[0046] Based on the channel link of the network communication channel, the operation control command reception log of the robot device that experienced the motion abnormality event is obtained, and the motion control command reception log is analyzed to obtain the instruction code content and reception time of each motion control command received by the robot device that experienced the motion abnormality event.

[0047] Based on the instruction code content, determine whether the corresponding motion control instruction contains interference code components; based on the reception time, determine whether the corresponding motion control instruction experiences a reception delay; if the corresponding motion control instruction contains interference code components or experiences a reception delay, then the corresponding motion control instruction is identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event; otherwise, the corresponding motion control instruction is not identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event.

[0048] Optionally, the device motion control correction module is used to perform motion control correction processing on the robot device based on the abnormal motion control command, including:

[0049] The abnormal motion control command is parsed to determine the location of all interference code components contained in the abnormal motion control command and / or the reception delay time of the abnormal motion control command; based on the location of all interference code components, interference code correction processing is performed on the abnormal motion control command; based on the reception delay time, the command execution progress of the abnormal motion control command on the corresponding robot device is advanced.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The device control method and system based on visual recognition and network authentication provided in this application collect and analyze images of the internal environment of the workshop to obtain the operating status information of all robotic devices within the workshop, and perform real-time visual motion recognition on the robotic devices. Based on the motion status information, it also identifies robotic devices experiencing abnormal motion events and determines their current network communication channels, facilitating a comprehensive screening of motion control commands for the robotic devices. Furthermore, it analyzes the motion control command reception logs of the robotic devices experiencing abnormal motion events to identify the abnormal motion control commands received by the robotic devices, achieving accurate and comprehensive identification of the motion control commands. Based on these abnormal motion control commands, it performs motion control correction processing on the robotic devices, ensuring the accuracy, safety, and reliability of the robot devices' motion control. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0053] Figure 1 This is a flowchart illustrating the device control method based on visual recognition and network authentication provided by the present invention.

[0054] Figure 2 A schematic diagram of the structure of the device control system based on visual recognition and network authentication provided by the present invention. Detailed Implementation

[0055] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, it should be noted that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, not the entire structure. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0056] The terms “comprising” and “having”, and any variations thereof, used in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0057] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0058] Please see Figure 1 As shown, an embodiment of this application provides a device control method based on visual recognition and network authentication, including:

[0059] Visual data is collected from the workshop area to obtain images of the internal environment; these images are then analyzed to obtain motion status information for all robotic devices within the workshop area.

[0060] Based on the motion state information, identify the robot equipment that has experienced an abnormal motion event within the workshop; perform communication network identification processing on the robot equipment that has experienced the abnormal motion event to obtain the network communication channel currently accessed by the robot equipment that has experienced the abnormal motion event;

[0061] Based on the network communication channel, obtain the motion control command reception log of the robot device that experienced the motion abnormality event; analyze the motion control command reception log to identify the abnormal motion control commands received by the robot device that experienced the motion abnormality event.

[0062] Based on the abnormal motion control command, motion control correction processing is performed on the robot device.

[0063] The beneficial effects of the above embodiments are that the device control method based on visual recognition and network authentication collects and analyzes internal environmental images of the workshop to obtain the operating status information of all robot devices within the workshop, and performs real-time visual motion recognition on the robot devices; based on the motion status information, it identifies robot devices that have experienced abnormal motion events and determines their current network communication channels, facilitating a comprehensive screening of motion control commands for the robot devices; further, it analyzes the motion control command reception logs of the robot devices that have experienced abnormal motion events to identify the abnormal motion control commands received by the robot devices, achieving accurate and comprehensive identification of the motion control commands of the robot devices. Based on these abnormal motion control commands, it performs motion control correction processing on the robot devices, ensuring the accuracy, safety, and reliability of the robot devices' motion control.

[0064] In another embodiment, visual acquisition is performed on the workshop area to obtain an image of the internal environment of the workshop area; the internal environment image is analyzed to obtain the motion state information of each robot device within the workshop area, including:

[0065] The workshop area is dynamically scanned and visually captured to obtain dynamic images of the internal environment. These images are then segmented into frames to obtain several internal environment image frames. Pixel contour recognition is performed on all internal environment image frames to obtain pixel contour feature information for each frame. Based on this pixel contour feature information, the motion trajectory information and heading angle change information of all robotic devices within the workshop area are obtained.

[0066] The beneficial effects of the above embodiments are that dynamic scanning and imaging of the workshop interior yields dynamic images of the workshop's internal environment. This enables dynamic visual tracking of the motion state of all robotic devices within the workshop, facilitating accurate identification of the actual motion state of the robots. Furthermore, the dynamic images of the internal environment are segmented into frames, and pixel contour recognition is performed on all frames to obtain corresponding pixel contour feature information. This allows for full-process tracking and determination of the robot's trajectory and heading angle within the workshop, ensuring accurate identification of the robot's operational status.

[0067] In another embodiment, visual acquisition of the workshop premises to obtain an image of the internal environment of the workshop premises includes: visual acquisition of the workshop premises using multiple rotatable cameras evenly arranged inside the workshop premises, specifically including:

[0068] Step S1: Using formula (1) below, based on the floor area and height of the workshop and the number of rotatable cameras, obtain the final minimum rotation radius of the rotatable cameras.

[0069]

[0070] In the above formula (1), R min The minimum rotation radius of the rotating camera is represented by S; the floor area of ​​the workshop is represented by H; the height of the workshop is represented by l; the length of the rotating camera is represented by N; and the number of rotating cameras is represented by N.

[0071] The rotatable camera starts shooting from vertically downwards. Each time, the rotation radius of the rotatable camera lens in its width and length directions is increased to perform rotational shooting. When the rotation radius is first greater than or equal to the final minimum rotation radius of the rotatable camera, the rotation radius is shortened to perform rotational shooting until the rotatable camera returns to vertically downwards. This cycle is repeated.

[0072] Step S2: Using formula (2) below, determine the rotational angular velocity of the rotatable camera based on its final minimum rotation radius and the number of all robotic devices within the workshop.

[0073]

[0074] In the above formula (2), ω represents the rotational angular velocity of the rotatable camera; D represents the number of all robotic devices inside the workshop; V min This indicates the minimum linear rotation speed of the preset rotatable camera; int() indicates rounding the value within the parentheses; Indicates the request The maximum value among 1 and 2;

[0075] Step S3: Using the formula (3) below, control the shooting frequency of the rotatable camera according to the movement speed of all robotic equipment inside the workshop.

[0076]

[0077] In the above formula (3), f represents the shooting frequency of the rotatable camera; v(a) represents the movement speed of the a-th robot device inside the workshop; max a∈[1,D] [v(a)] represents the maximum speed of all robotic devices within the workshop area.

[0078] The beneficial effects of the above embodiments are as follows: using the above formula (1), the final minimum rotation radius of the rotating camera is obtained according to the floor area and height of the workshop and the number of rotating cameras, thereby ensuring energy saving while enabling global visual acquisition of the workshop; then using the above formula (2), the rotation angular velocity of the rotating camera is determined according to the final minimum rotation radius of the rotating camera and the number of all robot devices inside the workshop, thereby ensuring that all robot devices can stably capture images and ensure the reliability of the system; then using the above formula (3), the shooting frequency of the rotating camera is controlled according to the movement speed of all robot devices inside the workshop, ensuring that the robot can perform tracking and shooting under each rotating camera.

[0079] In another embodiment, based on the motion state information, a robot device experiencing a motion anomaly within the workshop is identified; the robot device experiencing the motion anomaly undergoes communication network identification processing to obtain the network communication channel currently accessed by the robot device experiencing the motion anomaly, including:

[0080] By analyzing the motion trajectory information contained in the motion state information, the minimum offset distance between the actual motion trajectory and the expected motion path of the robot is obtained; by analyzing the motion heading angle change information contained in the motion state information, the rate of change of the robot's heading angle is obtained.

[0081] If the minimum offset distance is greater than a preset distance threshold, or the rate of change of the heading angle is greater than a preset rate threshold, then the corresponding robot device is identified as a robot device that has experienced a motion abnormality event; otherwise, the corresponding robot is identified as a robot device that has not experienced a motion abnormality event.

[0082] The wireless communication network where the robot device that experienced the motion abnormality event is located is identified to obtain the wireless communication gateway to which the robot device that experienced the motion abnormality event is connected; and based on the identity information of the data transmission destination terminals of all network communication channels currently connected to the wireless communication gateway, the network communication channel currently connected to the robot device that experienced the motion abnormality event is determined.

[0083] The beneficial effects of the above embodiments are that by analyzing the motion trajectory information and the change information of the heading angle contained in the motion state information, the minimum offset distance between the actual motion trajectory and the expected motion path of the robot and the rate of change of the heading angle of the robot are obtained. Then, a threshold comparison is performed on the minimum offset distance and the rate of change of the heading angle, which can accurately determine whether the robot has experienced a motion anomaly event, thus providing a reliable basis for subsequent communication network identification of the robot. Furthermore, the wireless communication network where the robot experiencing the motion anomaly event is located is identified, and the wireless communication gateway to which the robot experiencing the motion anomaly event is connected is obtained. This further determines the network communication channel currently accessed by the robot experiencing the motion anomaly event, facilitating the accurate acquisition of the motion control command reception status of the robot experiencing the motion anomaly event through the corresponding network communication channel.

[0084] In another embodiment, based on the network communication channel, the motion control command reception log of the robot device that experienced the motion anomaly is obtained; the motion control command reception log is analyzed to identify the abnormal motion control commands received by the robot device that experienced the motion anomaly, including:

[0085] Based on the channel link of the network communication channel, the operation control command reception log of the robot device that experienced the motion abnormality event is obtained. The motion control command reception log is analyzed to obtain the instruction code content and reception time of each motion control command received by the robot device that experienced the motion abnormality event.

[0086] Based on the instruction code content, determine whether the corresponding motion control instruction contains interference code components; based on the reception time, determine whether the corresponding motion control instruction has a reception delay; if the corresponding motion control instruction contains interference code components or has a reception delay, then the corresponding motion control instruction is identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event; otherwise, the corresponding motion control instruction is not identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event.

[0087] The beneficial effects of the above embodiments are that, based on the network communication channel currently accessed by the robot device experiencing a motion anomaly, the operation control command reception log of the robot device experiencing the motion anomaly is obtained. This allows for a comprehensive and accurate analysis of the motion control command code content and reception time of the corresponding robot device. Furthermore, based on the command code content, it is determined whether the corresponding motion control command contains interference code components; and based on the reception time, it is determined whether the corresponding motion control command experienced a reception delay. This accurately determines whether the robot device experiencing the motion anomaly received an abnormal motion control command, facilitating subsequent targeted motion control correction of the corresponding robot device.

[0088] In another embodiment, based on the abnormal motion control command, motion control correction processing is performed on the robot device, including:

[0089] The abnormal motion control command is parsed to determine the location of all interference code components contained in the abnormal motion control command and / or the reception delay time of the abnormal motion control command; based on the location of all interference code components, interference code correction processing is performed on the abnormal motion control command; based on the reception delay time, the command execution progress of the abnormal motion control command on the corresponding robot device is advanced.

[0090] The beneficial effects of the above embodiments are that by parsing the abnormal motion control command, determining the location of all interference code components contained in the abnormal motion control command and / or the reception delay time of the abnormal motion control command, the interference code correction processing and / or early execution of the abnormal motion control command can be performed on the abnormal motion control command, ensuring that the corresponding robot device can move correctly, and guaranteeing the accuracy, safety and reliability of the robot device's motion control.

[0091] Please see Figure 2 As shown, an embodiment of this application provides a device control system based on visual recognition and network authentication, including:

[0092] The visual acquisition and analysis module is used to acquire visual images of the workshop area to obtain internal environmental images of the workshop area; and to analyze the internal environmental images to obtain the motion state information of each robot device inside the workshop area.

[0093] The robot equipment identification module is used to identify robot equipment that has experienced abnormal motion events within the workshop based on the motion state information.

[0094] The network communication channel determination module is used to perform communication network identification processing on the robot device that has experienced a motion abnormality event, and to obtain the network communication channel currently accessed by the robot device that has experienced the motion abnormality event.

[0095] The motion control command recognition module is used to acquire the motion control command reception log of the robot device that has experienced a motion abnormality event based on the network communication channel; analyze the motion control command reception log to identify the abnormal motion control command received by the robot device that has experienced the motion abnormality event;

[0096] The device motion control correction module is used to perform motion control correction processing on the robot device based on the abnormal motion control command.

[0097] The beneficial effects of the above embodiments are that the device control system based on visual recognition and network authentication collects and analyzes images of the internal environment of the workshop to obtain the operating status information of all robot devices within the workshop, and performs real-time visual motion recognition on the robot devices; based on the motion status information, it identifies robot devices that have experienced abnormal motion events and determines their current network communication channels, facilitating a comprehensive screening of motion control commands for the robot devices; further, it analyzes the motion control command reception logs of the robot devices that have experienced abnormal motion events to identify the abnormal motion control commands received by the robot devices, achieving accurate and comprehensive identification of the motion control commands of the robot devices. Based on these abnormal motion control commands, it performs motion control correction processing on the robot devices, ensuring the accuracy, safety, and reliability of the robot devices' motion control.

[0098] In another embodiment, the visual acquisition and analysis module is used to visually acquire images of the workshop environment to obtain internal environmental images of the workshop; and to analyze these internal environmental images to obtain motion state information of all robotic devices within the workshop, including:

[0099] The workshop area is dynamically scanned and visually captured to obtain dynamic images of the internal environment. These images are then segmented into frames to obtain several internal environment image frames. Pixel contour recognition is performed on all internal environment image frames to obtain pixel contour feature information for each frame. Based on this pixel contour feature information, the motion trajectory information and heading angle change information of all robotic devices within the workshop area are obtained.

[0100] The beneficial effects of the above embodiments are that dynamic scanning and imaging of the workshop interior yields dynamic images of the workshop's internal environment. This enables dynamic visual tracking of the motion state of all robotic devices within the workshop, facilitating accurate identification of the actual motion state of the robots. Furthermore, the dynamic images of the internal environment are segmented into frames, and pixel contour recognition is performed on all frames to obtain corresponding pixel contour feature information. This allows for full-process tracking and determination of the robot's trajectory and heading angle within the workshop, ensuring accurate identification of the robot's operational status.

[0101] In another embodiment, the robot device identification module is used to identify robot devices that have experienced abnormal motion events within the workshop based on the motion state information, including:

[0102] By analyzing the motion trajectory information contained in the motion state information, the minimum offset distance between the actual motion trajectory and the expected motion path of the robot is obtained; by analyzing the motion heading angle change information contained in the motion state information, the rate of change of the robot's heading angle is obtained.

[0103] If the minimum offset distance is greater than a preset distance threshold, or the rate of change of the heading angle is greater than a preset rate threshold, then the corresponding robot device is identified as a robot device that has experienced a motion abnormality event; otherwise, the corresponding robot is identified as a robot device that has not experienced a motion abnormality event.

[0104] The network communication channel determination module is used to perform communication network identification processing on the robot device that has experienced a motion abnormality event, and to obtain the network communication channel currently accessed by the robot device that has experienced the motion abnormality event, including:

[0105] The wireless communication network where the robot device that experienced the motion abnormality event is located is identified to obtain the wireless communication gateway to which the robot device that experienced the motion abnormality event is connected; and based on the identity information of the data transmission destination terminals of all network communication channels currently connected to the wireless communication gateway, the network communication channel currently connected to the robot device that experienced the motion abnormality event is determined.

[0106] The beneficial effects of the above embodiments are that by analyzing the motion trajectory information and the change information of the heading angle contained in the motion state information, the minimum offset distance between the actual motion trajectory and the expected motion path of the robot and the rate of change of the heading angle of the robot are obtained. Then, a threshold comparison is performed on the minimum offset distance and the rate of change of the heading angle, which can accurately determine whether the robot has experienced a motion anomaly event, thus providing a reliable basis for subsequent communication network identification of the robot. Furthermore, the wireless communication network where the robot experiencing the motion anomaly event is located is identified, and the wireless communication gateway to which the robot experiencing the motion anomaly event is connected is obtained. This further determines the network communication channel currently accessed by the robot experiencing the motion anomaly event, facilitating the accurate acquisition of the motion control command reception status of the robot experiencing the motion anomaly event through the corresponding network communication channel.

[0107] In another embodiment, the motion control command identification module is used to acquire the motion control command reception log of the robot device that experienced the motion abnormality event based on the network communication channel; analyze the motion control command reception log to identify the abnormal motion control commands received by the robot device that experienced the motion abnormality event, including:

[0108] Based on the channel link of the network communication channel, the operation control command reception log of the robot device that experienced the motion abnormality event is obtained. The motion control command reception log is analyzed to obtain the instruction code content and reception time of each motion control command received by the robot device that experienced the motion abnormality event.

[0109] Based on the instruction code content, determine whether the corresponding motion control instruction contains interference code components; based on the reception time, determine whether the corresponding motion control instruction has a reception delay; if the corresponding motion control instruction contains interference code components or has a reception delay, then the corresponding motion control instruction is identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event; otherwise, the corresponding motion control instruction is not identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event.

[0110] The beneficial effects of the above embodiments are that, based on the network communication channel currently accessed by the robot device experiencing a motion anomaly, the operation control command reception log of the robot device experiencing the motion anomaly is obtained. This allows for a comprehensive and accurate analysis of the motion control command code content and reception time of the corresponding robot device. Furthermore, based on the command code content, it is determined whether the corresponding motion control command contains interference code components; and based on the reception time, it is determined whether the corresponding motion control command experienced a reception delay. This accurately determines whether the robot device experiencing the motion anomaly received an abnormal motion control command, facilitating subsequent targeted motion control correction of the corresponding robot device.

[0111] In another embodiment, the device motion control correction module is used to perform motion control correction processing on the robot device based on the abnormal motion control command, including:

[0112] The abnormal motion control command is parsed to determine the location of all interference code components contained in the abnormal motion control command and / or the reception delay time of the abnormal motion control command; based on the location of all interference code components, interference code correction processing is performed on the abnormal motion control command; based on the reception delay time, the command execution progress of the abnormal motion control command on the corresponding robot device is advanced.

[0113] The beneficial effects of the above embodiments are that by parsing the abnormal motion control command, determining the location of all interference code components contained in the abnormal motion control command and / or the reception delay time of the abnormal motion control command, the interference code correction processing and / or early execution of the abnormal motion control command can be performed on the abnormal motion control command, ensuring that the corresponding robot device can move correctly, and guaranteeing the accuracy, safety and reliability of the robot device's motion control.

[0114] In summary, this device control method and system based on visual recognition and network authentication collects and analyzes images of the internal environment of the workshop to obtain the operating status information of all robotic devices within the workshop, and performs real-time visual motion recognition on the robotic devices. Based on the motion status information, it identifies robotic devices experiencing abnormal motion events and determines their current network communication channels, facilitating a comprehensive screening of motion control commands. Furthermore, it analyzes the motion control command reception logs of the robotic devices experiencing abnormal motion events to identify the abnormal motion control commands received by the robotic devices, achieving accurate and comprehensive identification of the motion control commands. Based on these abnormal motion control commands, it performs motion control correction processing on the robotic devices, ensuring the accuracy, safety, and reliability of the robot's motion control.

[0115] The above is only one specific embodiment of the present invention, and any improvements made based on the concept of the present invention shall be considered within the scope of protection of the present invention.

Claims

1. A device control method based on visual recognition and network authentication, characterized in that, include: Visual data is captured of the workshop area to obtain an image of the internal environment; the image is then analyzed to obtain motion state information of all robotic devices within the workshop area. Based on the motion state information, identify robotic devices that have experienced abnormal motion events within the workshop area; The robot device that experienced the motion abnormality event is subjected to communication network identification processing to obtain the network communication channel currently accessed by the robot device that experienced the motion abnormality event. Based on the network communication channel, obtain the motion control command reception log of the robot device that experienced the motion abnormality event; analyze the motion control command reception log to identify the abnormal motion control command received by the robot device that experienced the motion abnormality event; Based on the abnormal motion control command, the robot device is subjected to motion control correction processing; The process of visually capturing images of the workshop premises to obtain internal environmental images includes: visually capturing images of the workshop premises using multiple rotatable cameras evenly arranged within the workshop premises, specifically including: Step S1: Using the formula (1) below, based on the floor area and height of the workshop and the number of rotatable cameras, obtain the final minimum rotation radius of the rotatable cameras. （1） In the above formula (1), This indicates the final minimum rotation radius of the rotating camera; Indicates the floor area of ​​the workshop area; Indicates the height of the workshop area; Indicates the length of the rotatable camera; Indicates the number of rotatable cameras; The rotatable camera starts shooting from vertically downwards. Each time, the rotation radius of the rotatable camera lens in its width and length directions is increased to perform rotational shooting. When the rotation radius is first greater than or equal to the final minimum rotation radius of the rotatable camera, the rotation radius is shortened to perform rotational shooting until the rotatable camera returns to vertically downwards. This cycle is repeated. Step S2: Using the formula (2) below, determine the rotational angular velocity of the rotatable camera based on its final minimum rotation radius and the number of all robotic devices within the workshop area. （2） In the above formula (2), This indicates the rotational angular velocity of a rotatable camera. This indicates the total number of robotic devices within the workshop area; This indicates the minimum linear speed of rotation for the preset rotatable camera; This indicates that the value within the parentheses is rounded down to the nearest integer. Indicates the request and The maximum value in; Step S3: Using the formula (3) below, control the shooting frequency of the rotatable camera according to the movement speed of all robot equipment inside the workshop. （3） In the above formula (3), This indicates the shooting frequency of the rotatable camera; The first [unit] inside the workshop area The movement speed of the robot device; This represents the maximum speed of all robotic devices within the workshop area.

2. The device control method based on visual recognition and network authentication as described in claim 1, characterized in that: Visual data is captured of the workshop area to obtain an image of the workshop's internal environment. This image is then analyzed to obtain motion state information for all robotic devices within the workshop area, including: The workshop area is dynamically scanned and visually captured to obtain dynamic images of the internal environment; the dynamic images of the internal environment are then processed into frames to obtain several internal environment image frames; pixel contour recognition processing is performed on all internal environment image frames to obtain pixel contour feature information for each internal environment image frame; based on the pixel contour feature information, the motion trajectory information and motion heading angle change information of all robot devices inside the workshop area are obtained.

3. The device control method based on visual recognition and network authentication as described in claim 1, characterized in that: Based on the motion state information, identify robotic devices that have experienced abnormal motion events within the workshop area; The robot device experiencing the motion anomaly undergoes communication network identification processing to obtain the network communication channel currently accessed by the robot device experiencing the motion anomaly, including: The motion trajectory information contained in the motion state information is analyzed to obtain the minimum offset distance between the actual motion trajectory and the desired motion path of the robot device; the motion heading angle change information contained in the motion state information is analyzed to obtain the rate of change of the motion heading angle of the robot device. If the minimum offset distance is greater than a preset distance threshold, or the rate of change of the motion heading angle is greater than a preset rate threshold, then the corresponding robot device is determined to be a robot device that has experienced a motion abnormality event; otherwise, the corresponding robot is determined to be a robot device that has not experienced a motion abnormality event. The wireless communication network where the robot device experiencing the motion abnormality event is located is identified to obtain the wireless communication gateway to which the robot device experiencing the motion abnormality event is connected; and based on the identity information of the data transmission destination terminals of all network communication channels currently connected to the wireless communication gateway, the network communication channel currently connected to the robot device experiencing the motion abnormality event is determined.

4. The device control method based on visual recognition and network authentication as described in claim 1, characterized in that: Based on the network communication channel, obtain the motion control command reception log of the robot device that experienced the motion anomaly; analyze the motion control command reception log to identify the abnormal motion control commands received by the robot device that experienced the motion anomaly, including: Based on the channel link of the network communication channel, the operation control command reception log of the robot device that experienced the motion abnormality event is obtained, and the motion control command reception log is analyzed to obtain the instruction code content and reception time of each motion control command received by the robot device that experienced the motion abnormality event. Based on the instruction code content, determine whether the corresponding motion control instruction contains interference code components; based on the reception time, determine whether the corresponding motion control instruction experiences a reception delay; if the corresponding motion control instruction contains interference code components or experiences a reception delay, then the corresponding motion control instruction is identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event; otherwise, the corresponding motion control instruction is not identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormality event.

5. The device control method based on visual recognition and network authentication as described in claim 1, characterized in that: Based on the abnormal motion control command, motion control correction processing is performed on the robot device, including: The abnormal motion control command is parsed to determine the location of all interference code components contained in the abnormal motion control command and / or the reception delay time of the abnormal motion control command; based on the location of all interference code components, interference code correction processing is performed on the abnormal motion control command; based on the reception delay time, the command execution progress of the abnormal motion control command on the corresponding robot device is advanced.

6. A device control system based on visual recognition and network authentication, characterized in that, include: The visual acquisition and analysis module is used to acquire visual images of the workshop area to obtain internal environmental images of the workshop area; and to analyze the internal environmental images to obtain motion state information of all robotic devices inside the workshop area. A robot equipment identification module is used to identify robot equipment that has experienced abnormal motion events within the workshop area based on the motion state information. The network communication channel determination module is used to perform communication network identification processing on the robot device that has experienced a motion abnormality event, and to obtain the network communication channel currently accessed by the robot device that has experienced a motion abnormality event. The motion control command recognition module is used to acquire the motion control command reception log of the robot device that has experienced a motion abnormality event based on the network communication channel; analyze the motion control command reception log to identify the abnormal motion control command received by the robot device that has experienced the motion abnormality event; The device motion control correction module is used to perform motion control correction processing on the robot device based on the abnormal motion control command. The process of visually capturing images of the workshop premises to obtain internal environmental images includes: visually capturing images of the workshop premises using multiple rotatable cameras evenly arranged within the workshop premises, specifically including: Step S1: Using the formula (1) below, based on the floor area and height of the workshop and the number of rotatable cameras, obtain the final minimum rotation radius of the rotatable cameras. （1） In the above formula (1), This indicates the final minimum rotation radius of the rotating camera; Indicates the floor area of ​​the workshop area; Indicates the height of the workshop area; Indicates the length of the rotatable camera; Indicates the number of rotatable cameras; The rotatable camera starts shooting from vertically downwards. Each time, the rotation radius of the rotatable camera lens in its width and length directions is increased to perform rotational shooting. When the rotation radius is first greater than or equal to the final minimum rotation radius of the rotatable camera, the rotation radius is shortened to perform rotational shooting until the rotatable camera returns to vertically downwards. This cycle is repeated. Step S2: Using the formula (2) below, determine the rotational angular velocity of the rotatable camera based on its final minimum rotation radius and the number of all robotic devices within the workshop area. （2） In the above formula (2), This indicates the rotational angular velocity of a rotatable camera. This indicates the total number of robotic devices within the workshop area; This indicates the minimum linear speed of rotation for the preset rotatable camera; This indicates that the value within the parentheses is rounded down to the nearest integer. Indicates the request and The maximum value in; Step S3: Using the formula (3) below, control the shooting frequency of the rotatable camera according to the movement speed of all robot equipment inside the workshop. （3） In the above formula (3), This indicates the shooting frequency of the rotatable camera; The first [unit] inside the workshop area The movement speed of the robot device; This represents the maximum speed of all robotic devices within the workshop area.

7. The device control system based on visual recognition and network authentication as described in claim 6, characterized in that: The visual acquisition and analysis module is used to acquire visual images of the workshop environment to obtain internal environmental images; and to analyze these images to obtain motion state information of all robotic devices within the workshop environment, including: The workshop area is dynamically scanned and visually captured to obtain dynamic images of the internal environment; the dynamic images of the internal environment are then processed into frames to obtain several internal environment image frames; pixel contour recognition processing is performed on all internal environment image frames to obtain pixel contour feature information for each internal environment image frame; based on the pixel contour feature information, the motion trajectory information and motion heading angle change information of all robot devices inside the workshop area are obtained.

8. The device control system based on visual recognition and network authentication as described in claim 6, characterized in that: The robot device identification module is used to identify robot devices that have experienced abnormal motion events within the workshop based on the motion state information, including: The motion trajectory information contained in the motion state information is analyzed to obtain the minimum offset distance between the actual motion trajectory and the desired motion path of the robot device; the motion heading angle change information contained in the motion state information is analyzed to obtain the rate of change of the motion heading angle of the robot device. If the minimum offset distance is greater than a preset distance threshold, or the rate of change of the motion heading angle is greater than a preset rate threshold, then the corresponding robot device is determined to be a robot device that has experienced a motion abnormality event; otherwise, the corresponding robot is determined to be a robot device that has not experienced a motion abnormality event. The network communication channel determination module is used to perform communication network identification processing on the robot device that has experienced a motion abnormality event, and to obtain the network communication channel currently accessed by the robot device that has experienced the motion abnormality event, including: The wireless communication network where the robot device experiencing the motion abnormality event is located is identified to obtain the wireless communication gateway to which the robot device experiencing the motion abnormality event is connected; and based on the identity information of the data transmission destination terminals of all network communication channels currently connected to the wireless communication gateway, the network communication channel currently connected to the robot device experiencing the motion abnormality event is determined.

9. The device control system based on visual recognition and network authentication as described in claim 6, characterized in that: The motion control command recognition module is used to obtain the motion control command reception log of the robot device that has experienced a motion abnormality event based on the network communication channel; Analyzing the motion control command reception logs identifies abnormal motion control commands received by the robot device experiencing the motion anomaly, including: Based on the channel link of the network communication channel, the operation control command reception log of the robot device that experienced the motion abnormality event is obtained, and the motion control command reception log is analyzed to obtain the instruction code content and reception time of each motion control command received by the robot device that experienced the motion abnormality event. Based on the instruction code content, determine whether the corresponding motion control instruction contains interference code components; based on the reception time, determine whether the corresponding motion control instruction experiences a reception delay; if the corresponding motion control instruction contains interference code components or experiences a reception delay, then the corresponding motion control instruction is identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormal event; otherwise, the corresponding motion control instruction is not identified as an abnormal motion control instruction received by the robot device that experienced the motion abnormal event. or, The device motion control correction module is used to perform motion control correction processing on the robot device based on the abnormal motion control command, including: The abnormal motion control command is parsed to determine the location of all interference code components contained in the abnormal motion control command and / or the reception delay time of the abnormal motion control command; based on the location of all interference code components, interference code correction processing is performed on the abnormal motion control command; based on the reception delay time, the command execution progress of the abnormal motion control command on the corresponding robot device is advanced.

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