Industrial robot control method and system using artificial intelligence

Abstract

The present invention relates to the field of robot control technology, and specifically discloses an industrial robot control method and system using artificial intelligence. The method comprises obtaining a workshop map of a production workshop, inserting equipment profiles into the workshop map based on the equipment information of the industrial equipment; obtaining operating data of the industrial equipment based on sensors built into the industrial equipment, analyzing the operating data to obtain an information diagram based on the workshop map containing the equipment profiles; determining a demand area containing demand levels based on the information diagram; determining the working parameters of the AI robot based on the demand area containing the demand levels, and generating control instructions based on the working parameters. The present invention analyzes the equipment data of each industrial equipment to determine the working process of the AI robot, and uses the AI robot to assist the industrial equipment in its work, acting as a "clone" of the staff, thereby reducing the contact time between the staff and the industrial equipment and indirectly improving the level of safety.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to an industrial robot control method and system using artificial intelligence. Background Art

[0002] With the advancement of random artificial intelligence technology and the development of robotics, the level of intelligence in the production process will become increasingly higher. Artificial intelligence technology, combined with robots, can complete a large amount of work.

[0003] In existing production activities, there are actually very few purely intelligent production lines. Only very large enterprises have the opportunity to build such production lines. Most of the production is carried out in a way that combines equipment and manual labor, that is, a semi-automatic method. This method has low costs and high flexibility, but because the staff need to work in coordination with the equipment, safety problems are likely to occur. Therefore, how to improve the safety of the staff in the semi-automatic production process is the technical problem that the technical solution of the present invention aims to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide an industrial robot control method and system using artificial intelligence to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An industrial robot control method using artificial intelligence, the method comprising:

[0007] Obtain a workshop map of the production workshop, and insert equipment outlines into the workshop map based on the equipment information of the industrial equipment;

[0008] Acquire the operating data of industrial equipment based on sensors built into the equipment, analyze the operating data, and obtain an information diagram based on a workshop map containing equipment outlines;

[0009] Determining a demand area containing demand levels according to the information diagram;

[0010] The working parameters of the AI robot are determined according to the demand area containing the demand level, and a control instruction is generated based on the working parameters and sent to the AI robot; wherein the working parameters include a working path and a working level, and the working level is used to control the amount of AI resources of the AI robot.

[0011] As a further solution of the present invention, the step of obtaining a workshop map of a production workshop and inserting equipment outlines into the workshop map according to equipment information of industrial equipment includes:

[0012] Get the workshop map of the production workshop;

[0013] Displaying a workshop map and receiving equipment information input by a user; the equipment information includes a housing outline, equipment location, and component information; the component information includes the location of each component, component importance, and correlation between different components;

[0014] Create equipment outlines in the shop floor map based on the shell outlines and equipment locations;

[0015] A node diagram is inserted into the device outline based on the component information. The nodes in the node diagram are circles, the radius of which is determined by the component importance. Connection lines are set between the nodes, and the width of the connection lines is determined by the correlation between the components. The node position is determined by the component position.

[0016] As a further solution of the present invention, the steps of acquiring the operating data of the industrial equipment based on the sensors built into the industrial equipment, analyzing the operating data, and obtaining an information diagram based on the workshop map containing the equipment outline include:

[0017] Establish a connection channel with sensors built into industrial equipment and obtain sensor data with time tags based on the connection channel;

[0018] Query the node corresponding to the sensor in the node graph and use the sensor data as the operating data of the node;

[0019] Analyze the operating data to determine the influence radius of each node;

[0020] The influence area of each node is determined according to the influence radius. When the influence areas of all nodes are determined, an information diagram is obtained.

[0021] As a further solution of the present invention: the step of analyzing the operating data to determine the influence radius of each node includes:

[0022] Create a matrix based on the node graph; each element in the matrix corresponds to a node, and each node corresponds to a component;

[0023] Based on matrix statistics of sensor data with time tags, the actual matrix at each moment is obtained; wherein, sensor data with the same time tag are inserted into the same matrix;

[0024] Arrange the actual matrix in chronological order, extract the data of each element in the arranged actual matrix at each moment within a preset time range, and obtain an array with row and column positions as labels;

[0025] Analyze the array of each row and column position to determine the influence radius of each node;

[0026] The process of determining the influence radius is as follows:

[0027] Where, Indicates the influence radius of a node. Indicates that the node includes The mean of the data in the array, Indicates that the node includes The standard deviation of the data in the array, Indicates that the independent variable is and The function is a preset function; Indicates that the node includes The weight of the sensor corresponding to the array is a preset value, and the weight has a dimension, which is used to The function values obtained from the array are converted to dimensionless values; Indicates the number of sensors included in the node.

[0028] As a further solution of the present invention: the step of determining the demand area containing the demand level according to the information diagram includes:

[0029] For any node, read the node's influence area;

[0030] Read the radius of the node, query all the connection lines of the node to other nodes, and obtain the radius of other nodes;

[0031] Determine the required value based on the radius of the current node and the radius of other nodes;

[0032] For any location in the information diagram, query the demand values of the influence areas of all nodes at that location, and superimpose all demand values as the characteristic value of that location;

[0033] Based on the characteristic values, the contour of the information diagram is identified, and the obtained area is called the demand area. The mean of the demand values in the demand area is calculated, and the demand level is determined based on the mean;

[0034] The process of determining the demand value is as follows:

[0035] Where, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the node that has a connection line with the current node. nodes, Indicates the total number of nodes that have connection lines with the current node. Indicates the node that has a connection line with the current node. The width of the connection line of each node, The preset maximum width.

[0036] As a further solution of the present invention, the steps of determining the working parameters of the AI robot according to the demand area containing the demand level, generating control instructions based on the working parameters, and sending the control instructions to the AI robot include:

[0037] Obtaining the location of each required area and determining the working path of the AI robot based on the location;

[0038] Obtain the demand level, and determine the length of time the AI robot stays in each demand area based on the demand level.

[0039] The technical solution of the present invention also provides an industrial robot control system using artificial intelligence, the system comprising:

[0040] The equipment profile insertion module is used to obtain the workshop map of the production workshop and insert the equipment profile into the workshop map according to the equipment information of the industrial equipment;

[0041] A schematic diagram generation module is used to obtain the operating data of industrial equipment based on sensors built into the industrial equipment, analyze the operating data, and obtain an information schematic diagram based on a workshop map containing the equipment outline;

[0042] A demand area determination module, configured to determine a demand area having a demand level according to the information diagram;

[0043] A control instruction generation module is used to determine the working parameters of the AI robot based on the demand area containing the demand level, generate control instructions based on the working parameters, and send them to the AI robot; wherein the working parameters include the working path and the working level, and the working level is used to control the amount of AI resources of the AI robot.

[0044] As a further solution of the present invention: the device profile insertion module includes:

[0045] A map acquisition unit, used to acquire a workshop map of a production workshop;

[0046] An equipment information receiving unit, configured to display a workshop map and receive equipment information input by a user; the equipment information includes a housing outline, equipment location, and component information; the component information includes the location of each component, component importance, and correlation between different components;

[0047] a contour determination unit for creating a device contour in a shop floor map based on a housing contour and a device position;

[0048] The outline insertion unit is used to insert a node diagram into the device outline based on the component information; the nodes in the node diagram are circles, the radius of the circle is determined by the component importance, and there are connecting lines between the nodes, the width of the connecting lines is determined by the correlation between the components; the node position is determined by the component position.

[0049] As a further solution of the present invention: the schematic diagram generating module includes:

[0050] A sensor data acquisition unit, configured to establish a connection channel with a sensor built into the industrial equipment and acquire sensor data containing a time tag based on the connection channel;

[0051] The sensor data application unit is used to query the corresponding node of the sensor in the node graph and use the sensor data as the operation data of the node;

[0052] a demand radius determination unit, configured to analyze the operation data and determine the influence radius of each node;

[0053] The schematic diagram output unit is used to determine the influence area of each node according to the influence radius, and obtain an information schematic diagram when the influence areas of all nodes are determined.

[0054] As a further solution of the present invention: the demand area determination module includes:

[0055] An area reading unit is used to read the influence area of any node;

[0056] The radius reading unit is used to read the radius of a node, query other nodes pointed to by all connection lines of the node, and obtain the radius of other nodes;

[0057] A demand value determining unit, configured to determine a demand value based on a radius of a current node and radii of other nodes;

[0058] A characteristic value generating unit is used to query the demand values of the influence areas of all nodes at any position in the information diagram, and superimpose all demand values as the characteristic value of the position;

[0059] The regional segmentation unit is used to identify the contour of the information diagram based on the characteristic value. The obtained area is called the demand area. The mean of the demand value in the demand area is calculated and the demand level is determined according to the mean;

[0060] The process of determining the demand value is as follows:

[0061] Where, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the node that has a connection line with the current node. nodes, Indicates the total number of nodes that have connection lines with the current node. Indicates the node that has a connection line with the current node. The width of the connection line of each node, The preset maximum width.

[0062] Compared with the existing technology, the beneficial effects of the present invention are: the present invention analyzes the equipment data of each industrial equipment, determines the working process of the AI robot, and uses the AI robot to assist the industrial equipment to work, acting as the "avatar" of the staff, reducing the contact time between the staff and the industrial equipment, and indirectly improving the safety level. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0064] Figure 1 This is a flowchart of an industrial robot control method using artificial intelligence.

[0065] Figure 2 This is the first sub-flow chart of the industrial robot control method using artificial intelligence.

[0066] Figure 3 This is the second sub-flow chart of the industrial robot control method using artificial intelligence.

[0067] Figure 4 This is the third sub-flow chart of the industrial robot control method using artificial intelligence.

[0068] Figure 5 This is the fourth sub-flow chart of the industrial robot control method using artificial intelligence.

[0069] Figure 6 This is a structural block diagram of the industrial robot control system that applies artificial intelligence. DETAILED DESCRIPTION

[0070] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] Figure 1This is a flowchart of an industrial robot control method using artificial intelligence. In an embodiment of the present invention, an industrial robot control method using artificial intelligence includes:

[0072] Step S100: obtaining a workshop map of a production workshop, and inserting equipment outlines into the workshop map according to equipment information of industrial equipment;

[0073] The production workshop is the working scene of industrial robots. The workshop map of the production workshop is obtained, and the equipment information of the industrial equipment in the production workshop is queried. According to the equipment information, the outline corresponding to the industrial equipment can be inserted into the workshop map, which is called the equipment outline.

[0074] Step S200: acquiring operating data of the industrial equipment based on sensors built into the industrial equipment, analyzing the operating data, and obtaining an information diagram based on a workshop map containing equipment outlines;

[0075] Industrial equipment is equipped with multiple sensors, which are basic components of industrial equipment. Based on the sensors built into the industrial equipment, the operating data of the industrial equipment can be obtained. By analyzing the operating data, the working status of the industrial equipment can be determined. By converting the working status into a workshop map containing the equipment outline, an information diagram can be obtained. Specifically, the working status determined based on the operating data actually represents the busyness of each industrial equipment, and the information diagram represents the busyness of all industrial equipment.

[0076] Step S300: determining a demand area having a demand level according to the information diagram;

[0077] By analyzing the information diagram, the demand situation at each location can be determined, and then the demand area can be determined. The demand area contains a parameter called the demand level, which indicates the degree of help needed.

[0078] Step S400: determining operating parameters of the AI robot based on the demand area containing the demand level, generating control instructions based on the operating parameters, and sending the control instructions to the AI robot; wherein the operating parameters include a working path and a working level, and the working level is used to control the amount of AI resources of the AI robot;

[0079] The demand area containing demand levels indicates the degree of need for help in various areas of the production workshop. The working parameters of the AI robot are determined based on the demand area containing demand levels. The AI robot is a robot with mobile and operating functions. The robot also has an AI recognition module built in, which can understand the scene and assist industrial equipment in working. The types of industrial equipment in the production workshop are limited, and the recognition accuracy requirements of the AI recognition module are very low. It only needs to be able to recognize a few simple tasks, and the cost of the AI recognition module is not high. At this time, the AI recognition module is equivalent to an auxiliary staff member, providing auxiliary work in the demand area, which can greatly improve production efficiency in the semi-automated production process.

[0080] Figure 2 This is a first sub-flow chart of the industrial robot control method using artificial intelligence. The steps of obtaining a workshop map of a production workshop and inserting equipment profiles into the workshop map based on equipment information of industrial equipment include:

[0081] Step S101: obtaining a workshop map of a production workshop;

[0082] Step S102: Displaying a workshop map and receiving device information input by a user; the device information includes a housing outline, device location, and component information; the component information includes the location of each component, component importance, and correlation between different components;

[0083] Step S103: creating a device outline in the workshop map according to the shell outline and the device location;

[0084] Step S104: inserting a node diagram into the device outline according to the component information; the nodes in the node diagram are circles, the radius of the circle is determined by the component importance, and connecting lines are provided between the nodes, the width of the connecting lines is determined by the correlation between the components; the node position is determined by the component position.

[0085] In an example of the technical solution of the present invention, the acquisition and application process of the workshop map is explained, the workshop map of the production workshop is obtained, the workshop map is displayed, and the equipment information and equipment location input by the user are received at the same time. The equipment information includes the shell outline. The shell outline is generally a three-dimensional outline, that is, the outline of the boundary of the industrial equipment, which can be directly read in the three-dimensional model of the industrial equipment. The equipment position is generally the relative position of several points on the equipment outline in the workshop. The simplest way is to use the origin of the three-dimensional model of the industrial equipment as the equipment position, as long as the positioning function can be realized.

[0086] In addition, the device information also includes component information, which includes which components are in the device, the importance of each component, and the correlation between each component. It should be noted that the component information is input by the user. Generally, not all components in an industrial device are important, but only a few core components need to be analyzed. Therefore, the complexity of the component information is actually very low.

[0087] Insert the top view of the shell outline into the workshop map to obtain the equipment outline. Then analyze the component information to determine multiple nodes, each node corresponds to a component. Count all the nodes, establish connecting lines between nodes, obtain a node graph, and insert the node graph into the equipment outline.

[0088] It should be noted that the nodes in the node diagram are circles, and the radius of the circle is determined by the importance of the component. The higher the component importance, the larger the radius. There are connecting lines between the nodes, and the width of the connecting lines is determined by the correlation between the components. The higher the correlation between the two components (for example, there is a direct connection relationship), the wider the line. In addition, the position of the node in the device outline is determined by the component position. The relationship between the position of the node in the device outline and the component position is not a strict quantitative relationship, as long as it conforms to the general directional relationship. The device outline containing the node diagram is actually a simplified two-dimensional diagram to represent the status of the device. The representation method is to insert multiple circles in the device outline. There may be connecting lines between the circles, thereby simplifying the representation of the device.

[0089] Figure 3 This is a second sub-flow diagram of the industrial robot control method using artificial intelligence. The steps of acquiring operating data of the industrial equipment based on sensors built into the industrial equipment, analyzing the operating data, and obtaining an information diagram based on a workshop map containing equipment outlines include:

[0090] Step S201: establishing a connection channel with a sensor built into the industrial equipment, and acquiring sensor data containing a time tag based on the connection channel;

[0091] Step S202: query the node corresponding to the sensor in the node graph, and use the sensor data as the operating data of the node;

[0092] Step S203: Analyze the operation data to determine the influence radius of each node;

[0093] Step S204: determining the influence area of each node according to the influence radius. When the influence areas of all nodes are determined, an information diagram is obtained.

[0094] In an example of the technical solution of the present invention, the generation process of the information schematic diagram is explained. It introduces a method of representing operating data in the equipment outline containing the node diagram, establishes a connection channel with the sensor built into the industrial equipment, obtains sensor data containing time tags based on the connection channel, and queries the corresponding node of the sensor in the node diagram. Since the node corresponds to the component, the number of components in the component information input by the user is limited. Therefore, many sensors may not correspond to the nodes in the node diagram. At this time, these sensor data still need to be saved, but they are not considered within the scope of this application. This application only analyzes sensors with corresponding relationships, queries the corresponding node of the sensor in the node diagram, uses the sensor data as the operating data of the node, analyzes the operating data, and determines the influence radius of each node.

[0095] Based on each node and its influence radius, an influence area can be created. When the influence area of all nodes is determined, an information diagram is obtained. It should be noted that the information diagram requires analysis of each node, not just the device outline.

[0096] Furthermore, the step of analyzing the operating data to determine the influence radius of each node includes:

[0097] Create a matrix based on the node graph; each element in the matrix corresponds to a node, and each node corresponds to a component;

[0098] Based on matrix statistics of sensor data with time tags, the actual matrix at each moment is obtained; wherein, sensor data with the same time tag are inserted into the same matrix;

[0099] Arrange the actual matrix in chronological order, extract the data of each element in the arranged actual matrix at each moment within a preset time range, and obtain an array with row and column positions as labels;

[0100] The array of each row and column position is analyzed to determine the influence radius of each node.

[0101] Once the node position in the node graph is determined, it will not be changed. A matrix can be created based on the positional relationship of the nodes in the node graph. Each element (row and column position) in the matrix corresponds to a node, and each node corresponds to a component. It should be noted that the matrix is actually just a statistical method. For example, a 3*3 matrix does not necessarily have data in all 9 positions. When there are only five components, there is data in only five positions. In other words, the matrix in this application is different from the matrix in mathematics.

[0102] Based on matrix statistics of sensor data containing time tags, the actual matrix at each moment is obtained, and the actual matrix is arranged in chronological order. This is a data regularization process. In the arranged actual matrix, the data of each element at each moment within the preset time range is extracted to obtain an array with row and column positions as labels. The array of each row and column position is analyzed to determine a value used to represent the radius. Since each row and column position corresponds to a node and the corresponding relationship remains unchanged, the determined value is the influence radius of each node.

[0103] The process of determining the influence radius is as follows:

[0104] Where, Indicates the influence radius of a node. Indicates that the node includes The mean of the data in the array, Indicates that the node includes The standard deviation of the data in the array, Indicates that the independent variable is and The function is a preset function; Indicates that the node includes The weight of the sensor corresponding to the array is a preset value, and the weight has a dimension, which is used to The function values obtained from the array are converted to dimensionless values; Indicates the number of sensors included in the node.

[0105] in, The simplest form of ; and These are all preset constants that affect the importance of the data of the node represented by the radius. What is considered important is determined by the user. Generally, the larger the mean, the more important it is, and the larger the standard deviation, the more important it is. In addition, each array corresponds to each sensor and represents the sensor data of a certain sensor within a preset time range.

[0106] Figure 4 The third sub-flow chart of the industrial robot control method using artificial intelligence, wherein the step of determining the demand area containing the demand level according to the information diagram includes:

[0107] Step S301: For any node, read the node's influence area;

[0108] Step S302: Read the radius of the node, query all the connection lines of the node to other nodes, and obtain the radius of other nodes;

[0109] Step S303: determining the demand value based on the radius of the current node and the radius of other nodes;

[0110] Step S304: For any position in the information diagram, query the demand values of the influence areas of all nodes at that position, and superimpose all demand values as the characteristic value of the position;

[0111] Step S305: performing contour recognition on the information diagram based on the characteristic values, and the obtained area is called the demand area. The mean of the demand values in the demand area is calculated, and the demand level is determined according to the mean.

[0112] In an example of the technical solution of the present invention, the demand area and its demand level are explained. For any node, the influence area of the node is read, and the demand area represents the influence range of a node; the radius of the node is read, and the radius of the node represents the importance of the node. The other nodes pointed to by all the connection lines of the node are queried to obtain the radius of other nodes. The importance of the current node can be corrected according to the radius of other nodes, and the demand value is determined according to the corrected radius. The demand value is inserted into each pixel point in the demand area to obtain the demand area containing the demand value; at this time, the demand area is a single-value area, and each node can obtain the influence area of each node through the same operation.

[0113] For any position in the information diagram, query all demand areas containing it, and query the demand value of the demand area at that location, so as to obtain the value of any position in the information diagram. At this time, the entire information diagram is equivalent to a single-value image. By applying a conventional contour recognition algorithm to the single-value image, the information diagram can be divided into regions to obtain multiple small areas, called demand areas. The mean of the values of each position in the demand area is calculated, and the demand level is determined according to the threshold reached by the mean.

[0114] Specifically, the process of determining the demand value is as follows:

[0115] Where, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the node that has a connection line with the current node. nodes, Indicates the total number of nodes that have connection lines with the current node. Indicates the node that has a connection line with the current node. The width of the connection line of each node, The preset maximum width.

[0116] The demand value is used to assign a value to the impact area. The larger the node radius, the higher the importance. In addition, the wider the connection line of the query node points to another node, the greater the correlation. The demand value can be determined by accumulating the radius of the query node and the radius of the related nodes according to the preset coefficient.

[0117] Figure 5 This is a fourth sub-flow diagram of the industrial robot control method using artificial intelligence. The steps of determining the operating parameters of the AI robot according to the demand area containing the demand level, generating control instructions based on the operating parameters, and sending them to the AI robot include:

[0118] Step S401: Acquire the location of each required area and determine the working path of the AI robot according to the location;

[0119] Step S402: Obtain the demand level, and determine the length of time the AI robot stays in each demand area according to the demand level.

[0120] In an example of the technical solution of the present invention, the control process of the AI robot is explained. The position of each demand area is obtained, and the position is used as a passing point. With the help of an existing path planning algorithm, the working path of the AI robot can be obtained; on this basis, for each demand area, its demand level is obtained, and the length of time the AI robot stays in each demand area is determined according to the demand level.

[0121] Furthermore, the location of the demand area can select the center of the area as the location. The working path and the stay time are actually parameters within a preset time period. The time period is generally a production cycle, such as one day. After determining the working path and stay time of the AI robot, the AI robot will reach each demand area along the working path during the production cycle, and then stay in each demand area for a certain period of time. During the stay time, the AI module of the AI robot is activated to assist the work of each industrial equipment. This is equivalent to arranging an auxiliary staff member. When the number of AI robots is large, multiple working paths can be generated, so that each industrial equipment can be assisted at a higher frequency.

[0122] Figure 6 The following is a structural block diagram of an industrial robot control system that applies artificial intelligence. In an embodiment of the present invention, an industrial robot control system that applies artificial intelligence, the system 10 includes:

[0123] The equipment profile insertion module 11 is used to obtain a workshop map of the production workshop and insert the equipment profile into the workshop map according to the equipment information of the industrial equipment;

[0124] a schematic diagram generating module 12 for acquiring operating data of the industrial equipment based on sensors built into the industrial equipment, analyzing the operating data, and obtaining an information schematic diagram based on a workshop map containing equipment outlines;

[0125] A demand area determination module 13 is configured to determine a demand area having a demand level according to the information diagram;

[0126] The control instruction generation module 14 is used to determine the working parameters of the AI robot based on the demand area containing the demand level, generate control instructions based on the working parameters, and send them to the AI robot; wherein the working parameters include the working path and the working level, and the working level is used to control the AI resource amount of the AI robot.

[0127] Furthermore, the device profile insertion module 11 includes:

[0128] A map acquisition unit, used to acquire a workshop map of a production workshop;

[0129] An equipment information receiving unit, configured to display a workshop map and receive equipment information input by a user; the equipment information includes a housing outline, equipment location, and component information; the component information includes the location of each component, component importance, and correlation between different components;

[0130] a contour determination unit for creating a device contour in a shop floor map based on a housing contour and a device position;

[0131] The outline insertion unit is used to insert a node diagram into the device outline based on the component information; the nodes in the node diagram are circles, the radius of the circle is determined by the component importance, and there are connecting lines between the nodes, the width of the connecting lines is determined by the correlation between the components; the node position is determined by the component position.

[0132] Specifically, the schematic diagram generating module 12 includes:

[0133] A sensor data acquisition unit, configured to establish a connection channel with a sensor built into the industrial equipment and acquire sensor data containing a time tag based on the connection channel;

[0134] The sensor data application unit is used to query the corresponding node of the sensor in the node graph and use the sensor data as the operation data of the node;

[0135] a demand radius determination unit, configured to analyze the operation data and determine the influence radius of each node;

[0136] The schematic diagram output unit is used to determine the influence area of each node according to the influence radius, and obtain an information schematic diagram when the influence areas of all nodes are determined.

[0137] Furthermore, the demand area determination module 13 includes:

[0138] An area reading unit is used to read the influence area of any node;

[0139] The radius reading unit is used to read the radius of a node, query other nodes pointed to by all connection lines of the node, and obtain the radius of other nodes;

[0140] A demand value determining unit, configured to determine a demand value based on a radius of a current node and radii of other nodes;

[0141] A characteristic value generating unit is used to query the demand values of the influence areas of all nodes at any position in the information diagram, and superimpose all demand values as the characteristic value of the position;

[0142] The regional segmentation unit is used to identify the contour of the information diagram based on the characteristic value. The obtained area is called the demand area. The mean of the demand value in the demand area is calculated and the demand level is determined according to the mean;

[0143] The process of determining the demand value is as follows:

[0144] Where, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the node that has a connection line with the current node. nodes, Indicates the total number of nodes that have connection lines with the current node. Indicates the node that has a connection line with the current node. The width of the connection line of each node, The preset maximum width.

[0145] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An industrial robot control method using artificial intelligence, characterized in that: The method comprises: Obtain a workshop map of the production workshop, and insert equipment outlines into the workshop map based on the equipment information of the industrial equipment; Acquire the operating data of industrial equipment based on sensors built into the equipment, analyze the operating data, and obtain an information diagram based on a workshop map containing equipment outlines; Determining a demand area containing demand levels according to the information diagram; Determining operating parameters of the AI robot based on the demand area containing the demand level, generating control instructions based on the operating parameters, and sending the control instructions to the AI robot; wherein the operating parameters include an operating path and an operating level, and the operating level is used to control the amount of AI resources of the AI robot; The steps of obtaining a workshop map of a production workshop and inserting equipment outlines into the workshop map according to equipment information of industrial equipment include: Get the workshop map of the production workshop; Displaying a workshop map and receiving equipment information input by a user; the equipment information includes a housing outline, equipment location, and component information; the component information includes the location of each component, component importance, and correlation between different components; Create equipment outlines in the shop floor map based on the shell outlines and equipment locations; Insert a node diagram into the device outline based on component information. The nodes in the node diagram are circles whose radius is determined by the component importance. Connecting lines are set between nodes, and the width of the connecting lines is determined by the correlation between the components. The node positions are determined by the component positions. The steps of acquiring the operating data of the industrial equipment based on the sensors built into the industrial equipment, analyzing the operating data, and obtaining an information diagram based on the workshop map containing the equipment outline include: Establish a connection channel with sensors built into industrial equipment and obtain sensor data with time tags based on the connection channel; Query the node corresponding to the sensor in the node graph and use the sensor data as the operating data of the node; Analyze the operating data to determine the influence radius of each node; Determine the influence area of each node according to the influence radius, and obtain an information diagram after the influence areas of all nodes are determined; The step of determining the demand area having the demand level according to the information diagram comprises: For any node, read the node's influence area; Read the radius of the node, query all the connection lines of the node to other nodes, and obtain the radius of other nodes; Determine the required value based on the radius of the current node and the radius of other nodes; For any location in the information diagram, query the demand values of the influence areas of all nodes at that location, and superimpose all demand values as the characteristic value of that location; Based on the characteristic values, the contour of the information diagram is identified, and the obtained area is called the demand area. The mean of the demand values in the demand area is calculated, and the demand level is determined based on the mean; The process of determining the demand value is as follows: Where, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the node that has a connection line with the current node. nodes, Indicates the total number of nodes that have connection lines with the current node. Indicates the node that has a connection line with the current node. The width of the connection line of each node, The preset maximum width.

2. The industrial robot control method using artificial intelligence according to claim 1, characterized in that: The step of analyzing the operating data to determine the influence radius of each node includes: Create a matrix based on the node graph; each element in the matrix corresponds to a node, and each node corresponds to a component; Based on matrix statistics of sensor data with time tags, the actual matrix at each moment is obtained; wherein, sensor data with the same time tag are inserted into the same matrix; Arrange the actual matrix in chronological order, extract the data of each element in the arranged actual matrix at each moment within a preset time range, and obtain an array with row and column positions as labels; Analyze the array of each row and column position to determine the influence radius of each node; The process of determining the influence radius is as follows: Where, Indicates the influence radius of a node. Indicates that the node includes The mean of the data in the array, Indicates that the node includes The standard deviation of the data in the array, Indicates that the independent variable is and The function is a preset function; Indicates that the node includes The weight of the sensor corresponding to the array is a preset value, and the weight has a dimension, which is used to The function values obtained from the array are converted to dimensionless values; Indicates the number of sensors included in the node.

3. The industrial robot control method using artificial intelligence according to claim 1, characterized in that: The steps of determining the working parameters of the AI robot according to the demand area containing the demand level, generating control instructions based on the working parameters, and sending the control instructions to the AI robot include: Obtaining the location of each required area and determining the working path of the AI robot based on the location; Obtain the demand level, and determine the length of time the AI robot stays in each demand area based on the demand level.

4. An industrial robot control system using artificial intelligence, characterized in that: The system comprises: The equipment profile insertion module is used to obtain the workshop map of the production workshop and insert the equipment profile into the workshop map according to the equipment information of the industrial equipment; A schematic diagram generation module is used to obtain the operating data of industrial equipment based on sensors built into the industrial equipment, analyze the operating data, and obtain an information schematic diagram based on a workshop map containing the equipment outline; A demand area determination module, configured to determine a demand area having a demand level according to the information diagram; a control instruction generation module, configured to determine operating parameters of the AI robot based on a demand region containing a demand level, generate control instructions based on the operating parameters, and send the control instructions to the AI robot; wherein the operating parameters include an operating path and an operating level, and the operating level is used to control the amount of AI resources of the AI robot; The device profile insertion module includes: A map acquisition unit, used to acquire a workshop map of a production workshop; An equipment information receiving unit, configured to display a workshop map and receive equipment information input by a user; the equipment information includes a housing outline, equipment location, and component information; the component information includes the location of each component, component importance, and correlation between different components; a contour determination unit for creating a device contour in a shop floor map based on a housing contour and a device position; The outline insertion unit is used to insert a node diagram into the device outline based on component information. The nodes in the node diagram are circles whose radius is determined by the component importance. There are connecting lines between the nodes, and the width of the connecting lines is determined by the correlation between the components. The node positions are determined by the component positions. The schematic diagram generating module includes: A sensor data acquisition unit, configured to establish a connection channel with a sensor built into the industrial equipment and acquire sensor data containing a time tag based on the connection channel; The sensor data application unit is used to query the corresponding node of the sensor in the node graph and use the sensor data as the operation data of the node; a demand radius determination unit, configured to analyze the operation data and determine the influence radius of each node; a schematic diagram output unit, configured to determine the influence area of each node according to the influence radius, and obtain an information schematic diagram after the influence areas of all nodes are determined; The demand area determination module includes: An area reading unit is used to read the influence area of any node; The radius reading unit is used to read the radius of a node, query other nodes pointed to by all connection lines of the node, and obtain the radius of other nodes; A demand value determining unit, configured to determine a demand value based on a radius of a current node and radii of other nodes; A characteristic value generating unit is used to query the demand values of the influence areas of all nodes at any position in the information diagram, and superimpose all demand values as the characteristic value of the position; The regional segmentation unit is used to identify the contour of the information diagram based on the characteristic value. The obtained area is called the demand area. The mean of the demand value in the demand area is calculated and the demand level is determined according to the mean; The process of determining the demand value is as follows: Where, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the node that has a connection line with the current node. nodes, Indicates the total number of nodes that have connection lines with the current node. Indicates the node that has a connection line with the current node. The width of the connection line of each node, The preset maximum width.

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