Industrial robot control method and system applying artificial intelligence

Abstract

The invention relates to the technical field of robot control, and particularly discloses an industrial robot control method and system applying artificial intelligence, and the method comprises the steps: obtaining a workshop map of a production workshop, and inserting an equipment contour in the workshop map according to the equipment information of industrial equipment; the method comprises the following steps: acquiring operation data of industrial equipment based on a sensor built in the industrial equipment, and analyzing the operation data to obtain an information schematic diagram based on a workshop map containing an equipment contour; determining a demand area containing a demand level according to the information schematic diagram; and 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. According to the method, the equipment data of each industrial equipment is analyzed, the working process of the AI robot is determined, the AI robot assists the industrial equipment to work, and the AI robot serves as a'branch 'of a worker, so that the contact time of the worker and the industrial equipment is shortened, and the safety degree is improved in a disguised manner.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control, and specifically to an industrial robot control method and system applying artificial intelligence. Background Art

[0002] With the progress of random artificial intelligence technology and the development of robot technology, the intelligent level of the production process will be higher and higher. Artificial intelligence technology cooperating with robots can complete a large amount of work.

[0003] In existing production activities, there are actually very few purely intelligent production lines. Only particularly large enterprises have the opportunity to build such production lines. Most are produced in the way of equipment plus manual labor, that is, semi-automated way. This way has low cost and high flexibility. However, since the staff needs to cooperate with the equipment, safety problems are likely to occur. Therefore, how to improve the safety level of the staff in the semi-automated production process is the technical problem that the technical solution of the present invention wants to solve. Summary of the Invention

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

[0005] To achieve the above purpose, the present invention provides the following technical solutions: An industrial robot control method applying artificial intelligence, the method includes: Obtain the workshop map of the production workshop, and insert the equipment outline in the workshop map according to the equipment information of the industrial equipment; Based on the sensors built in the industrial equipment, obtain the operation data of the industrial equipment, analyze the operation data, and obtain an information schematic diagram based on the workshop map containing the equipment outline; Determine the demand area containing the demand level according to the information schematic diagram; Determine the working parameters of the AI robot according to the demand area containing the demand level, generate a control instruction based on the working parameters, and send it 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.

[0006] As a further solution of the present invention: the step of obtaining the workshop map of the production workshop and inserting the equipment outline in the workshop map according to the equipment information of the industrial equipment includes: Obtain the workshop map of the production workshop; Display the workshop map, and receive the equipment information input by the user; the equipment information includes the housing outline, equipment position and component information; the component information includes the component position, component importance and correlation between different components of each component; Create an equipment outline in the workshop map according to the housing outline and equipment position; Insert a node diagram into the equipment 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, connection lines are provided between the nodes, and the width of the connection lines is determined by the relevance between the components; the node positions are determined by the component positions.

[0007] As a further solution of the present invention: the step of obtaining the operation data of the industrial equipment based on the sensors built in the industrial equipment and analyzing the operation data to obtain an information schematic diagram based on the workshop map containing the equipment outline includes: Establish a connection channel with the sensors built in the industrial equipment, and obtain sensing data with time tags based on the connection channel; Query the nodes corresponding to the sensors in the node diagram, and use the sensing data as the operation data of the nodes; Analyze the operation data to determine the influence radius of each node; Determine the influence area of each node according to the influence radius. When the influence areas of all nodes are determined, an information schematic diagram is obtained.

[0008] As a further solution of the present invention: the step of analyzing the operation data to determine the influence radius of each node includes: Create a matrix according to the node diagram; each element in the matrix corresponds to a node, and each node corresponds to a component; Statistically analyze the sensing data with time tags based on the matrix to obtain the actual matrix at each moment; among them, the sensing data with the same time tag is inserted into the same matrix; Arrange the actual matrices according to the time sequence, and extract the data of each element at each moment within a preset time range in the arranged actual matrices to obtain an array labeled with row and column positions; Analyze the arrays at each row and column position to determine the influence radius of each node; The process of determining the influence radius is: ; where represents the influence radius of a certain node, represents the data mean value of the th array included in this node, represents the data standard deviation of the th array included in this node, represents a function with independent variables and This function is a preset function; represents the The weights of the sensors corresponding to an array, where the weights are preset values with dimensions, and are used to convert the function values obtained from the th array into dimensionless values; represents the number of sensors included in this node.

[0009] As a further solution of the present invention: the step of determining the demand area containing the demand level according to the information schematic diagram includes: For any node, read the influence area of the node; Read the radius of the node, query other nodes pointed to by all connection lines of this node, and obtain the radii of other nodes; Determine the demand value according to the radius of the current node and the radii of other nodes; For any position in the information schematic diagram, query the demand values at this position of the influence areas of all nodes, and superimpose all the demand values as the characteristic value of this position; Based on the characteristic value, perform contour recognition on the information schematic diagram, and the obtained area is called the demand area. Calculate the average value of the demand values in the demand area, and determine the demand level according to the average value; The determination process of the demand value is: ; where is the demand value of a certain node, is a preset coefficient, is the radius of the current node, represents the th node existing a connection line with the current node, represents the total number of nodes existing a connection line with the current node, represents the connection line width of the th node existing a connection line with the current node, is the preset maximum width.

[0010] As a further solution of the present invention: the step of determining the working parameters of the AI robot according to the demand area containing the demand level, generating a control instruction based on the working parameters, and sending it to the AI robot includes: Obtain the positions of each demand area, and determine the working path of the AI robot according to the positions; Obtain the demand level, and determine the residence duration of the AI robot at each demand area according to the demand level.

[0011] The technical solution of the present invention also provides an industrial robot control system applying artificial intelligence, and the system includes: An equipment contour insertion module, which is used to obtain the workshop map of the production workshop and insert the equipment contour into the workshop map according to the equipment information of the industrial equipment; A schematic diagram generation module, configured to obtain the operation data of an industrial device based on sensors built in the industrial device, analyze the operation data, and obtain an information schematic diagram based on a workshop map containing the device outline; A demand area determination module, configured to determine a demand area containing a demand level according to the information schematic diagram; A control instruction generation module, configured to determine the working parameters of an AI robot according to the demand area containing the demand level, generate a control instruction based on the working parameters, and send the control instruction 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 AI resource amount of the AI robot.

[0012] As a further solution of the present invention: the device outline insertion module includes: A map acquisition unit, configured to acquire a workshop map of a production workshop; A device information receiving unit, configured to display the workshop map and receive device information input by a user; the device information includes a housing outline, a device position, and component information; the component information includes a component position, a component importance degree, and a correlation degree between different components of each component; An outline determination unit, configured to create a device outline in the workshop map according to the housing outline and the device position; An outline insertion unit, configured to insert 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 degree, connection lines are provided between the nodes, and the width of the connection line is determined by the correlation degree between the components; the node position is determined by the component position.

[0013] As a further solution of the present invention: the schematic diagram generation module includes: A sensing data acquisition unit, configured to establish a connection channel with sensors built in the industrial device and acquire sensing data with time tags based on the connection channel; A sensing data application unit, configured to query the nodes corresponding to the sensors in the node diagram and use the sensing data as the operation data of the nodes; 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 when the influence areas of all nodes are determined.

[0014] As a further solution of the present invention: the demand area determination module includes: An area reading unit, configured to read the influence area of a node for any node; A radius reading unit, configured to read the radius of a node, query other nodes pointed to by all connection lines of the node, and acquire the radii of the other nodes; A demand value determination unit, used to determine the demand value according to the radius of the current node and the radius of other nodes; The 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 the demand values ​​as the characteristic value of the position; A region segmentation unit is used to identify the contour of the information diagram based on the characteristic value. The obtained region is called the demand region. The mean value of the demand value in the demand region is calculated, and the demand level is determined according to the mean value. The process of determining the demand value is as follows: ; In the formula, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the first 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 first node that has a connection line with the current node. The connection line width of each node, The preset maximum width.

[0015] Compared with the prior art, 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, thereby reducing the contact time between the staff and the industrial equipment, and indirectly improving the safety level. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0017] Figure 1 The flowchart of the industrial robot control method using artificial intelligence.

[0018] Figure 2 This is a flowchart of the first sub-process of an industrial robot control method using artificial intelligence.

[0019] Figure 3 This is a second sub-flow chart of the industrial robot control method using artificial intelligence.

[0020] Figure 4 This is the third sub-process flowchart of the industrial robot control method using artificial intelligence.

[0021] Figure 5 It is the fourth sub - process block diagram of the industrial robot control method applying artificial intelligence.

[0022] Figure 6 It is the block diagram of the composition structure of the industrial robot control system applying artificial intelligence. Specific implementation manners

[0023] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the present invention will be 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 used to limit the present invention.

[0024] Figure 1 It is the flow chart of the industrial robot control method applying artificial intelligence. In an embodiment of the present invention, an industrial robot control method applying artificial intelligence, the method includes: Step S100: Obtain the workshop map of the production workshop, and insert the equipment outlines in the workshop map according to the equipment information of the industrial equipment; The production workshop is the working scenario of the industrial robot. Obtain the workshop map of the production workshop, query the equipment information of the industrial equipment in the production workshop, and according to the equipment information, the outlines corresponding to the industrial equipment can be inserted in the workshop map, which are called equipment outlines.

[0025] Step S200: Based on the sensors built in the industrial equipment, obtain the operation data of the industrial equipment, analyze the operation data, and obtain an information schematic diagram based on the workshop map containing the equipment outlines; There are multiple sensors in the industrial equipment, which belong to the basic components of the industrial equipment. Based on the sensors built in the industrial equipment, the operation data of the industrial equipment can be obtained. By analyzing the operation data, the working state of the industrial equipment can be judged, and the working state can be converted into the workshop map containing the equipment outlines to obtain an information schematic diagram. Specifically, the working state judged according to the operation data actually represents the busy degree of each industrial equipment, and the information schematic diagram represents the busy degree of all industrial equipment.

[0026] Step S300: Determine the demand area containing the demand level according to the information schematic diagram; By analyzing the information schematic diagram, the demand situation at each position can be determined, and then the demand area can be determined. The demand area contains a parameter of demand level, indicating the degree of need for help.

[0027] Step S400: Determine the working parameters of the AI robot according to the requirement area containing the requirement level, generate a control instruction based on the working parameters, and send it 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. The requirement area containing the requirement level represents the degree of help required in each area of the production workshop. Determine the working parameters of the AI robot according to the requirement area containing the requirement level. The AI robot is a robot with a moving function and an operating function. An AI recognition module is also built into the robot, 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 several simple operations, and the cost of the AI recognition module is not high. At this time, the AI recognition module is equivalent to an auxiliary worker, providing auxiliary work in the requirement area, which can greatly improve production efficiency in the semi-automated production process.

[0028] Figure 2 It is the first sub-process block diagram of the industrial robot control method applying artificial intelligence. The steps of obtaining the workshop map of the production workshop and inserting the equipment outline in the workshop map according to the equipment information of the industrial equipment include: Step S101: Obtain the workshop map of the production workshop. Step S102: Display the workshop map and receive the equipment information input by the user; the equipment information includes the housing outline, equipment position and component information; the component information includes the component position, component importance and the relevance between different components of each component. Step S103: Create an equipment outline in the workshop map according to the housing outline and the equipment position. Step S104: Insert a node diagram in the equipment outline according to the component information; the nodes in the node diagram are circles, and the radius of the circle is determined by the component importance. Connection lines are provided between the nodes, and the width of the connection lines is determined by the relevance between the components; the node positions are determined by the component positions.

[0029] In an example of the technical solution of the present invention, the acquisition and application process of the workshop map is described. Obtain the workshop map of the production workshop, display the workshop map, and at the same time receive the equipment information and equipment position input by the user. The equipment information includes the housing outline, and the housing outline is generally a three-dimensional outline, which is the outline of the boundary of the industrial equipment and can be directly read from 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.

[0030] In addition, the device information further includes component information, which refers to what components are in the device, the importance level of each component, and the relevance between components. 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.

[0031] Insert the top view of the housing contour into the workshop map to obtain the device contour. Then, analyze the component information to determine multiple nodes, with each node corresponding to a component. Count all the nodes, establish the connection lines between the nodes to obtain a node map, and insert the node map into the device contour.

[0032] It should be noted that the nodes in the node map are circles, and the radius of the circle is determined by the component importance level. The higher the component importance level, the larger the radius. There are connection lines between the nodes, and the width of the connection line is determined by the relevance between the components. The higher the relevance between two components (such as having a direct connection relationship), the larger its width. In addition, the position of the node in the device contour is determined by the component position. The relationship between the position of the node in the device contour and the component position is not a strict quantitative relationship, as long as it conforms to the general direction relationship. The device contour containing the node map actually constructs a simplified two-dimensional diagram to represent the state of the device. The representation method is to insert multiple circles into the device contour, and there may be connection lines between the circles, thus simplifying to represent the device.

[0033] Figure 3 It is the second sub-process block diagram of the industrial robot control method applying artificial intelligence. The steps of obtaining the operation data of the industrial device based on the sensors built in the industrial device and analyzing the operation data to obtain the information schematic diagram based on the workshop map containing the device contour include: Step S201: Establish a connection channel with the sensors built in the industrial device, and obtain the sensing data with time tags based on the connection channel. Step S202: Query the nodes corresponding to the sensors in the node map, and use the sensing data as the operation data of the node. Step S203: Analyze the operation data to determine the influence radius of each node. Step S204: Determine the influence area of each node according to the influence radius. When the influence areas of all nodes are determined, the information schematic diagram is obtained.

[0034] In an example of the technical solution of the present invention, the generation process of the information schematic diagram is described. It introduces a way to represent operation data in the device contour containing the node diagram, establishes a connection channel with the sensors built in the industrial device, obtains the sensing data with time tags based on the connection channel, and queries the nodes corresponding to the sensors in the node diagram. Since the nodes correspond to components and the number of components in the component information input by the user is limited, there may be many sensors that do not correspond to the nodes in the node diagram. At this time, these sensing data still need to be saved, but they are not within the scope considered in this application. This application only analyzes the sensors with corresponding relationships, queries the nodes corresponding to the sensors in the node diagram, takes the sensing data as the operation data of the nodes, and analyzes the operation data to determine the influence radius of each node.

[0035] Based on each node and its influence radius, an influence area can be created. When the influence areas of all nodes are determined, the information schematic diagram is obtained; it should be noted that the information schematic diagram needs to analyze each node, rather than just analyzing the device contour.

[0036] Further, the step of analyzing the operation data to determine the influence radius of each node includes: Create a matrix according to the node diagram; each element in the matrix corresponds to a node, and each node corresponds to a component; Statistically analyze the sensing data with time tags based on the matrix to obtain the actual matrix at each moment; among them, the sensing data with the same time tag is inserted into the same matrix; Arrange the actual matrix according to the time sequence, and extract the data at each moment within a preset time range for each element in the arranged actual matrix to obtain an array labeled with row and column positions; Analyze the arrays at each row and column position to determine the influence radius of each node.

[0037] Once the positions of the nodes in the node diagram are determined, they will not be changed. A matrix can be created according to the positional relationship of each node in the node diagram. 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, for a 3*3 matrix, there may not be data in all nine 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.

[0038] Sensing data with time tags is statistically analyzed based on a matrix to obtain the actual matrix at each moment. The actual matrices are arranged according to the time sequence, which belongs to the data regularization process. Data at each moment within a preset time range for each element in the arranged actual matrix is extracted to obtain an array labeled with row and column positions. The arrays at each row and column position are analyzed to determine a value used to represent the radius. Since each row and column position corresponds to a node and the correspondence remains unchanged, the determined value is the influence radius of each node.

[0039] Among them, the process of determining the influence radius is as follows: ; In the formula, represents the influence radius of a certain node, represents the data mean value of the th array included in this node, represents the data standard deviation of the th array included in this node, represents a function with independent variables and , and this function is a preset function; represents the weight of the sensor corresponding to the th array included in this node. The weight is a preset value and has dimensions, and is used to convert the function value obtained from the th array into a dimensionless value; represents the number of sensors included in this node.

[0040] Among them, the simplest form of is and are both preset constants. The influence radius represents the importance degree of the data of the node. How to define importance is determined by the user independently. Generally, it is set that the larger the mean value, the more important; the larger the standard deviation, the more important. In addition, each array corresponds to each sensor and represents the sensing data of a certain sensor within a preset time range.

[0041] Figure 4 is the third sub - process block diagram of the industrial robot control method applying artificial intelligence. The steps of determining the demand area containing the demand level according to the information schematic diagram include: Step S301: For any node, read the influence area of the node; Step S302: Read the radius of the node, query other nodes pointed to by all connection lines of this node, and obtain the radii of other nodes; Step S303: Determine the demand value according to the radius of the current node and the radii of other nodes; Step S304: For any position in the information schematic diagram, query the demand values at this position in the influence regions of all nodes, and sum up all the demand values as the feature value of this position. Step S305: Based on the feature value, perform contour recognition on the information schematic diagram. The obtained region is called the demand region, calculate the average value of the demand values within the demand region, and determine the demand level according to the average value.

[0042] In an example of the technical solution of the present invention, the demand region and its demand level are described. For any node, read the influence region of the node. The demand region represents the influence range of a node; read the radius of the node. The radius of the node represents the importance of the node. Query all the other nodes pointed to by the connection lines of this node, obtain the radii of the other nodes, and the importance of the current node can be corrected according to the radii of the other nodes. Determine the demand value according to the corrected radius, and insert the demand value into each pixel point in the demand region to obtain a demand region containing the demand value; at this time, the demand region is a single-value region. After performing the same operation on each node, the influence region of each node can be obtained.

[0043] For any position in the information schematic diagram, query all the demand regions containing it, and query the demand value of the demand region at this position, so as to obtain the value of any position in the information schematic diagram. At this time, the entire information schematic diagram is equivalent to a single-value image. Apply a conventional contour recognition algorithm to the single-value image to perform region segmentation on the information schematic diagram to obtain multiple small regions, which are called demand regions, calculate the average value of the values at each position in the demand region, and determine the demand level according to the threshold reached by the average value.

[0044] Specifically, the determination process of the demand value is as follows: ; where is the demand value of a certain node, is a preset coefficient, is the radius of the current node, represents the th node that has a connection line with the current node, represents the total number of nodes that have connection lines with the current node, represents the width of the connection line of the th node that has a connection line with the current node, is the preset maximum width.

[0045] The demand value is used to assign values to the influence region. The larger the node radius, the higher the importance. In addition, query another node pointed to by the connection line of the node. The larger the width of the connection line, the greater the relevance. Accumulate the radius of itself and the radii of related nodes according to the preset coefficient to determine the demand value.

[0046] Figure 5 It is the fourth sub - process block diagram of the control method for industrial robots applying artificial intelligence. The steps of determining the working parameters of the AI robot according to the demand area containing the demand level, generating a control instruction based on the working parameters, and sending it to the AI robot include: Step S401: Obtain the positions of each demand area, and determine the working path of the AI robot according to the positions; Step S402: Obtain the demand level, and determine the residence duration of the AI robot at each demand area according to the demand level.

[0047] In an example of the technical solution of the present invention, the control process of the AI robot is described. The positions of each demand area are obtained, and the positions are used as passing points. With the help of existing path - planning algorithms, the working path of the AI robot can be obtained; on this basis, for each demand area, its demand level is obtained, and the residence duration of the AI robot at each demand area is determined according to the demand level.

[0048] Furthermore, the position of the demand area can select the center of the area as the position. The working path and the residence duration are actually parameters within a preset time period, and the time period is generally a production cycle, such as one day; after determining the working path and residence duration of the AI robot, the AI robot will reach each demand area along the working path within the production cycle, and then stay at each demand area for a certain duration. During the residence duration, the AI module of the AI robot is activated to assist the work of each industrial device; it is equivalent to arranging an auxiliary worker. When the number of AI robots is large, multiple working paths can be generated so that each industrial device is assisted to work at a higher frequency.

[0049] Figure 6 It is the block diagram of the composition structure of the control system for industrial robots applying artificial intelligence. In an embodiment of the present invention, a control system for industrial robots applying artificial intelligence, the system 10 includes: An equipment contour insertion module 11, configured to obtain the workshop map of the production workshop and insert the equipment contour into the workshop map according to the equipment information of the industrial equipment; A schematic diagram generation module 12, configured to obtain the operation data of the industrial equipment based on the sensors built in the industrial equipment, analyze the operation data, and obtain an information schematic diagram based on the workshop map containing the equipment contour; A demand area determination module 13, configured to determine the demand area containing the demand level according to the information schematic diagram; The control instruction generation module 14 is configured to determine the working parameters of the AI robot according to the requirement area containing the requirement level, generate control instructions based on the working parameters, and send them 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.

[0050] Further, the device profile insertion module 11 includes: A map acquisition unit, configured to acquire the workshop map of the production workshop; A device information receiving unit, configured to display the workshop map and receive the device information input by the user; the device information includes a housing profile, a device position, and component information; the component information includes the component position, component importance, and the relevance between different components of each component; A profile determination unit, configured to create a device profile in the workshop map according to the housing profile and the device position; A profile insertion unit, configured to insert a node diagram into the device profile according to the component information; the nodes in the node diagram are circles, the radius of the circle is determined by the component importance, connection lines are provided between the nodes, and the width of the connection lines is determined by the relevance between the components; the node positions are determined by the component positions.

[0051] Specifically, the schematic diagram generation module 12 includes: A sensing data acquisition unit, configured to establish a connection channel with the sensors built in the industrial equipment, and acquire the sensing data with time tags based on the connection channel; A sensing data application unit, configured to query the nodes corresponding to the sensors in the node diagram, and use the sensing data as the running data of the nodes; A demand radius determination unit, configured to analyze the running data to 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 when the influence areas of all nodes are determined.

[0052] Furthermore, the requirement area determination module 13 includes: An area reading unit, configured to read the influence area of a node for any node; A radius reading unit, configured to read the radius of a node, query the other nodes pointed to by all the connection lines of the node, and obtain the radii of the other nodes; A demand value determination unit, configured to determine the demand value according to the radius of the current node and the radii of the other nodes; A feature value generation unit, configured to query the demand values at a position of the influence areas of all nodes for any position in the information schematic diagram, and superimpose all the demand values as the feature value of the position; The region segmentation unit is used to perform contour recognition on the information schematic diagram based on the eigenvalue. The obtained region is called the demand region, calculate the average value of the demand values within the demand region, and determine the demand level according to the average value; The determination process of the said demand value is as follows: ; In the formula, is the demand value of a certain node, is the preset coefficient, is the radius of the current node, represents the th node that has a connection line with the current node, represents the total number of nodes that have connection lines with the current node, represents the width of the connection line of the th node that has a connection line with the current node, is the preset maximum width.

[0053] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included in the protection scope 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 according to equipment information of industrial equipment; Acquire the operation data of industrial equipment based on sensors built into the industrial equipment, analyze the operation data, and obtain an information diagram based on a workshop map containing equipment outlines; Determining a demand area containing a demand level according to the information schematic diagram; 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.

2. The industrial robot control method using artificial intelligence according to claim 1, characterized in that: The step of obtaining a workshop map of a production workshop and inserting equipment profiles into the workshop map according to equipment information of industrial equipment comprises: 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 shell outline, equipment location and component information; the component information includes the component location of each component, component importance and correlation between different components; Create equipment outlines in the shop floor map based on shell outlines and equipment locations; A node diagram is inserted 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, connecting lines are set between the nodes, and the width of the connecting lines is determined by the correlation between the components; the node position is determined by the component position.

3. The industrial robot control method using artificial intelligence according to claim 1, characterized in that: The step of acquiring the operation data of the industrial equipment based on the sensor built into the industrial equipment, analyzing the operation data, and obtaining the information schematic diagram based on the workshop map containing the equipment outline includes: 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 operation data of the node; Analyze the operation data to determine the influence radius of each node; The influence area of ​​each node is determined according to the influence radius, and when the influence areas of all nodes are determined, an information schematic diagram is obtained.

4. The industrial robot control method using artificial intelligence according to claim 3, characterized in that: The step of analyzing the operation data to determine the influence radius of each node includes: Create a matrix from 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 the sensor data with the same time tag are inserted into the same matrix; Arrange the actual matrix in time 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: ; In the formula, Represents 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 an 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 arrays are converted to dimensionless values; Indicates the number of sensors included in the node.

5. The industrial robot control method using artificial intelligence according to claim 1, characterized in that: The step of determining the demand area containing the demand level according to the information schematic diagram comprises: For any node, read the node's influence area; Read the radius of the node, query other nodes pointed to by all the connection lines of the node, and obtain the radius of other nodes; Determine the demand value based on the radius of the current node and the radius of other nodes; 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 that position; Based on the characteristic values, the contour of the information diagram is identified, and the obtained area is called the demand area. The mean value of the demand value in the demand area is calculated, and the demand level is determined according to the mean value; The process of determining the demand value is as follows: ; In the formula, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the first 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 first node that has a connection line with the current node. The connection line width of each node, The preset maximum width.

6. The industrial robot control method using artificial intelligence according to claim 1, characterized in that: The step of determining the working parameters of the AI ​​robot according to the demand area containing the demand level, generating a control instruction based on the working parameters, and sending the control instruction to the AI ​​robot comprises: Obtaining the location of each required area, and determining the working path of the AI ​​robot according to the location; Obtain the demand level, and determine the stay time of the AI ​​robot in each demand area according to the demand level.

7. 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 operation data of the industrial equipment based on the sensors built into the industrial equipment, analyze the operation data, and obtain an information schematic diagram based on a workshop map containing the equipment outline; A demand area determination module, used to determine a demand area containing a demand level according to the information schematic diagram; A control instruction generation module is used to determine the working parameters of the AI ​​robot according to 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.

8. The industrial robot control system using artificial intelligence according to claim 7, characterized in that: The device profile insertion module includes: A map acquisition unit, used to acquire a workshop map of a production workshop; The equipment information receiving unit is used to display the workshop map and receive the equipment information input by the user; the equipment information includes the shell outline, equipment location and component information; the component information includes the component 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 the shell contour and the device location; The contour insertion unit is used to insert a node diagram into the device contour 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 set between the nodes, and the width of the connecting lines is determined by the correlation between the components; the node position is determined by the component position.

9. The industrial robot control system using artificial intelligence according to claim 7, characterized in that: The schematic diagram generating module comprises: A sensor data acquisition unit, used 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; A 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, used to analyze the operation data and determine the influence radius of each node; 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 after the influence areas of all nodes are determined.

10. The industrial robot control system using artificial intelligence according to claim 7, characterized in that: The demand area determination module includes: An area reading unit, used for reading the influence area of ​​any node; A 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 determination unit, used to determine the demand value according to the radius of the current node and the radius of other nodes; The 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 the demand values ​​as the characteristic value of the position; A region segmentation unit is used to identify the contour of the information diagram based on the characteristic value. The obtained region is called the demand region. The mean value of the demand value in the demand region is calculated, and the demand level is determined according to the mean value. The process of determining the demand value is as follows: ; In the formula, is the demand value of a node, is the preset coefficient, is the radius of the current node, Indicates the first 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 first node that has a connection line with the current node. The connection line width of each node, The preset maximum width.

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