Robot handling method, system, equipment and medium based on industrial Internet of Things

Abstract

The present application discloses a robot handling method, system, device and medium based on the industrial Internet of Things, and relates to the technical field of the industrial Internet of Things. The method includes: obtaining the standard processing parameters of the first production line and the scanning data after the first production line processes the workpiece, and determining the target second production line of the workpiece from the second production line according to the standard processing parameters and the scanning data; determining the target handling component type according to the scanning data, and determining at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the target handling component type; generating a movement control instruction according to the at least one selected type and the movement parameters to control the at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameters. The present application has the effect of improving the intelligence level of the production line.

Description

Technical Field

[0001] The present application relates to the technical field of industrial Internet of Things, and in particular to a robot handling method, system, equipment and medium based on the industrial Internet of Things. Background Art

[0002] In modern manufacturing, the handling method of production lines plays a vital role. Traditional handling methods include fixed handling methods and manual handling methods. Fixed handling methods usually use conveyor belts, elevators and various types of automated equipment, which can efficiently and stably handle a large number of identical or similar workpieces. This method can ensure high productivity and consistent product quality, but its lack of flexibility makes it difficult to adjust the production line configuration when production needs change; although the traditional manual handling method has high flexibility and can adapt to different workpieces and production environments, the handling method that relies on manpower has the risk of inefficiency, is prone to human errors, and increases the probability of work-related accidents.

[0003] At present, when transporting workpieces on a production line, the workpieces are usually transported from one production line to another by fixed transport methods or manual transport methods. The above transport methods have met the basic needs of production to a certain extent, but the intelligence level of the above transport methods is low, and under the above transport methods, the flexibility of the production line is low. Therefore, the traditional transport methods need to be improved in terms of intelligence level and production line flexibility. Summary of the invention

[0004] In order to improve the intelligence level of the production line, the present application provides a robot handling method, system, equipment and medium based on the industrial Internet of Things.

[0005] In the first aspect, the present application provides a robot handling method based on the industrial Internet of Things, which adopts the following technical solutions:

[0006] The robot handling method based on the industrial Internet of Things is applied to the industrial Internet of Things system, wherein the industrial Internet of Things system includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication. The method is executed by the management platform and includes:

[0007] Acquire standard processing parameters of the first production line and scan data after the first production line processes the workpiece, and determine a target second production line for the workpiece from the second production line according to the standard processing parameters and the scan data;

[0008] Determine the target transport component type according to the scanning data, and determine at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the target transport component type;

[0009] A movement control instruction is generated according to the at least one selected type and the movement parameter to control the at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameter.

[0010] By adopting the above technical scheme, the standard processing parameters of the first production line and the scanning data of the workpiece after processing by the first production line are obtained, and the target second production line of the workpiece is determined from the second production line according to the standard processing parameters and the scanning data, and then the target handling component type is determined according to the scanning data, and at least one selected type and corresponding movement parameters of the robot are determined according to the standard processing parameters and the target handling component type, and finally a movement control instruction is generated according to at least one selected type and the movement parameters to control at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameters. Compared with manual handling or fixed handling methods, the intelligence level of the production line is improved, and the flexibility of the production line is improved.

[0011] Optionally, the step of determining a target second production line for the workpiece from the second production line according to the standard processing parameters and the scanning data includes:

[0012] Acquire model data of the workpiece before entering the first production line, build a standard processing model corresponding to the workpiece according to the standard processing parameters and the model data, and generate an actual processing model of the workpiece according to the scanning data;

[0013] Registering the standard processing model and the actual processing model to obtain registration data;

[0014] Determine a representative point set P of the workpiece according to the actual processing model, and determine a corresponding point set Q of the representative point set P according to the standard processing model and the registration data;

[0015] Determine a coordinate deviation value according to the representative point set P and the corresponding point set Q, and construct a coordinate feature vector according to the coordinate deviation value;

[0016] The second production line is screened according to the coordinate feature vector to obtain the target second production line of the workpiece.

[0017] By adopting the above technical scheme, the model data of the workpiece before entering the first production line is obtained, and the standard processing model corresponding to the workpiece is constructed according to the standard processing parameters and the model data, and the actual processing model of the workpiece is generated according to the scanning data, and then the standard processing model and the actual processing model are aligned to obtain the alignment data, and then the representative point set P of the workpiece is determined according to the actual processing model, and the corresponding point set Q of the representative point set P is determined according to the standard processing model and the alignment data, and then the coordinate deviation value is determined according to the representative point set P and the corresponding point set Q, and the coordinate feature vector is constructed according to the coordinate deviation value, and then the second production line is screened according to the coordinate feature vector to obtain the target second production line of the workpiece.

[0018] Optionally, the step of constructing a coordinate feature vector according to the coordinate deviation value includes:

[0019] Determine the corresponding maximum coordinate deviation and minimum coordinate deviation according to the coordinate deviation value;

[0020] Determine the coordinate average value corresponding to the coordinate absolute value according to the coordinate absolute value;

[0021] Determine a corresponding coordinate offset according to the coordinate deviation value and the coordinate average value, and determine a corresponding coordinate standard deviation according to the coordinate offset and the coordinate average value;

[0022] A coordinate feature vector is constructed according to the maximum coordinate deviation, the minimum coordinate deviation, the coordinate average value and the coordinate standard deviation.

[0023] By adopting the above technical scheme, the corresponding maximum coordinate deviation and minimum coordinate deviation are first determined according to the coordinate deviation value, and then the coordinate average value corresponding to the coordinate absolute value is determined according to the coordinate absolute value, and then the corresponding coordinate offset is determined according to the coordinate deviation value and the coordinate average value, and the corresponding coordinate standard deviation is determined according to the coordinate offset and the coordinate average value, and finally the coordinate feature vector is constructed according to the maximum coordinate deviation, the minimum coordinate deviation, the coordinate average value and the coordinate standard deviation.

[0024] Optionally, the step of screening the second production line according to the coordinate feature vector to obtain the target second production line of the workpiece includes:

[0025] Acquire historical processing data of each second production line, the historical processing data including historical standard processing parameters and historical scanning data, and construct conditional feature vectors corresponding to each second production line according to the historical processing data;

[0026] Calculating the fitness evaluation value of each of the second production lines and the workpiece according to the condition feature vector and the coordinate feature vector;

[0027] The second production line corresponding to the minimum value among the fitness evaluation values ​​is used as the target second production line for the workpiece.

[0028] By adopting the above technical solution, the historical processing data of each second production line is first obtained, the historical processing data including historical standard processing parameters and historical scanning data, and the conditional feature vector corresponding to each second production line is constructed according to the historical processing data, and then the fitness evaluation value of each second production line and the workpiece is calculated according to the conditional feature vector and the coordinate feature vector, and finally the second production line corresponding to the minimum value in the fitness evaluation value is used as the target second production line for the workpiece.

[0029] Optionally, the step of determining the type of the target transport component according to the scan data includes:

[0030] Generating an actual processing model of the workpiece according to the scanning data, and extracting features of the workpiece according to the actual processing model to obtain corresponding feature data;

[0031] Inputting the characteristic data into a pre-generated target component parameter model to obtain corresponding target component parameters;

[0032] The target component parameters are matched in a first preset table to obtain a corresponding target component type; the first preset table includes handling component parameters and handling component types, and the first preset table is used to represent a mapping relationship between handling component parameters and handling component types.

[0033] By adopting the above technical solution, an actual processing model of the workpiece is first generated according to the scanning data, and features of the workpiece are extracted according to the actual processing model to obtain corresponding feature data, and then the feature data is input into a pre-generated target component parameter model to obtain corresponding target component parameters, and finally the target component parameters are matched in a first preset table to obtain a corresponding target component type, the first preset table including handling component parameters and handling component types, and the first preset table is used to represent a mapping relationship between handling component parameters and handling component types.

[0034] Optionally, the step of generating the pre-generated target component parameter model includes:

[0035] Acquire model training data, the model training data including the transport component parameters corresponding to the transport component and feature data of different transport components, and divide the model training data into k data subsets of the same size; k is a positive integer greater than 1;

[0036] Based on the k-fold cross validation method, k rounds of training and validation are performed on the pre-constructed target component parameter model according to the data subset to obtain the pre-generated target component parameter model.

[0037] By adopting the above technical solution, the model training data is first obtained, the model training data includes the handling component parameters corresponding to the handling component and the characteristic data of different handling components, and the model training data is divided into k data subsets of the same size. k is a positive integer greater than 1, and then based on the k-fold cross-validation method, the pre-constructed target component parameter model is trained and verified for k rounds according to the data subsets to obtain the pre-generated target component parameter model.

[0038] Optionally, the movement parameter includes the number of robots, and the step of determining at least one selected type and corresponding movement parameter corresponding to the robot according to the standard processing parameters and the target transport component type includes:

[0039] Matching the target handling component type in a second preset table to obtain at least one optional type corresponding to the robot; the second preset table includes handling component types and robot types, and the second preset table is used to represent the mapping relationship between the handling component types and the robot types;

[0040] determining a processing rate of the first production line according to the standard processing parameters, and determining a handling force requirement of the first production line according to the processing rate;

[0041] The number of robots corresponding to the at least one optional type of robot is obtained, and at least one selected type corresponding to the robot and the number of robots corresponding to the at least one selected type of robot are determined according to the at least one optional type, the number of robots and the handling force requirement.

[0042] By adopting the above technical solution, the target handling component type is first matched in the second preset table to obtain at least one optional type corresponding to the robot. The second preset table includes handling component types and robot types, and the second preset table is used to represent the mapping relationship between handling component types and robot types, and then the processing rate of the first production line is determined according to the standard processing parameters, and the handling force requirement of the first production line is determined according to the processing rate, and then the number of robots corresponding to at least one optional type of robot is obtained, and at least one selected type corresponding to the robot and the number of robots corresponding to at least one selected type of robot are determined according to at least one optional type, the number of robots and the handling force requirement.

[0043] In the second aspect, the present application also provides a robot handling system based on the industrial Internet of Things, which adopts the following technical solutions:

[0044] A robot handling system based on the industrial Internet of Things includes a management platform, a sensor network platform and an object platform that are sequentially connected in communication, wherein the management platform is configured with:

[0045] a target second production line determination module, used to obtain standard processing parameters of the first production line and scan data of the workpiece after the first production line processes the workpiece, and determine the target second production line of the workpiece from the second production line according to the standard processing parameters and the scan data;

[0046] a robot type determination module, configured to determine a target transport component type according to the scan data, and to determine at least one selected type and corresponding movement parameters of the robot corresponding to the target transport component type according to the standard processing parameters and the target transport component type;

[0047] The robot control module is used to generate a movement control instruction according to the at least one selected type and the movement parameters to control the at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameters.

[0048] In a third aspect, the present application also provides a computer device, which adopts the following technical solution:

[0049] A computer device comprises a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the method described in the first aspect when executing the computer program.

[0050] In a fourth aspect, the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0051] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the method described in the first aspect.

[0052] In summary, the present application includes at least the following beneficial technical effects: obtaining standard processing parameters of the first production line and scanning data of the workpiece after processing by the first production line, and determining the target second production line of the workpiece from the second production line according to the standard processing parameters and the scanning data, and then determining the target handling component type according to the scanning data, and determining at least one selected type and corresponding movement parameters of the robot according to the standard processing parameters and the target handling component type, and finally generating a movement control instruction according to at least one selected type and movement parameters to control at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameters, which improves the intelligence level of the production line and the flexibility of the production line compared to manual handling or fixed handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present application.

[0054] Figure 2It is a structural diagram of one application scenario of the system of an embodiment of the present application.

[0055] Figure 3 It is a structural diagram of another application scenario of the system of an embodiment of the present application.

[0056] Figure 4 It is a structural block diagram of the computer device of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-4 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] The embodiment of the present application discloses a robot handling method based on the industrial Internet of Things.

[0059] Reference Figure 1 The method of robot handling based on industrial Internet of Things is applied to the industrial Internet of Things system. The industrial Internet of Things system includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication. The method is executed by the management platform, including:

[0060] Step S11, acquiring standard processing parameters of the first production line and scanning data of the workpiece after the first production line processes the workpiece, and determining a target second production line for the workpiece from the second production line according to the standard processing parameters and the scanning data.

[0061] It can be understood that the standard processing parameters are processing parameters set for the first production line; there are at least two second production lines, and the target second production line is a production line determined from the second production lines.

[0062] Step S12, determining the type of the target transport component according to the scanning data, and determining at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the type of the target transport component.

[0063] It is understandable that for any two workpieces processed by the first production line, due to errors in the processing process, such as errors caused by manual operation of the processing equipment in the first production line, or errors caused by aging of the processing equipment in the first production line, it is necessary to select a suitable handling component to move or transport each workpiece; in addition, for the same type of handling component, there are usually multiple types of robots corresponding to it. For example, for handling component X, there are two types of robots corresponding to it, Y1 and Y2. The robot of type Y1 is equipped with handling component X, and the robot of type Y2 is equipped with handling components X and Z. Therefore, for the robot of type Y2, one type of workpiece can be transported by handling component X, or it can be switched to handling component Z by a handling component switching instruction, and another type of workpiece can be transported according to handling component Z.

[0064] Step S13, generating a movement control instruction according to at least one selected type and the movement parameter to control at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameter.

[0065] In the above embodiment, standard processing parameters of the first production line and scanning data of the workpiece after processing by the first production line are obtained, and the target second production line of the workpiece is determined from the second production line according to the standard processing parameters and the scanning data, and then the target handling component type is determined according to the scanning data, and at least one selected type and corresponding moving parameters of the robot corresponding to the standard processing parameters and the target handling component type are determined, and finally a moving control instruction is generated according to at least one selected type and the moving parameters to control at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the moving parameters, which improves the intelligence level of the production line and the flexibility of the production line compared to manual handling or fixed handling.

[0066] As a further embodiment of the robot handling method, the step of determining a target second production line for the workpiece from the second production line according to the standard processing parameters and the scanning data includes:

[0067] Step S21 , obtaining model data of the workpiece before entering the first production line, and constructing a standard processing model corresponding to the workpiece according to standard processing parameters and the model data, and generating an actual processing model of the workpiece according to the scanning data.

[0068] Step S22, registering the standard processing model and the actual processing model to obtain registration data.

[0069] Step S23, determining a representative point set P of the workpiece according to the actual processing model, and determining a corresponding point set Q of the representative point set P according to the standard processing model and the registration data.

[0070] It can be understood that the coordinates in the representative point set P and the coordinates in the corresponding point set Q are in a one-to-one correspondence.

[0071] Step S24, determining a coordinate deviation value according to the representative point set P and the corresponding point set Q, and constructing a coordinate feature vector according to the coordinate deviation value.

[0072] It can be understood that the coordinate deviation value is the distance between the coordinates in the representative point set P and the corresponding coordinates in the corresponding point set Q.

[0073] Step S25, screening the second production line according to the coordinate feature vector to obtain the target second production line of the workpiece.

[0074] In the above embodiment, the model data of the workpiece before entering the first production line is obtained, and a standard processing model corresponding to the workpiece is constructed according to the standard processing parameters and the model data, and the actual processing model of the workpiece is generated according to the scanning data, and then the standard processing model and the actual processing model are aligned to obtain the alignment data, and then the representative point set P of the workpiece is determined according to the actual processing model, and the corresponding point set Q of the representative point set P is determined according to the standard processing model and the alignment data, and then the coordinate deviation value is determined according to the representative point set P and the corresponding point set Q, and the coordinate feature vector is constructed according to the coordinate deviation value, and then the second production line is screened according to the coordinate feature vector to obtain the target second production line of the workpiece.

[0075] As a further implementation of the robot handling method, the step of constructing a coordinate feature vector according to the coordinate deviation value includes:

[0076] Step S31, determining the corresponding maximum coordinate deviation and minimum coordinate deviation according to the coordinate deviation value.

[0077] It is understandable that the coordinate deviation value is multiple values, a set of coordinate deviation values D={d 1, d 2, …… , d n } , the maximum coordinate deviation D max =max(D) , minimum coordinate deviation D min =min(D) .

[0078] Step S32, determining the coordinate average value corresponding to the absolute value of the coordinate according to the absolute value of the coordinate.

[0079] It can be understood that the coordinate mean D mean The calculation formula is:

[0080]

[0081] Step S33, determining the corresponding coordinate offset according to the coordinate deviation value and the coordinate average value, and determining the corresponding coordinate standard deviation according to the coordinate offset and the coordinate average value.

[0082] It is understandable that for each coordinate deviation value in the set, the corresponding coordinate offset needs to be calculated. O i The calculation formula is:

[0083]

[0084] Step S34, constructing a coordinate feature vector according to the maximum coordinate deviation, the minimum coordinate deviation, the coordinate average value and the coordinate standard deviation.

[0085] It can be understood that the coordinate feature vector F can be expressed as:

[0086]

[0087] in, S is the coordinate standard deviation.

[0088] In the above implementation, the corresponding maximum coordinate deviation and minimum coordinate deviation are first determined according to the coordinate deviation value, and then the coordinate average corresponding to the coordinate absolute value is determined according to the coordinate absolute value. Then, the corresponding coordinate offset is determined according to the coordinate deviation value and the coordinate average, and the corresponding coordinate standard deviation is determined according to the coordinate offset and the coordinate average. Finally, a coordinate feature vector is constructed based on the maximum coordinate deviation, the minimum coordinate deviation, the coordinate average and the coordinate standard deviation.

[0089] As a further implementation of the robot handling method, the step of screening the second production line according to the coordinate feature vector to obtain the target second production line of the workpiece includes:

[0090] Step S41, acquiring historical processing data of each second production line, the historical processing data including historical standard processing parameters and historical scanning data, and constructing a conditional feature vector corresponding to each second production line according to the historical processing data.

[0091] It can be understood that the conditional feature vector E of the second production line can be expressed as , is the maximum deviation of the historical processing data of the second production line, is the minimum deviation of the historical processing data of the second production line, is the average deviation of the historical processing data of the second production line, s is the standard deviation of the historical processing data of the second production line;

[0092] In this embodiment, the conditional feature vector is calculated based on the historical standard processing parameters and the historical scanning data, and the calculation method is the same as the calculation method of the coordinate feature vector.

[0093] Step S42, calculating the fitness evaluation value of each second production line and the workpiece according to the condition feature vector and the coordinate feature vector.

[0094] It should be noted that the calculation formula of the fitness evaluation value A is:

[0095]

[0096] in, w 1. w 2. w 3 and w 4 is the preset weight value.

[0097] Step S43, taking the second production line corresponding to the minimum value among the fitness evaluation values ​​as the target second production line for the workpiece.

[0098] In the above embodiment, the historical processing data of each second production line is first obtained, the historical processing data includes historical standard processing parameters and historical scanning data, and the conditional feature vector corresponding to each second production line is constructed based on the historical processing data. Then, the fitness evaluation value of each second production line and the workpiece is calculated based on the conditional feature vector and the coordinate feature vector. Finally, the second production line corresponding to the minimum value in the fitness evaluation value is used as the target second production line for the workpiece.

[0099] As a further implementation of the robot handling method, the step of determining the type of the target handling component according to the scanning data includes:

[0100] Step S51 , generating an actual processing model of the workpiece according to the scan data, and extracting features of the workpiece according to the actual processing model to obtain corresponding feature data.

[0101] Step S52: input the characteristic data into a pre-generated target component parameter model to obtain corresponding target component parameters.

[0102] Step S53, matching the target component parameters in the first preset table to obtain the corresponding target component type.

[0103] The first preset table includes transport component parameters and transport component types, and the first preset table is used to indicate a mapping relationship between the transport component parameters and the transport component types.

[0104] In the above-mentioned embodiment, an actual processing model of the workpiece is first generated according to the scanning data, and features of the workpiece are extracted according to the actual processing model to obtain corresponding feature data, and then the feature data is input into a pre-generated target component parameter model to obtain corresponding target component parameters, and finally the target component parameters are matched in a first preset table to obtain corresponding target component types, the first preset table includes handling component parameters and handling component types, and the first preset table is used to represent the mapping relationship between handling component parameters and handling component types.

[0105] As a further implementation of the robot handling method, the step of generating a pre-generated parameter model of the target component includes:

[0106] Step S61, obtaining model training data, the model training data including handling component parameters corresponding to the handling components and feature data of different handling components, and dividing the model training data into k data subsets of the same size, where k is a positive integer greater than 1.

[0107] Step S62 , based on a k-fold cross-validation method and according to the data subset, the pre-constructed target component parameter model is trained and validated for k rounds to obtain a pre-generated target component parameter model.

[0108] In the above implementation, model training data is first obtained, the model training data includes the transport component parameters corresponding to the transport component and the characteristic data of different transport components, and the model training data is divided into k data subsets of the same size. k is a positive integer greater than 1, and then based on the k-fold cross-validation method, the pre-constructed target component parameter model is trained and validated for k rounds according to the data subsets to obtain the pre-generated target component parameter model.

[0109] As a further embodiment of the robot handling method, the movement parameters include the number of robots, and the step of determining at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the target handling component type includes:

[0110] Step S71, matching the target transport component type in the second preset table to obtain at least one optional type corresponding to the robot.

[0111] The second preset table includes handling component types and robot types, and the second preset table is used to represent a mapping relationship between the handling component types and the robot types.

[0112] Step S72, determining the processing rate of the first production line according to the standard processing parameters, and determining the handling force requirement of the first production line according to the processing rate.

[0113] It is understandable that the greater the processing rate of the first production line, the greater the handling force requirement of the first production line; in this embodiment, a function is used to represent the relationship between the handling force requirement and the processing rate, with the processing rate as the independent variable and the handling force requirement as the dependent variable.

[0114] Step S73, obtaining the number of robots corresponding to at least one optional type of robot, and determining at least one selected type of robot corresponding to the robot and the number of robots corresponding to the at least one selected type of robot according to the at least one optional type, the number of robots and the handling force requirement.

[0115] It can be understood that, in step S73, assuming that the handling force requirement is 100, at least one of the optional types is A and B, the handling force corresponding to the robot of type A is 10, and the handling force corresponding to the robot of type B is 20; at this time, the number of robots of type A is 10, and the number of robots of type B is 20, then there are three options: select robots of type A, the number is 10; select robots of type B, the number is 5; select robots of type A and type B at the same time, the number of robots of type A X and the number of robots of type B Y satisfy 10X+20Y=100, and both X and Y are not 0. At this time, you can make a choice according to the actual situation, for example, select the solution with the least number of robots, that is, select 5 robots of type B.

[0116] In the above implementation, the target handling component type is first matched in the second preset table to obtain at least one optional type corresponding to the robot. The second preset table includes handling component types and robot types, and the second preset table is used to represent the mapping relationship between handling component types and robot types, and then the processing rate of the first production line is determined according to the standard processing parameters, and the handling force requirement of the first production line is determined according to the processing rate, and then the number of robots corresponding to at least one optional type of robot is obtained, and at least one selected type corresponding to the robot and the number of robots corresponding to at least one selected type of robot are determined according to the at least one optional type, the number of robots and the handling force requirement.

[0117] The embodiments of the present application also disclose a robot handling system based on the industrial Internet of Things.

[0118] refer to Figure 2 The robot handling system based on the industrial Internet of Things includes a management platform, a sensor network platform and an object platform which are connected in sequence by communication. The management platform is configured with:

[0119] A target second production line determination module is used to obtain standard processing parameters of the first production line and scan data of the workpiece after the first production line processes the workpiece, and determine the target second production line of the workpiece from the second production line according to the standard processing parameters and the scan data;

[0120] A robot type determination module, used to determine the type of the target transport component according to the scanning data, and to determine at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the type of the target transport component;

[0121] The robot control module is used to generate a movement control instruction according to at least one selected type and movement parameters to control at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameters.

[0122] The overall framework of another application scenario of the robot handling system based on the industrial Internet of Things of this application is as follows: Figure 3 As shown, it may include a user platform, a service platform, a management platform, a sensor network platform and an object platform that interact in sequence, forming a five-platform architecture based on the industrial Internet of Things. Among them, the management platform includes a target second production line determination module, a robot type determination module and a robot control module; the sensor network platform includes a sensor database and several sensor network sub-platforms, and each sensor network sub-platform can communicate with the sensor database.

[0123] Specifically, in another application scenario mentioned above, the robot handling system based on industrial Internet of Things includes a management platform, which is configured to: obtain standard processing parameters of the first production line and scanning data of the first production line after processing the workpiece, and determine the target second production line of the workpiece from the second production line according to the standard processing parameters and the scanning data; determine the target handling component type according to the scanning data, and determine at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the target handling component type; generate movement control instructions according to at least one selected type and movement parameters to control at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameters.

[0124] Through the interaction between the various functional platforms of the industrial Internet of Things-based robot handling system based on the above three platforms or five platforms, a complete closed-loop information operation logic is established, ensuring the orderly operation of perception information and control information and realizing intelligent equipment management.

[0125] The robot handling system based on industrial Internet of Things of the present invention can implement any one of the robot handling methods based on industrial Internet of Things, and the specific working process of the robot handling system based on industrial Internet of Things of the present invention can refer to the corresponding process in the above-mentioned robot handling method based on industrial Internet of Things.

[0126] The embodiment of the present application also discloses a computer device.

[0127] refer to Figure 4A computer device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, any of the above-mentioned robot handling methods based on the industrial Internet of Things is implemented.

[0128] The embodiment of the present application also discloses a computer-readable storage medium.

[0129] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned industrial Internet of Things-based robot handling methods.

[0130] Among them, computer-readable storage media can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0131] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A robot handling method based on industrial Internet of Things, characterized in that: Applied to an industrial Internet of Things system, the industrial Internet of Things system includes a management platform, a sensor network platform and an object platform that are sequentially communicatively connected, and the method is executed by the management platform, including: Acquire standard processing parameters of the first production line and scan data after the first production line processes the workpiece, and determine a target second production line for the workpiece from the second production line according to the standard processing parameters and the scan data; Determine the target transport component type according to the scanning data, and determine at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the target transport component type; the movement parameters include the number of robots; generating a movement control instruction according to the at least one selected type and the movement parameter to control the at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameter; The step of determining the target second production line of the workpiece from the second production line according to the standard processing parameters and the scanning data comprises: Acquire model data of the workpiece before entering the first production line, build a standard processing model corresponding to the workpiece according to the standard processing parameters and the model data, and generate an actual processing model of the workpiece according to the scanning data; Registering the standard processing model and the actual processing model to obtain registration data; Determine a representative point set P of the workpiece according to the actual processing model, and determine a corresponding point set Q of the representative point set P according to the standard processing model and the registration data; Determine a coordinate deviation value according to the representative point set P and the corresponding point set Q, and construct a coordinate feature vector according to the coordinate deviation value; Screening the second production line according to the coordinate feature vector to obtain the target second production line of the workpiece; The step of constructing a coordinate feature vector according to the coordinate deviation value comprises: Determine the corresponding maximum coordinate deviation and minimum coordinate deviation according to the coordinate deviation value; Determine, according to the absolute value of the coordinate, a coordinate average value corresponding to the absolute value of the coordinate; Determine a corresponding coordinate offset according to the coordinate deviation value and the coordinate average value, and determine a corresponding coordinate standard deviation according to the coordinate offset and the coordinate average value; Constructing a coordinate feature vector according to the maximum value of the coordinate deviation, the minimum value of the coordinate deviation, the coordinate average value and the coordinate standard deviation; The step of screening the second production line according to the coordinate feature vector to obtain the target second production line of the workpiece includes: Acquire historical processing data of each second production line, the historical processing data including historical standard processing parameters and historical scanning data, and construct conditional feature vectors corresponding to each second production line according to the historical processing data; Calculating the fitness evaluation value of each of the second production lines and the workpiece according to the condition feature vector and the coordinate feature vector; taking the second production line corresponding to the minimum value among the fitness evaluation values ​​as the target second production line for the workpiece; The step of determining the type of the target transport component according to the scanning data comprises: Generating an actual processing model of the workpiece according to the scanning data, and extracting features of the workpiece according to the actual processing model to obtain corresponding feature data; Inputting the characteristic data into a pre-generated target component parameter model to obtain corresponding target component parameters; The target component parameters are matched in a first preset table to obtain a corresponding target component type; the first preset table includes handling component parameters and handling component types, and the first preset table is used to represent a mapping relationship between handling component parameters and handling component types.

2. The robot handling method based on industrial Internet of Things according to claim 1 is characterized in that: The step of generating the pre-generated target component parameter model comprises: Acquire model training data, the model training data including the transport component parameters corresponding to the transport component and feature data of different transport components, and divide the model training data into k data subsets of the same size; k is a positive integer greater than 1; Based on the k-fold cross validation method, k rounds of training and validation are performed on the pre-constructed target component parameter model according to the data subset to obtain the pre-generated target component parameter model.

3. The robot handling method based on industrial Internet of Things according to claim 1 is characterized in that: The step of determining at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the target transport component type comprises: Matching the target handling component type in a second preset table to obtain at least one optional type corresponding to the robot; the second preset table includes handling component types and robot types, and the second preset table is used to represent the mapping relationship between the handling component types and the robot types; determining a processing rate of the first production line according to the standard processing parameters, and determining a handling force requirement of the first production line according to the processing rate; The number of robots corresponding to the at least one optional type of robot is obtained, and at least one selected type corresponding to the robot and the number of robots corresponding to the at least one selected type of robot are determined according to the at least one optional type, the number of robots and the handling force requirement.

4. The robot handling system based on industrial Internet of Things is characterized by: It includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication, and the management platform is configured with: a target second production line determination module, used to obtain standard processing parameters of the first production line and scan data of the workpiece after the first production line processes the workpiece, and determine the target second production line of the workpiece from the second production line according to the standard processing parameters and the scan data; A robot type determination module, configured to determine the type of a target transport component according to the scanning data, and to determine at least one selected type and corresponding movement parameters corresponding to the robot according to the standard processing parameters and the type of the target transport component; the movement parameters include the number of robots; a robot control module, configured to generate a movement control instruction according to the at least one selected type and the movement parameter, so as to control the at least one selected type of robot to move the workpiece from the first production line to the target second production line based on the movement parameter; The step of determining the target second production line of the workpiece from the second production line according to the standard processing parameters and the scanning data comprises: Acquire model data of the workpiece before entering the first production line, build a standard processing model corresponding to the workpiece according to the standard processing parameters and the model data, and generate an actual processing model of the workpiece according to the scanning data; Registering the standard processing model and the actual processing model to obtain registration data; Determine a representative point set P of the workpiece according to the actual processing model, and determine a corresponding point set Q of the representative point set P according to the standard processing model and the registration data; Determine a coordinate deviation value according to the representative point set P and the corresponding point set Q, and construct a coordinate feature vector according to the coordinate deviation value; Screening the second production line according to the coordinate feature vector to obtain the target second production line of the workpiece; The step of constructing a coordinate feature vector according to the coordinate deviation value comprises: Determine the corresponding maximum coordinate deviation and minimum coordinate deviation according to the coordinate deviation value; Determine, according to the absolute value of the coordinate, a coordinate average value corresponding to the absolute value of the coordinate; Determine a corresponding coordinate offset according to the coordinate deviation value and the coordinate average value, and determine a corresponding coordinate standard deviation according to the coordinate offset and the coordinate average value; Constructing a coordinate feature vector according to the maximum value of the coordinate deviation, the minimum value of the coordinate deviation, the coordinate average value and the coordinate standard deviation; The step of screening the second production line according to the coordinate feature vector to obtain the target second production line of the workpiece includes: Acquire historical processing data of each second production line, the historical processing data including historical standard processing parameters and historical scanning data, and construct conditional feature vectors corresponding to each second production line according to the historical processing data; Calculating the fitness evaluation value of each of the second production lines and the workpiece according to the condition feature vector and the coordinate feature vector; taking the second production line corresponding to the minimum value among the fitness evaluation values ​​as the target second production line for the workpiece; The step of determining the type of the target transport component according to the scanning data comprises: Generating an actual processing model of the workpiece according to the scanning data, and extracting features of the workpiece according to the actual processing model to obtain corresponding feature data; Inputting the characteristic data into a pre-generated target component parameter model to obtain corresponding target component parameters; The target component parameters are matched in a first preset table to obtain a corresponding target component type; the first preset table includes handling component parameters and handling component types, and the first preset table is used to represent a mapping relationship between handling component parameters and handling component types.

5. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the method according to any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 3.

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