General servo driving method and system based on artificial intelligence

Abstract

The invention provides a general servo driving method and system based on artificial intelligence, and relates to the technical field of general servo driving, and the method comprises the steps: obtaining a three-dimensional image of a to-be-cut device, a three-dimensional image of a target device, and servo driving device information; based on the three-dimensional image of the to-be-cut device, the three-dimensional image of the target device and the information of the servo driving device, multiple sets of primary selection operation schemes are determined by using an image processing model; based on the three-dimensional image of the to-be-cut device and the multiple sets of primary selection operation schemes, using a generative adversarial network to generate a simulation operation video of each set of primary selection operation scheme; determining a target operation scheme based on the simulation operation video of each primary selection operation scheme; and controlling a servo driving device to perform servo driving based on the target operation scheme. According to the method, the optimal operation scheme of the cutting processing device can be quickly determined.

Description

Technical Field

[0001] The present invention relates to the technical field of servo equipment control, and in particular to a universal servo driving method and system based on artificial intelligence. Background Art

[0002] The servo drive system has important application value in the operation of cutting devices. It can achieve high-precision and high-efficiency operation by precisely controlling mechanical movement. Traditional device cutting operations usually rely on fixed schemes and lack the ability to dynamically adjust according to the specific characteristics of the device to be cut. In addition, the selection and adjustment of the operation scheme are overly dependent on the operator's experience and are prone to human errors. In some complex and high-precision tasks, it often takes a lot of time to determine and select the operation scheme, resulting in low efficiency.

[0003] Therefore, how to quickly determine the optimal operation plan for cutting and processing devices is a problem that needs to be solved urgently. Summary of the invention

[0004] The main technical problem solved by the present invention is how to quickly determine the optimal operation plan of a cutting processing device.

[0005] According to a first aspect, the present invention provides a general servo drive method based on artificial intelligence, including: obtaining a three-dimensional image of a device to be cut, a three-dimensional image of a target device, and information of a servo drive device; based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device, using an image processing model to determine multiple sets of preliminary operation plans; based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation plans, using a generative adversarial network to generate simulated operation videos of each set of preliminary operation plans; determining a target operation plan based on the simulated operation videos of each set of preliminary operation plans; and controlling the servo drive device to perform servo drive based on the target operation plan.

[0006] In a possible implementation, determining the target operation scheme based on the simulated operation video of each set of preliminary operation schemes includes: processing the simulated operation video of each set of preliminary operation schemes based on an information processing model to determine the simulated operation information of each set of preliminary operation schemes; determining multiple sets of preferred operation schemes, the similarities between different preferred operation schemes, and multiple excellent operation step information of the preliminary operation schemes based on the simulated operation information of each set of preliminary operation schemes; constructing a knowledge graph, the knowledge graph including multiple preferred operation scheme nodes and multiple edges between the multiple preferred operation scheme nodes, the node features of the multiple preferred operation scheme nodes including the simulated operation video of the preferred operation scheme, multiple excellent operation step information of the preliminary operation scheme, and the edges between different preferred operation scheme nodes represent the similarities between different preferred operation schemes; processing the knowledge graph based on a graph convolutional network to determine the target operation scheme.

[0007] In one possible implementation, the image processing model is a deep neural network model.

[0008] In a possible implementation, the information processing model is a Transformer model.

[0009] According to a second aspect, the present invention provides a universal servo drive system based on artificial intelligence, comprising:

[0010] An acquisition module, used to acquire a three-dimensional image of a device to be cut, a three-dimensional image of a target device, and information of a servo drive device;

[0011] A preliminary selection scheme generating module, used to determine a plurality of preliminary selection operation schemes using an image processing model based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device;

[0012] A simulation operation generation module, used to generate a simulation operation video of each set of preliminary operation schemes using a generative adversarial network based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation schemes;

[0013] A target solution determination module, used to determine a target operation solution based on the simulated operation video of each set of preliminary operation solutions;

[0014] The control module is used to control the servo drive device to perform servo drive based on the target operation scheme.

[0015] In one possible implementation, the target solution determination module is also used to: process the simulated operation video of each set of preliminary operation solutions based on the information processing model to determine the simulated operation information of each set of preliminary operation solutions; determine multiple sets of preferred operation solutions, the similarities between different preferred operation solutions, and multiple excellent operation step information of the preliminary operation solutions based on the simulated operation information of each set of preliminary operation solutions; construct a knowledge graph, the knowledge graph includes multiple preferred operation solution nodes and multiple edges between the multiple preferred operation solution nodes, the node features of the multiple preferred operation solution nodes include the simulated operation video of the preferred operation solution, multiple excellent operation step information of the preliminary operation solution, and the edges between different preferred operation solution nodes represent the similarities between different preferred operation solution nodes; and process the knowledge graph based on a graph convolutional network to determine the target operation solution.

[0016] In one possible implementation, the image processing model is a deep neural network model.

[0017] In a possible implementation, the information processing model is a Transformer model.

[0018] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement a method as described above, the method comprising: obtaining a three-dimensional image of the device to be cut, a three-dimensional image of the target device, and information of a servo drive device; based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device, using an image processing model to determine multiple sets of preliminary operation schemes; based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation schemes, using a generative adversarial network to generate simulated operation videos of each set of preliminary operation schemes; determining a target operation scheme based on the simulated operation videos of each set of preliminary operation schemes; and controlling the servo drive device to perform servo drive based on the target operation scheme.

[0019] According to the fourth aspect, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned general servo drive method based on artificial intelligence, the method comprising: obtaining a three-dimensional image of the device to be cut, a three-dimensional image of the target device, and servo drive device information; based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the servo drive device information, using an image processing model to determine multiple sets of preliminary operation schemes; based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation schemes, using a generative adversarial network to generate simulated operation videos of each set of preliminary operation schemes; determining a target operation scheme based on the simulated operation videos of each set of preliminary operation schemes; and controlling the servo drive device to perform servo drive based on the target operation scheme.

[0020] The present invention provides a general servo drive method and system based on artificial intelligence. The method includes obtaining a three-dimensional image of a device to be cut, a three-dimensional image of a target device, and information of a servo drive device; based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device, using an image processing model to determine multiple sets of preliminary operation plans; based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation plans, using a generative adversarial network to generate simulated operation videos of each set of preliminary operation plans; determining a target operation plan based on the simulated operation videos of each set of preliminary operation plans; and controlling the servo drive device to perform servo drive based on the target operation plan. The method can quickly determine the optimal operation plan for cutting and processing devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of an application scenario of a general servo driving method based on artificial intelligence provided by an embodiment of the present invention;

[0022] Figure 2 A schematic flow chart of a general servo driving method based on artificial intelligence provided by an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of a process for determining a target operation scheme provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of a universal servo drive system based on artificial intelligence provided by an embodiment of the present invention;

[0025] Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention;

[0026] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present invention better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present invention are not shown or described in the specification, this is to avoid the core part of the present invention being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0028] Figure 1 A schematic diagram of an application scenario of a universal servo drive method based on artificial intelligence provided by an embodiment of the present invention. Figure 1 The application scenario of the general servo driving method based on artificial intelligence may include a server 11, a network 12, a terminal 13 and a storage device 14.

[0029] In some embodiments, the server 11 may be a single server or a server group. The server 11 may access information and / or data stored in the terminal 13 or the storage device 14 through the network 12. In some embodiments, the server 11 may be used to execute Figure 2 The general servo drive method based on artificial intelligence is shown in.

[0030] The network 12 may facilitate the exchange of information and / or data. In some embodiments, the network 12 may be any form of wired or wireless network, or any combination thereof.

[0031] Terminal 13 may refer to one or more terminal devices used by a user. In some embodiments, terminal 13 may include one or more combinations of a mobile device, a tablet computer, a laptop computer, and the like.

[0032] The storage device 14 may store data and / or instructions. For example, the storage device 14 may store data instructions of a general servo driving method based on artificial intelligence.

[0033] In an embodiment of the present invention, there is provided Figure 2 A general servo driving method based on artificial intelligence is shown, and the general servo driving method based on artificial intelligence includes steps S1 to S5:

[0034] Step S1, obtaining a three-dimensional image of a device to be cut, a three-dimensional image of a target device, and information of a servo drive device;

[0035] The three-dimensional image of the device to be cut refers to a digital model of the device to be cut generated by three-dimensional scanning technology. The three-dimensional image of the device to be cut can be used to describe the geometric shape, size and other information of the device to be cut. The three-dimensional image of the device to be cut is usually obtained using a three-dimensional scanner or a depth camera, such as a laser scanner to generate an accurate three-dimensional contour.

[0036] The target device refers to the device model whose shape and size are expected after the device operation is completed.

[0037] The three-dimensional image of the target device refers to a digital model of the target device drawn by computer-aided design (CAD) software, which can be used to describe the ideal shape, size and other information expected to be achieved after the device to be cut is completed.

[0038] Servo drive device information refers to key parameters related to the servo drive device. Servo drive device information can be used to describe the performance and operating status of the equipment. For example, servo drive device information can include motion range, accuracy, speed and load capacity. Servo drive device information is usually read directly through the device interface or control system, such as obtaining real-time data through PLC (Programmable Logic Controller) or host computer software.

[0039] Step S2, based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device, using an image processing model to determine a plurality of preliminary operation schemes;

[0040] The image processing model is a deep neural network model. The input of the image processing model is the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the servo drive device information. The output of the image processing model is multiple sets of preliminary operation plans.

[0041] The multiple sets of preliminary operation plans are a collection of multiple simulated operation plans generated by a deep neural network model.

[0042] The deep neural network model is a machine learning model that simulates the structure of human brain neurons, including deep neural networks (DNN). The deep neural network can include multiple processing layers, each of which is composed of multiple neurons. Each neuron performs matrix transformation on the data and can extract high-level features from the input data through multiple layers of nonlinear transformation. The deep neural network can process complex multidimensional data, such as images, text, etc., and learn the potential laws in the data through training. The deep neural network can generate multiple sets of preliminary operation plans by extracting geometric features from the image and analyzing the constraints of the servo drive device in combination with the fully connected layer. The advantage of the deep neural network is that it can automatically learn the complex relationships in the data and comprehensively analyze the geometric shape, operation accuracy and equipment limitations, so as to provide a variety of feasible solutions for subsequent optimization.

[0043] Step S3, based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation schemes, using a generative adversarial network to generate a simulated operation video of each set of preliminary operation schemes;

[0044] The simulation operation video of each set of preliminary operation schemes is a virtual simulation video of the operation process generated by the Generative Adversarial Network (GAN). The simulation operation video of each set of preliminary operation schemes is a continuous time series data that can record the dynamic changes of tool movement, operation steps and effects during the operation.

[0045] The input of the generative adversarial network is a three-dimensional image of the device to be cut and multiple sets of preliminary operation plans, and the output of the generative adversarial network is a simulated operation video of each set of preliminary operation plans. The generative adversarial network is a way to implement artificial intelligence.

[0046] The Generative Adversarial Network (GAN) consists of a generator and a discriminator. The generator is responsible for generating simulated data, while the discriminator is used to distinguish generated data from real data. The two compete with each other and make progress together, generating high-quality data through adversarial training. The generator of the Generative Adversarial Network generates simulated operation videos for each set of solutions based on the three-dimensional image of the device to be cut and multiple sets of preliminary operation solutions, while the discriminator evaluates the authenticity of the simulated operation videos for each set of solutions to ensure that they are highly consistent with the actual operation process.

[0047] The 3D image of the device to be cut is an accurate description of the shape and structure of the object, and contains all the geometric information of the device to be cut. This information is crucial for predicting the interaction between the tool and the material during the cutting process. Based on the 3D image of the device to be cut, the generator can ensure that the details such as the cutting path and tool movement in the generated simulation video meet the actual physical constraints.

[0048] Each set of preliminary operation plans provides specific cutting parameters and steps, such as cutting speed, feed rate, tool trajectory, etc. This information provides the generator with clear operation instructions, making the generated simulation video more specific and targeted.

[0049] Different preliminary operation scenarios represent different operation strategies, ensuring that the generated simulation videos cover a variety of possible situations, increasing the diversity and coverage of the results.

[0050] Step S4, determining a target operation scheme based on the simulated operation video of each set of preliminary operation schemes;

[0051] In some embodiments, Figure 3 A schematic diagram of a process for determining a target operation scheme provided by an embodiment of the present invention, wherein the target operation scheme includes steps S21 to S24:

[0052] Step S21, processing the simulated operation video of each set of preliminary operation schemes based on the information processing model to determine the simulated operation information of each set of preliminary operation schemes;

[0053] The simulated operation information of each set of preliminary operation plans is determined after analyzing the simulated operation video through an information processing model.

[0054] The simulated operation information of each set of preliminary operation plans includes the number of operations, the operation score of each step, the operation time, the material utilization rate, the energy consumption, the tool wear degree and the error rate.

[0055] The simulated operation information of each set of preliminary operation schemes can fully describe the operation effect and performance of each set of preliminary operation schemes. For example, the operation score can evaluate the quality of each cutting step. The higher the operation score, the better the accuracy, stability and effect of the operation step. The number of operations and operation time are used to calculate the efficiency of the scheme. Energy consumption and tool wear are used to evaluate energy saving and sustainability. The error rate can measure the operation accuracy. By analyzing the simulated operation information of each set of preliminary operation schemes, excellent operation steps can be identified and the preferred operation scheme can be determined.

[0056] The information processing model is a Transformer model, the input of the information processing model is each set of preliminary operation schemes, and the output of the information processing model is the simulated operation information of each set of preliminary operation schemes. The Transformer model is an implementation method of artificial intelligence.

[0057] The Transformer model is a deep learning model based on the self-attention mechanism, which is good at processing sequence data such as simulated operation videos. Through the self-attention mechanism, the Transformer model can capture the global dependencies between video frames, such as the continuity of tool motion trajectories and the dynamic changes of the operation area. In addition, the Transformer model can combine visual features and timing information to generate comprehensive simulated operation information, such as the number of operations, operation scores, material utilization rate and other data.

[0058] In some embodiments, the information processing model includes a video feature extraction layer, an operation parameter analysis layer, and a comprehensive evaluation layer. The video feature extraction layer, the operation parameter analysis layer, and the comprehensive evaluation layer all include a Transformer structure. The input of the video feature extraction layer is a simulated operation video of each set of preliminary operation schemes, and the output of the video feature extraction layer is key operation steps, tool motion trajectories, and operation process stability indicators. The input of the operation parameter analysis layer is key operation steps and tool motion trajectories, and the output of the operation parameter analysis layer is the number of operations, the operation score of each step, the operation time, material utilization, energy consumption, tool wear degree and error rate. The input of the comprehensive evaluation layer is the number of operations, the operation score of each step, the operation time, material utilization, energy consumption, tool wear degree and error rate, and the operation process stability index, and the output of the comprehensive evaluation layer is the simulated operation information of each set of preliminary operation schemes.

[0059] The video feature extraction layer can extract key features from the simulated operation video, including the tool's motion trajectory, key operation steps, and stability indicators of the operation process. The operation parameter analysis layer can calculate the number of operations, the operation score of each step, the operation time, material utilization, energy consumption, tool wear degree, error rate and other parameters by analyzing the key operation steps and tool motion trajectory. The comprehensive evaluation layer can generate simulated operation information for each set of preliminary operation plans by combining the operation parameters and the stability indicators of the operation process. This hierarchical design not only improves the accuracy and efficiency of the system, but also enhances the interpretability of the model.

[0060] By breaking down the task into multiple steps and refining the results of the previous step in each step, the accuracy of the information can be gradually improved. This layered processing can enhance the accuracy of the final output data because each layer can focus on optimizing the accuracy of its specific task, thereby improving the stability and reliability of the entire model.

[0061] Step S22, determining multiple sets of preferred operation schemes, similarities between different preferred operation schemes, and multiple excellent operation step information of the preliminary operation schemes based on the simulated operation information of each set of preliminary operation schemes;

[0062] In some embodiments, multiple sets of preferred operation schemes, similarities between different preferred operation schemes, and multiple excellent operation step information of the preliminary operation schemes can be determined by a deep neural network model. The input of the deep neural network model is the simulated operation information of each set of preliminary operation schemes, and the output of the deep neural network model is multiple sets of preferred operation schemes, similarities between different preferred operation schemes, and multiple excellent operation step information of the preliminary operation schemes.

[0063] Multiple sets of preferred operation plans refer to a collection of plans selected from the preliminary operation plans that perform well in multiple evaluation dimensions and each has its own advantages.

[0064] The similarity between different optimal operation schemes is a parameter used to quantify the similarity between multiple sets of optimal operation schemes in key features.

[0065] The information of multiple excellent operation steps of the preliminary operation plan is a set of operation steps with excellent performance extracted from the preliminary operation plan.

[0066] Step S23, constructing a knowledge graph, wherein the knowledge graph includes multiple preferred operation solution nodes and multiple edges between the multiple preferred operation solution nodes, the node features of the multiple preferred operation solution nodes include a simulated operation video of the preferred operation solution, multiple excellent operation step information of the preliminary operation solution, and the edges between different preferred operation solution nodes represent the similarity between different preferred operation solutions;

[0067] The knowledge graph is a structured data representation method, which consists of multiple nodes and edges between nodes, and can be used to express the relationship between different entities. In some embodiments, the knowledge graph is used to represent the similarity and correlation between different preferred operation schemes. Each preferred operation scheme node represents a preferred operation scheme, such as scheme A, scheme B, scheme C, etc. The node features include a simulated operation video of the scheme and multiple excellent operation step information of the preliminary operation scheme. The edges connect different preferred operation scheme nodes to indicate the similarity between these schemes.

[0068] Step S24, processing the knowledge graph based on the graph convolutional network to determine the target operation plan.

[0069] The target operation plan is the operation plan that best suits the current operation requirements from multiple sets of preferred operation plans. The target operation plan is generated through graph convolutional network (GCN) analysis based on the preferred operation plan nodes and their relationships in the knowledge graph. The target operation plan is one of multiple preferred operation plans. The target operation plan can be used to optimize the operation process, reduce human intervention, and adapt to dynamic needs. The input of the graph convolutional network is the knowledge graph, and the output of the graph convolutional network is the target operation plan.

[0070] Each node (preferred operation plan node) contains the behavioral characteristics of the operation plan under specific conditions (simulated operation video, multiple excellent operation step information of the preliminary operation plan), and the edge represents the similarity score between the preferred operation plans.

[0071] Excellent operation step information has excellent performance. By using excellent operation step information as node features, the graph neural network model can understand which operation details are crucial to achieving high-quality cutting.

[0072] The simulated operation video shows the expected operation effects under different conditions. The simulated operation video contains rich visual information, such as particle motion trajectory, separation effect, etc. The simulated operation video can intuitively reflect the effect of the operation scheme, so that the model can more comprehensively understand the actual performance of different operation schemes. Compared with static images or simple numerical parameters, video data can capture the dynamic characteristics of the operation process that change over time. This is especially important for evaluating complex operation processes, because many key operation details can only be accurately identified in the dynamic process.

[0073] By organizing different preliminary operation plans into a graph structure, the complex relationships between the various plans can be better captured. For example, which plans have similar operation steps or effects, and which plans have significant differences, so that the graph convolutional network can better determine the target operation plan. Graph Convolutional Network (GCN) is a deep learning model for processing knowledge graphs. Graph convolutional networks can use node features and edge features in knowledge graphs to learn the complex relationships between operation plans. In this way, the graph convolutional network can comprehensively consider the characteristics of each preferred operation plan and the similarity between them, so as to recommend the most suitable and optimal target operation plan.

[0074] Step S5: controlling the servo drive device to perform servo drive based on the target operation scheme.

[0075] After the target operation scheme is determined, the servo drive device is controlled to operate the device to be cut based on the target operation scheme.

[0076] Based on the same inventive concept, Figure 4 A schematic diagram of a universal servo drive system based on artificial intelligence provided by an embodiment of the present invention, the universal servo drive system based on artificial intelligence includes:

[0077] An acquisition module 41 is used to acquire a three-dimensional image of a device to be cut, a three-dimensional image of a target device, and information of a servo drive device;

[0078] A preliminary selection scheme generating module 42 is used to determine a plurality of preliminary selection operation schemes using an image processing model based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device;

[0079] A simulation operation generation module 43 is used to generate a simulation operation video of each set of preliminary operation schemes using a generative adversarial network based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation schemes;

[0080] A target solution determination module 44 is used to determine a target operation solution based on the simulated operation video of each set of preliminary operation solutions;

[0081] The control module 45 is used to control the servo drive device to perform servo drive based on the target operation scheme.

[0082] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 5 As shown, including:

[0083] It includes: a processor 51; a memory 52; and a computer program; wherein the computer program is stored in the memory 52 and is configured to be executed by the processor 51 to implement the general servo drive method based on artificial intelligence as provided above, the method including: obtaining a three-dimensional image of the device to be cut, a three-dimensional image of the target device, and servo drive device information; based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the servo drive device information, using an image processing model to determine multiple sets of preliminary operation plans; based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation plans, using a generative adversarial network to generate simulated operation videos of each set of preliminary operation plans; determining a target operation plan based on the simulated operation videos of each set of preliminary operation plans; and controlling the servo drive device to perform servo drive based on the target operation plan.

[0084] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by the processor 51, implements the aforementioned general servo drive method based on artificial intelligence, the method comprising: obtaining a screen recording running video and data transmission description text of the user's favorite software; obtaining a three-dimensional image of the device to be cut, a three-dimensional image of the target device, and servo drive device information; based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the servo drive device information, using an image processing model to determine multiple sets of preliminary operation plans; based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation plans, using a generative adversarial network to generate a simulated operation video of each set of preliminary operation plans; determining a target operation plan based on the simulated operation video of each set of preliminary operation plans; and controlling the servo drive device to perform servo drive based on the target operation plan.

[0085] The general servo drive method based on artificial intelligence provided in the embodiment of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (ultra-mobile personal computers, UMPCs), handheld computers, netbooks, personal digital assistants (personal digital assistants, PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (augmented reality, AR) \ virtual reality (virtual reality, VR) devices, smart home devices, car computers and other electronic devices, and the embodiment of the present application does not impose any restrictions on this.

[0086] Taking the mobile phone 100 as an example of the electronic device, Figure 6 A schematic structural diagram of the mobile phone 100 is shown.

[0087] like Figure 6 As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0088] The processing module 110 may include one or more processing units, for example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0089] The processing module 110 can be used to: obtain a three-dimensional image of the device to be cut, a three-dimensional image of the target device, and information of a servo drive device; determine multiple sets of preliminary operation plans using an image processing model based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device; generate simulated operation videos of each set of preliminary operation plans using a generative adversarial network based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation plans; determine a target operation plan based on the simulated operation videos of each set of preliminary operation plans; and control the servo drive device to perform servo drive based on the target operation plan.

[0090] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0091] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0092] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0093] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0094] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A general servo driving method based on artificial intelligence, characterized in that: include: Obtaining a three-dimensional image of the device to be cut, a three-dimensional image of the target device, and information of a servo drive device; Based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device, a plurality of sets of preliminary operation schemes are determined using an image processing model; Based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation schemes, using a generative adversarial network to generate a simulated operation video of each set of preliminary operation schemes; Determine a target operation plan based on the simulated operation video of each set of preliminary operation plans; The servo drive device is controlled to perform servo drive based on the target operation scheme.

2. The universal servo driving method based on artificial intelligence as claimed in claim 1, characterized in that: Determining the target operation scheme based on the simulated operation video of each set of the preliminary operation schemes includes: Processing the simulated operation video of each set of preliminary operation schemes based on the information processing model to determine the simulated operation information of each set of preliminary operation schemes; Determine multiple sets of preferred operation schemes, similarities between different preferred operation schemes, and multiple excellent operation step information of the preliminary operation schemes based on the simulated operation information of each set of preliminary operation schemes; Constructing a knowledge graph, wherein the knowledge graph includes a plurality of preferred operation solution nodes and a plurality of edges between the plurality of preferred operation solution nodes, wherein the node features of the plurality of preferred operation solution nodes include a simulated operation video of the preferred operation solution and a plurality of excellent operation step information of the preliminary operation solution, and the edges between different preferred operation solution nodes represent the similarity between the different preferred operation solutions; The knowledge graph is processed based on a graph convolutional network to determine a target operation plan.

3. The universal servo driving method based on artificial intelligence as claimed in claim 1, characterized in that: The image processing model is a deep neural network model.

4. The universal servo driving method based on artificial intelligence as claimed in claim 2, characterized in that: The information processing model is a Transformer model.

5. A universal servo drive system based on artificial intelligence, characterized in that: include: An acquisition module, used to acquire a three-dimensional image of a device to be cut, a three-dimensional image of a target device, and information of a servo drive device; A preliminary selection scheme generating module, used to determine a plurality of preliminary selection operation schemes using an image processing model based on the three-dimensional image of the device to be cut, the three-dimensional image of the target device, and the information of the servo drive device; A simulation operation generation module, used to generate a simulation operation video of each set of preliminary operation schemes using a generative adversarial network based on the three-dimensional image of the device to be cut and the multiple sets of preliminary operation schemes; A target solution determination module, used to determine a target operation solution based on the simulated operation video of each set of preliminary operation solutions; The control module is used to control the servo drive device to perform servo drive based on the target operation scheme.

6. The universal servo drive system based on artificial intelligence as claimed in claim 5, characterized in that: The target solution determination module is also used for: Processing the simulated operation video of each set of preliminary operation schemes based on the information processing model to determine the simulated operation information of each set of preliminary operation schemes; Determine multiple sets of preferred operation schemes, similarities between different preferred operation schemes, and multiple excellent operation step information of the preliminary operation schemes based on the simulated operation information of each set of preliminary operation schemes; Constructing a knowledge graph, wherein the knowledge graph includes a plurality of preferred operation solution nodes and a plurality of edges between the plurality of preferred operation solution nodes, wherein the node features of the plurality of preferred operation solution nodes include a simulated operation video of the preferred operation solution and a plurality of excellent operation step information of the preliminary operation solution, and the edges between different preferred operation solution nodes represent the similarity between the different preferred operation solutions; The knowledge graph is processed based on a graph convolutional network to determine a target operation plan.

7. The universal servo drive system based on artificial intelligence as claimed in claim 5, characterized in that: The image processing model is a deep neural network model.

8. The universal servo drive system based on artificial intelligence as claimed in claim 6, characterized in that: The information processing model is a Transformer model.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the general servo drive method based on artificial intelligence as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the universal servo drive method based on artificial intelligence as described in any one of claims 1 to 4 is implemented.

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