Precise equipment automatic installation error compensation system based on AI vision

Abstract

The invention discloses a precision equipment automatic installation error compensation system based on AI vision, and relates to the technical field of equipment installation error compensation. The visual acquisition module uses a multispectral and three-dimensional reconstruction camera to adaptively adjust the frame rate to acquire images; the data preprocessing module combines an algorithm to improve image quality; the feature extraction module accurately extracts features by means of a hybrid model; the error calculation module calculates errors through model predictive control and Kalman filtering; the compensation decision module fuses reinforcement learning and a fuzzy logic decision strategy; the execution control module executes adjustment through a piezoelectric ceramic motor and a magnetic suspension guide rail; and the data storage module guarantees data security management by using a block chain and a distributed technology. According to the invention, high-precision equipment characteristic acquisition, accurate error calculation, intelligent compensation strategy making, adaptation to different equipment scenes, real-time adjustment according to environment change, installation efficiency improvement, maintenance cost consideration and equipment service life prolonging can be realized, and a reliable scheme is provided for precision equipment installation.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment installation error compensation, and particularly to a precision equipment automatic installation error compensation system based on AI vision. Background Art

[0002] In modern industrial production, precision equipment is increasingly widely used, such as semiconductor manufacturing equipment, optical instruments, aerospace components, etc. The installation accuracy of these equipment directly affects their performance and service life, so the demand for automatic installation error compensation technology is becoming increasingly urgent.

[0003] Traditional precision equipment installation methods mainly rely on manual operation and simple mechanical auxiliary tools. Manual installation is not only inefficient, but also easily affected by factors such as the skill level, experience and fatigue of operators, making it difficult to ensure the consistency of installation accuracy. Even in some automatic installation systems, there are obvious limitations. Early automatic installation systems were mainly based on mechanical positioning and sensor feedback, but the accuracy of these sensors was limited and difficult to meet the installation requirements of high-precision equipment. At the same time, these systems lack the ability to adapt to complex environments and dynamic changes, and cannot adjust the installation strategy in real time to compensate for errors.

[0004] With the development of computer vision technology, some vision-based installation error compensation systems have begun to appear. However, most of the existing vision systems use simple image processing algorithms, and the extraction of equipment features is not accurate enough, and it is easily interfered by environmental factors such as light and noise. In addition, these systems lack intelligence in error calculation and compensation decision-making, often using fixed rules and algorithms, and cannot be flexibly adjusted according to different equipment types and installation scenarios. Moreover, the existing systems rarely consider the impact of uncertain factors and environmental changes during the equipment installation process on the installation accuracy, resulting in poor error compensation effects in actual applications. Therefore, it is of great practical significance to develop a high-precision, intelligent and adaptable precision equipment automatic installation error compensation system based on AI vision. Summary of the Invention

[0005] The precision equipment automatic installation error compensation system based on AI vision proposed by the present invention is to solve the problems mentioned in the above prior art.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A precision equipment automatic installation error compensation system based on AI vision, comprising:

[0007] Vision acquisition module: Configure industrial cameras with multi-spectral imaging and 3D reconstruction functions, and distribute them at the positions in the equipment installation area; the camera frame rate is adaptively adjusted according to the equipment installation speed v, image complexity C, and light change rate r. The formula is f = v × k1 × C × (1 + r × k2), where k1 and k2 are empirical coefficients, k1 = 2, k2 = 0.1. The camera is equipped with an adaptive optical system. At the same time, a microelectromechanical system (MEMS) inertial sensor is integrated to monitor the camera attitude in real time, and the acquired images are corrected in real time using the attitude data;

[0008] Data preprocessing module: First, use the non-local means denoising algorithm combined with deep learning image restoration technology to remove image noise. The deep learning image restoration model is based on the generative adversarial network (GAN) architecture, and the generator and discriminator are trained against each other. After that, use the multi-scale image fusion method based on wavelet transform to fuse multi-spectral images into a single high-information image. The formula is where I fusion is the fused image, I i is each spectral image, and w i is the corresponding weight, which is determined by calculating the structural similarity index (SSIM) of the image; then, use the deep learning-based reconstruction algorithm to reconstruct the low-resolution image into an image;

[0009] Feature extraction module: Build a hybrid feature extraction model based on Transformer and convolutional neural network (CNN). The model uses the attention mechanism to strengthen the regional features by weighting, and uses the multi-scale dilated convolution technology to obtain feature information of different scales without increasing the number of parameters. Introduce meta-learning technology to learn and adapt on different types of equipment installation data;

[0010] Error calculation module: Combine the design and installation standard data of the equipment and the extracted equipment features, and use the error calculation method based on model predictive control (MPC). By establishing a dynamic model of equipment installation, predict the position and attitude of the equipment at future time steps, and calculate the prediction error in combination with the current image data. The formula is where E pred is the prediction error, T is the prediction time step, is the predicted position and attitude, is the true value; at the same time, considering the uncertainty factors in the equipment installation process, use the Kalman filter algorithm to estimate and correct the error;

[0011] Furthermore, it also includes the following modules:

[0012] Compensation Decision-making Module: A compensation strategy is formulated by combining reinforcement learning and fuzzy logic control. Deep Q-Network (DQN) algorithm is used in reinforcement learning, with error data, device status, and environmental information introduced as inputs. By continuously trying different compensation actions, rewards or punishments are obtained based on the compensation effect to optimize the compensation strategy. Fuzzy logic control formulates a fuzzy rule base according to the magnitude, change rate of the error, and physical characteristics of the device. After fuzzifying the error information, compensation decisions are generated based on the rule base. In addition, the Bayesian optimization algorithm is introduced to optimize the parameters of reinforcement learning and fuzzy logic control.

[0013] Execution Control Module: A piezoelectric ceramic motor and a magnetic levitation guide are equipped as the execution mechanisms. The driving of the piezoelectric ceramic motor adopts an adaptive sliding mode control method. According to the real-time status of the device and compensation instructions, the driving voltage and frequency of the motor are adaptively adjusted. The formula is where V is the driving voltage, e is the error, k p 、k i 、k d are control parameters, which are determined by the adaptive adjustment algorithm;

[0014] Data Storage Module: A hybrid storage technology based on the distributed file system DFS is adopted to encrypt and store the data installed on the device, including error data, compensation strategies, and installation results. Image data is stored in the distributed file system, and the distributed hash table DHT technology is used to realize data retrieval and access.

[0015] Environmental Monitoring Module: The module uses temperature and humidity sensors, three-axis acceleration vibration sensors, and spectrometers to monitor the temperature T, humidity H, vibration acceleration a, and light spectrum distribution S(λ) of the device installation environment in real time. When the environmental parameters exceed the preset range, the environmental impact error E is calculated according to the formula, where α, β, γ, δ are weight coefficients, (T env , H set , S set (λ)) are the preset environmental parameter values, (a set , a x , a y , a z ) are the vibration acceleration components; The environmental monitoring module sends the environmental impact error information to the compensation decision-making module, and the compensation decision-making module adjusts the compensation strategy according to the environmental changes.

[0016] Remote Monitoring Module: The module realizes data transmission with the system based on 5G communication technology, establishes a secure remote connection through virtual private network (VPN) technology. The remote monitoring terminal adopts augmented reality and virtual reality technologies. Operators can view the real-time images, error data, and compensation process of the device installation site through a head-mounted display device (HMD).

[0017] Furthermore, the camera in the visual acquisition module uses light field imaging technology to record the intensity and direction information of light, obtain the light field image of the device, and through the light field reconstruction algorithm, realize the three-dimensional modeling of the device. Utilizing the all-focus characteristic of the light field image, image different device components at different depths. The light field reconstruction algorithm is based on Fourier transform and phase retrieval technology, and the formula is where I(x, y, z) is the reconstructed three-dimensional image, and L(u, v, x, y) is the light field image, and are the Fourier transform and the inverse Fourier transform respectively, and H(u, v, z) is the light field propagation model.

[0018] Furthermore, the hybrid model in the feature extraction module adopts knowledge distillation technology to transfer the knowledge of a complex pre-trained model into the model, reducing the computational amount and storage requirements of the model while ensuring the accuracy of feature extraction. By minimizing the KL divergence between the student model and the teacher model to achieve knowledge distillation, where p(i) and q(i) are the output probability distributions of the teacher model and the student model respectively.

[0019] Furthermore, when calculating the error, the error calculation module introduces the topological data analysis (TDA) method. By constructing the topological space of the device installation state, analyze the topological structure changes of device features to identify abnormal situations and error patterns during the device installation process; use the persistent homology algorithm to calculate the persistent homology group of the topological space and extract topological features. The formula is where H n (X) is the n-dimensional persistent homology group, is the boundary operator, and combine the topological features with the traditional error calculation results.

[0020] Furthermore, when formulating the compensation strategy, the compensation decision module considers the maintenance cycle and cost factors of the device, and establishes a device maintenance cost model C maintain = C repair + C downtime , where C repair is the repair cost, and C downtime is the downtime cost; by optimizing the compensation strategy, minimize the maintenance cost of the device on the premise of ensuring the installation accuracy; at the same time, combine the service life prediction model of the device and adjust the compensation strategy according to the wear condition and remaining life of the device.

[0021] Furthermore, the piezoelectric ceramic motor and the magnetic levitation guide rail in the execution control module adopt a cooperative control strategy. By establishing a coupled dynamic model of the motor and the guide rail, analyzing the interaction relationship between the two, and designing a cooperative control algorithm, the cooperative control algorithm is based on a method combining model predictive control (MPC) and adaptive control. According to the real-time state of the device and the compensation instruction, the movement of the motor and the levitation force of the guide rail are adjusted simultaneously. Using sensor fusion technology, the data of the position sensor of the motor, the levitation force sensor of the guide rail, and the attitude sensor of the device are fused to provide feedback information for cooperative control.

[0022] Furthermore, the data storage module adopts the technology of federated learning to perform federated learning on local devices at the installation site to protect user data privacy. Each local device uses local data to train the model and only uploads the gradient or parameter updates of the model to the central server. The central server updates the global model by aggregating the data.

[0023] Furthermore, it also includes:

[0024] Self-learning module: Adopting a method combining online learning and meta-learning, online learning uses the device installation data collected in real time to continuously update the parameters of the model, enabling the system to adapt to changes during the device installation process. Meta-learning adapts to new device installation tasks and environmental changes by analyzing historical learning processes. The self-learning module transfers the knowledge learned in the installation of one type of device to the installation of other types of devices through knowledge transfer ability, improving the versatility and adaptability of the system. By regularly evaluating the performance indicators of the system, the parameters and strategies of self-learning are automatically adjusted to optimize the performance of the system.

[0025] Compared with the existing technologies, the beneficial effects of the present invention are:

[0026] In terms of accuracy, the system adopts advanced technologies such as multi-spectral imaging, three-dimensional reconstruction, and light field imaging, combined with a hybrid feature extraction model based on Transformer and CNN, which can accurately extract the feature information of the device, greatly improving the accuracy of error calculation. At the same time, algorithms such as model predictive control and Kalman filtering are used to accurately estimate and correct the error, ensuring that the device installation meets extremely high accuracy requirements.

[0027] In terms of intelligence level, the system formulates compensation strategies by using reinforcement learning, fuzzy logic control, and Bayesian optimization algorithms. It can automatically adjust the compensation plan according to different device types, installation scenarios, and environmental changes, realizing intelligent error compensation. The self-learning module continuously optimizes the system's performance and improves the system's adaptability and versatility through online learning and meta-learning. In terms of adaptability, the environmental monitoring module monitors parameters such as temperature, humidity, vibration, and light in the installation environment in real time. When the environment changes, the system can timely adjust the compensation strategy to reduce the impact of environmental factors on the installation accuracy. At the same time, the system adopts federated learning and blockchain technologies to protect user data privacy and can perform data sharing and collaborative learning at different installation sites to improve the overall performance of the system.

[0028] In terms of efficiency, high-speed 5G communication and intelligent collaborative control strategies enable the system to quickly respond to and execute compensation instructions, greatly shortening the device installation time. In addition, the system also considers the device's maintenance cycle and cost factors, and by optimizing the compensation strategy, reduces the device's maintenance cost and extends the device's service life. In short, the patent system provides an efficient, high-precision, and intelligent solution for the automatic installation of precision equipment. Brief Description of the Drawings

[0029] Figure 1 It is a schematic block diagram of the automatic installation error compensation system for precision equipment based on AI vision proposed by the present invention;

[0030] Figure 2 It is a schematic block diagram for comparing the improvement of the processing accuracy of each module of the automatic installation error compensation system for precision equipment based on AI vision proposed by the present invention;

[0031] Figure 3 It is a schematic line diagram of the installation error compensation effect of the automatic installation error compensation system for precision equipment based on AI vision proposed by the present invention under different device types. Detailed Embodiments

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0034] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined. In addition, the terms "mounted", "connected", "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the drawings.

[0035] Refer to Figures 1 - 3 : A specific implementation manner of a precision equipment automatic installation error compensation system based on AI vision:

[0036] I. Overall system architecture The precision equipment automatic installation error compensation system based on AI vision mainly consists of a vision acquisition module, a data preprocessing module, a feature extraction module, an error calculation module, a compensation decision module, an execution control module, and a data storage module. In addition, it also includes an environment monitoring module, a remote monitoring module, and a self-learning module. Each module works together to achieve the error compensation of the precision equipment automatic installation.

[0037] II. Specific implementation of each module:

[0038] Visual acquisition module: At key positions in the installation area of precision equipment, such as directly above and on the sides of the equipment, multiple high-resolution industrial cameras with multi-spectral imaging and 3D reconstruction functions are reasonably arranged. The frame rate of these cameras is adaptively adjusted according to the formula f = v × k1 × C × (1 + r × k2). Among them, the equipment installation speed v can be obtained through the speed sensor on the installation equipment; the image complexity C is obtained by calculating the image entropy, and the greater the image entropy, the higher the complexity; the light change rate r is monitored in real time by the light sensor. For example, when the equipment installation speed is fast, the image complexity is high, and the light changes greatly, the camera frame rate will increase accordingly. The adaptive optical system equipped with the camera can automatically adjust parameters such as the focal length and aperture of the lens according to the ambient light intensity and the surface reflection characteristics of the equipment to ensure clear images are captured. The microelectromechanical system (MEMS) inertial sensor monitors the attitude of the camera in real time. Once camera jitter is detected, the acquired images are immediately corrected using the attitude data to reduce image deviation.

[0039] Data preprocessing module: First, the non-local means denoising algorithm combined with deep learning image restoration technology is used to remove image noise. The deep learning image restoration model is based on the generative adversarial network (GAN) architecture. The generator attempts to generate the denoised image, and the discriminator judges whether the image is a real denoised image or a generated image. Through the adversarial training of the two, the denoising effect is improved and image details are retained. Then, the multi-scale image fusion method based on wavelet transform is used to fuse the multi-spectral images into a single high-information image. The fusion formula is where the weight w i is determined by calculating the structural similarity index (SSIM) of the image. The larger the SSIM value, the more similar the structure of the spectral image is to other images, and the greater the weight. Finally, the super-resolution reconstruction algorithm based on deep learning is used to reconstruct the low-resolution image into a high-resolution image to improve the clarity and details of the image.

[0040] Feature extraction module: A hybrid feature extraction model based on Transformer and convolutional neural network (CNN) is constructed. Transformer is responsible for capturing the global features and long-range dependencies of the image, while CNN focuses on extracting local detail features. The model uses an attention mechanism to weight and strengthen the features of key regions in the image, such as key installation parts and reference points of the equipment. The multi-scale dilated convolution technology is used to expand the receptive field without increasing the number of parameters to obtain feature information at different scales. At the same time, meta-learning technology is introduced to perform rapid learning and adaptation on different types of precision equipment installation data to improve the generalization ability of the model.

[0041] Error calculation module: Combining the design and installation standard data of the device and the extracted device features, an error calculation method based on model predictive control (MPC) is adopted. By establishing a dynamic model of device installation, the position and attitude of the device at multiple future time steps are predicted. The prediction error calculation formula is where T is the prediction time step length, is the predicted position and attitude, is the true value. At the same time, considering the uncertain factors in the device installation process, the Kalman filter algorithm is used to estimate and correct the error to improve the accuracy of error calculation.

[0042] Compensation decision module: A compensation strategy is formulated by combining reinforcement learning and fuzzy logic control. Reinforcement learning adopts the deep Q-network (DQN) algorithm, which takes error data, device status, and environmental information as inputs. By continuously trying different compensation actions, rewards or punishments are obtained according to the compensation effect to optimize the compensation strategy. Fuzzy logic control formulates a fuzzy rule base according to the magnitude, change rate of the error, and physical characteristics of the device. After fuzzifying the error information, compensation decisions are generated based on the rule base. The Bayesian optimization algorithm is introduced to optimize the parameters of reinforcement learning and fuzzy logic control to improve the accuracy and efficiency of compensation decisions.

[0043] Execution control module: High-precision piezoelectric ceramic motors and magnetic levitation guides are equipped as actuators. The drive of the piezoelectric ceramic motor adopts a method based on adaptive sliding mode control, and the drive voltage is adjusted according to the formula where e is the error, k p 、k i 、k d are control parameters determined by the adaptive adjustment algorithm. The magnetic levitation guide uses electromagnetic force to levitate the device, reducing friction and improving the accuracy and stability of device movement.

[0044] Execution control module: Adopts a double-closed-loop control structure, with the inner loop being the position loop and the outer loop being the speed loop to ensure that the device is accurately adjusted according to the compensation instructions. The data storage module adopts a hybrid storage technology based on blockchain and distributed file system (DFS). Key data of device installation, such as error data, compensation strategies, and installation results, are encrypted and stored through blockchain technology. The consensus mechanism of the blockchain adopts the practical Byzantine fault tolerance algorithm (PBFT) to improve the efficiency and reliability of data storage. A large amount of image data is stored in the distributed file system, and the distributed hash table (DHT) technology is used to achieve fast retrieval and access to data. At the same time, a deep learning-based image compression algorithm is used to compress the data to reduce the data storage space.

[0045] Environmental Monitoring Module: Through high-precision temperature and humidity sensors, three-axis acceleration vibration sensors, and spectrometers, it monitors the temperature T, humidity H, vibration acceleration a, and light spectral distribution S(λ) of the equipment installation environment in real time. When the environmental parameters exceed the preset range, according to the formula

[0046]

[0047] calculate the environmental impact error E env , where α, β, γ, δ are weight coefficients, (T set , H set , S set (λ)) are the preset environmental parameter values, and (a x , a y , a z ) are the vibration acceleration components.

[0048] Environmental Monitoring Module: Sends the environmental impact error information to the compensation decision-making module. The compensation decision-making module adjusts the compensation strategy according to environmental changes, such as compensating for errors caused by environmental factors by adjusting the installation order of the equipment or adding additional shock-absorbing measures. The remote monitoring module realizes high-speed data transmission with the system based on 5G communication technology to ensure the real-time nature of remote monitoring. A secure remote connection is established through Virtual Private Network (VPN) technology to ensure the security of data transmission. The remote monitoring terminal adopts augmented reality (AR) and virtual reality (VR) technologies, and operators can immerse themselves in viewing the real-time images, error data, and compensation process of the equipment installation site through a head-mounted display device (HMD).

[0049] Remote Monitoring Module: Has an intelligent early warning function. When the error exceeds the preset threshold or the system fails, it notifies the operator in a timely manner via text messages, emails, etc. At the same time, it supports hierarchical management of remote operation permissions, and operators with different permissions have different operation scopes and control levels. The self-learning module adopts a method combining online learning and meta-learning. Online learning uses the equipment installation data collected in real time to continuously update the parameters of the model, enabling the system to adapt to changes during the equipment installation process. Meta-learning learns how to adapt to new equipment installation tasks and environmental changes more quickly by analyzing historical learning processes.

[0050] Self-learning Module: Has the ability of knowledge transfer, and can transfer the knowledge learned in the installation of one type of equipment to the installation of other types of equipment, improving the versatility and adaptability of the system. By regularly evaluating the performance indicators of the system, such as installation accuracy, compensation efficiency, etc., it automatically adjusts the parameters and strategies of self-learning to continuously optimize the performance of the system.

[0051] III. Data Representation of Beneficial Effects:

[0052] To verify the beneficial effects of this system, 100 precision equipment installation tasks were selected for a comparative experiment. Among them, this system was used in 50 tasks, and the traditional installation method was used in the other 50 tasks. The results are as follows:

[0053] Comparison items Traditional installation method The patent system Installation accuracy (average error, unit: mm) 0.15 0.02 Installation time (average per time, unit: minutes) 60 20 Environmental adaptability (installation success rate when the environment changes) 70％ 95％ Maintenance cost (subsequent maintenance cost per installation on average, unit: yuan) 500 100

[0054] As can be seen from the above data, this patented system has significant advantages in terms of installation accuracy, installation time, environmental adaptability, and maintenance cost, fully demonstrating the beneficial effects of this patent.

[0055] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered by the protection scope of the present invention.

Claims

1. An AI vision-based precision equipment automatic installation error compensation system, characterized in that, Including: Visual acquisition module: Configure industrial cameras with multi-spectral imaging and 3D reconstruction functions, distributed at positions in the equipment installation area; the camera frame rate is adaptively adjusted according to the equipment installation speed v, image complexity C, and light change rate r. The formula is f = v × k1 × C × (1 + r × k2), where k1 and k2 are empirical coefficients, k1 = 2, k2 = 0.

1. The camera is equipped with an adaptive optical system. At the same time, a microelectromechanical system MEMS inertial sensor is integrated to monitor the camera attitude in real time, and the acquired images are corrected in real time using the attitude data; Data preprocessing module: First, the non-local mean denoising algorithm is combined with the deep learning image inpainting technology to remove image noise. The deep learning image inpainting model is based on the generative adversarial network (GAN) architecture, where the generator and discriminator are trained against each other. Then, the multi-scale image fusion method based on wavelet transform is used to fuse multi-spectral images into a single high-information image. The formula is where I fusion is the fused image, I i is each spectral image, and w i is the corresponding weight, which is determined by calculating the structural similarity index (SSIM) of the image. Then, a deep learning-based reconstruction algorithm is used to reconstruct the low-resolution image into an image; Feature extraction module: Construct a hybrid feature extraction model based on Transformer and convolutional neural network CNN. The model adopts an attention mechanism to weight and strengthen regional features. Use multi-scale dilated convolution technology to obtain feature information of different scales without increasing the number of parameters. Introduce meta-learning technology to learn and adapt on different types of equipment installation data; Error calculation module: Combining the design and installation standard data of the device and the extracted device features, using an error calculation method based on model predictive control (MPC), by establishing a dynamic model of device installation, predicting the position and attitude of the device at future time steps, and calculating the prediction error in combination with the current image data. The formula is where E pred is the prediction error, T is the prediction time step, is the predicted position and attitude, is the true value; meanwhile, considering the uncertainty factors during the equipment installation process, the Kalman filtering algorithm is used to estimate and correct the error.

2. The precision equipment automatic installation error compensation system based on AI vision according to claim 1, wherein Also included: Compensation decision module: Use a method combining reinforcement learning and fuzzy logic control to formulate a compensation strategy. Reinforcement learning adopts the deep Q-network DQN algorithm, introducing error data, equipment status, and environmental information as inputs. By continuously trying different compensation actions, rewards or punishments are obtained according to the compensation effect to optimize the compensation strategy. Fuzzy logic control formulates a fuzzy rule base according to the magnitude, change rate of the error, and physical characteristics of the equipment. After fuzzifying the error information, a compensation decision is generated based on the rule base; In addition, the Bayesian optimization algorithm is introduced to optimize the parameters of reinforcement learning and fuzzy logic control; Execution control module: Equipped with a piezoelectric ceramic motor and a magnetic levitation guide rail as the actuator, the piezoelectric ceramic motor is driven by a method based on adaptive sliding mode control. According to the real-time state of the device and the compensation instruction, the driving voltage and frequency of the motor are adaptively adjusted. The formula is where V is the driving voltage, e is the error, and k p , k i , k d are control parameters determined by an adaptive adjustment algorithm; Data storage module: Adopt a hybrid storage technology based on the distributed file system DFS to encrypt and store the data of equipment installation, including error data, compensation strategies, and installation results. Image data is stored in the distributed file system, and the distributed hash table DHT technology is used to realize data retrieval and access. Environmental monitoring module: The module uses a temperature and humidity sensor, a three-axis acceleration vibration sensor, and a spectral analyzer to monitor the temperature T, humidity H, vibration acceleration a, and light spectral distribution S(λ) of the equipment installation environment in real time. When the environmental parameters exceed the preset range, the environmental impact error E is calculated according to the formula where α, β, γ, and δ are weight coefficients, (T env , H set , S set (λ)) are the preset environmental parameter values, and (a set , a x , a y , a z ) are the vibration acceleration components; the environmental monitoring module sends the environmental impact error information to the compensation decision-making module, and the compensation decision-making module adjusts the compensation strategy according to the environmental changes.

3. The precision equipment automatic installation error compensation system based on AI vision according to claim 1, characterized in that, Also included: Remote monitoring module: The module realizes data transmission with the system based on 5G communication technology, establishes a secure remote connection through virtual private network VPN technology. The remote monitoring terminal adopts augmented reality and virtual reality technologies. Operators can view the real-time images, error data, and compensation process of the equipment installation site through a head-mounted display device HMD.

4. The precision equipment automatic installation error compensation system based on AI vision according to claim 1, characterized in that The camera in the visual acquisition module uses light field imaging technology to record the intensity and direction information of light, obtain the light field image of the device, and through the light field reconstruction algorithm, realize the three-dimensional modeling of the device. Utilizing the all-focusing characteristic of the light field image, image different device components at different depths. The light field reconstruction algorithm is based on Fourier transform and phase retrieval technology, and the formula is where I(x, y, z) is the reconstructed three-dimensional image, and L(u, v, x, y) is the light field image, and are the Fourier transform and the inverse Fourier transform respectively, and H(u, v, z) is the light field propagation model.

5. The precision equipment automatic installation error compensation system based on AI vision according to claim 1, characterized in that The hybrid model in the feature extraction module uses knowledge distillation technology to transfer the knowledge of a complex pre-trained model into the model, reducing the computational amount and storage requirements of the model while ensuring the feature extraction accuracy, by minimizing the KL divergence between the student model and the teacher model. Knowledge distillation is achieved, where p(i) and q(i) are the output probability distributions of the teacher model and the student model, respectively.

6. The precision equipment automatic installation error compensation system based on AI vision according to claim 1, wherein When calculating the error, the error calculation module introduces the topological data analysis (TDA) method. By constructing the topological space of the device installation state, it analyzes the topological structure changes of device features to identify abnormal situations and error patterns during the device installation process. The persistent homology algorithm is used to calculate the persistent homology groups of the topological space and extract topological features. The formula is where H n (X) is the n-dimensional persistent homology group, is the boundary operator, which combines the topological features with the traditional error calculation results.

7. The precision equipment automatic installation error compensation system based on AI vision according to claim 2, characterized in that When formulating the compensation strategy, the compensation decision-making module considers the maintenance cycle and cost factors of the equipment, and establishes an equipment maintenance cost model C maintain = C repair + C downtime , where C repair is the repair cost, and C downtime is the downtime cost; by optimizing the compensation strategy, the equipment maintenance cost is minimized on the premise of ensuring the installation accuracy; at the same time, combined with the service life prediction model of the equipment, the compensation strategy is adjusted according to the wear condition and remaining life of the equipment.

8. The AI vision-based precision equipment automatic installation error compensation system according to claim 2, wherein, The piezoelectric ceramic motor and the magnetic levitation guide rail in the execution control module adopt a cooperative control strategy. By establishing a coupling dynamics model of the motor and the guide rail, analyzing the interaction relationship between the two, designing a cooperative control algorithm. The cooperative control algorithm is based on a method combining model predictive control MPC and adaptive control. According to the real-time state of the equipment and the compensation instruction, the movement of the motor and the levitation force of the guide rail are adjusted simultaneously. Using sensor fusion technology, the data of the position sensor of the motor, the levitation force sensor of the guide rail, and the attitude sensor of the equipment are fused to provide feedback information for cooperative control.

9. The precision equipment automatic installation error compensation system based on AI vision according to claim 2, characterized in that, The data storage module adopts the technology of federated learning to perform federated learning on local devices at the installation site, protecting the privacy of user data; each local device uses local data to train the model and only uploads the gradient or parameter update of the model to the central server, and the central server updates the global model by aggregating the data.

10. The precision equipment automatic installation error compensation system based on AI vision according to claim 1, characterized in that, It also includes: Self-learning module: Adopting a method combining online learning and meta-learning, online learning uses the device installation data collected in real time to continuously update the parameters of the model, enabling the system to adapt to changes during the device installation process; Meta-learning adapts to new device installation tasks and environmental changes by analyzing the historical learning process. The self-learning module transfers the knowledge learned in the installation of one type of device to the installation of other types of devices through its knowledge transfer ability, improving the versatility and adaptability of the system. By regularly evaluating the performance metrics of the system, it automatically adjusts the parameters and strategies of self-learning to optimize the performance of the system.

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