Precise three-dimensional photogrammetry and automatic positioning attitude control system

Abstract

The invention belongs to the field of three-dimensional photogrammetry, and provides a precise three-dimensional photogrammetry and automatic positioning attitude control system, which comprises a precise three-dimensional photographing module used for photographing a multi-view image of a target object; the image preprocessing module is used for denoising and enhancing the multi-view image shot by the precise three-dimensional shooting module; the feature point extraction module is used for automatically detecting and extracting feature points in the preprocessed image; the three-dimensional reconstruction module is used for performing three-dimensional reconstruction by utilizing a stereoscopic vision algorithm according to the feature points; the precise three-dimensional photogrammetry and automatic positioning of a target object are realized by integrating a plurality of modules such as a precise three-dimensional photogrammetry module, an image preprocessing module, a feature point extraction module, a three-dimensional reconstruction module and an attitude estimation module, and the steps such as graying processing and histogram equalization are adopted in the image preprocessing stage. And the image quality is improved.

Description

Technical Field

[0001] The invention belongs to the field of three-dimensional photogrammetry, in particular to a precise three-dimensional photogrammetry and automatic positioning attitude control system. Background Art

[0002] In the current field of photogrammetry and attitude control, achieving precise three-dimensional photogrammetry and automatic positioning of target objects is an important but challenging task. Existing three-dimensional photogrammetry systems usually have some problems, especially when dealing with complex scenes or irregularly shaped target objects. These problems include but are not limited to unstable image quality, inaccurate feature point extraction, errors in three-dimensional reconstruction results, inaccurate attitude estimation, and limited positioning accuracy.

[0003] Specifically, existing systems usually find it difficult to maintain the stability of image quality when taking multi-view images of the target object, which leads to difficulties in subsequent image processing steps. In addition, in the feature point extraction stage, existing algorithms may not be able to effectively identify and extract key feature points in the image, thereby affecting the accuracy of 3D reconstruction. During the 3D reconstruction process, due to the influence of various factors (such as camera parameters, lighting conditions, etc.), the reconstruction results often have certain errors.

[0004] To this end, those skilled in the art have proposed a precise three-dimensional photogrammetry and automatic positioning attitude control system to solve the problems raised by the background technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a precise three-dimensional photogrammetry and automatic positioning attitude control system to solve the problems existing in the prior art.

[0006] A precision three-dimensional photogrammetry and automatic positioning attitude control system, including a precision three-dimensional photography module for capturing multi-viewing images of a target object;

[0007] An image preprocessing module, used for denoising and enhancing the multi-view images taken by the precision 3D photography module;

[0008] A feature point extraction module, used for automatically detecting and extracting feature points in the preprocessed image;

[0009] The three-dimensional reconstruction module uses a stereoscopic vision algorithm to perform three-dimensional reconstruction based on the feature points. The formula of the stereoscopic vision algorithm is: Where Z is the depth of the target object, f is the focal length of the camera, B is the baseline distance, and d is the parallax;

[0010] The posture estimation module estimates the posture of the target object based on the three-dimensional reconstruction result using the extended Kalman filter algorithm. The state update formula of the extended Kalman filter algorithm is: k =F k-1 X k-1 +B k-1 u k-1 +w k-1 , where X k is the current state estimate, F k-1 is the state transfer matrix, B k-1 is the control input matrix, u k-1 is the control input, w k-1 is the process noise.

[0011] Preferably, the image preprocessing module also includes grayscale processing and histogram equalization steps, wherein the grayscale processing formula is G(x,y)=0.299R(x,y)+0.587G(x,y)+0.114B(x,y), wherein G(x,y) represents the grayscale value of the pixel located at the coordinate (x,y), R(x,y), G(x,y), and B(x,y) represent the component values ​​of the pixel in the three color channels of R, G, and B, respectively, and the weight coefficient in the formula is determined based on the empirical value of the human eye's sensitivity to different colors.

[0012] Preferably, the feature point extraction module uses a scale-invariant feature transformation algorithm to detect and describe feature points. The key steps of the algorithm include constructing a scale space, detecting extreme points, calculating key point directions, and generating feature descriptors, wherein the feature descriptors are obtained by calculating a gradient direction histogram within a key point neighborhood.

[0013] Preferably, the three-dimensional reconstruction module further comprises an error correction module, which is used to perform error correction on the reconstruction result according to the three-dimensional coordinates of the known reference points. The error correction uses the least squares method to optimize the objective function, which is specifically: Where P i est is the coordinate of the reconstructed reference point, P i true are the known reference point coordinates, and n is the number of reference points.

[0014] Preferably, the attitude estimation module further includes an accelerometer and a gyroscope sensor for assisting in estimating the initial attitude and angular velocity of the target object. The accelerometer and gyroscope data fusion adopts a complementary filtering algorithm, and its formula is θ fused =aθ gyro +(1-a)θ accel , where θ fused is the fused attitude angle, θ gyro is the attitude angle measured by the gyroscope, θaccel is the attitude angle calculated by the accelerometer, and a is the weight coefficient.

[0015] Preferably, the system also includes a feedback control module for adjusting the control strategy to optimize the positioning accuracy according to the difference between the actual positioning result of the automatic positioning module and the expected positioning target. The feedback control module adopts a proportional-integral-differential control algorithm and introduces an anti-integral saturation algorithm to avoid excessive accumulation of integral terms. The anti-integral saturation algorithm is implemented by limiting the growth rate of the integral term, that is, when the integral term exceeds a preset threshold, it is limited to a threshold range.

[0016] Preferably, the control output formula of the control algorithm is:

[0017] Where u(t) is the control output, e(t) is the error, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, e max is the maximum value limit of the integral term, and sgn(e(τ)) is the sign function used to determine the sign of the error.

[0018] Preferably, the system further comprises an automatic positioning module, which realizes precise positioning through a closed-loop servo system, wherein the closed-loop servo system comprises a position sensor and a servo driver, and the servo driver realizes precise control of the motor by using vector control technology, and its control equation is v ref =T -1 (θ est ) cmd , where v ref is the motor reference speed vector, T -1 is the inverse transformation of the motor model, θ est is the motor state estimation, u cmd For control instructions.

[0019] Preferably, the system also includes a data storage module for storing the multi-view images, three-dimensional reconstruction results and posture estimation data. The data storage module adopts a compression algorithm to reduce storage space occupancy. The compression algorithm adopts the JPEG2000 standard, and its discrete wavelet transform process can be expressed as Y=ΨX, where X represents the original image data, Ψ represents the wavelet transform matrix, and Y represents the transformed coefficient. The compression process includes discrete wavelet transform, quantization, entropy coding and other steps.

[0020] Preferably, the system realizes data communication and collaborative work among modules through an integrated software platform, and the integrated software platform is deployed on a server, adopts a distributed architecture, and realizes asynchronous communication among modules through a message queue middleware.

[0021] Through the above technical solution,

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The system realizes precise 3D photogrammetry and automatic positioning of the target object by integrating multiple modules such as precise 3D photography module, image preprocessing module, feature point extraction module, 3D reconstruction module and attitude estimation module. In the image preprocessing stage, grayscale processing and histogram equalization are used to improve the image quality. In the feature point extraction stage, the scale-invariant feature transform (SIFT) algorithm is used to accurately extract the key feature points in the image. In the 3D reconstruction stage, the stereo vision algorithm is used to perform 3D reconstruction, and the error correction module is used to correct the errors of the reconstruction results. In the attitude estimation stage, the extended Kalman filter algorithm is used to estimate the attitude of the target object, and the accelerometer and gyroscope sensors are used to assist in estimating the initial attitude and angular velocity. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a module block diagram of the image processing module in the present invention;

[0025] Figure 2 is a system module block diagram of the present invention; DETAILED DESCRIPTION

[0026] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0027] Embodiment 1: As shown in the attached Figure 1 As shown: The present invention provides a precise three-dimensional photogrammetry and automatic positioning posture control system, including a precise three-dimensional photography module for capturing multi-viewing images of a target object;

[0028] An image preprocessing module, used for denoising and enhancing the multi-view images taken by the precision 3D photography module;

[0029] A feature point extraction module is used to automatically detect and extract feature points in the preprocessed image;

[0030] The 3D reconstruction module uses the stereo vision algorithm to perform 3D reconstruction based on the feature points. The formula of the stereo vision algorithm is: Where Z is the depth of the target object, f is the focal length of the camera, B is the baseline distance, and d is the parallax;

[0031] The attitude estimation module uses the extended Kalman filter algorithm to estimate the attitude of the target object based on the 3D reconstruction results. The state update formula of the extended Kalman filter algorithm is: k =F k-1X k-1 +B k-1 u k-1 +w k-1 , where X k is the current state estimate, F k-1 is the state transfer matrix, B k-1 is the control input matrix, u k-1 is the control input, w k-1 is the process noise.

[0032] Preferably, the image preprocessing module also includes grayscale processing and histogram equalization steps, wherein the grayscale processing formula is G(x,y)=0.299R(x,y)+0.587G(x,y)+0.114B(x,y), wherein G(x,y) represents the grayscale value of the pixel at the coordinate (x,y), R(x,y), G(x,y), and B(x,y) represent the component values ​​of the pixel in the three color channels of R, G, and B, respectively, and the weight coefficient in the formula is determined based on the empirical value of the sensitivity of the human eye to different colors. Adding grayscale processing and histogram equalization steps to the image preprocessing module can further improve the quality of the image and provide clearer and more accurate image data for subsequent feature point extraction and three-dimensional reconstruction. In the present patent solution, grayscale processing and histogram equalization are used to effectively remove noise and interference in the image, enhance the contrast and clarity of the image, make feature point extraction more accurate, and make three-dimensional reconstruction results more reliable.

[0033] Furthermore, the feature point extraction module adopts the scale-invariant feature transform (SIFT) algorithm to detect and describe feature points. The key steps of the SIFT algorithm include constructing a scale space, detecting extreme points, calculating the direction of key points, and generating feature descriptors. The feature descriptors are obtained by calculating the gradient direction histogram in the neighborhood of the key points. They can accurately extract the key feature points in the image and are highly robust to changes in image scale, rotation, and illumination. Using the SIFT algorithm for feature point extraction can ensure that stable and accurate feature point data can be obtained under different shooting conditions, providing a reliable foundation for subsequent 3D reconstruction.

[0034] As can be seen from the above, the system uses a precision 3D photography module to capture multi-view images of the target object, and uses an image preprocessing module to perform denoising, enhancement, and grayscale processing to improve image quality; then, the feature point extraction module uses the SIFT algorithm to accurately extract key feature points in the image. Compared with the prior art, the present invention has achieved significant improvements in image preprocessing and feature point extraction, providing a more accurate and reliable data basis for subsequent 3D reconstruction and posture estimation, thereby improving the measurement accuracy and positioning accuracy of the entire system.

[0035] Embodiment 2: As shown in the attached Figure 1 To Attachment Figure 2 As shown: This embodiment is basically the same as the previous embodiment, except that the 3D reconstruction module further includes an error correction module for performing error correction on the reconstruction result according to the 3D coordinates of the known reference points. The error correction uses the least squares method to optimize the objective function, specifically: Where P i est is the coordinate of the reconstructed reference point, P i true are the known reference point coordinates, n is the number of reference points, and an error correction module is added to the 3D reconstruction module, which can further reduce the error of the 3D reconstruction result and improve the reconstruction accuracy. The error correction module is used to correct the reconstruction result, which can eliminate the errors caused by factors such as camera parameters and lighting conditions, making the reconstruction result closer to the actual situation.

[0036] Furthermore, the attitude estimation module also includes an accelerometer and a gyroscope sensor to assist in estimating the initial attitude and angular velocity of the target object. The accelerometer and gyroscope data fusion adopts a complementary filtering algorithm, and its formula is θ fused =aθ gyro +(1-a)θ accel , where θ fused is the fused attitude angle, θ gyro is the attitude angle measured by the gyroscope, θ accel is the attitude angle calculated by the accelerometer, a is the weight coefficient, and accelerometer and gyroscope sensors are added to the attitude estimation module to assist in estimating the initial attitude and angular velocity of the target object, which can further improve the accuracy and real-time performance of attitude estimation. The data provided by the accelerometer and gyroscope sensors are used for attitude estimation, which can be combined with the three-dimensional reconstruction results to obtain more accurate attitude information of the target object, while improving the response speed and real-time performance of the system.

[0037] Preferably, the system also includes a feedback control module, which is used to adjust the control strategy to optimize the positioning accuracy according to the difference between the actual positioning result of the automatic positioning module and the expected positioning target. The feedback control module adopts a proportional-integral-differential control algorithm and introduces an anti-integral saturation algorithm to avoid excessive accumulation of integral terms. The anti-integral saturation algorithm is implemented by limiting the growth rate of the integral term, that is, when the integral term exceeds a preset threshold, it is limited within the threshold range. The feedback control module is used to adjust the control strategy to optimize the positioning accuracy according to the difference between the actual positioning result of the automatic positioning module and the expected positioning target. It can realize real-time feedback and adjustment of the positioning results, further improve the positioning accuracy, and use the feedback control module to monitor and adjust the positioning results, so that the positioning error can be discovered and corrected in time to ensure the accuracy and stability of the positioning results.

[0038] From the above, it can be seen that the error correction module is integrated into the three-dimensional reconstruction module, and the least squares method is used to optimize the reconstruction results, thereby improving the accuracy of three-dimensional reconstruction; at the same time, the attitude estimation module not only uses the extended Kalman filter algorithm for attitude estimation, but also introduces accelerometers and gyroscope sensors for auxiliary estimation, and fuses data through the complementary filtering algorithm, further improving the accuracy and stability of attitude estimation; in addition, the addition of the feedback control module can adjust the control strategy according to the difference between the actual positioning result and the expected target, thereby optimizing the positioning accuracy.

[0039] Furthermore, the control output formula of the control algorithm is

[0040] Where u(t) is the control output, e(t) is the error, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, e max is the maximum value limit of the integral term, sgn(e(τ)) is the sign function, which is used to determine the sign of the error. The advantage of this formula is that it can comprehensively consider multiple factors such as error, proportion, integration and differentiation to achieve accurate calculation and adjustment of the control output. The use of this control algorithm can achieve accurate control of the control process and ensure the accuracy and stability of the positioning results. At the same time, the algorithm also introduces an anti-integral saturation algorithm to avoid system instability caused by excessive accumulation of integral terms.

[0041] Embodiment 3: As shown in the attached Figure 1 To Attachment Figure 2 As shown: Based on the first embodiment, the system also includes an automatic positioning module, which realizes precise positioning through a closed-loop servo system. The closed-loop servo system includes a position sensor and a servo driver. The servo driver uses vector control technology to realize precise control of the motor. Its control equation is v ref =T -1 (θ est ) cmd , where v ref is the motor reference speed vector, T -1 is the inverse transformation of the motor model, θ est is the motor state estimation, u cmd To control the instructions, the module achieves precise positioning through a closed-loop servo system, which can further improve the positioning accuracy and stability. The closed-loop servo system can achieve precise control of the motor control and ensure the accuracy and stability of the positioning results. At the same time, the system also uses vector control technology to achieve efficient and precise control of the motor.

[0042] Preferably, the system also includes a data storage module for storing multi-view images, three-dimensional reconstruction results and posture estimation data. The data storage module uses a compression algorithm to reduce storage space occupancy. The compression algorithm uses the JPEG2000 standard. Its discrete wavelet transform process can be expressed as Y=ΨX, where X represents the original image data, Ψ represents the wavelet transform matrix, and Y represents the transformed coefficient. Its compression process includes discrete wavelet transform, quantization, entropy coding and other steps. This module is used to store multi-view images, three-dimensional reconstruction results and posture estimation data. The advantage of this module is that it can achieve long-term storage and efficient management of data. The use of the data storage module can ensure the integrity and security of the data, and facilitate subsequent data analysis and processing. The module also uses the JPEG2000 standard for compressed storage, which reduces the storage space occupancy.

[0043] Furthermore, the system realizes data communication and collaborative work between modules through an integrated software platform. The integrated software platform is deployed on the server, adopts a distributed architecture, and realizes asynchronous communication between modules through message queue middleware. The integrated software platform is used to realize data communication and collaborative work between modules, which can further improve the overall performance and reliability of the system. The use of an integrated software platform can realize unified management and scheduling of each module, ensuring smooth data communication and collaborative work between modules. At the same time, the platform also adopts technical means such as distributed architecture and message queue middleware to improve the scalability and real-time performance of the system.

[0044] From the above, it can be seen that the present invention not only optimizes data storage and reduces space occupancy through the JPEG2000 standard compression algorithm, but also constructs an integrated software platform to achieve efficient collaboration and data communication between modules, ensuring the overall performance and reliability of the system, and ultimately forming a comprehensive solution that can handle both static and dynamic scenes and has high-precision measurement and positioning capabilities.

[0045] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

[0046] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0047] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0048] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0049] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0050] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0051] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A precise three-dimensional photogrammetry and automatic positioning attitude control system, characterized in that: include: Precision 3D photography module, used to capture multi-view images of target objects; An image preprocessing module, used for denoising and enhancing the multi-view images taken by the precision 3D photography module; A feature point extraction module, used for automatically detecting and extracting feature points in the preprocessed image; The three-dimensional reconstruction module uses a stereoscopic vision algorithm to perform three-dimensional reconstruction based on the feature points. The formula of the stereoscopic vision algorithm is: Where Z is the depth of the target object, f is the focal length of the camera, B is the baseline distance, and d is the parallax; The posture estimation module estimates the posture of the target object based on the three-dimensional reconstruction result using the extended Kalman filter algorithm. The state update formula of the extended Kalman filter algorithm is: k =F k-1 X k-1 +B k-1 u k-1 +w k-1 , where X k is the current state estimate, F k-1 is the state transfer matrix, B k-1 is the control input matrix, u k-1 is the control input, w k-1 is the process noise.

2. A precise three-dimensional photogrammetry and automatic positioning attitude control system as claimed in claim 1, characterized in that: The image preprocessing module also includes grayscale processing and histogram equalization steps, wherein the grayscale processing formula is G(x,y)=0.299R(x,y)+0.587G(x,y)+0.114B(x,y), wherein G(x,y) represents the grayscale value of the pixel located at the coordinate (x,y), R(x,y), G(x,y), and B(x,y) represent the component values ​​of the pixel in the three color channels of R, G, and B, respectively, and the weight coefficient in the formula is determined based on the empirical value of the human eye's sensitivity to different colors.

3. A precise three-dimensional photogrammetry and automatic positioning attitude control system as claimed in claim 2, characterized in that: The feature point extraction module uses a scale-invariant feature transformation algorithm to detect and describe feature points. The key steps of the algorithm include constructing a scale space, detecting extreme points, calculating key point directions, and generating feature descriptors, wherein the feature descriptors are obtained by calculating the gradient direction histogram within the key point neighborhood.

4. A precise three-dimensional photogrammetry and automatic positioning attitude control system as claimed in claim 3, characterized in that: The 3D reconstruction module also includes an error correction module for performing error correction on the reconstruction result according to the 3D coordinates of the known reference points. The error correction uses the least squares method to optimize the objective function, specifically: Where P i est is the coordinate of the reconstructed reference point, P i true are the known reference point coordinates, and n is the number of reference points.

5. A precise three-dimensional photogrammetry and automatic positioning attitude control system as claimed in claim 4, characterized in that: The attitude estimation module also includes an accelerometer and a gyroscope sensor, which are used to assist in estimating the initial attitude and angular velocity of the target object. The accelerometer and gyroscope data fusion adopts a complementary filtering algorithm, and its formula is θ fused =aθ gyro +(1-a)θ accel , where θ fused is the fused attitude angle, θ gyro is the attitude angle measured by the gyroscope, θ accel is the attitude angle calculated by the accelerometer, and a is the weight coefficient.

6. A precise three-dimensional photogrammetry and automatic positioning attitude control system as claimed in claim 1, characterized in that: The system also includes a feedback control module, which is used to adjust the control strategy to optimize the positioning accuracy according to the difference between the actual positioning result of the automatic positioning module and the expected positioning target. The feedback control module adopts a proportional-integral-differential control algorithm and introduces an anti-integral saturation algorithm to avoid excessive accumulation of integral terms. The anti-integral saturation algorithm is implemented by limiting the growth rate of the integral term, that is, when the integral term exceeds a preset threshold, it is limited to a threshold range.

7. A precise three-dimensional photogrammetry and automatic positioning attitude control system as claimed in claim 6, characterized in that: The control output formula of the control algorithm is: Where u(t) is the control output, e(t) is the error, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, e max is the maximum value limit of the integral term, and sgn(e(τ)) is the sign function used to determine the sign of the error.

8. The precise three-dimensional photogrammetry and automatic positioning attitude control system according to claim 1, characterized in that: The system also includes an automatic positioning module, which realizes precise positioning through a closed-loop servo system. The closed-loop servo system includes a position sensor and a servo driver. The servo driver uses vector control technology to realize precise control of the motor. Its control equation is v ref =T -1 (θ est ) cmd , where v ref is the motor reference speed vector, T -1 is the inverse transformation of the motor model, θ est is the motor state estimation, u cmd For control instructions.

9. The precise three-dimensional photogrammetry and automatic positioning attitude control system according to claim 1, characterized in that: The system also includes a data storage module for storing the multi-view images, three-dimensional reconstruction results and posture estimation data. The data storage module uses a compression algorithm to reduce storage space occupancy. The compression algorithm uses the JPEG2000 standard. Its discrete wavelet transform process can be expressed as Y=ΨX, where X represents the original image data, Ψ represents the wavelet transform matrix, and Y represents the transformed coefficient. Its compression process includes discrete wavelet transform, quantization, entropy coding and other steps.

10. The precise three-dimensional photogrammetry and automatic positioning attitude control system according to claim 1, characterized in that: The system realizes data communication and collaborative work among modules through an integrated software platform, which is deployed on a server, adopts a distributed architecture, and realizes asynchronous communication among modules through a message queue middleware.

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