Die design cooperation system based on digital video and audio processing

Abstract

The invention provides a mold design collaboration system based on digital video and audio processing, and the system comprises a video and audio collection module which is used for collecting video image data of structural display of a produced mold model by a worker, and capturing voice information explained by the worker; the video and audio processing module is used for processing the video image data and the voice information data; the mold drawing module is used for customizing a mold in a drawing mode and generating a mold graph according to the drawing content; the mold design module is used for converting the generated mold graph into a three-dimensional structure model, and analyzing and optimizing the three-dimensional structure model; the real-time cooperation module is used for sharing information and providing various mold cooperation tools; and the user management module is used for performing safety management on the user. The die design difficulty is reduced, the working efficiency of die design is improved, die design information sharing is achieved, the safety of die design information is guaranteed, and design data leakage is prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of mold design, and particularly to a mold design collaboration system based on digital video and audio processing. Background Art

[0002] In the process of mold design, efficient collaboration among design team members is crucial. The traditional mold design process is relatively cumbersome and requires high technical requirements for designers. Designers need to design the three-dimensional shape of the mold through software or hand-drawing, which takes a lot of time. And the traditional mold design collaboration methods mainly rely on face-to-face communication, email communication, and two-dimensional drawing transmission, etc. However, these methods have many limitations. For example, face-to-face communication is restricted by time and space, and it is difficult for team members to communicate in real time at different locations; email communication has untimely information transmission and is difficult to intuitively express complex design problems. Two-dimensional drawings cannot fully display the three-dimensional structure and dynamic working process of the mold, which is prone to cause understanding deviations. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a mold design collaboration system based on digital video and audio processing to solve or at least partially solve the above problems existing in the prior art.

[0004] To achieve the above purpose, the present invention provides a mold design collaboration system based on digital video and audio processing, and the system includes:

[0005] Video and audio acquisition module: used to acquire video images of staff showing the structure of the produced mold model, and capture the voice information of the staff explaining the mold model structure during the structure display;

[0006] Video and audio processing module: used to perform processing operations on the video image data and voice information data acquired by the video and audio acquisition module;

[0007] Mold painting module: used for staff to paint a customized mold by painting, and automatically generate a mold graphic according to the painting content. The painting method includes painting customization of the shape and color of the mold;

[0008] Mold design module: used to convert the generated mold graphic into a three-dimensional structure model, and analyze and optimize the three-dimensional structure model of the mold;

[0009] Real-time collaboration module: used to share the information of the video and audio processing module and the mold design module, and provide a variety of mold collaboration tools;

[0010] User management module: used to perform security management on users and set different operation permissions for different users.

[0011] Further, the processing operation on the video image data collected by the audio-visual acquisition module specifically includes the following steps:

[0012] S11. Divide the collected video image data into multiple video image data blocks;

[0013] S12. Calculate the grayscale video images of each video image data block respectively, average the pixels of each video image data block into the gray levels of the grayscale video images, and calculate the average pixels of the gray levels, which is expressed as follows:

[0014]

[0015] Among them, is the average pixel of the gray level, R x and R y are the number of pixels of the video image data block in the x-axis and y-axis directions respectively, and R w is the number of gray levels in the video image data block;

[0016] S13. Set a gray threshold, crop the gray levels greater than the gray threshold in the grayscale video image, and evenly distribute the part greater than the gray threshold to each gray level, so as to perform image enhancement on the video image data, which is expressed as follows:

[0017]

[0018] Among them, R z is the gray threshold, and f is the truncation coefficient.

[0019] Further, the processing operation on the voice information data collected by the audio-visual acquisition module specifically includes the following steps:

[0020] S21. Collect the original environmental noise signal in the working environment for a long period of time;

[0021] S22. Perform preprocessing operations on the original environmental noise signal, and the preprocessing operations include removing outliers and interference spikes;

[0022] S23. Analyze the preprocessed original environmental noise signal, analyze its spectral characteristics, and determine the frequency components and energy distribution of the original environmental noise signal;

[0023] S24. Based on the frequency components and energy distribution of the original environmental noise signal, calculate the average amplitude of the original environmental noise signal, and the discrete value of the amplitude of the original environmental noise signal in the current period relative to the average amplitude. According to the average amplitude and discrete value of the original environmental noise signal, set the fluctuation ranges of the average amplitude and discrete value respectively;

[0024] S25. Preprocess the environmental noise signals in the collected voice information data based on the average amplitude and the fluctuation range of the discrete values, and calculate the average amplitude and the fluctuation range of the discrete values of the environmental noise signals in the collected voice information data.

[0025] S26. Determine whether the average amplitude and the fluctuation range of the discrete values of the environmental noise signals in the voice information data are within the fluctuation range set in step S24, classify the environmental noise signals in the voice information data according to the judgment result, and select corresponding noise reduction algorithms for different types of environmental noise signals.

[0026] Furthermore, the mold painting module specifically includes the following steps:

[0027] S31. Collect various types of molds, decompose the composition structures of the various types of molds respectively, and decompose them into their respective composition structures.

[0028] S32. The staff selects the type of mold to be customized according to the requirements, and respectively paints and customizes the respective composition structures of the mold by painting, and marks the dimensions of the respective composition structures.

[0029] S33. Generate a mold graphic according to the painting content and the dimension markings of each composition structure.

[0030] S34. Use the respective composition structures of the various types of molds collected and the dimensions of the respective composition structures as mold training data, and input the mold training data into the model for training and learning, so as to establish a mold judgment model.

[0031] S35. Input the generated mold graphic into the mold judgment model, and judge whether the respective composition structures and the dimensions of the respective composition structures of the generated mold graphic are reasonable through the mold judgment model.

[0032] Furthermore, the conversion of the generated mold graphic into a three-dimensional structure model specifically includes the following steps:

[0033] S41. Based on the generated reasonable mold graphic, obtain the pixel depth of the mold graphic.

[0034] S42. Calculate the coordinate system of the generated mold graphic, and calculate the pixel points of the mold graphic according to the coordinate system, which is expressed as follows:

[0035]

[0036] Where a and b are respectively the pixel points of the mold graphic in the pixel coordinate system, x and y are respectively the x-axis and y-axis coordinate points of the mold graphic in the physical coordinate system, d x and d yare the physical sizes of the pixel points on the x-axis and y-axis respectively, and a0 and b0 are the offsets between the pixel coordinate system and the physical coordinate system;

[0037] S43. Based on steps S31 - S32, calculate the system coordinate system according to the pixel depth of the mold pattern and the pixel points, which is expressed as follows:

[0038]

[0039] Among them, X, Y, and Z are the coordinates of the X-axis, Y-axis, and Z-axis of the system respectively, and K is the pixel depth of the mold pattern;

[0040] S44. Calculate the coordinate system of the three-dimensional structure model of the mold based on the system coordinate system, and establish the three-dimensional structure model according to the coordinate system of the three-dimensional structure model.

[0041] Furthermore, the analysis and optimization of the three-dimensional structure model of the mold specifically include the following steps:

[0042] S51. Segment the three-dimensional structure model to obtain each three-dimensional structure surface of the three-dimensional structure model;

[0043] S52. Based on each three-dimensional structure surface, extract the geometric features of each three-dimensional structure surface, which is expressed as follows:

[0044]

[0045] Among them, p x is the geometric feature of the x-th three-dimensional structure surface, Q x is the feature vector of the x-th three-dimensional structure surface, is the average geometric feature of the three-dimensional structure surfaces adjacent to the three-dimensional structure surface x, U x is the average feature vector of the three-dimensional structure surfaces adjacent to the three-dimensional structure surface x, V x is the distribution vector of the x-th three-dimensional structure surface;

[0046] S53. Based on the geometric features of each three-dimensional structure surface extracted, conduct feature analysis on the three-dimensional structure model of the mold, obtain the types of mold parts required to generate the mold according to the feature analysis, and optimize the assembly method of the mold structure according to the types of mold parts.

[0047] Furthermore, the real-time collaboration module adopts the WebSocket protocol of network communication technology. The WebSocket protocol conducts full-duplex communication on a single TCP connection to achieve real-time connection between staff members.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] The present invention provides a mold design collaboration system based on digital video and audio processing. The system collects video images and voice information of the mold model completed in production through the audio and video acquisition module. The audio and video processing module processes the collected video image and voice information data. The mold painting module is used to paint the customized mold shape and color. The mold design module converts the customized mold into a three-dimensional structure model. The real-time collaboration module shares system information and provides mold collaboration tools. The user management module performs security management on user login and permissions. The present invention reduces the design difficulty of the mold, improves the work efficiency of mold design, realizes the sharing of mold design information among staff, avoids design delays caused by poor communication, and effectively protects the security of mold design information and prevents the leakage of important design data. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the following drawings are only the preferred embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 FIG. is a schematic structural diagram of a mold design collaboration system based on digital video and audio processing provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following describes the principles and features of the present invention in conjunction with the drawings. The listed embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0053] Referring to Figure 1 , this embodiment provides a mold design collaboration system based on digital video and audio processing. The system includes:

[0054] An audio and video acquisition module: used to collect video image data of the mold model completed in production by the staff for structural display, and capture the voice information of the staff explaining the mold model structure during the structural display. Specifically, it includes:

[0055] A high-definition camera is used to collect video image data of the mold model structure display. The high-definition camera has a multi-angle adjustment function, breaking through the limitation of the traditional fixed viewing angle. For example, when the staff shows the internal structure of the mold model, the camera angle can be flexibly adjusted to clearly capture and transmit the internal details to the audio and video acquisition module, ensuring that every key part of the mold model can be accurately presented, providing intuitive materials for in-depth discussion of design details;

[0056] A high-fidelity microphone is used to capture the voice information explained by the staff. The high-fidelity microphone uses advanced acoustic technology to accurately capture the voice information of the staff. At the same time, it integrates an efficient noise reduction algorithm to effectively reduce the interference of background noise and ensure the clear transmission of voice information.

[0057] Video and audio processing module: Used to perform processing operations on the video images and voice information data collected by the video and audio acquisition module, specifically including:

[0058] The processing operations performed on the video image data collected by the video and audio acquisition module specifically include the following steps:

[0059] S11. Divide the collected video image data into multiple video image data blocks;

[0060] S12. Calculate the grayscale video images of each video image data block respectively, average the pixels of each video image data block into the gray levels of each grayscale video image, and calculate the average pixels of the gray levels, which is expressed as follows:

[0061]

[0062] Among them, is the average pixel of the gray level, R x and R y are the number of pixels of the video image data block in the x-axis and y-axis directions respectively, and R w is the number of gray levels in the video image data block;

[0063] S13. Set a gray threshold, crop the gray levels greater than the gray threshold in the grayscale video image, and evenly distribute the part greater than the gray threshold to each gray level, so as to perform image enhancement on the video image data, which is expressed as follows:

[0064]

[0065] Among them, R z is the gray threshold, and f is the truncation coefficient.

[0066] The processing operations performed on the voice information data collected by the video and audio acquisition module specifically include the following steps:

[0067] S21. Collect the original environmental noise signals in the working environment for a long period of time;

[0068] S22. Perform preprocessing operations on the original environmental noise signals, and the preprocessing operations include removing outliers and interference spikes;

[0069] S23. Analyze the pre - processed original environmental noise signal, analyze its spectral characteristics, and determine the frequency components and energy distribution of the original environmental noise signal;

[0070] S24. Based on the frequency components and energy distribution of the original environmental noise signal, calculate the average amplitude of the original environmental noise signal and the discrete value of the amplitude of the original environmental noise signal in the current time period relative to the average amplitude. According to the average amplitude and discrete value of the original environmental noise signal, set the fluctuation ranges of the average amplitude and discrete value respectively;

[0071] S25. Based on the fluctuation ranges of the average amplitude and discrete value, perform pre - processing operations on the environmental noise signals in the collected voice information data, and calculate the average amplitude and the fluctuation range of the discrete value of the environmental noise signals in the collected voice information data;

[0072] S26. Judge whether the fluctuation ranges of the average amplitude and discrete value of the environmental noise signal in the voice information data are within the fluctuation ranges set in step S24. If the fluctuation ranges of the average amplitude and discrete value are within the set fluctuation ranges, it is preferably judged as steady - state noise. On the contrary, it is judged as non - steady - state noise; for steady - state noise, the spectral subtraction method is preferably used, and for non - steady - state noise, the Wiener filtering method is more effective;

[0073] Perform processing according to the selected noise reduction algorithm. Taking the spectral subtraction method as an example, first estimate the noise power spectrum at the current moment according to the noise sample, subtract the noise power spectrum from the power spectrum of the mixed signal, and then convert the frequency - domain signal back to the time - domain through inverse Fourier transform to obtain the preliminarily noise - reduced voice signal. Then, perform post - processing (such as clipping, filtering) on the preliminarily noise - reduced voice signal to remove residual noise spikes and musical noise.

[0074] Mold painting module: It is used for staff to paint customized molds by painting and automatically generate mold graphics according to the painting content. The painting method includes customizing the shape and color of the mold, and specifically includes the following steps:

[0075] S31. Collect various types of molds, decompose the composition structures of various types of molds respectively, and decompose them into their respective component structures;

[0076] S32. The staff selects the type of mold to be customized according to the requirements, and respectively performs painting customization on each component structure of the mold by painting, and marks the dimensions of each component structure;

[0077] S33. Generate mold graphics according to the painting content and dimension markings of each component structure;

[0078] S34. Use the components and dimensions of various types of molds collected as mold training data, and input the mold training data into the model for training and learning to establish a mold judgment model.

[0079] S35. Input the generated mold graphics into the mold judgment model, and use the mold judgment model to determine whether the components and dimensions of the generated mold graphics are reasonable. If reasonable, proceed to the next step.

[0080] Mold design module: used to convert the generated mold graphics into a three-dimensional structure model and analyze and optimize the three-dimensional structure model of the mold, specifically including:

[0081] The conversion of the generated mold graphics into a three-dimensional structure model specifically includes the following steps:

[0082] S41. Based on the generated reasonable mold graphics, obtain the pixel depth of the mold graphics.

[0083] S42. Calculate the coordinate system of the generated mold graphics, and calculate the pixel points of the mold graphics according to the coordinate system, expressed as follows:

[0084]

[0085] where a and b are the pixel points of the mold graphics in the pixel coordinate system, x and y are the x-axis and y-axis coordinate points of the mold graphics in the physical coordinate system, d x and d y are the physical sizes of the pixel points on the x-axis and y-axis respectively, and a0 and b0 are the offsets between the pixel coordinate system and the physical coordinate system.

[0086] S43. Based on steps S31 - S32, calculate the system coordinate system according to the pixel depth and pixel points of the mold graphics, expressed as follows:

[0087]

[0088] where X, Y, and Z are the X-axis, Y-axis, and Z-axis coordinates of the system, and K is the pixel depth of the mold graphics.

[0089] S44. Calculate the three-dimensional structure model coordinate system based on the system coordinate system, and establish a three-dimensional structure model according to the three-dimensional structure model coordinate system.

[0090] The analysis and optimization of the three-dimensional structure model of the mold specifically include the following steps:

[0091] S51. Segment the three-dimensional structure model to obtain each three-dimensional structure surface of the three-dimensional structure model.

[0092] S52. Based on each three-dimensional structural plane, extract the geometric features of each three-dimensional structural plane, which are expressed as follows:

[0093]

[0094] Among them, p x is the geometric feature of the xth three-dimensional structural plane, and Q x is the feature vector of the xth three-dimensional structural plane. is the average geometric feature of the three-dimensional structural planes adjacent to the three-dimensional structural plane x, and U x is the average feature vector of the three-dimensional structural planes adjacent to the three-dimensional structural plane x, and V x is the distribution vector of the xth three-dimensional structural plane;

[0095] S53. Based on the geometric features of each extracted three-dimensional structural plane, perform feature analysis on the three-dimensional structural model of the mold, obtain the types of mold parts required to generate the mold according to the feature analysis, and analyze the mold structure according to the types of mold parts to determine whether there is a simpler and more convenient structure assembly method for the mold structure.

[0096] Real-time collaboration module: Used to share the information of the audio and video processing module and the mold design module, and provide a variety of mold collaboration tools, specifically including:

[0097] The real-time collaboration module adopts the WebSocket protocol of network communication technology. The WebSocket protocol conducts full-duplex communication on a single TCP connection, reducing communication latency, realizing real-time connection between staff, and ensuring the instant transmission of data such as the audio and video processing module, the mold design module, and operation instructions. No matter where the members are, they can conduct real-time collaboration at any time;

[0098] The real-time collaboration module integrates rich and practical mold collaboration tools. For example: The electronic whiteboard collaboration tool uses vector drawing technology and supports multiple people to draw and mark on the whiteboard at the same time, and the operation process is displayed in real time. Staff can directly discuss and modify the design plan through the whiteboard; The file transfer collaboration tool uses a high-speed transmission protocol to ensure the fast and stable sharing of design documents, facilitating members to consult and refer to relevant materials at any time.

[0099] User management module: Used to perform security management on users and set different operation permissions for different users, specifically including:

[0100] User Role and Permission Assignment Technology: The user management module sets different operation permissions for different users through a rigorous user role and permission assignment mechanism. The user administrator has the highest permissions and can not only conduct final approval on the mold design scheme to ensure that the design direction meets the project requirements, but also adjust user permissions to ensure the security of the model library data. Staff with ordinary permissions can only perform basic operations such as viewing mold models and participating in collaborative discussions, avoiding damage to important design data caused by incorrect or unauthorized operations.

[0101] Security Authentication and Encryption Technology: To ensure the security of user permission management, the user management module adopts multiple security authentication methods, such as username and password authentication, fingerprint recognition authentication, etc., to prevent illegal users from logging in. At the same time, it records user operation logs in detail for traceability in case of security issues, and uses encryption technologies such as SSL / TLS encryption protocols to encrypt data and encrypt the storage of mold models to prevent data from being stolen or tampered with, comprehensively ensuring the security and confidentiality of mold design information.

[0102] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A mold design collaboration system based on digital audio and video processing, characterized in that, The system includes: An audio-visual acquisition module: used to acquire video image data of the staff demonstrating the structure of the completed mold model, and capture the voice information of the staff explaining the mold model structure during the structure demonstration; An audio-visual processing module: used to perform processing operations on the video image data and voice information data acquired by the audio-visual acquisition module; A mold painting module: used for the staff to paint a customized mold by painting, and automatically generate a mold graphic according to the painting content. The painting method includes painting and customizing the shape and color of the mold; A mold design module: used to convert the generated mold graphic into a three-dimensional structure model and analyze and optimize the three-dimensional structure model of the mold; A real-time collaboration module: used to share the information of the audio-visual processing module and the mold design module, and provide a variety of mold collaboration tools; A user management module: used to perform security management on users and set different operation permissions for different users.

2. The mold design collaboration system based on digital video and audio processing according to claim 1, characterized in that, The processing operation on the video image data acquired by the audio-visual acquisition module specifically includes the following steps: S11. Divide the acquired video image data into multiple video image data blocks; S12. Calculate the grayscale video images of each video image data block respectively, average the pixels of each video image data block into the gray levels of each grayscale video image, and calculate the average pixel of the gray level, which is expressed as follows: Among them, is the average pixel of the gray level, R x and R y are the number of pixels of the video image data block in the x-axis and y-axis directions respectively, R w is the number of gray levels in the video image data block; S13. Set a grayscale threshold, crop the gray levels in the grayscale video image that are greater than the grayscale threshold, and evenly distribute the part greater than the grayscale threshold to each gray level, so as to perform image enhancement on the video image data, which is expressed as follows: where R z is the gray threshold and f is the intercept coefficient.

3. The mold design collaboration system based on digital video and audio processing according to claim 1, characterized in that, The processing operation on the voice information data acquired by the audio-visual acquisition module specifically includes the following steps: S21. Acquire the original environmental noise signal in the working environment for a long period of time; S22. Perform preprocessing operations on the original environmental noise signal. The preprocessing operations include removing outliers and interference spikes; S23. Analyze the preprocessed original environmental noise signal, analyze its spectral characteristics, and determine the frequency components and energy distribution of the original environmental noise signal; S24. Based on the frequency components and energy distribution of the original environmental noise signal, calculate the average amplitude of the original environmental noise signal and the discrete value of the original environmental noise signal amplitude in the current period relative to the average amplitude. According to the average amplitude and discrete value of the original environmental noise signal, set the fluctuation ranges of the average amplitude and discrete value respectively; S25. Based on the fluctuation ranges of the average amplitude and discrete value, perform preprocessing operations on the environmental noise signal in the acquired voice information data, and calculate the average amplitude and discrete value fluctuation range of the environmental noise signal in the voice information data; S26. Judge whether the average amplitude and discrete value fluctuation range of the environmental noise signal in the voice information data are within the fluctuation ranges set in step S24, and classify the environmental noise signal in the voice information data according to the judgment result. For different types of environmental noise signals, select corresponding noise reduction algorithms.

4. A die design collaboration system based on digital video and audio processing according to claim 1, characterized in that, The mold painting module specifically includes the following steps: S31. Collect various types of molds, decompose the constituent structures of each type of mold separately, and break them down into individual constituent structures; S32. The staff selects the type of mold to be customized according to requirements, and separately customizes the constituent structures of the mold by painting, and marks the dimensions of each constituent structure; S33. Generate a mold graphic based on the painting content and dimension markings of each constituent structure; S34. Use the constituent structures of various types of molds collected and the dimensions of each constituent structure as mold training data, and input the mold training data into the model for training and learning to establish a mold judgment model; S35. Input the generated mold graphic into the mold judgment model, and use the mold judgment model to determine whether the constituent structures and dimensions of the generated mold graphic are reasonable.

5. A mold design collaboration system based on digital video and audio processing according to claim 4, characterized in that The conversion of the generated mold graphic into a three-dimensional structure model specifically includes the following steps: S41. Based on the generated reasonable mold graphic, obtain the pixel depth of the mold graphic; S42. Calculate the coordinate system of the generated mold graphic, and calculate the pixel points of the mold graphic according to the coordinate system, expressed as follows: where a and b are respectively pixel points of the mold pattern in the pixel coordinate system, x and y are respectively the x-axis and y-axis coordinate points of the mold pattern in the physical coordinate system, d x and d y are respectively the physical sizes of the pixel point on the x-axis and y-axis, and a0 and b0 are respectively the offsets between the pixel coordinate system and the physical coordinate system; S43. Based on steps S31 - S32, calculate the system coordinate system according to the pixel depth and pixel points of the mold graphic, expressed as follows: Where X, Y, and Z are the X-axis, Y-axis, and Z-axis coordinates of the system respectively, and K is the pixel depth of the mold graphic; S44. Calculate the three-dimensional structure model coordinate system of the mold based on the system coordinate system, and establish a three-dimensional structure model according to the three-dimensional structure model coordinate system.

6. A die design collaboration system based on digital video and audio processing according to claim 1, characterized in that The analysis and optimization of the three-dimensional structure model of the mold specifically includes the following steps: S51. Segment the three-dimensional structure model to obtain each three-dimensional structure surface of the three-dimensional structure model; S52. Based on each three-dimensional structure surface, extract the geometric features of each three-dimensional structure surface, expressed as follows: where p x is the geometric feature of the x-th three-dimensional structural plane, Q x is the eigenvector of the x-th three-dimensional structural plane, is the average geometric feature of the three-dimensional structural planes adjacent to the x-th three-dimensional structural plane, U x is the average eigenvector of the three-dimensional structural planes adjacent to the x-th three-dimensional structural plane, V x is the distribution vector of the x-th three-dimensional structural plane; S53. Based on the geometric features of each three-dimensional structure surface extracted, perform feature analysis on the three-dimensional structure model of the mold, obtain the type of mold parts required to generate the mold according to the feature analysis, and optimize the assembly method of the mold structure according to the type of mold parts.

7. A mold design collaboration system based on digital video and audio processing according to claim 1, characterized in that, The real-time collaboration module uses the WebSocket protocol of network communication technology. The WebSocket protocol performs full-duplex communication on a single TCP connection to achieve real-time connection between staff.

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