A real-time imaging dynamic navigation system for breast tumor interventional surgery

Abstract

This invention discloses a real-time imaging dynamic navigation system for interventional surgery of breast tumors, comprising an acousto-optic scanning device, a Redis client, a Redis server, a database, a modeling platform, a Three.js dynamic driving engine, and a display terminal. The acousto-optic scanning device is positioned above the breast tumor for non-contact scanning and data acquisition during surgery. The Redis client, Redis server, and database acquire, distribute, and store the acquired data, respectively. The modeling platform constructs a model from the received data. The Three.js dynamic driving engine, consisting of a breast tumor data input terminal, an initial data driving unit, an incremental data driving unit, a rendering unit, and a dynamic image output terminal, further processes the model in a data-driven manner. This invention's dynamic navigation system achieves real-time data acquisition, transmission, and processing, generating intuitive and dynamic navigation images that provide high-quality image guidance for surgery, improving the success rate and accuracy of the procedure.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, specifically, it relates to a real-time imaging dynamic navigation system for interventional surgery of breast tumors. Background Technology

[0002] Interventional surgery for breast tumors is a minimally invasive treatment method. It uses precision instruments such as catheters to deliver drugs, perform embolization, or apply physical ablation directly to the tumor site under preoperative and intraoperative image guidance to directly kill solid tumors. This method is non-surgical, minimally invasive, and reduces damage to surrounding normal tissues. Preoperative image guidance usually uses ultrasound, CT, and MRI images to obtain information such as the size, shape, location, and boundaries of the breast tumor to determine the surgical scope and stage, thereby formulating a reasonable preoperative treatment plan. Intraoperative image guidance usually uses ultrasound imaging to assist in the implementation of the surgery. Preoperative and intraoperative imaging plays a guiding role in the surgery. However, during the actual surgical procedure, the current status of the breast tumor has changed due to the patient's current condition and the real-time changes in the shape of the breast. The previous imaging data can no longer reflect the current status of the breast tumor in real time. Intraoperative ultrasound imaging is limited by the contact operation of the probe and the prevention of wound infection, so it cannot collect data in real time. This static imaging not only cannot achieve true real-time synchronization, but also has limitations in the identification of the boundaries of the breast tumor and deep tissues.

[0003] In the prior art, patent publication number CN118490354A describes a real-time image navigation system and related device for breast tumor surgery. This patent uses image fusion technology to fuse preoperative MRI breast tumor images, CT breast tumor images, and B-ultrasound breast tumor images to generate a comprehensive breast tumor image. Simultaneously, the patent uses B-ultrasound to acquire breast tumor images during surgery to assist in the implementation of the procedure. This comprehensive breast tumor image and the B-ultrasound-acquired breast tumor image help analyze the location and status of the breast tumor. However, the comprehensive breast tumor image in this patent is preoperative data, not real-time data acquisition during surgery. Furthermore, the intraoperative B-ultrasound image is limited by the contact operation of the ultrasound probe, failing to achieve real-time synchronization during surgery and unable to provide accurate and intuitive dynamic navigation images, thus affecting the success rate and accuracy of the surgery. Therefore, there is an urgent need for a dynamic navigation system that can accurately acquire data in real-time during surgery and intuitively display breast tumor images. Summary of the Invention

[0004] To overcome the above-mentioned shortcomings in the prior art, the present invention provides a dynamic navigation system for interventional surgery of breast tumors, which uses an acousto-optic scanning device to accurately acquire breast tumor data in real time during the operation, and generates intuitive breast tumor images through real-time transmission, distribution, storage, modeling, and data-driven generation of the data.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A real-time imaging dynamic navigation system for interventional surgery of breast tumors includes an acoustic-optical scanning device for acquiring data of the breast tumor, a Redis client for acquiring data acquired by the acoustic-optical scanning device, a Redis server for distributing data from the Redis client, a database for storing data from the Redis server, a modeling platform for building models based on the data from the Redis server, a Three.js dynamic driving engine for data-driven and image rendering based on the data from the modeling platform and the database, and a display terminal for displaying dynamic navigation images of the breast tumor generated by the Three.js dynamic driving engine.

[0007] The Three.js dynamic driving engine includes a breast tumor data input terminal connected to both the modeling platform and the database, an initial data driving unit connected to the breast tumor data input terminal, an incremental data driving unit connected to the initial data driving unit, a rendering unit connected to the incremental data driving unit, and a dynamic image output terminal connected to the rendering unit. The rendering unit is connected to the initial data driving unit, and the dynamic image output terminal is connected to the display terminal.

[0008] Furthermore, the initial data-driven unit includes a data verification module connected to the breast tumor data input terminal, a data flow monitoring module and a historical data evolution module connected to the data verification module, a data flow acquisition module connected to the data flow monitoring module, and a model comparison and judgment module connected to the data flow acquisition module, wherein the historical data evolution module is connected to the data flow monitoring module.

[0009] Furthermore, the incremental data driving unit includes a cumulative storage increment module connected to the model comparison and judgment module, an incremental threshold judgment module connected to the cumulative storage increment module, and a reset storage data module connected to the incremental threshold judgment module, wherein the reset storage data module is connected to the rendering unit.

[0010] Furthermore, the rendering unit includes a coloring module that sets the color of the breast tumor image according to the different biological components using the color property of the Three.js tool.

[0011] Furthermore, the rendering unit includes a rotation module that uses the position property of the Three.js tool to rotate the breast tumor image.

[0012] Furthermore, the rendering unit includes a transparency settings module that uses the opacity option of the Three.js tool to make non-breast tumor biological tissue images transparent.

[0013] Furthermore, the modeling platform is connected to a lightweight processing module for reducing the weight of the constructed model.

[0014] Furthermore, the Redis client obtains data transmitted from the acoustic-optical scanning device in real time via the RS485 communication interface.

[0015] Furthermore, the Redis client, Redis server, database, modeling platform, Three.js dynamic driver engine, and display terminal communicate with each other based on the ProfiNet communication protocol.

[0016] Furthermore, the acousto-optic scanning device includes a scanning mirror for real-time scanning of breast tumors, an optical exciter and an ultrasonic array connected to the scanning mirror, a data acquisition unit connected to the ultrasonic array, and a data transmission module connected to the data acquisition unit, wherein the data transmission module is connected to the Redis client.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] (1) This invention not only realizes real-time synchronous data acquisition of breast tumors during surgery through an acousto-optic scanning device, but also overcomes the limitations of B-ultrasound images in identifying the boundaries and deep tissues of breast tumors through acousto-optic scanning method, making the acquired data more accurate. By setting up a Redis client, a Redis server, and a database to acquire, distribute, and store the acquired breast tumor data, a real-time high-speed data acquisition and transmission network is formed that allows for one-time acquisition, multiple distribution, and reusability of data. Moreover, the Redis client and Redis server have fast data parsing speed, ensuring the real-time nature of the data. After the model is built on the modeling platform, the initial data driving unit and incremental data driving unit in the Three.js dynamic driving engine complete the data processing of dynamic images in a data-driven manner. The rendering unit then performs rendering highlighting operations to enhance the visualization effect. Through intuitive and dynamic navigation images, high-quality image guidance is provided for the surgery, making the surgical operation more accurate and improving the surgical efficiency and success rate.

[0019] (2) The initial data driving unit of the present invention realizes the driving of the model by the initial data through the setting of the data verification module, data flow monitoring module, historical data evolution module, data flow acquisition module and model comparison and judgment module, which ensures the accuracy of the initial image. At the same time, the incremental data driving unit completes the incremental data driving through the cumulative storage incremental module, incremental threshold judgment module and reset storage data module, realizing the intuitive display of the dynamic changes of breast tumors during surgery. This data processing method of driving the model with real-time data not only improves the data processing efficiency, but also further improves the quality of the output image.

[0020] (3) The coloring module of this invention uses the color property of the Three.js tool to color the breast tumor image according to the different biological components, making the breast tumor more prominent. The rotation module uses the position property of the Three.js tool to rotate the breast tumor image. By rotating the breast tumor image, the observation angle of the breast tumor is increased. The transparency setting module uses the opacity option of the Three.js tool to make the non-breast tumor biological tissue image transparent, further enhancing the prominence of the breast tumor. The breast tumor image after coloring, rotation and transparency setting rendering operations is more intuitive and enhances the navigation effect.

[0021] (4) The lightweight processing module of the present invention reduces the consumption of computing resources by lightweighting the model, avoids the delay in data processing caused by overly complex calculations, and ensures the real-time output of dynamic images.

[0022] (5) The Redis client of this invention obtains data from the audio-visual scanning device in real time through the RS485 communication interface, ensuring real-time data transmission. In addition, the Redis client, Redis server, database, modeling platform, Three.js dynamic driving engine, and display terminal build a high-precision closed-loop data communication system through the ProfiNet communication protocol, further enhancing the real-time performance and data security of data transmission.

[0023] (6) The acousto-optic scanning device of the present invention includes a scanning mirror, an optical exciter, an ultrasonic array, a data acquisition device, and a data transmission module. The scanning mirror is placed above the breast tumor and scans in a non-contact manner during the operation. According to the different optical absorption spectra of each basic biological component, it not only realizes rapid imaging during the operation, but also provides information-rich optical contrast through acousto-optic scanning, which improves the imaging resolution and imaging depth, and further improves the imaging quality. Attached Figure Description

[0024] Figure 1 This is a schematic diagram illustrating the structural principle of the real-time imaging dynamic navigation system of the present invention;

[0025] Figure 2 This is a schematic diagram illustrating the structural principle of the Three.js dynamic driving engine of this invention;

[0026] Figure 3 This is a schematic diagram of the rendering unit of the real-time imaging dynamic navigation system of the present invention;

[0027] Figure 4 This is a schematic diagram illustrating the data processing flow of the Three.js dynamic driver engine of this invention;

[0028] Figure 5 This is a schematic diagram comparing the cross-sectional imaging quality of the acousto-optic scanning of the present invention with that of traditional images.

[0029] In the above figures, the component names corresponding to the reference numerals are as follows:

[0030] 1-Acousto-optic scanning device, 2-Redis client, 3-Redis server, 4-Database, 5-Modeling platform, 6-Lightweight processing module, 7-Three.js dynamic driving engine, 8-Display terminal, 101-Scanning mirror, 102-Optical exciter, 103-Ultrasound array, 104-Data acquisition unit, 105-Data transmission module, 701-Breast tumor data input terminal, 702-Initial data driving unit, 703-Incremental data driving unit, 704-Rendering unit, 705-Dynamic image output terminal, 702a-Data verification module, 702b-Historical data evolution module, 702c-Data stream monitoring module, 702d-Data stream acquisition module, 702e-Model comparison and judgment module, 703a-Cumulative storage increment module, 703b-Incremental threshold judgment module, 703c-Reset storage data module, 704a-Shaping module, 704b-Rotation module, 704c-Transparency setting module. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments. The embodiments of the present invention include, but are not limited to, the following embodiments.

[0032] Example

[0033] like Figures 1-5 As shown, this embodiment provides a real-time imaging dynamic navigation system for interventional surgery of breast tumors, including an acousto-optic scanning device 1, a Redis client 2, a Redis server 3, a database 4, a modeling platform 5, a Three.js dynamic driving engine 7, and a display terminal 8. The acousto-optic scanning device 1 is placed above the breast tumor to perform scanning and data acquisition in a non-contact manner during the procedure. Compared to intraoperative ultrasound imaging, which is limited by probe contact operation and the need to prevent wound infection, this real-time dynamic data acquisition method achieves true synchronous image guidance during the procedure. Furthermore, this novel acousto-optic imaging method provides rich optical contrast. Based on the different optical absorption spectra of each basic biological component in the breast tumor, the acousto-optic scanning device 1 modulates the emitted light into multiple wavelengths. Hemoglobin, melanin, water, myoglobin, nucleic acids, and fat can all be imaged endogenously. Acousto-optic imaging has advantages in identifying the boundaries of breast tumors and deep tissues, overcoming the limitations of ultrasound imaging. Figure 5 As shown, a and c are schematic diagrams of the imaging quality of the acousto-optic scanning cross-section, while b and d are schematic diagrams of the imaging quality of the cross-section of traditional imaging methods such as ultrasound, CT, and MRI. Compared with traditional imaging methods, acousto-optic imaging has better quality.

[0034] In this embodiment, Redis, in Redis client 2 and Redis server 3, is an abbreviation for Remote Dictionary Server. Redis is an open-source in-memory data structure storage system that uses memory as its primary storage medium. It supports various data types and is widely used for data acquisition, caching, and distribution. After the audio-visual scanning device 1 collects data, it can transmit the data to Redis client 2 in real time. Redis client 2 then sends the data to Redis server 3 for distribution. In this embodiment, database 4 uses GoldenDB, a domestically developed database with advantages such as lightweight, high speed, stability, and cross-platform compatibility. Furthermore, its open-source nature ensures data security and low cost. In this embodiment, Redis client 2 and Redis server 3 can be deployed locally or remotely as needed. Both Redis client 2 and Redis server 3 have fast data parsing speeds, and both local and remote deployments ensure data real-time performance. Redis client 2, Redis server 3, and database 4 respectively acquire, distribute, and store the collected breast tumor data, forming a real-time high-speed data acquisition and transmission network that allows for one-time acquisition, multiple distributions, and reusable data.

[0035] In this embodiment, modeling platform 5 uses the WebGL platform. WebGL is an abbreviation for Web Graphics Library, a 3D graphics protocol. After receiving breast tumor data from the Redis server, the WebGL platform performs modeling operations according to modeling instructions. The 3D model built by the WebGL platform cannot respond to changes in the breast tumor during surgery in real time. Therefore, the Three.js dynamic driving engine 7 is used to drive the model based on the real-time collected breast tumor data, thereby truly realizing the real-time and synchronous nature of the generated image and dynamically reflecting the changes in the breast tumor during surgery. Three.js is a powerful graphics data processor based on the JavaScript programming language, which can achieve high-quality 3D graphics driving and rendering through a simple application programming interface. Through the connection with database 4, Three.js can drive the 3D model with the collected data in real time, enabling the model to dynamically reflect the changes in the state of the breast tumor. At the same time, by using Three.js tool components, further rendering of the breast tumor image is achieved for better observation of the breast tumor.

[0036] In this embodiment, the Three.js dynamic driving engine 7 includes a breast tumor data input terminal 701, an initial data driving unit 702, an incremental data driving unit 703, a rendering unit 704, and a dynamic image output terminal 705. The breast tumor data input terminal 701 is connected to the modeling platform 5 to obtain model data. It is also connected to the database 4 to obtain real-time and historical breast tumor data. The breast tumor data input terminal 701 transmits the acquired data to the initial data driving unit 702 to generate the initial image before changes in the breast tumor's state. During the surgery, the state of the breast tumor changes. Incremental data drives the initial image generated by the initial data driving unit 702 through the incremental data driving unit 703, thereby generating an image with the changed state. The rendering unit 704 further renders the images generated by the initial data driving unit 702 and the incremental data driving unit 703. The rendered dynamic navigation image of the breast tumor is finally displayed through the display terminal 8. By comparing the rendered images generated by the initial data driving unit 702 and the incremental data driving unit 703, the dynamic changes in the state of the breast tumor during the surgery can be clearly and intuitively reflected, providing dynamic navigation for the implementation process of the surgery.

[0037] The initial data driving unit 702 in this embodiment includes a data verification module 702a, a data stream monitoring module 702c, a historical data evolution module 702b, a data stream acquisition module 702d, and a model comparison and judgment module 702e. The data verification module 702a verifies the accuracy of the acquired breast tumor data. If the data is inaccurate, the historical data evolution module 702b is used for data comparison. The data stream monitoring module 702c monitors the data stream as a whole after judgment to check whether the data stream is running normally. Specifically, it checks whether there is any missing data. If there is missing data, an alarm is sent. If there is no missing data, the data stream runs normally. After acquiring the data stream, the data stream acquisition module 702d performs model comparison through the model comparison and judgment module 702e, thereby generating comparison data and updating model data. The operation of each module ensures the accuracy of the initial image.

[0038] The incremental data driving unit 703 in this embodiment includes an incremental storage module 703a, an incremental threshold judgment module 703b, and a data reset module 703c. The incremental storage module 703a accumulates and stores the acquired incremental breast tumor data. The incremental threshold judgment module 703b is used to determine whether the storage increment is greater than a preset threshold. When the increment is not greater than the threshold, the original data is maintained. When the increment is greater than the threshold, the incremental data is stored. Then, the data reset module 703c generates a new image. This embodiment ensures the real-time performance of the image while also considering optimizing the system's operating performance and reducing the image update frequency. By comparing the incremental data and the threshold, it is determined whether the image needs to be updated. In practice, the threshold will be reduced as much as possible to increase the model's update frequency.

[0039] In this embodiment, the rendering unit 704 renders the breast tumor image generated by the initial data-driven unit 702 and the incremental data-driven unit 703. The rendering unit 704 includes a coloring module 704a, a rotation module 704b, and a transparency setting module 704c. Specifically, the coloring module 704a sets the color of the breast tumor image according to different biological components in the breast tumor. Specifically, it uses the color property of the Three.js tool to set different colors for biological components such as hemoglobin, melanin, water, myoglobin, nucleic acid, and fat, thereby enhancing the visualization effect. The rotation module 704b uses the position property of the Three.js tool to rotate the breast tumor image, increasing the observation angle of the breast tumor. The transparency setting module 704c uses the opacity option of the Three.js tool to make the non-breast tumor biological tissue image transparent, further enhancing the prominence of the breast tumor.

[0040] Breast tumors contain a variety of biological components. To avoid the tedious process of repeated modeling and to avoid increasing computational complexity and system resource consumption, this embodiment sets up a lightweight processing module 6 while highlighting the key features of breast tumor images. Lightweight processing is performed during the modeling process on the modeling platform 5. In specific implementation, the Blender tool is used to delete some biological tissues around the breast tumor, such as fat and water, from the model. Blender is a powerful model processing software that provides a wealth of components to achieve model lightweighting.

[0041] In this embodiment, Redis client 2 acquires data from acoustic-optical scanning device 1 in real time via an RS485 communication interface. RS485 is a communication protocol standard with advantages such as simple interface structure, high transmission rate, low cost, and easy installation. The RS485 communication interface used in this embodiment has a maximum data transmission rate of 10Mbps, ensuring real-time data transmission. In this embodiment, Redis client 2, Redis server 3, database 4, modeling platform 5, Three.js dynamic driver engine 7, and display terminal 8 establish a high-precision closed-loop data communication system through the ProfiNet communication protocol. ProfiNet is an Ethernet-based communication transmission protocol that provides deterministic and isochronous transmission time for critical process data with jitter less than 1 microsecond. Furthermore, ProfiNet integrates PROFIsafe functional safety technology, allowing the use of the same cable for standard and safety-related communication, simplifying the system architecture and meeting stringent safety standards. The adoption of the ProfiNet communication protocol further enhances the real-time performance and data security of this real-time imaging dynamic navigation system.

[0042] In this embodiment, the acousto-optic scanning device 1 includes a scanning mirror 101, an optical exciter 102, an ultrasonic array 103, a data acquisition unit 104, and a data transmission module 105. The scanning mirror 101 is placed above the breast tumor to perform non-contact scanning during the procedure. The light generated by the optical exciter 102 is irradiated onto the breast tumor tissue through the scanning mirror 101. The light absorption domain of the biological tissue generates ultrasonic signals. The ultrasonic array 103 receives and records these ultrasonic signals. The data acquisition unit 104 acquires the ultrasonic signals from the ultrasonic array 103 in real time. The data transmission module 105 transmits the acquired signals. In this embodiment, the data acquisition capability can be further improved by increasing the size of the ultrasonic array and increasing the number of channels in the data acquisition unit.

[0043] In use, this invention first collects breast tumor data by scanning the breast tumor using an acoustic-optical scanning device 1. Then, the collected breast tumor data is acquired, distributed, and stored using a Redis client 2, a Redis server 3, and a database 4, respectively. Next, a model is constructed using a modeling platform 5. Then, the initial data-driven unit 702 and the incremental data-driven unit 703 in the Three.js dynamic driving engine 7 perform dynamic image data processing in a data-driven manner, and the rendering unit 704 performs rendering operations. Finally, the dynamic navigation image of the breast tumor is displayed on a display terminal 8. This invention collects data in a non-contact manner during surgery using acoustic-optical scanning. The breast tumor data not only overcomes the limitations of ultrasound imaging due to probe-contact operation, achieving real-time dynamic data acquisition and true real-time synchronization, but also overcomes the limitations of ultrasound imaging in identifying breast tumor boundaries and deep tissues, improving the quality of acquired data. The data transmission network, consisting of a Redis client 2, a Redis server 3, and a database 4, enables real-time acquisition, distribution, and storage of the acquired data, supporting modeling processing and the generation of dynamic navigation images for breast tumors. The accurate and intuitive dynamic navigation images generated by the dynamic navigation system of this invention provide high-quality image guidance for surgery, improving the success rate and accuracy of the procedure. The deployment and connection of the acoustic-optical scanning device 1, Redis client 2, Redis server 3, database 4, modeling platform 5, Three.js dynamic driving engine 7, display terminal 8, and lightweight processing module 6 in this embodiment can be implemented using existing conventional hardware and software designs; the specific software settings and circuit structures are not detailed in this embodiment.

[0044] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any changes made based on the design principles of the present invention, or any non-creative modifications made thereon, shall fall within the scope of protection of the present invention.

Claims

1. A real-time imaging dynamic navigation system for interventional surgery of breast tumors, characterized in that, The system includes an acoustic-optical scanning device (1) for non-contact data acquisition of breast tumors, a Redis client (2) for acquiring data from the acoustic-optical scanning device (1), a Redis server (3) for distributing data from the Redis client (2), a database (4) for storing data from the Redis server (3), a modeling platform (5) for building models from the data from the Redis server (3), a Three.js dynamic driving engine (7) for data-driven and image rendering by combining data from the modeling platform (5) and the database (4), and a display terminal (8) for displaying dynamic navigation images of breast tumors generated by the Three.js dynamic driving engine (7), wherein: The Three.js dynamic driving engine (7) includes a breast tumor data input terminal (701) connected to both the modeling platform (5) and the database (4), an initial data driving unit (702) connected to the breast tumor data input terminal (701), an incremental data driving unit (703) connected to the initial data driving unit (702), a rendering unit (704) connected to the incremental data driving unit (703), and a dynamic image output terminal (705) connected to the rendering unit (704). The rendering unit (704) is connected to the initial data driving unit (702), and the dynamic image output terminal (705) is connected to the display terminal (8). The initial data driving unit (702) includes a data verification module (702a) connected to the breast tumor data input terminal (701), a data flow monitoring module (702c) and a historical data evolution module (702b) connected to the data verification module (702a), a data flow acquisition module (702d) connected to the data flow monitoring module (702c), and a model comparison and judgment module (702e) connected to the data flow acquisition module (702d). The historical data evolution module (702a) is further defined as follows: 2b) Connected to the data flow monitoring module (702c); the incremental data driving unit (703) includes an incremental storage module (703a) connected to the model comparison judgment module (702e), an incremental threshold judgment module (703b) connected to the incremental storage module (703a), and a reset storage data module (703c) connected to the incremental threshold judgment module (703b), wherein the reset storage data module (703c) is connected to the rendering unit (704).

2. The real-time imaging dynamic navigation system for interventional breast tumor surgery according to claim 1, characterized in that: The rendering unit (704) includes a coloring module (704a) that sets the color of the breast tumor image according to the different biological components using the color property of the Three.js tool.

3. The real-time imaging dynamic navigation system for interventional breast tumor surgery according to claim 1, characterized in that: The rendering unit (704) includes a rotation module (704b) that uses the position property of the Three.js tool to rotate the breast tumor image.

4. The real-time imaging dynamic navigation system for interventional breast tumor surgery according to claim 1, characterized in that: The rendering unit (704) includes a transparency setting module (704c) that performs transparency processing on non-breast tumor biological tissue images using the opacity option of the Three.js tool.

5. The real-time imaging dynamic navigation system for interventional breast tumor surgery according to claim 1, characterized in that: The modeling platform (5) is connected to a lightweight processing module (6) for reducing the weight of the constructed model.

6. The real-time imaging dynamic navigation system for interventional breast tumor surgery according to claim 1, characterized in that: The Redis client (2) obtains data transmitted from the acoustic and optical scanning device (1) in real time through the RS485 communication interface.

7. A real-time imaging dynamic navigation system for interventional breast tumor surgery according to claim 6, characterized in that: The Redis client (2), Redis server (3), database (4), modeling platform (5), Three.js dynamic driver engine (7), and display terminal (8) communicate with each other based on the ProfiNet communication protocol.

8. A real-time imaging dynamic navigation system for interventional breast tumor surgery according to any one of claims 1-7, characterized in that: The acousto-optic scanning device (1) includes a scanning mirror (101) for real-time scanning of breast tumors, an optical exciter (102) and an ultrasonic array (103) connected to the scanning mirror (101), a data acquisition unit (104) connected to the ultrasonic array (103), and a data transmission module (105) connected to the data acquisition unit (104), wherein the data transmission module (105) is connected to the Redis client (2).

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