Artificial joint operation image transmission method and system

Abstract

The invention discloses an artificial joint operation image transmission method and system, and relates to the technical field of image transmission, and the system comprises an initial equipment building module, a software and system configuration adjustment module, an image transmission execution switching module and a postoperative data processing optimization evaluation module. A specific sensor, a lighting assembly and a positioning module are installed in an operating microscope, various computing devices and data acquisition modules are deployed in a specific area of an operating room, software and system configuration is adjusted according to illness state data of a patient after the devices are set up, and key operation nodes are set as monitoring time points in an operation. The method comprises the following steps: acquiring position coordinates and movement speed data of an instrument, evaluating whether to switch a transmission execution scheme or not, acquiring image transmission data, such as a transmission delay rate and the like, at each monitoring time point after an operation is finished, comparing a standard value to evaluate transmission performance, and judging whether optimization is needed or not, so as to improve the transmission quality of an operation image.

Description

Technical Field

[0001] This invention relates to the field of image transmission technology, and specifically to a method and system for transmitting surgical images of artificial joints. Background Technology

[0002] With the rapid development of medical technology, the precision and success rate of artificial joint surgery increasingly rely on high-quality surgical image transmission. Traditional surgical image transmission methods often suffer from problems such as transmission delays and image blurring when faced with complex surgical environments and high image quality requirements, seriously affecting the smooth progress of the surgery. For example, in artificial joint replacement surgery, doctors need to observe the fine structures of the surgical site and the position of instruments in real time and clearly in order to operate precisely. Therefore, there is a need for an artificial joint surgical image transmission method and system.

[0003] Existing technology, such as the invention application patent with publication number CN109379558A, discloses an artificial joint surgery image transmission system, including a remote control terminal, an IoT server, a touch screen display, a graphics image transmission system, a beamforming scanning control module, and a probe. The remote control terminal is connected to the touch screen display via the IoT server, and the touch screen display is connected to the graphics image transmission system, the beamforming scanning control module, and the probe. The remote control terminal includes a mobile operating terminal and a PC operating terminal, and also includes a wireless communication module and a Bluetooth module. The touch screen display includes a data acquisition module, a data processing module, an imaging processing module, a user exchange processing imaging display module, a central processing unit, and a power supply module. This artificial joint surgery image transmission system facilitates information integration, real-time monitoring and control, has high safety performance, and can clearly observe the situation during surgery, which is conducive to the application and promotion of artificial joint surgery.

[0004] Regarding the above-mentioned solutions, the inventors of this application have discovered at least the following technical problems: 1. Existing technologies struggle to precisely adjust image acquisition and processing equipment based on the patient's condition. When faced with complex conditions, such as severe joint wear or significant anatomical variations, it is difficult to obtain high-resolution, clear surgical images. Doctors struggle to clearly identify lesion details, easily leading to surgical errors and affecting surgical outcomes. Furthermore, there is a lack of a mechanism for dynamically switching transmission paths. During surgery, if signal interference or network congestion occurs, it is impossible to quickly switch from a quantum channel to a millimeter-wave channel as this solution does, resulting in frequent transmission delays and packet loss, causing image lag and interruptions. Doctors are unable to obtain real-time images of the surgical site, delaying the surgical process.

[0005] 2. Existing technologies struggle to flexibly allocate computing resources based on the patient's condition and the stage of surgery. During critical surgical procedures, it's impossible to allocate more CPU core resources to optical nodes processing medical image data in a timely manner, resulting in slow image data processing and impacting the timeliness of image transmission. Furthermore, the lack of flexibility in bandwidth allocation prevents a significant increase in the bandwidth allocation for surgical image transmission when needed, affecting both image quality and transmission speed.

[0006] 3. Existing technologies often lack real-time monitoring and early warning of surgical image transmission performance. When transmission performance deteriorates, such as excessive transmission delay or large image guidance errors, medical staff may not be able to detect it in time and take proactive measures to optimize it, potentially leading to forced surgical interruption or increased surgical risks. Furthermore, it is difficult to effectively evaluate and optimize transmission performance based on data after surgery, which hinders the continuous improvement of transmission technology. Summary of the Invention

[0007] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a method and system for transmitting surgical images of artificial joints.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for transmitting images of artificial joint surgery, including: Step 1, setting up the initial equipment: when artificial joint surgery is about to be performed in the target operating room, the initial equipment of the target operating room is set up.

[0009] Step 2: Adjustment of software and system configuration: After the initial equipment setup in the target operating room is completed, acquire the patient's condition data for the target operating room, and then adjust and analyze the software and system configuration of the initial equipment.

[0010] Step 3: Switching the image transmission execution: When the patient in the target operating room is undergoing surgery, several monitoring time points are set, and then at each monitoring time point, it is evaluated whether the surgical images of the patient in the target operating room need to be switched to a different transmission execution scheme.

[0011] Step 4: Postoperative data processing optimization and evaluation: After the patient in the target operating room has completed the surgery, the surgical image transmission data of the patient in the target operating room at each monitoring time point is obtained, so as to evaluate whether the surgical image transmission performance of the target operating room equipment needs to be optimized.

[0012] The beneficial effects of this invention are as follows: 1. In this embodiment, by installing four sets of 16MP CMOS sensors on a surgical microscope, combined with a multispectral LED array and a built-in laser interferometry positioning module, high-resolution, high-definition images of the surgical site can be acquired. Surgeons can clearly observe subtle lesions such as joint wear and bone hyperplasia, accurately determine the anatomical structure of the surgical site, and thus perform joint replacement, lesion removal, and other operations more precisely during surgery, reducing surgical errors and improving the success rate. The image transmission scheme is dynamically adjusted according to the surgical progress, and the transmission path is switched in a timely manner at key operational nodes, such as prosthesis implantation and bone resurfacing, improving the real-time performance and resolution of image transmission. Surgeons can obtain precise positional information of instruments and key areas in real time, ensuring that surgical instruments accurately reach the target position, avoiding damage to surrounding normal tissues, and further improving the precision of the surgery.

[0013] 2. In this embodiment of the invention, the software and system configuration of the initial device are intelligently adjusted based on the patient's condition data. For patients with varying degrees of medical complexity, the image encoding algorithm is optimized, model training data is adjusted, and CPU core resources are allocated accordingly, enabling the device to quickly process and transmit image data that meets the surgical requirements. Doctors do not need to wait excessively for clear images, reducing downtime during surgery and improving overall surgical efficiency. During the surgery, when a switch in transmission execution scheme is detected, the device can quickly switch from a quantum channel to a millimeter-wave channel and reconfigure the transmission power, bandwidth resources, and CPU core allocation. This rapid transmission switching mechanism ensures the continuity and stability of image transmission, avoiding surgical interruptions due to transmission problems and significantly shortening surgical time.

[0014] 3. This invention establishes a comprehensive early warning mechanism for surgical image transmission performance. By integrating internal hospital instant messaging software, equipping smart wearable devices, and deploying voice broadcasting, it comprehensively covers relevant areas. Once abnormalities occur in surgical image transmission performance, relevant personnel can receive an immediate warning and take timely measures to optimize and maintain the transmission, ensuring the stability of surgical image transmission and reducing the risk of surgical safety being affected by image transmission failures. The use of quantum-millimeter-wave hybrid transmission equipment provides redundant transmission paths. When the quantum channel experiences signal interference or excessively high bit error rates, it can automatically switch to the millimeter-wave channel, ensuring continuous and stable transmission of surgical image data, providing reliable communication support for the smooth progress of surgery, and enhancing surgical safety.

[0015] 4. In this embodiment of the invention, postoperative analysis of surgical image transmission data evaluates the performance of surgical image transmission, providing data support for subsequent equipment maintenance and performance optimization. Continuously improving the image transmission system allows it to better adapt to different surgical needs, continuously enhancing the quality of surgical image transmission. This, in turn, promotes the improvement of the entire medical team's diagnostic and treatment capabilities for artificial joint surgery, improves the overall quality of medical care in the hospital, and enables personalized medical services by adjusting equipment configuration and image transmission schemes according to the patient's specific condition. Patients with different conditions can receive image transmission guarantees most suitable for their surgical needs, helping doctors to develop more precise surgical plans, provide higher-quality medical services, promote postoperative recovery, and improve patient satisfaction. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the implementation steps of the method of the present invention.

[0018] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Examples of embodiments of the present invention Figure 1 As shown, a method for transmitting images during artificial joint surgery includes: Step 1, setting up the initial equipment: when artificial joint surgery is about to be performed in the target operating room, the initial equipment is set up in the target operating room.

[0021] In a specific embodiment, the initial setup of the target operating room is carried out as follows: A1. On the surgical microscope, four sets of 16MP CMOS sensors are fixed at the preset installation points using matching screws. Then, a multispectral LED array is installed near the microscope's illumination system and connected to the power control module via a dedicated cable. The built-in laser interference positioning module is installed inside the microscope's optical tube and fixed using a precision mechanical structure.

[0022] It should be noted that the precision mechanical structure includes a mounting base, shock absorption components, fine-tuning devices, and a fixing frame.

[0023] A2. Within a 1-meter radius ring area centered on the operating table in the operating room, NVIDIA IGX Orin devices for optical nodes are mounted on a custom rack and connected to other devices via PCIe Gen5 cables, creating a mesh interconnect architecture with a bandwidth of 512GB / s. FPGA-Xilinx Versal HBM series devices for mechanical nodes are also mounted in designated locations on the rack. ADAS1135 128-channel ADC chips for biological nodes are integrated into a dedicated data acquisition module, which is installed near the patient's vital signs monitoring equipment and connected to other nodes in the edge computing cluster via high-speed data cables.

[0024] A3. The developed dual-mode base station will have its quantum channel component, consisting of a 1550nm single-photon transmitter based on the BB84 protocol, installed high up in the operating room and connected to the relevant data processing equipment using dedicated fiber optic cables. Simultaneously, the millimeter-wave channel component, employing a SiGe 60GHz transceiver array, will be installed near the surgical area but in a position that does not interfere with the surgical procedure and connected to the antenna system via RF cables. For the adaptive beamforming antenna, a 3D-printed liquid metal phase controller will be installed inside the antenna's internal structure and connected to the control module via circuitry.

[0025] Step 2: Adjustment of software and system configuration: After the initial equipment setup in the target operating room is completed, acquire the patient's condition data for the target operating room, and then adjust and analyze the software and system configuration of the initial equipment.

[0026] In a specific embodiment, the adjustment and analysis of the software and system configuration of the initial device is carried out as follows: B1. Obtain the condition data corresponding to the patient in the target operating room. The condition data includes the joint wear area of ​​the surgical site, the volume of bone hyperplasia at the surgical site, the variation of the joint anatomical structure at the surgical site, the bone density at the surgical site, the patient's own heart rate variation, and the patient's own blood pressure fluctuation range. Then, based on the condition data corresponding to the patient in the target operating room, the condition matching value corresponding to the patient in the target operating room is obtained.

[0027] B2. If the condition matching value of the target operating room patient is less than or equal to the first threshold, the dynamic light field block coding model will maintain the default octree segmentation algorithm setting, the average region partitioning time will be kept at 0.3ms, the ResNet-3D+Transformer model will use the regular training dataset, the dynamic priority matrix will be set to the regular mode, and the joint region will be encoded with a uniform medium priority of 50 levels to balance image quality and transmission efficiency. The sEMG sensor array in the bioelectric synchronization system will maintain a sampling rate of 10kHz, the motion feature extraction algorithm based on wavelet transform will use the default parameters, the jitter of the phase-locked loop synchronization circuit of the photonic resonance controller will be maintained at <0.1ps, the 1550nm single-photon transmitter of the quantum channel will maintain the default transmission power, the SiGe 60GHz transceiver array of the millimeter-wave channel will maintain a 16×16 MIMO transmission power, and the CPU core allocation ratio of the optical node for processing medical image data will be maintained at 60%.

[0028] B3. If the condition fit value of the target operating room patient is greater than or equal to the first threshold and less than the second threshold, the octree segmentation algorithm in the dynamic light field block coding model will be slightly optimized to shorten the average region segmentation time to 0.25ms. In the ResNet-3D+Transformer model training, approximately 10% of sample data similar to the current condition characteristics will be added. For relatively obvious areas of joint wear and bone hyperplasia, the dynamic priority matrix will be increased to level 60-70. The sampling rate of the sEMG sensor array in the bioelectric synchronization system will be increased to 11kHz. The phase-locked loop synchronization circuit of the photonic resonance controller will be fine-tuned to reduce jitter to <0.09ps. The transmission power of the 1550nm single-photon transmitter in the quantum channel of the transmission system will be increased by 5%, the transmission power of the SiGe 60GHz transceiver array in the millimeter-wave channel will be increased by 5%, and the cycle of the TSN time-sensitive switch will be shortened to 0.9μs. At the same time, the CPU core allocation ratio of the optical node for processing medical image data will be increased to 63%.

[0029] B4. If the condition matching value of the target operating room patient is greater than or equal to the second threshold, the dynamic light field block coding model will be significantly optimized with an octree segmentation algorithm, reducing the average region segmentation time to less than 0.2ms. The ResNet-3D+Transformer model will be expanded with 30% more sample data related to complex conditions. For key lesion areas, the dynamic priority matrix will be increased to 90-100 levels. The bioelectric synchronization system will increase the sampling rate of the sEMG sensor array to 12kHz. The phase-locked loop synchronization circuit of the photonic resonance controller will be deeply optimized, reducing jitter to <0.08ps. The transmission power of the 1550nm single-photon transmitter in the quantum channel of the transmission system will be increased by 20%, and the transmission power of the SiGe 60GHz transceiver array in the millimeter-wave channel will be increased by 15%. The cycle of the TSN time-sensitive switch will be shortened to 0.8μs. At the same time, the CPU core allocation ratio of the optical node for processing medical image data will be increased to 70%.

[0030] In a specific embodiment, the analysis obtains the condition adaptation value corresponding to the target operating room patient. The specific analysis process is as follows: C1. The joint wear area and bone hyperplasia volume of the surgical site corresponding to the target operating room patient are used as input items and imported into the joint lesion degree assessment value analysis model. After the joint lesion degree assessment value analysis model is used for calculation and analysis, the joint lesion degree assessment value corresponding to the target operating room patient is finally output.

[0031] It should be noted that the analysis process for the joint lesion severity assessment values ​​corresponding to patients in the target operating room is as follows: the joint wear area and bone hyperplasia volume at the surgical site corresponding to patients in the target operating room are normalized, and the normalized joint wear area and bone hyperplasia volume at the surgical site corresponding to patients in the target operating room are denoted as follows: and Substitute into the analysis formula The assessment value of the degree of joint lesions corresponding to the target operating room patient was obtained.

[0032] C2. Input the anatomical structure variation of the joints and bone density of the surgical site corresponding to the patient in the target operating room into the anatomical structure evaluation value analysis model. After the anatomical structure evaluation value analysis model performs calculations and analysis, the final output is the anatomical structure evaluation value corresponding to the patient in the target operating room.

[0033] It should be noted that the analysis process for the anatomical structure assessment values ​​corresponding to the target operating room patients is as follows: the variability of joint anatomy and bone density at the surgical site corresponding to the target operating room patients are normalized, and the normalized variability of joint anatomy and bone density at the surgical site corresponding to the target operating room patients are denoted as follows: and Substitute into the analysis formula The anatomical structure assessment values ​​corresponding to the target operating room patient are obtained.

[0034] C3. Input the patient's own heart rate variability and blood pressure fluctuation range corresponding to the target operating room patient into the patient physiological status assessment value analysis model. After the calculation and analysis of the patient physiological status assessment value analysis model, the final output is the patient physiological status assessment value corresponding to the target operating room patient.

[0035] It should be noted that the analysis process for the physiological status assessment values ​​corresponding to the patients in the target operating room is as follows: the patient's own heart rate variability and blood pressure fluctuation range are normalized, and the normalized heart rate variability and blood pressure fluctuation range corresponding to the patients in the target operating room are denoted as follows: and Substitute into the analysis formula The physiological status assessment value of the patient corresponding to the target operating room patient is obtained.

[0036] C4. Record the joint lesion severity assessment value, anatomical structure assessment value, and patient physiological status assessment value corresponding to the target operating room patient as follows: , and Substitute into the calculation formula: In the process, the condition matching value corresponding to the patient in the target operating room is obtained. ,in, , , These are the standard joint lesion severity assessment values, standard anatomical structure assessment values, and standard patient physiological status assessment values ​​corresponding to the established operating room patients. , , These are the weighting factors corresponding to the assessment values ​​of the degree of joint lesions in operating room patients, the weighting factors corresponding to the assessment values ​​of anatomical structure, and the weighting factors corresponding to the assessment values ​​of the patient's physiological state. , , These are the differences in the assessment values ​​for the degree of joint lesions in patients requiring permission to operate in the operating room, the differences in the assessment values ​​for permitted anatomical structures, and the differences in the assessment values ​​for permitted patients' physiological states. It is a mathematical constant.

[0037] It should be noted that, , , All are greater than 0 and less than 1.

[0038] It should also be noted that a large amount of clinical data from patients undergoing artificial joint surgery was collected, covering different age groups, genders, disease types, and severity. For the assessment value of joint lesion severity, the distribution of data such as joint wear area and bone hyperplasia volume under different joint diseases (such as osteoarthritis and rheumatoid arthritis) was analyzed, and the average value of each disease under a stable state was taken as the standard assessment value of joint lesion severity. Similarly, for the assessment value of anatomical structure, the standard anatomical structure assessment value was determined by comprehensively statistically analyzing the variability of joint anatomy and bone density data in the normal population. For the assessment value of patient physiological status, the standard patient physiological status assessment value was derived based on statistical data of heart rate variability and blood pressure fluctuation range in healthy individuals. A multidisciplinary expert team composed of orthopedic specialists, anesthesiologists, and radiologists was organized. Based on their extensive clinical experience, the experts assessed and scored the importance of joint lesion severity, anatomical structure, and patient physiological status in artificial joint surgery. Through expert consultation methods such as the Delphi method, expert opinions were repeatedly solicited, and the weighting factors for each assessment value were determined after comprehensive summarization.

[0039] Step 3: Switching the image transmission execution: When the patient in the target operating room is undergoing surgery, several monitoring time points are set, and then at each monitoring time point, it is evaluated whether the surgical images of the patient in the target operating room need to be switched to a different transmission execution scheme.

[0040] In a specific embodiment, the evaluation process for determining whether the transmission execution scheme of the surgical images of the target operating room patient needs to be switched at each monitoring time point is as follows: Each key operation node corresponding to the surgery of the target operating room patient is set as a monitoring time point. At each monitoring time point, the position coordinates and movement speed data of the instruments corresponding to the surgery of the target operating room patient are collected. This yields the distance and movement speed between the instruments and key parts corresponding to the surgery of the target operating room patient at each monitoring time point. The distance and movement speed between the instruments and key parts corresponding to the surgery of the target operating room patient at each monitoring time point are compared with preset thresholds for the distance and movement speed between the instruments and key parts. If the distance between the instruments and key parts corresponding to the surgery of the target operating room patient at a certain monitoring time point is less than the preset distance, and the movement speed is greater than the preset movement speed threshold, then the evaluation for the surgical images of the target operating room patient at that monitoring time point requires a switch in the transmission execution scheme. Simultaneously, the transmission execution scheme for the surgical images of the target operating room patient at that monitoring time point is switched.

[0041] It should be noted that multiple high-precision optical cameras are deployed in the operating room, surrounding the surgical area to ensure no blind spots. Specialized optical markers, typically made of reflective material, are attached to surgical instruments, allowing for clear imaging under camera illumination. The optical cameras are connected to a data processing unit, which receives and analyzes the image data captured by the cameras. The acquisition process involves the optical cameras continuously capturing images of the optical markers on the surgical instruments during the surgery. By analyzing the positions of the marker images captured by different cameras, the three-dimensional coordinates of the instruments are calculated using triangulation principles. Simultaneously, based on the changes in the marker positions in consecutive images and the time interval between image acquisitions, the movement speed of the instruments is calculated. For example, if the marker moves 5 pixels along the X-axis in two adjacent frames, and the image acquisition interval is 0.01 seconds, the movement speed of the instruments along the X-axis can be calculated using the known conversion ratio between camera pixels and actual distance. The data processing unit transmits the calculated position coordinates and movement speed data to the surgical image transmission system in real time for subsequent analysis and decision-making.

[0042] In a specific embodiment, the switching of the transmission execution scheme for the surgical images of the target operating room patient at the monitoring time point is carried out as follows: In the quantum-millimeter wave hybrid transmission device, the transmission path is automatically switched from the quantum channel to the millimeter wave channel. During the switching process, the transmission power of the millimeter wave channel is reconfigured, increasing the transmission power by 10%, and network bandwidth resources are reallocated, increasing the proportion of bandwidth used for surgical image transmission from the original 60% to 80% of the total bandwidth. In the edge computing cluster, the CPU core allocation ratio of the mechanical nodes is reduced from 20% to 15%, and these 5% of CPU core resources are allocated to the optical nodes responsible for processing surgical image data.

[0043] Step 4: Postoperative data processing optimization and evaluation: After the patient in the target operating room has completed the surgery, the surgical image transmission data of the patient in the target operating room at each monitoring time point is obtained, so as to evaluate whether the surgical image transmission performance of the target operating room equipment needs to be optimized.

[0044] In a specific embodiment, the acquisition process of surgical image transmission data corresponding to the target operating room patient is as follows: The transmission device records key parameters during the surgical image transmission process. Through the management software of the transmission device, the surgical image transmission data corresponding to the target operating room patient at each monitoring time point can be exported. The surgical image transmission data includes transmission delay rate, spatial resolution, power consumption, and image guidance error. The transmission delay rate, spatial resolution, power consumption, and image guidance error corresponding to the target operating room patient at each monitoring time point are used as input items and imported into the surgical image transmission evaluation value analysis model. After the operation and analysis of the surgical image transmission evaluation value analysis model, the surgical image transmission evaluation value corresponding to the target operating room patient is finally output.

[0045] It should be noted that the analysis process for the surgical image transmission evaluation values ​​corresponding to the target operating room patient is as follows: the transmission delay rate, spatial resolution, power consumption, and image guidance error of the target operating room patient at each monitoring time point are normalized, and the normalized transmission delay rate, spatial resolution, power consumption, and image guidance error of the target operating room patient at each monitoring time point are denoted as follows: , , and ,in, This indicates the number corresponding to each monitoring time point. u is any integer greater than 2, and u is also the sum of all monitoring time points. Substitute these values ​​into the analysis formula. Obtain the anatomical structure assessment value corresponding to the target operating room patient. ,in, , , , These represent the standard transmission delay rate, standard spatial resolution, standard power consumption, and standard image guidance error for each operating room patient at a given monitoring time point. , , , These are the weighting factors corresponding to the transmission delay rate, spatial resolution, power consumption, and image guidance error of the operating room patients at the set monitoring time points.

[0046] In one specific embodiment, the evaluation process for determining whether the surgical image transmission performance corresponding to the target operating room equipment needs optimization is as follows: The evaluation value of the surgical image transmission for the target operating room patient is compared with the evaluation value of the surgical image transmission for the standard operating room patient. If the evaluation value of the surgical image transmission for the target operating room patient is greater than or equal to the evaluation value of the surgical image transmission for the standard operating room patient, then the evaluation of the surgical image transmission performance corresponding to the target operating room equipment does not require optimization. If the evaluation value of the surgical image transmission for the target operating room patient is less than the evaluation value of the surgical image transmission for the standard operating room patient, then the evaluation of the surgical image transmission performance corresponding to the target operating room equipment needs optimization. If the evaluation of the surgical image transmission performance corresponding to the target operating room equipment needs optimization, then an early warning is issued regarding the performance of the surgical image transmission performance corresponding to the target operating room equipment.

[0047] In a specific embodiment, the warning process for the surgical image transmission performance corresponding to the target operating room equipment is as follows: By integrating the hospital's internal instant WeChat, the system automatically pushes warning messages to relevant personnel accounts, including the warning level and abnormal performance data. Key personnel in the operating room are equipped with smart wearable devices and smart bracelets. When the warning is triggered, the device will notify with vibration and prompt sound, and the warning content will be displayed on the screen simultaneously. At the same time, voice broadcasts are deployed in the operating room and surrounding technical areas to automatically broadcast the warning when it is triggered, providing comprehensive coverage of the relevant areas.

[0048] Examples of embodiments of the present invention Figure 2 As shown, an artificial joint surgery image transmission system includes: an initial equipment setup module, a software and system configuration adjustment module, an image transmission execution switching module, and a postoperative data processing optimization and evaluation module.

[0049] Initial Equipment Setup Module: Used to set up the initial equipment for the target operating room when artificial joint surgery is about to be performed.

[0050] Software and system configuration adjustment module: After the initial equipment setup of the target operating room is completed, it acquires the patient's condition data corresponding to the target operating room, and then adjusts and analyzes the software and system configuration of the initial equipment.

[0051] Image transmission execution switching module: used to set several monitoring time points when a patient in the target operating room is undergoing surgery, and then evaluate at each monitoring time point whether the surgical images of the patient in the target operating room need to be switched to a different transmission execution scheme.

[0052] Postoperative data processing optimization and evaluation module: After the patient in the target operating room has completed surgery, it acquires the surgical image transmission data of the patient at each monitoring time point, thereby evaluating whether the surgical image transmission performance of the target operating room equipment needs to be optimized.

[0053] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for transmitting surgical images of an artificial joint, characterized in that, include: Step 1: Initial Equipment Setup: When an artificial joint surgery is about to be performed in the target operating room, the initial equipment setup for the target operating room is carried out. Step 2: Adjustment of software and system configuration: After the initial equipment setup in the target operating room is completed, acquire the patient's condition data in the target operating room, and then adjust and analyze the software and system configuration of the initial equipment. Step 3: Switching the image transmission execution: When the patient in the target operating room is undergoing surgery, several monitoring time points are set, and then at each monitoring time point, it is evaluated whether the surgical images of the patient in the target operating room need to be switched to a different transmission execution scheme. Step 4: Postoperative data processing optimization and evaluation: After the patient in the target operating room has completed the surgery, the surgical image transmission data of the patient in the target operating room at each monitoring time point is obtained, so as to evaluate whether the surgical image transmission performance of the target operating room equipment needs to be optimized.

2. The method for transmitting surgical images of an artificial joint as described in claim 1, characterized in that, The initial setup of the target operating room involves the following process: A1. On the surgical microscope, use the matching screws to fix the four sets of 16MP CMOS sensors to the preset mounting points. Then, install the multispectral LED array near the microscope's illumination system and connect it to the power control module through a dedicated cable. Install the built-in laser interference positioning module inside the microscope's optical tube and fix it through a precision mechanical structure. A2. Within a 1-meter radius ring area centered on the operating table in the operating room, the NVIDIA IGX Orin devices of the optical nodes are installed on a custom rack and connected to other devices via PCIe Gen5 cables to build a mesh interconnect architecture with a bandwidth of 512GB / s. The FPGA-Xilinx Versal HBM series devices of the mechanical nodes are also installed in designated locations on the rack. The ADAS1135 128-channel ADC chip of the biological nodes is integrated into a dedicated data acquisition module, which is installed close to the patient's vital signs monitoring equipment and connected to other nodes of the edge computing cluster via high-speed data cables. A3. The developed dual-mode base station will have its quantum channel component, consisting of a 1550nm single-photon transmitter based on the BB84 protocol, installed high up in the operating room and connected to the relevant data processing equipment using dedicated fiber optic cables. Simultaneously, the millimeter-wave channel component, employing a SiGe 60GHz transceiver array, will be installed near the surgical area but in a position that does not interfere with the surgical procedure and connected to the antenna system via RF cables. For the adaptive beamforming antenna, a 3D-printed liquid metal phase controller will be installed inside the antenna's internal structure and connected to the control module via circuitry.

3. The method for transmitting surgical images of an artificial joint as described in claim 2, characterized in that, The analysis and adjustment process for the software and system configuration of the initial device is as follows: B1. Obtain the corresponding medical data of the patients in the target operating room. The medical data includes the joint wear area at the surgical site, the volume of bone hyperplasia at the surgical site, the variation of joint anatomy at the surgical site, the bone density at the surgical site, the patient's own heart rate variation, and the patient's own blood pressure fluctuation range. Then, based on the corresponding medical data of the patients in the target operating room, analyze and obtain the corresponding medical condition fit value of the patients in the target operating room. B2. If the patient's condition matching value in the target operating room is less than or equal to the first threshold, the dynamic light field block coding model will maintain the default octree segmentation algorithm setting, the average region partitioning time will be kept at 0.3ms, the ResNet-3D+Transformer model will use the regular training dataset, the dynamic priority matrix will be set to the regular mode, and the joint region will be encoded with a uniform medium priority of 50 levels to balance image quality and transmission efficiency. The sEMG sensor array in the bioelectric synchronization system will maintain a sampling rate of 10kHz, the motion feature extraction algorithm based on wavelet transform will use the default parameters, the phase-locked loop synchronization circuit jitter of the photonic resonance controller will be kept at <0.1ps, the 1550nm single-photon transmitter of the quantum channel will maintain the default transmission power, the SiGe 60GHz transceiver array of the millimeter-wave channel will maintain a 16×16 MIMO transmission power, and the CPU core allocation ratio of the optical node for processing medical image data will be kept at 60%. B3. If the condition fit value of the target operating room patient is greater than or equal to the first threshold and less than the second threshold, the octree segmentation algorithm in the dynamic light field block coding model will be slightly optimized to shorten the average region segmentation time to 0.25ms. In the ResNet-3D+Transformer model training, approximately 10% of sample data similar to the current condition characteristics will be added. For relatively obvious areas of joint wear and bone hyperplasia, the dynamic priority matrix will be increased to level 60-70. The sampling rate of the sEMG sensor array in the bioelectric synchronization system will be increased to 11kHz. The phase-locked loop synchronization circuit of the photonic resonance controller will be fine-tuned to reduce jitter to <0.09ps. The transmission power of the 1550nm single-photon transmitter in the quantum channel of the transmission system will be increased by 5%, the transmission power of the SiGe 60GHz transceiver array in the millimeter-wave channel will be increased by 5%, and the cycle of the TSN time-sensitive switch will be shortened to 0.9μs. At the same time, the CPU core allocation ratio of the optical node for processing medical image data will be increased to 63%. B4. If the condition matching value of the target operating room patient is greater than or equal to the second threshold, the dynamic light field block coding model will be significantly optimized with an octree segmentation algorithm, reducing the average region segmentation time to less than 0.2ms. The ResNet-3D+Transformer model will be expanded with 30% more sample data related to complex conditions. For key lesion areas, the dynamic priority matrix will be increased to 90-100 levels. The bioelectric synchronization system will increase the sampling rate of the sEMG sensor array to 12kHz. The phase-locked loop synchronization circuit of the photonic resonance controller will be deeply optimized, reducing jitter to <0.08ps. The transmission power of the 1550nm single-photon transmitter in the quantum channel of the transmission system will be increased by 20%, and the transmission power of the SiGe 60GHz transceiver array in the millimeter-wave channel will be increased by 15%. The cycle of the TSN time-sensitive switch will be shortened to 0.8μs. At the same time, the CPU core allocation ratio of the optical node for processing medical image data will be increased to 70%.

4. The method for transmitting surgical images of an artificial joint as described in claim 3, characterized in that, The analysis yielded the condition fit values ​​for the target operating room patients. The specific analysis process is as follows: C1. Input the joint wear area and bone hyperplasia volume of the surgical site corresponding to the patient in the target operating room as input items, and import them into the joint lesion severity assessment value analysis model. After the joint lesion severity assessment value analysis model is used for calculation and analysis, the final output is the joint lesion severity assessment value corresponding to the patient in the target operating room. C2. Input the variability of joint anatomy and bone density of the surgical site corresponding to the target operating room patient into the anatomical structure evaluation value analysis model. After the calculation and analysis of the anatomical structure evaluation value analysis model, the final output is the anatomical structure evaluation value corresponding to the target operating room patient. C3. Input the patient's own heart rate variability and blood pressure fluctuation range corresponding to the target operating room patient into the patient physiological status assessment value analysis model. After the calculation and analysis of the patient physiological status assessment value analysis model, the final output is the patient physiological status assessment value corresponding to the target operating room patient. C4. Record the joint lesion severity assessment value, anatomical structure assessment value, and patient physiological status assessment value corresponding to the target operating room patient as follows: , and Substitute into the calculation formula: In the process, the condition matching value corresponding to the patient in the target operating room is obtained. ,in, , , These are the standard joint lesion severity assessment values, standard anatomical structure assessment values, and standard patient physiological status assessment values ​​corresponding to the established operating room patients. , , These are the weighting factors corresponding to the assessment values ​​of the degree of joint lesions in operating room patients, the weighting factors corresponding to the assessment values ​​of anatomical structure, and the weighting factors corresponding to the assessment values ​​of the patient's physiological state. , , These are the differences in the assessment values ​​for the degree of joint lesions in patients requiring permission to operate in the operating room, the differences in the assessment values ​​for permitted anatomical structures, and the differences in the assessment values ​​for permitted patients' physiological states. It is a mathematical constant.

5. The method for transmitting surgical images of an artificial joint as described in claim 4, characterized in that, The process of assessing whether the transmission execution scheme needs to be switched for the surgical images of the target operating room patient at each monitoring time point is as follows: Each key operation node corresponding to the patient's surgery in the target operating room is set as a monitoring time point. At each monitoring time point, the position coordinates and movement speed data of the instruments corresponding to the patient's surgery in the target operating room are collected. This yields the distance and movement speed between the instruments and key parts of the patient's surgery in the target operating room at each monitoring time point. The distance and movement speed between the instruments and key parts of the patient's surgery in the target operating room at each monitoring time point are compared with preset thresholds for the distance and movement speed between the instruments and key parts of the patient's surgery in the target operating room. If the distance between the instruments and key parts of the patient's surgery in the target operating room at a certain monitoring time point is less than the preset distance and the movement speed is greater than the preset movement speed threshold, then the transmission execution scheme of the patient's surgery image in the target operating room at that monitoring time point needs to be switched. At the same time, the transmission execution scheme of the patient's surgery image in the target operating room at that monitoring time point is switched.

6. The method for transmitting surgical images of an artificial joint as described in claim 5, characterized in that, The switching of the transmission execution scheme for the surgical images of the target operating room patient at the monitored time point is as follows: In the quantum-millimeter-wave hybrid transmission device, the transmission path is automatically switched from the quantum channel to the millimeter-wave channel. During the switching process, the transmission power of the millimeter-wave channel is reconfigured, increasing the transmission power by 10%. Network bandwidth resources are reallocated, increasing the proportion of bandwidth used for surgical image transmission from 60% to 80% of the total bandwidth. In the edge computing cluster, the CPU core allocation ratio of the mechanical nodes is reduced from 20% to 15%, and these 5% of CPU core resources are allocated to the optical nodes responsible for processing surgical image data.

7. The method for transmitting surgical images of an artificial joint as described in claim 6, characterized in that, The specific process for acquiring the surgical image transmission data corresponding to the target operating room patient is as follows: During the transmission of surgical images, the transmission equipment records key parameters. Through the management software of the transmission equipment, the surgical image transmission data of the target operating room patient at each monitoring time point can be exported. The surgical image transmission data includes transmission delay rate, spatial resolution, power consumption, and image guidance error. The transmission delay rate, spatial resolution, power consumption, and image guidance error of the target operating room patient at each monitoring time point are used as input items and imported into the surgical image transmission evaluation value analysis model. After the operation and analysis of the surgical image transmission evaluation value analysis model, the surgical image transmission evaluation value corresponding to the target operating room patient is finally output.

8. The method for transmitting surgical images of an artificial joint as described in claim 7, characterized in that, The evaluation process for determining whether the surgical image transmission performance of the target operating room equipment needs optimization is as follows: The surgical image transmission evaluation value for the target operating room patient is compared with the set standard operating room patient surgical image transmission evaluation value. If the surgical image transmission evaluation value for the target operating room patient is greater than or equal to the set standard operating room patient surgical image transmission evaluation value, then the surgical image transmission performance of the target operating room equipment does not need to be optimized. If the surgical image transmission evaluation value for the target operating room patient is less than the set standard operating room patient surgical image transmission evaluation value, then the surgical image transmission performance of the target operating room equipment needs to be optimized. If the surgical image transmission performance of the target operating room equipment needs to be optimized, then an early warning is issued for the surgical image transmission performance of the target operating room equipment.

9. The method for transmitting surgical images of an artificial joint as described in claim 8, characterized in that, The warning process for assessing the surgical image transmission performance of the target operating room equipment is as follows: By integrating with the hospital's internal WeChat platform, the system automatically pushes early warning messages to relevant personnel accounts, including the warning level and abnormal performance data. Key personnel in the operating room are equipped with smart wearable devices and smart bracelets. When an early warning is triggered, the device will notify the user with vibration and sound, and the warning content will be displayed on the screen simultaneously. At the same time, voice broadcasts are deployed in the operating room and surrounding technical areas to automatically broadcast warnings, providing comprehensive coverage of the relevant areas.

10. An artificial joint surgery image transmission system for implementing the artificial joint surgery image transmission method according to any one of claims 1-9, characterized in that, include: Initial equipment setup module: Used to set up the initial equipment in the target operating room when artificial joint surgery is about to be performed; Software and system configuration adjustment module: After the initial equipment setup of the target operating room is completed, it acquires the patient's condition data corresponding to the target operating room, and then adjusts and analyzes the software and system configuration of the initial equipment. Image transmission execution switching module: used to set several monitoring time points when a patient in the target operating room is undergoing surgery, and then evaluate at each monitoring time point whether the surgical images of the patient in the target operating room need to be switched to a different transmission execution scheme; Postoperative data processing optimization and evaluation module: After the patient in the target operating room has completed surgery, it acquires the surgical image transmission data of the patient at each monitoring time point, thereby evaluating whether the surgical image transmission performance of the target operating room equipment needs to be optimized.

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