Intelligent operating table network access system

Abstract

The invention relates to the technical field of medical information, in particular to an intelligent operating table network access system which comprises a network diagram construction module, a path optimization module, a network monitoring module and a fault prediction module. According to the operating table network access system, path optimization is carried out through the network load, so that the whole operating table network access system can process data more accurately and efficiently, and through real-time data simulation and fault point analysis, the structural stability of the network is improved, the fault probability is remarkably reduced, the optimal data path selection and real-time network adjustment are realized, and the network access efficiency is improved. The data transmission speed and accuracy in an operation are greatly improved, the immediate availability and accurate delivery of key data in the operation process are ensured, the safety and efficiency of the operation are improved, real-time monitoring and predictive fault analysis are performed on network health, the response capability of the system to emergencies is enhanced, and the operation safety and efficiency are improved. The continuity and reliability of the network in the operation process are ensured, and the recovery result of the patient and the overall operation experience are optimized.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a network access system for an intelligent operating table. Background Art

[0002] The field of healthcare information technology involves the use of information technology tools and solutions to improve the quality and efficiency of healthcare services. This includes electronic health records (EHR), patient management systems, and medical devices connected through the Internet. It helps medical institutions manage patient information more efficiently, optimize clinical workflows, and improve patient care through remote monitoring and real-time data analysis. The implementation of healthcare information technology aims to improve the transparency and accessibility of medical services while ensuring the security and privacy of data.

[0003] Among them, the intelligent operating table network access system refers to connecting the operating table to the network system within the hospital or clinic, enabling it to receive and send critical medical data. Such a system enables the operating table to synchronize information with medical equipment in real time, support remote monitoring and operation, and enhance the efficiency of collaboration in the operating room. In this way, the surgical team can control the surgical process more accurately and optimize the patient's recovery results. The main purpose of the intelligent operating table network access system is to improve surgical safety, efficiency and results, especially in complex or delicate operations.

[0004] Although existing medical information technology has improved the transparency and accessibility of medical services, it has certain limitations in terms of network access and real-time data processing on the operating table. For example, data exchange during surgery relies on fixed network paths and lacks the ability to dynamically adjust, which leads to delays or losses in data transmission when the network is congested or equipment fails, affecting the smooth progress of the operation. Existing systems do not have sufficient predictive maintenance strategies, making it difficult to identify potential network failure points in advance, limiting the ability of medical institutions to respond in high-risk operations. The lack of effective real-time network status assessment and fault prediction functions affects the accuracy and availability of medical data at critical moments, thereby affecting surgical outcomes and patient safety. The shortcomings are particularly significant when dealing with complex or emergency surgical scenarios, resulting in unnecessary surgical risks and prolonged recovery time. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an intelligent operating table network access system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A network access system for an intelligent operating table comprises:

[0007] The network graph construction module collects data on routers and switches in the operating table network, identifies device types and connection speeds, integrates devices and connections according to the graph model to form nodes and edges, initializes and regularly updates the attributes of each node and edge, reflects changes in the network structure in real time, and generates a network topology graph;

[0008] The path optimization module extracts node and connection data from the network topology diagram, calculates the shortest path between nodes, determines the optimal data transmission route based on the delay and bandwidth of the edge, evaluates the efficiency of each path, dynamically adjusts the route to match the changes in network status, and obtains the optimal path data;

[0009] The network monitoring module uses the optimal path data to monitor the real-time traffic and node performance in the operating table network access, analyzes the network load based on the real-time data, and if a traffic peak and a node failure are detected, intelligently adjusts the path and re-evaluates the network status to obtain the adjusted network status;

[0010] The fault prediction module evaluates the structural stability of the intelligent operating table network access according to the adjusted network status, obtains the potential fault point analysis results, simulates the fault scenario through real-time network data, tests the network's response capability, quantitatively analyzes the failure probability of multiple nodes and connections, and obtains the network access health status assessment results.

[0011] As a further solution of the present invention, the steps of obtaining the device type and the connection speed are specifically as follows:

[0012] Collect data from routers and switches in the operating table network, analyze the data transmission characteristics and performance parameters of each device, and obtain device data characteristics;

[0013] Classifying the device data characteristics, identifying the types of routers and switches according to the functions and roles of the devices, and obtaining device type classification results;

[0014] The device type classification results are used to analyze the data transmission rate of the operating table network access, and the connection speed of the router and switch under each device type is tested using the formula:

[0015]

[0016] Calculate the connection speed of differentiated devices to obtain device type and connection speed;

[0017] Among them, v i represents the connection speed of the ith device, d i represents the amount of data transmitted within the target time, ti represents the time required for data transmission, ki is the adjustment coefficient, and zha is the data transmission efficiency weight of the device.

[0018] As a further solution of the present invention, the steps of obtaining the network topology map are specifically as follows:

[0019] Based on the device type and connection speed, the information is integrated according to a graph model, with the device as a node and the connection as an edge, and the attributes of each node and edge are initialized to generate a graph model data structure;

[0020] The graph model data structure is dynamically updated, and the attributes of nodes and edges are adjusted according to the real-time data feedback of the devices in the intelligent operating table network access, so as to reflect the network status in real time and obtain the adjusted graph model:

[0021] The adjusted graph model is visualized to optimize the layout of nodes using the formula:

[0022]

[0023] Calculate the location of the nodes and obtain the network topology;

[0024] Among them, T j represents the layout position of node j, v j represents the connection speed of node j, α j and β j are the visual criticality and performance weight parameters, n j is the number of nodes j.

[0025] As a further solution of the present invention, the step of obtaining the optimal path data is specifically as follows:

[0026] Extract each node and connection data from the network topology map, identify the physical distance and network delay of the connection, and obtain connection characteristic data;

[0027] The connection characteristic data is used in combination with the bandwidth information between nodes to perform path scoring using the formula:

[0028]

[0029] Calculate the score S of each path p , obtain the path scoring record;

[0030] Among them, BW p represents the bandwidth of path p, Delay p represents the delay of path p, Cost p represents the cost of path p, λ1, λ2, λ3 and λ4 are weighting coefficients;

[0031] According to the path scoring records, priority is sorted according to the scores, the path with the highest score is selected as the optimal path, and the optimal path data is obtained.

[0032] As a further solution of the present invention, the steps of obtaining the intelligent adjustment of the path are specifically:

[0033] Based on the optimal path data, the network access points of the intelligent operating table are monitored, traffic data and node performance are collected in real time, node load and performance status are analyzed, performance bottlenecks are identified, and path adjustment demand analysis results are obtained;

[0034] Based on the path adjustment demand analysis results, a dynamic routing algorithm is applied to optimize the network path, and a path adjustment plan is generated with reference to the real-time network load and node performance;

[0035] Execute and test the path adjustment plan to verify the reliability and efficiency of the network, using the formula:

[0036]

[0037] Calculate the performance p of the adjusted path adj , obtain intelligent adjustment of the path;

[0038] Among them, c j represents the load of the jth node, L j is the length of the node on the path, r j is the criticality score of the node, β j is the adjustment factor, n j is the number of nodes j.

[0039] As a further solution of the present invention, the step of acquiring the adjusted network status is specifically:

[0040] According to the intelligent adjustment of the path, the performance data of the intelligent operating table network access is collected and analyzed, the data flow and node response time in the network are detected, the impact of the intelligent adjustment of the path on the overall performance of the network is evaluated, and the network performance analysis results are obtained;

[0041] Based on the network performance analysis results, the stability and carrying capacity of the network are estimated using the formula:

[0042]

[0043] Calculate the overall network stability index s new , get the network status assessment result;

[0044] Among them, t j is the response time of the jth node, t max is the maximum acceptable value of the response time, w j is the weight coefficient of the node, n j is the number of nodes j;

[0045] According to the network status evaluation result, it is determined whether the network meets the predetermined performance standard, the effect of network adjustment is evaluated, and iterative optimization is performed to obtain the adjusted network status.

[0046] As a further solution of the present invention, the step of obtaining the potential fault point analysis result is specifically:

[0047] According to the adjusted network state, a structure detection of the intelligent operating table network is performed to identify the configuration states of the differentiated nodes and connections, and obtain a network structure detection result;

[0048] Using the network structure detection results, the load capacity of the node is evaluated using the formula:

[0049]

[0050] Calculate the load and fault tolerance of each node and connection to obtain the node fault tolerance score f(x);

[0051] Among them, x1 represents the node load, x2 represents the number of connections, x3 represents the fault data, a, b, c and d o represents the adjustment factor;

[0052] According to the node fault tolerance scores, the scores are prioritized, overloaded nodes and connections with insufficient fault tolerance are identified, and potential fault point analysis results are obtained.

[0053] As a further solution of the present invention, the steps of obtaining the network access health status assessment result are specifically:

[0054] Using the potential failure point analysis results, extract the data of each failure point, including the node where the failure occurred, the load and the failure response time, to obtain a failure occurrence record;

[0055] According to the fault occurrence record, the impact of the fault is quantified using the formula:

[0056]

[0057] Calculate the impact of each fault point and obtain the fault point impact score g(x);

[0058] Among them, x4 represents the load at the fault point, x5 represents the response time, and d a , f, h and Z are adjustment coefficients;

[0059] Based on the impact score of the fault point and the comparison with the set health threshold, the overall health status of the intelligent operating table network is identified to obtain the network access health status assessment result.

[0060] Compared with the prior art, the advantages and positive effects of the present invention are:

[0061] In the present invention, by collecting the data of routers and switches in the operating table network and forming a dynamically updated network structure diagram, real-time monitoring of traffic and node performance, and path optimization according to the network load, the entire operating table network access system can process data more accurately and efficiently. Through real-time data simulation and fault point analysis, the structural stability of the network is improved and the probability of failure is significantly reduced. Optimized data path selection and real-time network adjustment greatly improve the speed and accuracy of data transmission during surgery, ensure the immediate availability and accurate delivery of key data during surgery, and improve the safety and efficiency of surgery. Real-time monitoring of network health and predictive fault analysis enhance the system's responsiveness to emergencies, ensure the continuity and reliability of the network during surgery, and optimize the patient's recovery results and overall surgical experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a system flow chart of the present invention;

[0063] Figure 2 A flowchart of the steps of obtaining the device type and connection speed of the present invention;

[0064] Figure 3 A flowchart of the steps for obtaining a network topology map of the present invention;

[0065] Figure 4 A flowchart of the steps of obtaining the optimal path data of the present invention;

[0066] Figure 5 A flow chart of the steps for obtaining the intelligent adjustment of the path of the present invention;

[0067] Figure 6 A flowchart of the steps for obtaining the adjusted network status of the present invention;

[0068] Figure 7 A flowchart of the steps for obtaining the potential fault point analysis results of the present invention;

[0069] Figure 8 The present invention is a flowchart of the steps for obtaining the network access health status assessment results. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0072] See also Figure 1 , an intelligent operating table network access system includes:

[0073] The network graph construction module collects data on routers and switches in the operating table network, identifies device types and connection speeds, integrates devices and connections according to the graph model to form nodes and edges, initializes and regularly updates the attributes of each node and edge, reflects changes in the network structure in real time, and generates a network topology graph;

[0074] The path optimization module extracts node and connection data from the network topology, calculates the shortest path between nodes, determines the optimal data transmission route based on the edge delay and bandwidth, and evaluates the efficiency of each path, dynamically adjusts the route to match the changes in network status, and obtains the optimal path data;

[0075] The network monitoring module uses the optimal path data to monitor the real-time traffic and node performance in the operating table network access, analyzes the network load based on the real-time data, and if a traffic peak or node failure is detected, intelligently adjusts the path and re-evaluates the network status to obtain the adjusted network status;

[0076] The fault prediction module evaluates the structural stability of the intelligent operating table's network access based on the adjusted network status, obtains the potential fault point analysis results, simulates fault scenarios through real-time network data, tests the network's response capabilities, conducts quantitative analysis of the failure probabilities of multiple nodes and connections, and obtains the network access health status assessment results.

[0077] The device type and connection speed include router model, switch model, and connection speed value. The network topology includes nodes and edges. The optimal path data includes the shortest path between node pairs, data transmission route, and path efficiency. The intelligent adjustment of the path includes the adjusted route, path delay, and path bandwidth. The adjusted network status includes real-time traffic, node performance, and network load. The potential fault point analysis results include the fault point location, fault impact range, and fault cause. The network access health status assessment results include the quantitative score of the fault point impact, fault scenario response capability, and network health status.

[0078] See also Figure 2 , the specific steps for obtaining device type and connection speed are:

[0079] Collect data from routers and switches in the operating table network, analyze the data transmission characteristics and performance parameters of each device, and obtain device data characteristics;

[0080] The data transmission characteristics and performance parameters are integrated, including the transmission rate, data processing capability and connection stability of the equipment. The characteristics are obtained through real-time monitoring and historical data comparative analysis, which can more accurately determine the role and performance level of each device in the network, and provide basic data support for the construction of the network diagram. The process also involves real-time updating of device data to ensure that the reflection of the network structure is accurate. The update operation includes the identification of new equipment and the re-evaluation of the performance of old equipment to ensure the accuracy and real-time nature of the network topology diagram and obtain the device data characteristics.

[0081] Classify the device data characteristics, identify the types of routers and switches according to the functions and roles of the devices, and obtain the device type classification results;

[0082] Automatically identify the type and function of each router and switch, including the identification of key performance indicators such as the transmission rate, data throughput and network interface type of the device. The identification process uses a clustering analysis algorithm based on device performance parameters, which can effectively distinguish network devices of different types and functions, such as backbone routers and edge switches. It also includes checking the device operating system and firmware version to evaluate the latest functions and security performance of the device. Through detailed analysis, network devices can be more reasonably configured and optimized, improving the overall performance and security of the network, and obtaining device type classification results.

[0083] The device type classification results are used to analyze the data transmission rate of the operating table network access, and the connection speed of the routers and switches under each device type is tested using the formula:

[0084]

[0085] Calculate the connection speed of differentiated devices to obtain device type and connection speed;

[0086] Among them, v i represents the connection speed of the ith device, d i Represents the amount of data transmitted within the target time, t i represents the time required for data transmission, k i is the adjustment coefficient, λ i The data transmission efficiency weight of the device;

[0087] Use network monitoring tools to measure the data transmission volume of routers and switches under each device type in real time. i For example, for a router with a 1Gbps connection, 10GB of data is transferred in 10 seconds, that is, d i =10GB;

[0088] Measure the time t required for data transmission i , in this case t i = 10 seconds;

[0089] In order to calculate the connection speed, the adjustment factor k is introduced i , which is based on the performance score of the device. If the performance score of the device is set to 0.8, then k i =0.8;

[0090] The data transmission efficiency weight of the device λ i It is also a factor to consider, setting the weight λ i =1.2;

[0091] Substitute the values ​​into the formula to calculate the connection speed:

[0092]

[0093] The result shows that under the current device performance and network conditions, the actual connection speed of the router is 2.31Gbps. This numerical result reflects the degree of match between the data transmission capability of the router and the set goals of the network building module in the actual network environment, and provides a basis for further optimizing the network structure and improving data transmission efficiency.

[0094] See also Figure 3 , the specific steps for obtaining the network topology map are:

[0095] Based on the device type and connection speed, the information is integrated according to the graph model, with the device as the node and the connection as the edge. The properties of each node and edge are initialized to generate the graph model data structure.

[0096] Identify the devices connected to the smart operating table network, such as routers and switches, and integrate them into nodes and edges by type and connection speed. The collection of this data includes identifying the model of each device and detecting the network speed. This process involves real-time monitoring and data collection of network performance. Network management software is used to record the status and performance of each device in real time, such as transmission rate and response time. The data is automatically summarized through the network monitoring system to generate a real-time performance report. The type of device is determined by checking the hardware specifications and configuration files, while the connection speed is measured through network testing tools such as rate testing software. The device layout and performance indicators in the network are displayed, and a graph model data structure is generated.

[0097] Dynamically update the graph model data structure, adjust the attributes of nodes and edges according to the real-time data feedback of the devices in the intelligent operating table network access, reflect the network status in real time, and obtain the adjusted graph model;

[0098] Continuous data updates are performed through network performance management software. This process includes real-time capture of changes in the status of network devices, such as load increase, connection interruption, and the addition of new devices, and automatic updates of the properties of nodes and edges in the graph model. Changes in network load will affect the weight parameters of the nodes, and the addition of new devices requires adding new nodes to the graph and setting up connections. Advanced network analysis tools can be used to visually inspect and adjust the network topology to adapt to changes in network behavior, reflect the current operating status of the network, and provide data support for rapid diagnosis of network faults to obtain an adjusted graph model.

[0099] Visualize the adjusted graph model and optimize the layout of nodes using the formula:

[0100]

[0101] Calculate the location of the nodes and obtain the network topology;

[0102] Among them, T j represents the layout position of node j, v j represents the connection speed of node j, α j and β j are the visual criticality and performance weight parameters, n j is the number of nodes j;

[0103] Parameter meaning and setting value:

[0104] αj is the visual importance parameter of node j, which is adjusted according to the traffic and criticality of the node. This parameter is determined by the data provided by the network traffic analyzer to ensure that high-traffic nodes are more prominent in the topology map;

[0105] β j is the performance weight parameter of node j, which is adjusted based on the processing capacity and number of connections of the node, using data obtained from the device performance report provided by the network management system;

[0106] v j Represents the connection speed of node j. This value is obtained through actual network speed test and reflects the data transmission capacity of the node.

[0107] There are three nodes in the setup network, and the specific parameters are as follows:

[0108] α1=1.2, β1=1.5, v1=100Mbps;

[0109] α2=1.0, β2=1.3, V2=150Mbps;

[0110] α3=1.5, β3=1.7, v3=120Mbps;

[0111] Substitute the parameters into the formula for calculation:

[0112]

[0113] Result 19.2 shows that the coordinate of the calculated layout position of node j in the topology map is 19.2. This position is calculated based on the visual importance and performance weight of the node, which helps to optimize the display of the network topology map and ensure that important and high-performance nodes are properly highlighted in the network map, making it easier for network administrators to monitor and manage the network.

[0114] See also Figure 4 , the specific steps for obtaining the optimal path data are:

[0115] Extract each node and connection data from the network topology map, identify the physical distance and network delay of the connection, and obtain connection characteristic data;

[0116] The connection data between nodes is extracted through the network topology diagram, and the interactivity and connectivity between nodes are analyzed by combining the calculation of physical distance and network delay. The connection data not only contains the location information of each node in the network, but also covers the specific data of transmission delay and bandwidth between nodes. By analyzing the data, network administrators can optimize routing strategies and improve the efficiency of data transmission, provide the necessary basic information for path scoring and path selection, and obtain connection characteristic data.

[0117] Using the connection characteristic data and the bandwidth information between nodes, the path score is calculated using the formula:

[0118]

[0119] Calculate the score S of each path p , obtain the path scoring record;

[0120] Among them, BW p represents the bandwidth of path p, Delay p represents the delay of path p, Cost p represents the cost of path p, λ1, λ2, λ3 and λ4 are weighting coefficients;

[0121] Parameter meaning and setting value:

[0122] BW pThe path bandwidth is in Mbps, which is obtained through monitoring data of network devices. Taking 100Mbps as an example, it indicates the data transmission capacity of the current path.

[0123] Delay p is the path delay in milliseconds, measured by a network delay test tool, with a value of 50ms, indicating the time it takes for data to travel from the sending point to the receiving point;

[0124] Cost p is the path cost, which reflects the economic cost of maintaining the path. It is calculated using the data provided by the finance department. Taking 10 as an example, it includes factors such as equipment depreciation and energy consumption.

[0125] λ1, λ2, λ3 and λ4 are weight coefficients, which are set according to network management policies and business requirements. 0.3, 0.5, 0.2 and 1 are taken as examples. λ4 is used as a normalization coefficient to ensure that the score is within a reasonable range.

[0126] Substitute the parameters into the formula for calculation:

[0127]

[0128] The result 30.64 shows that considering the weighted impact of bandwidth, latency, and cost, the comprehensive score of this path is 30.64, which is a path with better performance. The score helps decision makers understand the performance of each path and then select the best path to optimize the performance and cost efficiency of the entire network.

[0129] According to the path scoring data, the priority is sorted according to the score, the path with the highest score is selected as the optimal path, and the optimal path data is obtained;

[0130] Based on the calculated scoring data of each path, the performance score of the analysis path is the highest, and the path with the highest comprehensive score is selected as the optimal path in the network. This process involves the screening and processing of batch data. Through actual network testing and historical data analysis, it is determined that the path stands out due to low latency, high bandwidth and maximum cost-effectiveness. It is used for network traffic management and optimization, improving the operating efficiency of the entire network, and obtaining the optimal path data.

[0131] See also Figure 5 , the specific steps for obtaining the intelligent adjustment of the path are:

[0132] Based on the optimal path data, the network access points of the intelligent operating table are monitored, traffic data and node performance are collected in real time, node load and performance status are analyzed, performance bottlenecks are identified, and path adjustment demand analysis results are obtained;

[0133] The process of monitoring the operating table network access point involves real-time collection of node traffic and performance data, setting the frequency and type of data collected by the monitoring node to ensure coverage of various network usage situations. The data includes the number of packets per second, error rate, delay time and bandwidth utilization. Based on the collected data, data aggregation technology is used to perform preliminary processing of the information, such as using statistical methods such as average and median to evaluate the overall performance status of the network, and then using anomaly detection algorithms to identify data points that are out of the normal range, indicating high traffic or performance bottlenecks. This identification is performed by comparing real-time data with performance baselines, analyzing the details of key areas that need to be adjusted in the network, and obtaining path adjustment demand analysis results.

[0134] Based on the results of path adjustment demand analysis, the dynamic routing algorithm is used to optimize the network path, and the path adjustment plan is generated by referring to the real-time network load and node performance;

[0135] After identifying the high-traffic points and performance bottlenecks of the network, the process of applying dynamic routing algorithms to optimize network paths adjusts routing decisions based on real-time monitoring data sets, which includes analyzing traffic patterns, node performance, and current network topology in the data sets. The dynamic routing algorithm calculates multiple paths based on the data and evaluates the performance indicators of each path, such as latency, bandwidth, and packet loss rate. The algorithm selects an optimal path that meets the lowest latency and highest bandwidth utilization while also considering the reliability and fault recovery capabilities of the network. It lists the configuration steps for the new path and the expected network performance improvement effect, and generates a path adjustment plan.

[0136] Execute and test the path adjustment plan to verify the reliability and efficiency of the network, using the formula:

[0137]

[0138] Calculate the performance p of the adjusted path adj , obtain intelligent adjustment of the path;

[0139] Among them, c j represents the load of the jth node, L j is the length of the node on the path, r j is the criticality score of the node, β j is the adjustment factor, n j is the number of nodes j.

[0140] Parameter meaning and setting value:

[0141] c j It represents the load of node j in Mbps, which is obtained by real-time monitoring through network monitoring tools. For example, the current load of a node is 30Mbps.

[0142] rj Score the importance of node j, ranging from 1 to 5. This value is obtained through historical data analysis and the experience evaluation of network administrators. For example, the importance score of the node is 2;

[0143] L j is the distance length of node j on the network path, in meters, calculated by network topology management software, for example, the path length of a node is 100 meters;

[0144] β j It is the adjustment factor of the impact of path length on performance, ranging from 0 to 1. This value is dynamically adjusted according to the congestion of the network. When the congestion is low, it is lowered to reduce the impact of the length. When the congestion is high, it is increased. For example, it is set to 0.5 to balance the impact of path length and node importance.

[0145] Substitute the parameters into the formula for calculation:

[0146]

[0147] The results show that the performance index of the current network path is 0.7746. This value reflects the comprehensive performance status of the network path after considering the node load, importance and length on the path. A higher value means better network performance. The current path configuration effectively improves network performance. This result helps network administrators evaluate and optimize network path design to ensure data transmission efficiency and network stability.

[0148] See also Figure 6 , the specific steps for obtaining the adjusted network status are:

[0149] According to the intelligent adjustment of the path, the performance data of the intelligent operating table network access is collected and analyzed, the data flow and node response time in the network are detected, the impact of the intelligent adjustment of the path on the overall performance of the network is evaluated, and the network performance analysis results are obtained;

[0150] In the process of monitoring the operating table network access points, advanced network traffic analysis technology is involved. Data is collected from each access point in real time through sensors and monitoring software, including the bandwidth utilization, delay time and stability indicators of each node. The monitoring tool will automatically analyze the data to identify high-traffic points and performance bottlenecks. This process ensures accurate data collection and preliminary analysis, provides a basis for subsequent network optimization, and obtains network performance analysis results based on the monitoring results.

[0151] Based on the results of network performance analysis, the stability and carrying capacity of the network are estimated using the formula:

[0152]

[0153] Calculate the overall network stability index snew , get the network status assessment result;

[0154] Among them, t j is the response time of the jth node, t max is the maximum acceptable value of the response time, w j is the weight coefficient of the node, n j is the number of nodes j;

[0155] Parameter meaning and setting value:

[0156] t j The response time of the node is obtained in real time through the network monitoring system. The response time of node A is set to 120 milliseconds;

[0157] t max is the maximum response time, which is set according to the network design standard and depends on the network design requirements and service level agreement (SLA), and is set to 200 milliseconds;

[0158] w j The importance weight of the node is obtained based on the role of the node in the network and the traffic data analysis. For example, if node A processes batch critical data flow, its importance score is higher and the weight is set to 0.8.

[0159] Substitute the parameters into the formula for calculation:

[0160]

[0161] The result of 0.48 indicates that the network stability is low and further adjustments are needed to improve network performance. This result shows that the response time of node A still has room for improvement relative to the maximum allowed value and performance needs to be improved by adjusting the network configuration or increasing resources.

[0162] Based on the network status evaluation results, determine whether the network meets the predetermined performance standards, evaluate the effect of network adjustment, and perform iterative optimization to obtain the adjusted network status;

[0163] In-depth analysis based on network performance data not only involves data collection, but also involves comparing historical data and real-time data through advanced algorithm models to evaluate the impact of various network adjustment measures. Network analysis tools use a variety of parameters, such as packet loss rate, throughput, and various delay indicators, to comprehensively evaluate the overall performance of the network. Through comprehensive data, network engineers can accurately identify network segments whose performance has improved or deteriorated due to adjustments, and explain in detail the various performance indicators after network adjustment, as well as the specific values ​​of the indicators compared with the predetermined performance standards, providing decision-makers with direct basis for whether the network needs to be further optimized and obtaining the adjusted network status.

[0164] See also Figure 7 , the specific steps for obtaining the potential fault point analysis results are:

[0165] According to the adjusted network status, the structure of the intelligent operating table network is detected, the configuration status of the differentiated nodes and connections is identified, and the network structure detection result is obtained;

[0166] To analyze the adjusted network status, it is necessary to use advanced network monitoring tools to collect real-time data, including the collection of core indicators such as node response time, data transmission rate, and connection stability. The data is monitored in real time through network sensors and data collection and uploaded to the data analysis platform. Then, the network topology analysis tool is used to draw a structural diagram of the entire network and detect the status of each node and connection. The process includes specific operations such as node activity detection, connection delay and packet loss rate testing. Through detailed evaluation, problem areas in the network can be accurately identified, such as nodes with high latency or frequent link disconnection, which can intuitively display the overall health status and key problem areas of the network and obtain network structure detection results.

[0167] Using the network structure detection results, the load capacity of the node is evaluated using the formula:

[0168]

[0169] Calculate the load and fault tolerance of each node and connection to obtain the node fault tolerance score f(x);

[0170] Among them, x1 represents the node load, x2 represents the number of connections, x3 represents the fault data, a, b, c and d o represents the adjustment factor;

[0171] Parameter meaning and calculation process:

[0172] x1 represents the node load, which is obtained in real time through the network monitoring system. For example, the data transmission rate of a node at peak time is 100Mbps;

[0173] x2 represents the number of connections. The number of active connections recorded by the network management software is 50;

[0174] x3 is the failure data. The number of failures of this node in the past month obtained from the network log analysis system is 3;

[0175] Weight parameters a, b, c and normalization coefficient d o Based on performance data and expert experience, we set a = 0.5, b = 2, c = 1.5, d o =10;

[0176] 1. Calculate the node load item:

[0177]

[0178] 2. Calculate the number of connections:

[0179]

[0180] 3. Calculate the fault item:

[0181] c·|x3|=1.5·3=4.5;

[0182] 4. Substitute the above results into the formula to obtain:

[0183]

[0184] The result shows that according to the current weight and node performance parameters, the node's comprehensive fault tolerance score is 501.864. This score reflects the stability and reliability of the node in the network. A higher score means that the node has good fault tolerance.

[0185] According to the node fault tolerance scores, the scores are prioritized, overloaded nodes and connections with insufficient fault tolerance are identified, and potential fault point analysis results are obtained;

[0186] Utilizing the node fault tolerance score and combining it with the network structure detection results, we conduct an in-depth analysis to screen out all nodes and connections with fault tolerance below the average level. This screening is based on the fault tolerance score and a set threshold. For example, the threshold is set to 80% of the average network score. We conduct a potential fault point analysis on nodes and connections. By comparing and analyzing the fault data of nodes and connections, the current load conditions, and the network structure location, we identify the network parts with the highest probability of failure. This process not only relies on the analysis results of automated tools, but also requires the experience and judgment of network engineers to ensure the accuracy of the analysis, maintain and optimize the network, ensure that the network can continue to operate stably, and obtain the potential fault point analysis results.

[0187] See also Figure 8 , the specific steps for obtaining the network access health status assessment results are:

[0188] Using the potential failure point analysis results, extract the data of each failure point, including the node where the failure occurred, the load, and the failure response time, and obtain the failure occurrence record;

[0189] When extracting data from the potential fault point analysis results, all nodes and connections in the network are monitored to accurately capture the specific location and time of the fault. The monitoring data includes the load of each node and the response time of each fault. The data is automatically recorded and stored in the database through a real-time network monitoring system. The database is updated as frequently as once per second to ensure the real-time and accuracy of the data. Potential fault points in the network can be accurately identified and the impact on network operation can be analyzed for further analysis and formulation of fault prevention measures to obtain a record of fault occurrence.

[0190] According to the fault occurrence records, the impact of the fault is quantified using the formula:

[0191]

[0192] Calculate the impact of each fault point and obtain the fault point impact score g(x);

[0193] Among them, x4 represents the load at the fault point, x5 represents the response time, and d a , f, h and Z are adjustment coefficients;

[0194] Parameter meaning and setting value:

[0195] x4 represents the load of the fault point in Mbps. The real-time data obtained from the network monitoring system shows that the load of a certain fault point is 80Mbps when the fault occurs.

[0196] x5 represents the fault response time in seconds. According to the records of the fault response system, the response time of this fault point is 30 seconds.

[0197] d a is the weight parameter of the load, which is set to 0.5 according to the capacity and service quality requirements of the network, which reflects the sensitivity of the load anomaly to the network health in the scoring system;

[0198] f is the weight parameter of response time, which is set to 0.3, considering the severity of the impact of prolonged response time on the service;

[0199] h and Z are adjustment coefficients used to balance the impact of response time, which are set to 2 and 0.1 respectively. These two coefficients help adjust the sensitivity of the formula to adapt to the characteristics of different networks;

[0200] Substitute the parameters into the formula for calculation:

[0201]

[0202] The results show that the comprehensive impact score of the fault point is 855.34, indicating that the fault point has a greater negative impact on the health of the network and needs to be dealt with as a priority. This score is higher than the conventional threshold (set at around 500), indicating that the fault is highly severe and poses a threat to the stability of network operation. The score directly affects fault management and response strategies. A high score prompts network managers to take emergency measures to restore network service quality.

[0203] Based on the impact score of the fault point and the set health threshold, the overall health status of the intelligent operating table network is identified and the network access health status assessment result is obtained;

[0204] By comprehensively evaluating the impact scores of all fault points, a network health threshold can be set. This threshold is determined based on past fault handling experience and network performance standards. For example, setting a threshold of 500 points means that any fault point that exceeds this score needs to be handled immediately. By summing up the scores of all fault points and calculating the average, the overall health score of the entire network is obtained. This score reflects the current operating status and potential risk level of the network. If the total score exceeds the threshold, further technical intervention is performed, such as route adjustment or hardware replacement, to ensure that the network can operate stably and reduce the risk of major failures in the future, thereby obtaining a network access health status assessment result.

[0205] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent operating table network access system, characterized in that: The system comprises: The network graph construction module collects data on routers and switches in the operating table network, identifies device types and connection speeds, integrates devices and connections according to the graph model to form nodes and edges, initializes and regularly updates the attributes of each node and edge, reflects changes in the network structure in real time, and generates a network topology graph; The path optimization module extracts node and connection data from the network topology diagram, calculates the shortest path between nodes, determines the optimal data transmission route based on the delay and bandwidth of the edge, evaluates the efficiency of each path, dynamically adjusts the route to match the changes in network status, and obtains the optimal path data; The network monitoring module uses the optimal path data to monitor the real-time traffic and node performance in the operating table network access, analyzes the network load based on the real-time data, and if a traffic peak and a node failure are detected, intelligently adjusts the path and re-evaluates the network status to obtain the adjusted network status; The fault prediction module evaluates the structural stability of the intelligent operating table network access according to the adjusted network status, obtains the potential fault point analysis results, simulates the fault scenario through real-time network data, tests the network's response capability, quantitatively analyzes the failure probability of multiple nodes and connections, and obtains the network access health status assessment results.

2. The intelligent operating table network access system according to claim 1, characterized in that: The steps for obtaining the device type and connection speed are specifically as follows: Collect data from routers and switches in the operating table network, analyze the data transmission characteristics and performance parameters of each device, and obtain device data characteristics; Classifying the device data characteristics, identifying the types of routers and switches according to the functions and roles of the devices, and obtaining device type classification results; The device type classification results are used to analyze the data transmission rate of the operating table network access, and the connection speed of the router and switch under each device type is tested using the formula: Calculate the connection speed of differentiated devices to obtain device type and connection speed; Among them, v i represents the connection speed of the ith device, d i Represents the amount of data transmitted within the target time, t i represents the time required for data transmission, k i is the adjustment coefficient, λ i The data transmission efficiency weight of the device.

3. The intelligent operating table network access system according to claim 2, characterized in that: The steps of obtaining the network topology map are specifically as follows: Based on the device type and connection speed, the information is integrated according to a graph model, with the device as a node and the connection as an edge, and the attributes of each node and edge are initialized to generate a graph model data structure; Dynamically update the graph model data structure, adjust the attributes of nodes and edges according to the real-time data feedback of the equipment in the intelligent operating table network access, reflect the network status in real time, and obtain the adjusted graph model; The adjusted graph model is visualized to optimize the layout of nodes using the formula: Calculate the location of the nodes and obtain the network topology; Among them, T j represents the layout position of node j, v j represents the connection speed of node j, α j and β j are the visual criticality and performance weight parameters, n j is the number of nodes j.

4. The intelligent operating table network access system according to claim 3 is characterized in that: The steps for obtaining the optimal path data are specifically as follows: Extract each node and connection data from the network topology map, identify the physical distance and network delay of the connection, and obtain connection characteristic data; The connection characteristic data is used in combination with the bandwidth information between nodes to perform path scoring using the formula: Calculate the score S of each path p , obtain the path scoring record; Among them, BW p represents the bandwidth of path p, Delay p represents the delay of path p, Cost p represents the cost of path p, λ1, λ2, λ3 and λ4 are weighting coefficients; According to the path scoring records, priority is sorted according to the scores, the path with the highest score is selected as the optimal path, and the optimal path data is obtained.

5. The intelligent operating table network access system according to claim 4, characterized in that: The steps for obtaining the intelligent adjustment of the path are specifically as follows: Based on the optimal path data, the network access points of the intelligent operating table are monitored, traffic data and node performance are collected in real time, node load and performance status are analyzed, performance bottlenecks are identified, and path adjustment demand analysis results are obtained; Based on the path adjustment demand analysis results, a dynamic routing algorithm is applied to optimize the network path, and a path adjustment plan is generated with reference to the real-time network load and node performance; Execute and test the path adjustment plan to verify the reliability and efficiency of the network, using the formula: Calculate the performance pad of the adjusted path j , obtain intelligent adjustment of the path; Among them, c j represents the load of the jth node, L j is the length of the node on the path, r j is the criticality score of the node, β j is the adjustment factor, n j is the number of nodes j.

6. The intelligent operating table network access system according to claim 5, characterized in that: The steps for obtaining the adjusted network status are specifically as follows: According to the intelligent adjustment of the path, the performance data of the intelligent operating table network access is collected and analyzed, the data flow and node response time in the network are detected, the impact of the intelligent adjustment of the path on the overall performance of the network is evaluated, and the network performance analysis results are obtained; Based on the network performance analysis results, the stability and carrying capacity of the network are estimated using the formula: Calculate the overall network stability index s new , get the network status assessment result; Among them, t j is the response time of the jth node, t max is the maximum acceptable value of the response time, w j is the weight coefficient of the node, n j is the number of nodes j; According to the network status evaluation result, it is determined whether the network meets the predetermined performance standard, the effect of network adjustment is evaluated, and iterative optimization is performed to obtain the adjusted network status.

7. The intelligent operating table network access system according to claim 6, characterized in that: The steps for obtaining the potential fault point analysis result are specifically as follows: According to the adjusted network state, a structure detection of the intelligent operating table network is performed to identify the configuration states of the differentiated nodes and connections, and obtain a network structure detection result; Using the network structure detection results, the load capacity of the node is evaluated using the formula: Calculate the load and fault tolerance of each node and connection to obtain the node fault tolerance score f(x); Among them, x1 represents the node load, x2 represents the number of connections, x3 represents the fault data, a, b, c and d o represents the adjustment factor; According to the node fault tolerance scores, the scores are prioritized, overloaded nodes and connections with insufficient fault tolerance are identified, and potential fault point analysis results are obtained.

8. The intelligent operating table network access system according to claim 7, characterized in that: The steps for obtaining the network access health status assessment result are specifically as follows: Using the potential failure point analysis results, extract the data of each failure point, including the node where the failure occurred, the load and the failure response time, to obtain a failure occurrence record; According to the fault occurrence record, the impact of the fault is quantified using the formula: Calculate the impact of each fault point and obtain the fault point impact score g(x); Among them, x4 represents the load at the fault point, x5 represents the response time, and d a , f, h and Z are adjustment coefficients; Based on the impact score of the fault point and the comparison with the set health threshold, the overall health status of the intelligent operating table network is identified to obtain the network access health status assessment result.

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