A hospital multi-active data center network management system based on software defined network

Abstract

The application discloses a hospital multi-active data center network management system based on a software defined network, relates to the technical field of data management, and discloses a network management system controlled based on an SDN controller, which comprises a traffic collection module, a prediction module, a scheduling module, a multi-active data module and a security module; the traffic collection module is used for collecting historical traffic data of a hospital, analyzing and managing the acquired traffic data, obtaining historical data of traffic loads of various devices of the hospital, and storing the data; the prediction module predicts future traffic loads of devices of the hospital based on historical data acquired in the traffic collection module and uploads the prediction result to the scheduling module; the application can predict future traffic loads of devices of the hospital, reasonably distribute network traffic to various data centers and corresponding servers according to the prediction result, flexibly configure the network, quickly adjust the distribution of network resources, and improve the medical experience of patients.

Description

Technical Field

[0001] This invention relates to the field of data management technology, specifically to a hospital multi-active data center network management system based on software-defined networking. Background Technology

[0002] A multi-active data center refers to the construction of multiple data centers that are simultaneously active. These data centers are distributed in different geographical locations and connected to each other via a high-speed network. They can synchronize and interact with data in real time or near real time, and each can provide services to the outside world. Unlike the traditional primary-backup data center model, where the backup data center is mostly idle and only activated when the primary data center fails, each center in a multi-active data center undertakes business processing tasks in daily operations and jointly provides services to users. With the continuous advancement of hospital informatization, hospitals have accumulated massive amounts of medical data, such as patient electronic medical records, medical imaging data, and test results. Many critical business systems rely on stable and reliable data centers to operate. Traditional data center network management methods have many shortcomings when facing the needs of hospital multi-active data centers. The network configuration lacks flexibility and it is difficult to quickly adjust the allocation of network resources according to different business scenarios, resulting in slow response of some systems during peak business periods, affecting the patient's medical experience and the hospital's operational efficiency. To address this, we propose a hospital multi-active data center network management system based on software-defined networking. Summary of the Invention

[0003] To address the aforementioned technical issues, a hospital multi-active data center network management system based on software-defined networking is provided. This technical solution resolves the problem of slow system response during peak business periods.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a hospital multi-active data center network management system based on software-defined networking, wherein the network management system is controlled based on an SDN controller and includes: a traffic collection module, a prediction module, a scheduling module, a multi-active data module and a security module;

[0005] The traffic collection module is used to collect historical patient traffic data of the hospital, analyze and manage the acquired patient traffic data, obtain historical traffic load data of various hospital equipment, and store the data.

[0006] The prediction module predicts the future flow load of hospital equipment based on historical data obtained from the flow collection module, and uploads the prediction results to the scheduling module.

[0007] Based on the prediction results from the prediction module, and taking into account the traffic scheduling algorithm and the sensitivity of the business, the scheduling module distributes network traffic from various business terminals in the hospital to various data centers and corresponding servers to share the load.

[0008] The multi-active data module includes multiple hospital data centers distributed in different geographical locations. Each data center is equipped with a server cluster for storing and processing medical data, and also has network access equipment for connecting to external networks. The data centers are connected through high-speed dedicated network links to synchronize and mutually support business operations. When a business device fails, the corresponding business can be seamlessly taken over.

[0009] The security module continuously monitors data access requests in the network, intercepts illegal access and attack behaviors, and ensures network security.

[0010] Preferably, the flow collection module uses various types of monitoring equipment. Infrared sensor counters are installed at each entrance and exit of the hospital, and infrared sensors record the flow. Cameras are deployed in the corridors on each floor and at the entrances of departments inside the hospital to capture patient flow data. Based on the data recorded in the physician's medical record report, patient flow data of different departments is obtained.

[0011] The prediction module acquires and analyzes stored historical data. After preprocessing the data, it uses time series analysis to analyze the trend of traffic changes in different time periods, identify peak and trough periods and periodic patterns of hospital traffic, and uses correlation analysis algorithms to analyze the intrinsic relationship between traffic flow and the use of different departments and equipment in the hospital to obtain historical hospital equipment usage load data.

[0012] Preferably, assuming the analysis covers n days, with m observation points per day, then the average pedestrian flow P on day i is... i Represented as:

[0013]

[0014] Where P(i,j) represents the pedestrian flow at the j-th observation time point on the i-th day, and j represents the observation time point.

[0015] Preferably, the formula for calculating the overall average pedestrian flow is:

[0016]

[0017] Where P represents the overall average pedestrian flow during the entire analysis period;

[0018] Further calculation of fluctuation indicators in pedestrian flow:

[0019]

[0020] Where αp is the standard deviation of the flow rate.

[0021] Preferably, peak and off-peak periods are further determined by setting a relative threshold. Peak periods are defined as those exceeding the overall average pedestrian flow plus a certain multiple of the standard deviation, and off-peak periods are defined as those below the overall average pedestrian flow minus a certain multiple of the standard deviation. Let the threshold multiple be k. Then, the determination of peak periods is expressed as:

[0022] P(t>P+kαp

[0023] Where P(t) represents the flow of people at time t;

[0024] The criteria for determining the trough period are:

[0025] P(t)<P-kαp

[0026] The hospital's peak and off-peak periods were calculated and analyzed.

[0027] Preferably, if there is a linear relationship between the historical hospital equipment usage load data and the usage of different departments and equipment in the hospital, then the analysis formula is:

[0028] Q=β0+β1P(t)+∈

[0029] Where Q is the predicted output value, β0 is the intercept term, β1 is the regression coefficient, and ∈ is the error term; the intercept term and regression coefficient are calculated based on the least squares method.

[0030] Preferably, the scheduling module allocates network traffic from each service terminal to the data center and servers. Guided by the prediction results of the prediction module, it initiates a traffic allocation mechanism, with the traffic allocation formula as follows:

[0031]

[0032] Where F represents the actual network traffic value from the server allocated to the data center server, Ft represents the amount of network traffic generated by the business terminal at time t, At is the final weight coefficient of the network traffic of the business terminal allocated to the server in the data center, which comprehensively reflects the appropriateness of allocating network traffic to a specific server in a specific data center, taking into account factors such as business sensitivity, link bandwidth utilization, server load, and network latency. d∈D represents a specific data center, and s∈Sd is one of the servers in data center d. The above formula means that the network traffic generated by the business terminal is allocated to each data center and server according to the proportion of the final weight coefficient to the total weight coefficient, thereby achieving the goal of allocating network traffic from various business terminals in the hospital to each data center and corresponding server based on a comprehensive consideration of multiple factors, and sharing the network load.

[0033] Preferably, the multi-active data module is composed of multiple hospital data centers, including server clusters and network access devices. The server clusters are used for data management, and the network access devices are used for connecting to external networks. The data centers are closely connected through high-speed dedicated network links, which use fiber optic transmission media for transmission. After a server cluster in one data center receives a new patient electronic medical record data entry request, the data center will send the new data to other data centers through the network link. After receiving the synchronized data, the other data centers will perform data update operations on their local server clusters.

[0034] Preferably, when one server fails, the multi-active data module, upon detecting the failure, transfers the registration requests originally destined for the failed server to its own internal server cluster for processing.

[0035] Preferably, the security module controls the data in the network from different dimensions, captures network nodes, and parses the header information and payload content of data packets to identify the source and intent of access requests and to understand the legitimacy of data access.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] This invention, through a prediction module utilizing the rich historical data acquired by the traffic collection module, can predict future traffic load on hospital equipment. Based on these predictions, network traffic is rationally allocated to various data centers and corresponding servers, preventing server performance degradation due to traffic overload and reducing idle server resources. This ensures that server resources throughout the network are fully and appropriately utilized, maximizing their computing and storage capabilities, improving overall server resource utilization efficiency, enabling flexible network configuration, rapid adjustment of network resource allocation, and enhancing the patient's medical experience. Attached Figure Description

[0038] Figure 1 This is a framework diagram of the network management system of the present invention. Detailed Implementation

[0039] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0040] Reference Figure 1 As shown, a hospital multi-active data center network management system based on software-defined networking is described. The network management system is controlled by an SDN controller and includes: a traffic collection module, a prediction module, a scheduling module, a multi-active data module, and a security module.

[0041] The traffic collection module is used to collect historical patient traffic data of the hospital, analyze and manage the acquired patient traffic data, obtain historical traffic load data of various hospital equipment, and store the data.

[0042] The prediction module predicts the future flow load of hospital equipment based on historical data obtained from the flow collection module, and uploads the prediction results to the scheduling module.

[0043] Based on the prediction results from the prediction module, and taking into account the traffic scheduling algorithm and the sensitivity of the business, the scheduling module distributes network traffic from various business terminals in the hospital to various data centers and corresponding servers to share the load.

[0044] The multi-active data module includes multiple hospital data centers distributed in different geographical locations. Each data center is equipped with a server cluster for storing and processing medical data, and also has network access equipment for connecting to external networks. The data centers are connected through high-speed dedicated network links to synchronize and mutually support business operations. When a business device fails, the corresponding business can be seamlessly taken over.

[0045] The security module continuously monitors data access requests in the network, intercepts illegal access and attack behaviors, and ensures network security.

[0046] This application is based on software definition, which refers to the concept and method of flexibly controlling, managing and allocating underlying resources through software in the related technical concepts of software-defined networking and software-defined storage. In this application document, it is referred to as an SDN controller.

[0047] The prediction module, leveraging the rich historical data acquired by the traffic collection module, can make relatively accurate predictions of future hospital equipment traffic load. Based on these predictions, the scheduling module, combined with traffic scheduling algorithms and considerations of business sensitivity, rationally allocates network traffic to various data centers and corresponding servers. This avoids performance degradation of some servers due to traffic overload, while also reducing idle resources on other servers, ensuring that server resources throughout the network are fully and appropriately utilized, maximizing their computing and storage capabilities, and improving the overall utilization efficiency of server resources. During traffic scheduling, the bandwidth utilization of network links is considered. Based on the prediction results, the scheduling module can know in advance which services will generate large traffic during specific time periods, and then rationally arrange traffic flow to avoid an unbalanced state where some links are congested while other links have idle bandwidth.

[0048] By comparing the actual traffic distribution with the prediction results, it was found that the traffic fluctuation of a certain routine business was greater than expected during a specific period, resulting in an imbalance in the load of some servers. Administrators can then adjust the relevant parameters in the prediction module or the traffic distribution weights in the scheduling module to optimize subsequent traffic scheduling and make the network operation more stable and efficient.

[0049] The flow collection module uses various types of monitoring equipment. Infrared sensor counters are installed at various entrances and exits of the hospital, and infrared sensors record the flow. Cameras are deployed in the corridors of each floor and at the entrances of departments inside the hospital to capture patient flow data. Based on the data recorded in the doctors' medical records, patient flow data of different departments is obtained.

[0050] This application utilizes various monitoring devices, including infrared sensor counters installed at hospital entrances and exits, to accurately record the entry and exit of personnel. This provides basic data for understanding the overall inflow and outflow of personnel in the hospital, as well as the flow trends at different times.

[0051] The prediction module acquires and analyzes stored historical data. After preprocessing the data, it uses time series analysis to analyze the trend of traffic changes in different time periods, identify peak and trough periods and periodic patterns of hospital traffic, and uses correlation analysis algorithms to analyze the intrinsic relationship between traffic flow and the use of different departments and equipment in the hospital to obtain historical hospital equipment usage load data.

[0052] This application, through analysis of historical hospital equipment usage flow load data, enables hospital administrators to clearly understand the actual usage and load levels of different departments and equipment at various time periods.

[0053] Suppose the analysis covers n days, with m observation points each day, then the average pedestrian flow P on day i is... i Represented as:

[0054]

[0055] Where P(i,j) represents the pedestrian flow at the j-th observation time point on the i-th day, and j represents the observation time point.

[0056] The formula for calculating the overall average pedestrian flow is:

[0057]

[0058] Where P represents the overall average pedestrian flow during the entire analysis period;

[0059] Further calculation of fluctuation indicators in pedestrian flow:

[0060]

[0061] Where αp is the standard deviation of the flow rate.

[0062] To further define peak and off-peak periods, a relative threshold is set. Peak periods are defined as those exceeding the overall average pedestrian flow plus a certain multiple of the standard deviation, and off-peak periods are defined as those below the overall average pedestrian flow minus a certain multiple of the standard deviation. Let the threshold multiple be k. Then, the peak period determination is expressed as:

[0063] P(t>P+kαp

[0064] Where P(t) represents the flow of people at time t;

[0065] The criteria for determining the trough period are:

[0066] P(t)<P-kαp

[0067] The hospital's peak and off-peak periods were calculated and analyzed.

[0068] If there is a linear relationship between historical hospital equipment usage load data and the usage of different departments and equipment within the hospital, then the analysis formula is:

[0069] Q=β0+β1P(t)+∈

[0070] Where Q is the predicted output value, β0 is the intercept term, β1 is the regression coefficient, and ∈ is the error term; the intercept term and regression coefficient are calculated based on the least squares method.

[0071] The scheduling module distributes network traffic from each service terminal to the data center and servers. Guided by the prediction results of the prediction module, it initiates the traffic distribution mechanism. The traffic distribution formula is as follows:

[0072]

[0073] Where F represents the actual network traffic value from the server allocated to the data center server, Ft represents the amount of network traffic generated by the business terminal at time t, At is the final weight coefficient of the network traffic of the business terminal allocated to the server in the data center, which comprehensively reflects the appropriateness of allocating network traffic to a specific server in a specific data center, taking into account factors such as business sensitivity, link bandwidth utilization, server load, and network latency. d∈D represents a specific data center, and s∈Sd is one of the servers in data center d. The above formula means that the network traffic generated by the business terminal is allocated to each data center and server according to the proportion of the final weight coefficient to the total weight coefficient, thereby achieving the goal of allocating network traffic from various business terminals in the hospital to each data center and corresponding server based on a comprehensive consideration of multiple factors, and sharing the network load.

[0074] This application determines the weighting coefficient by comprehensively considering the factor of business sensitivity, which can accurately distinguish the urgency and importance of different businesses' demand for network resources. The link bandwidth utilization rate, as part of the weighting coefficient, will prompt the traffic allocation process to prioritize the data centers and servers connected by links with relatively ample bandwidth and reasonable utilization rate to carry network traffic.

[0075] The multi-active data module consists of multiple hospital data centers, including server clusters and network access devices. The server clusters are used for data management, and the network access devices are used to connect to external networks. The data centers are closely connected through high-speed dedicated network links using fiber optic transmission media. When a server cluster in one data center receives a request to enter new patient electronic medical record data, the data center will send the new data to other data centers through the network link. After receiving the synchronized data, the other data centers will perform data update operations on their local server clusters.

[0076] This application uses multiple hospital data centers to form a multi-active data module, which means that the data has multiple copies stored in different geographical locations. Even if a data center suffers data damage or loss due to force majeure, other data centers still retain complete data copies and can provide services at any time.

[0077] When a server in the multi-active data module fails, other normal servers, upon detecting the failure, redirect the registration requests originally destined for the failed server to their own internal server cluster for processing.

[0078] During peak hours, when many patients are registering at self-service registration machines, if a server responsible for processing registration requests suddenly fails, the automatic transfer mechanism of the multi-active data module will quickly redirect these ongoing registration operations to other normal servers. Patients can still complete their registration smoothly, and subsequent medical procedures can proceed as scheduled, maintaining the smooth operation of the hospital's overall business processes.

[0079] The security module controls network data from different dimensions, capturing network nodes and parsing packet header information and payload content to identify the source and intent of access requests and to understand the legitimacy of data access.

[0080] The hospital network in this application stores a massive amount of highly sensitive medical data, including patients' basic personal information, detailed medical history, diagnostic results, and various medical imaging data. By capturing network nodes and parsing data packet content to understand the legitimacy of data access, the security module can accurately block access attempts from illegal external sources or unauthorized internal personnel, preventing this sensitive data from being stolen and leaked.

[0081] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A hospital multi-active data center network management system based on software-defined networking, characterized in that, The network management system is controlled based on an SDN controller and includes: a traffic collection module, a prediction module, a scheduling module, a multi-active data module, and a security module. The traffic collection module is used to collect historical patient traffic data of the hospital, analyze and manage the acquired patient traffic data, obtain historical traffic load data of various hospital equipment, and store the data. The prediction module predicts the future flow load of hospital equipment based on historical data obtained from the flow collection module, and uploads the prediction results to the scheduling module. Based on the prediction results from the prediction module, and taking into account the traffic scheduling algorithm and the sensitivity of the business, the scheduling module distributes network traffic from various business terminals in the hospital to various data centers and corresponding servers to share the load. The multi-active data module includes multiple hospital data centers distributed in different geographical locations. Each data center is equipped with a server cluster for storing and processing medical data, and also has network access equipment for connecting to external networks. The data centers are connected through high-speed dedicated network links to synchronize and mutually support business operations. When a business device fails, the corresponding business can be seamlessly taken over. The security module continuously monitors data access requests in the network, intercepts illegal access and attack behaviors, and ensures network security. The flow collection module uses various types of monitoring equipment. Infrared sensor counters are installed at various entrances and exits of the hospital, and infrared sensors record the flow. Cameras are deployed in the corridors and department entrances of each floor inside the hospital to capture patient flow data. Based on the data recorded in the doctors' medical records, patient flow data of different departments is obtained. The prediction module acquires and analyzes stored historical data. After preprocessing the data, it uses time series analysis to analyze the trend of traffic changes in different time periods, identify the peak and trough periods and periodic patterns of hospital traffic, and uses correlation analysis algorithms to analyze the intrinsic relationship between traffic and the use of different departments and equipment in the hospital, thereby obtaining historical hospital equipment usage load data. The scheduling module distributes network traffic from each service terminal to the data center and servers. Guided by the prediction results of the prediction module, it initiates the traffic distribution mechanism. The traffic distribution formula is as follows: Where F represents the actual network traffic value from the server allocated to the data center server, Ft represents the amount of network traffic generated by the business terminal at time t, At is the final weight coefficient of the network traffic of the business terminal allocated to the server in the data center, which comprehensively reflects the appropriateness of allocating network traffic to a specific server in a specific data center, taking into account factors such as business sensitivity, link bandwidth utilization, server load, and network latency. d∈D represents a specific data center, and s∈Sd is one of the servers in data center d. The above formula means that the network traffic generated by the business terminal is allocated to each data center and server according to the proportion of the final weight coefficient to the total weight coefficient, thereby achieving the goal of allocating network traffic from various business terminals in the hospital to each data center and corresponding server based on a comprehensive consideration of multiple factors, and sharing the network load.

2. The hospital multi-active data center network management system based on software-defined networking according to claim 1, characterized in that, Suppose the analysis covers n days, with m observation points each day, then the average pedestrian flow P on day i is... i Represented as: Where P(i,j) represents the pedestrian flow at the j-th observation time point on the i-th day, and j represents the observation time point.

3. A hospital multi-active data center network management system based on software-defined networking according to claim 2, characterized in that, The formula for calculating the overall average pedestrian flow is: Where P represents the overall average pedestrian flow over the entire analysis period; further calculations are made of pedestrian flow fluctuation indicators: Where αp is the standard deviation of the flow rate.

4. A hospital multi-active data center network management system based on software-defined networking as described in claim 3, characterized in that, To further define peak and off-peak periods, a relative threshold is set. Peak periods are defined as those exceeding the overall average pedestrian flow plus a certain multiple of the standard deviation, and off-peak periods are defined as those below the overall average pedestrian flow minus a certain multiple of the standard deviation. Let the threshold multiple be k. Then, the peak period determination is expressed as: P(t>P+kαp Where P(t) represents the flow of people at time t; The criteria for determining the trough period are: P(t)<P-kαp The hospital's peak and off-peak periods were calculated and analyzed.

5. A hospital multi-active data center network management system based on software-defined networking according to claim 4, characterized in that, If there is a linear relationship between historical hospital equipment usage load data and the usage of different departments and equipment within the hospital, then the analysis formula is: Q=β0+β1P(t)+∈ Where Q is the predicted output value, β0 is the intercept term, β1 is the regression coefficient, and ∈ is the error term; the intercept term and regression coefficient are calculated based on the least squares method.

6. A hospital multi-active data center network management system based on software-defined networking according to claim 1, characterized in that, The traffic allocation formula represents the distribution of network traffic generated by business terminals based on the proportion of the final weight coefficient of each data center and server to the total weight coefficient. This achieves the goal of distributing network traffic from various business terminals in the hospital to each data center and corresponding server based on a comprehensive consideration of multiple factors, thereby sharing the network load.

7. A hospital multi-active data center network management system based on software-defined networking according to claim 1, characterized in that, The multi-active data module consists of multiple hospital data centers, including server clusters and network access devices. The server clusters are used for data management, and the network access devices are used for connecting to external networks. The data centers are closely connected through high-speed dedicated network links using fiber optic transmission media. When a server cluster in one data center receives a request to enter new patient electronic medical record data, the data center sends the new data to other data centers through the network link. After receiving the synchronized data, the other data centers update the data on their local server clusters.

8. A hospital multi-active data center network management system based on software-defined networking according to claim 1, characterized in that, When a server in the multi-active data module fails, other normal servers, upon detecting the failure, redirect the registration requests originally destined for the failed server to their own internal server cluster for processing.

9. A hospital multi-active data center network management system based on software-defined networking according to claim 1, characterized in that, The security module controls network data from different dimensions, capturing network nodes and parsing packet header information and payload content to identify the source and intent of access requests and to understand the legitimacy of data access.

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