A Dynamic Security Resource Allocation Method for a Medical Consortium Blockchain

Abstract

The present invention discloses a method for dynamically and securely configuring resources in a medical alliance chain, which relates to the field of communication technologies. The method includes the following steps: counting several nodes in the network architecture of the medical alliance chain nodes, marking them as each link node, monitoring the operating states of each link node based on a preset dynamic monitoring period, analyzing the comprehensive load evaluation index values of each link node, and comparing them with a preset operating load index threshold, thereby generating an operating load evaluation label for each link node. According to the operating load evaluation labels of each link node, the number of redundant backup nodes for each link node is obtained through processing, and resource configuration for each link node is performed. The present invention can achieve precise allocation of system resources in the medical alliance chain, can adjust the resource allocation strategy and the enabling mechanism of redundant backup nodes according to the real-time load conditions of each link node, and improves the operating efficiency and stability of the entire medical alliance chain system.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and particularly to a method for dynamically and securely allocating resources in a medical consortium chain. Background Art

[0002] With the continuous acceleration of the digitalization process in the medical industry and the explosive growth of medical data, in a medical consortium chain, each link node undertakes a large number of medical data transmission, storage, and processing tasks. The traditional medical information management method often focuses on data storage and simple access control, and can no longer meet the current requirements for efficient and secure management of medical data, and is also likely to affect the stable operation and service quality of the entire medical consortium chain.

[0003] The prior art, such as the invention patent with the publication number of CN113133024B, is a method and device for allocating network resources. By improving the limitations of the prior art in the tuning range and tuning effect of network resource allocation, it realizes the problem of optimizing the allocation of network resources in the entire local area network. The method includes: obtaining detection data of multiple network devices, where the detection data includes the air interface data of the network devices; obtaining the path loss between multiple network devices according to the detection data of the multiple network devices; obtaining the network health scores corresponding to multiple network configurations through a neural network algorithm and a deep learning algorithm, where the multiple network configurations are determined according to the path loss between the multiple network devices; and selecting the network configuration with the highest network health score to configure the multiple network devices.

[0004] The prior art, such as the invention patent with the publication number of CN113259999B, is a method and device for allocating network resources. The method includes: a terminal device receiving configuration information from a network device, where the configuration information includes information on a first threshold corresponding to at least one candidate cell, and the first threshold includes at least one of a threshold of Doppler frequency offset, a threshold of the change rate of timing advance TA, or a threshold of the change rate of Doppler frequency offset; the terminal device determining a target cell from at least one candidate cell according to the configuration information; and the terminal device initiating a handover to the target cell.

[0005] In view of the above solutions, the current resource allocation methods usually only analyze and process the overall network resource allocation. When it comes to a medical consortium chain, due to the sensitivity of its data and the diversity of the functions of each link node, the requirements for the accuracy and dynamic adaptability of resource allocation are extremely high. At this time, if only the overall network resources are allocated from a macroscopic level, it is impossible to comprehensively ensure the efficient operation and data security of actual nodes in different service scenarios and dynamic change environments. Therefore, the current general resource allocation methods are difficult to meet the refined and dynamic requirements under the complex network architecture of a medical consortium chain, and the overall intelligence and adaptability also need to be further improved. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method for dynamically and securely allocating resources in a medical consortium chain, which can effectively solve the problems involved in the above-mentioned background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: The present invention provides a method for dynamically and securely allocating resources in a medical consortium chain,

[0008] including the following steps: S1, counting several nodes in the network architecture of the medical consortium chain nodes and marking them as each link node;

[0009] S2, based on a preset dynamic monitoring period, and monitoring the operating status of each link node within the dynamic monitoring period, and analyzing the comprehensive load evaluation index value of each link node;

[0010] S3, based on the comprehensive load evaluation index value of each link node, and comparing it with a preset operating load index threshold, thereby generating an operating load evaluation label for each link node;

[0011] S4, according to the operating load evaluation label of each link node, obtaining the number of redundant backup nodes of each link node through processing, and performing resource allocation for each link node.

[0012] As a preferred technical solution, the monitoring of the operating status of each link node is specifically as follows: within the preset dynamic monitoring period, the operating status of each link node is monitored based on a preset dynamic monitoring frequency, and the operating status data of each link node is obtained. The operating status data of each link node includes hardware performance parameters and data transmission parameters.

[0013] The hardware performance parameters include the CPU usage rate, memory occupancy rate, hard disk storage capacity consumption speed, hard disk average read and write speed, and device temperature of each link node within the dynamic monitoring period.

[0014] The data transmission parameters include the cumulative number of bytes of data transmitted, average data transmission rate, cumulative amount of transmitted data, data packet loss rate, and network interface bandwidth usage rate of each link node within the dynamic monitoring period.

[0015] As a preferred technical solution, the analysis of the comprehensive load evaluation index value of each link node is specifically as follows: based on the hardware performance parameters, the hardware operating load index of each link node is analyzed and processed.

[0016] Based on the data transmission parameters, the data transmission load index of each link node is analyzed and processed.

[0017] Based on the hardware operation load index and data transmission load index of each link node, the comprehensive load evaluation index value of each link node is obtained through comprehensive analysis and processing.

[0018] The comprehensive load evaluation index value of each link node is the numerical result of quantifying the hardware operation load index and data transmission load index of each link node, and is used to characterize the operation load degree of each link node.

[0019] As a preferred technical solution, the specific analysis process of the hardware operation load index of each link node is as follows: Extract the reference hardware performance parameters stored in the database, and the reference hardware performance parameters include reference CPU usage rate, reference memory occupancy rate, reference consumption speed of hard disk storage capacity, reference hard disk read and write speed, and reference device temperature.

[0020] Based on the hardware performance parameters and reference hardware performance parameters, the hardware operation load index of each link node is obtained through analysis and processing.

[0021] The hardware operation load index of each link node is the numerical result of quantifying the hardware performance parameters and reference hardware performance parameters, and is used to characterize the hardware operation load status of each link node.

[0022] As a preferred technical solution, the specific analysis process of the data transmission load index of each link node is as follows: Extract the reference data transmission parameters stored in the database, and the reference data transmission parameters include reference cumulative data transmission bytes, reference average data transmission rate, reference cumulative transmitted data volume, reference data packet loss rate, and reference network interface bandwidth usage rate.

[0023] Based on the data transmission parameters and reference data transmission parameters, the data transmission load index of each link node is obtained through analysis and processing.

[0024] The data transmission load index of each link node is the numerical result of quantifying the data transmission parameters and reference data transmission parameters, and is used to characterize the data transmission load degree of each link node.

[0025] As a preferred technical solution, the specific process of generating the operation load evaluation label of each link node is as follows: According to the comprehensive load evaluation index value of each link node and the preset operation load index threshold, the operation load evaluation label of each link node is generated by comparison.

[0026] The operation load evaluation label includes normal operation load and abnormal operation load.

[0027] If the operation load index of a certain link node is lower than or equal to the preset operation load index threshold, the operation load evaluation label of the link node is defined as normal operation load.

[0028] If the operating load index of a link node is higher than the preset operating load index threshold, then define the operating load evaluation label of the link node as abnormal operating load.

[0029] As a preferred technical solution, the process of obtaining the number of redundant backup nodes for each link node is as follows: Based on the operating load evaluation labels of each link node, if the operating load evaluation label of a link node is normal operating load, then define the number of redundant backup nodes of this link node as zero. If the operating load evaluation label of a link node is abnormal operating load, then mark this link node as a link node requiring capacity expansion, and thus count each link node requiring capacity expansion.

[0030] Based on the comprehensive load evaluation index values of each link node, extract the difference between the operating load index of each link node requiring capacity expansion and the preset operating load index threshold, and record it as the capacity expansion reference value. Thus, count the capacity expansion reference values of each link node requiring capacity expansion, and perform mapping matching with the number of redundant backup nodes corresponding to each capacity expansion reference value interval defined in the database to obtain the number of redundant backup nodes for each link node requiring capacity expansion.

[0031] As a preferred technical solution, the comprehensive load evaluation index value of each link node is specifically calculated by the following formula:

[0032] , where is the comprehensive load evaluation index value of the i-th link node, is the hardware operating load index of the i-th link node, is the data transmission load index of the i-th link node, is the weight of the hardware operating load index, is the weight of the data transmission load index, i is the number of each link node, , and m is the number of link nodes.

[0033] As a preferred technical solution, the method for dynamically configuring secure resources in a medical consortium chain further includes dynamically configuring secure resources for redundant backup nodes. The specific process is as follows: Based on the number of redundant backup nodes of each link node, count each redundant backup node.

[0034] Perceive and monitor each redundant backup node during a preset configuration period to obtain the usage load data of each redundant backup node, specifically including the usage access frequency, maximum number of connected people, and average read / write operation rate of each redundant backup node.

[0035] Extract the redundant backup node reference load data stored in the database, where the redundant backup node reference load data includes the reference usage access frequency, the reference maximum number of concurrent connections, and the reference average read / write operation rate.

[0036] Based on the usage load data of each redundant backup node and the redundant backup node reference load data, analyze and process to obtain the usage load index of each redundant backup node. The usage load index of each redundant backup node is a numerical result obtained by quantifying the usage load data of each redundant backup node and the redundant backup node reference load data, and is used to characterize the usage load degree of each redundant backup node.

[0037] According to the usage load index of each redundant backup node, compare it with the redundant backup node usage load index threshold. If the usage load index of a certain redundant backup node is less than or equal to the redundant backup node usage load index threshold, no storage resource configuration is performed on this redundant backup node. If the usage load index of a certain redundant backup node is greater than the redundant backup node usage load index threshold, storage resource configuration is performed on this redundant backup node with a preset storage capacity.

[0038] As a preferred technical solution, the usage load index of each redundant backup node is specifically calculated by the following formula:

[0039] , where is the usage load index of the j-th redundant backup node, is the usage access frequency of the j-th redundant backup node, is the reference usage access frequency, The maximum number of concurrent connections of the j-th redundant backup node, is the reference maximum number of concurrent connections, The average read / write operation rate of the j-th redundant backup node, is the reference average read / write operation rate, is the weight of the usage access frequency, is the weight of the maximum number of concurrent connections, is the weight of the average read / write operation rate, j is the number of each redundant backup node, , n is the number of redundant backup nodes, and e is the natural constant.

[0040] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0041] (1) By providing a dynamic security resource configuration method for a medical consortium chain, the present invention realizes the precise allocation of resources in the medical consortium chain system, can adjust the resource allocation strategy and the enabling mechanism of redundant backup nodes according to the real-time load conditions of each link node, and improves the operation efficiency and stability of the entire medical consortium chain system;

[0042] (2) By analyzing the comprehensive load evaluation index values of each link node, the present invention can effectively evaluate and grasp the operation efficiency and pressure status of each link node in the medical alliance chain system, providing a solid foundation for the optimization and stable operation of the system. At the same time, it provides a clear direction for the reasonable allocation of resources, contributing to the realization of dynamic and precise management of the medical alliance chain system;

[0043] (3) By generating operation load evaluation tags for each link node and configuring node backup resources based on the operation load evaluation tags, the present invention improves the convenience and pertinence of management, contributing to long-term performance monitoring and trend analysis. By tracking and recording the changes in the evaluation tags of each link node over different time periods, potential performance problems and trend laws can be discovered, ensuring the sustainable development and stable operation of the medical alliance chain system;

[0044] (4) By analyzing the number of redundant backup nodes of each link node, the present invention significantly enhances the reliability and stability of the system. By accurately calculating and configuring the number of redundant backup nodes, when the main link node fails or faces high load pressure and cannot work properly, the redundant backup nodes can be quickly enabled to take over its work, ensuring the uninterrupted transmission and processing of medical data, improving the scalability and adaptability of the system. At the same time, it helps to enhance the security of the entire medical alliance chain. By reasonably distributing the redundant backup nodes, the risk of data loss and leakage can be reduced, providing a strong guarantee for information security;

[0045] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic diagram of the connection of system modules of the present invention;

[0047] Figure 2 is a scatter diagram showing the relationship between the data transmission load index and the operation load index of link nodes involved in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Please refer to Figure 1As shown in the figure, an embodiment of the present invention provides a method for dynamically configuring secure resources in a medical consortium blockchain, including: S1, counting several nodes in the network architecture of the medical consortium blockchain nodes and marking them as each link node.

[0050] In this embodiment, the network architecture of the medical consortium blockchain nodes refers to a distributed network structure system composed of computer devices or systems corresponding to multiple relevant medical institution entities participating in medical data interaction, sharing, and storage. In this architecture, each node has certain computing power, storage capacity, and network communication capabilities. Nodes are interconnected through specific network protocols and communication links to form a network entity that cooperates with each other and is relatively independent.

[0051] For example, as one of the nodes, a hospital's internal medical information system contains a large amount of information such as patient medical record data, diagnosis and treatment records, and medical images. These hospital nodes transmit and interact data with other nodes through high-speed network connections.

[0052] It should be noted that several nodes in the node network architecture are the key support elements for the effective operation of the entire medical consortium blockchain. These nodes cover multiple key roles and institutions in the medical field, including but not limited to various levels of hospital nodes, medical research institution nodes, and medical supervision department nodes, etc.

[0053] It should also be noted that counting several nodes in the network architecture of the medical consortium blockchain nodes can be achieved by using network detection technologies and tools. For example, by deploying specialized network scanning software, sending specific detection data packets in the network environment relied on by the medical consortium blockchain, and identifying and locating active network devices and nodes based on the response mechanism of the network protocol.

[0054] S2, based on a preset dynamic monitoring period, monitor the operating status of each link node during the dynamic monitoring period and analyze the comprehensive load evaluation index values of each link node.

[0055] In this embodiment, the process of monitoring the operating status of each link node is as follows: During the preset dynamic monitoring period, monitor the operating status of each link node based on a preset dynamic monitoring frequency, and obtain the operating status data of each link node. The operating status data of each link node includes hardware performance parameters and data transmission parameters.

[0056] It should be understood that the preset dynamic monitoring period needs to be flexibly customized according to the actual operating characteristics and business requirements of the medical alliance chain. Analyze the business traffic pattern of the medical alliance chain, and conduct a detailed analysis of the transmission of medical data in the alliance chain over a period of time in the past, including peak and trough periods of data transmission. At the same time, considering the importance differences of each link node in the medical alliance chain, for core link nodes that undertake key medical services, such as the central data exchange node of a large hospital or the data analysis server node of a medical research institution, since any problem with them may have a significant impact on the operation of the entire alliance chain, a relatively short and more frequent monitoring period needs to be set.

[0057] It should also be understood that even if a monitoring period is preset, during actual operation, it may be adjusted according to specific circumstances. In this embodiment, if there are obvious changes in the operation status data of each link node and the duration is relatively long, the system will reduce the monitoring period and conduct more frequent monitoring. If the operation status data of each link node has been in a stable state, the system will appropriately extend the monitoring period to save resources. Therefore, presetting the monitoring period does not affect the essence of dynamic monitoring. It is just a way to manage and optimize the monitoring process. By reasonably presetting the monitoring period, problems can be detected in a timely manner while maximizing the efficiency and resource utilization rate of the monitoring system.

[0058] The hardware performance parameters include the CPU usage rate, memory occupancy rate, hard disk storage capacity consumption speed, average hard disk read and write speed, and device temperature of each link node during the dynamic monitoring period.

[0059] It should be understood that the CPU usage rate is directly provided by the task manager built into the operating system of each link node. The memory occupancy rate can be obtained through professional system performance monitoring software, such as Prometheus, by reading memory-related data structures and interfaces provided by the operating system to obtain memory usage information, and thus obtain the memory occupancy rate.

[0060] It should also be understood that the hard disk storage capacity consumption speed can be calculated by the storage area network (SAN) controller through centralized management and monitoring of the hard disk. The average hard disk read and write speed is calculated by the software tool CrystalDiskMark for testing hard disk performance by sending a large number of data read and write requests to the hard disk and measuring the speed at which the hard disk completes these requests within a certain period of time.

[0061] It should be noted that the device temperature refers to the average temperature of the hardware devices of each link node.

[0062] The data transmission parameters include the cumulative number of bytes of data transmitted by each link node during the dynamic monitoring period, the average data transmission rate, the cumulative amount of transmitted data, the data packet loss rate, and the network interface bandwidth utilization rate.

[0063] It should be understood that the cumulative number of bytes of data transmitted can be obtained by using the network protocol analysis tool Wireshark to capture network data packets and calculating based on the size and quantity of the data packets. The average data transmission rate can be calculated by using the network speed test tool Speedtest to send test data to the server and measure the response time.

[0064] It should also be understood that the data packet loss rate can be calculated by using a network performance test tool to perform a packet loss rate test.

[0065] It should be noted that the network interface bandwidth utilization rate refers to the ratio of the actual amount of data transmitted by the network interface within a unit time to the theoretical maximum amount of transmitted data, which reflects the usage efficiency of the network interface.

[0066] In this embodiment, the process of analyzing the comprehensive load evaluation index value of each link node is as follows: Based on the hardware performance parameters, the hardware operation load index of each link node is analyzed and processed.

[0067] In this embodiment, the process of analyzing the hardware operation load index of each link node is as follows: Extract the reference hardware performance parameters stored in the database. The reference hardware performance parameters include the reference CPU usage rate, the reference memory occupancy rate, the reference consumption speed of the hard disk storage capacity, the reference read / write speed of the hard disk, and the reference temperature of the device.

[0068] It should be noted that the database is used to store reference index data, and the reference index data includes first reference index data, second reference index data, and third reference index data.

[0069] The first reference index data includes reference hardware performance parameters and reference data transmission parameters, and also includes the number of redundant backup nodes corresponding to each expansion reference value range and the reference load data of the redundant backup nodes, and also includes the operation load index threshold and the redundant backup node usage load index threshold.

[0070] The second reference index data includes the CPU usage rate weight, the memory occupancy rate weight, the hard disk storage capacity consumption speed weight, the average hard disk read / write speed weight, and the device temperature weight, and also includes the cumulative number of bytes of data transmitted weight, the average data transmission rate weight, the cumulative amount of transmitted data weight, the data packet loss rate weight, and the network interface bandwidth utilization rate weight.

[0071] The third reference index data includes a hardware operation load index weight and a data transmission load index weight, and also includes a usage access frequency weight, a maximum connection number weight, and an average read / write operation rate weight.

[0072] Based on the hardware performance parameters and the reference hardware performance parameters, the hardware operation load index of each link node is analyzed and processed.

[0073] The hardware operation load index of each link node is a numerical result of quantifying the hardware performance parameters and the reference hardware performance parameters, and is used to characterize the hardware operation load status of each link node.

[0074] In a specific embodiment, the hardware operation load index of each link node has the following specific formula:

[0075] ,

[0076] where, is the hardware operation load index of the i-th link node, is the CPU usage rate of the i-th link node, is the reference CPU usage rate, is the memory occupancy rate of the i-th link node, is the reference memory occupancy rate, is the hard disk storage capacity consumption speed of the i-th link node, is the reference hard disk storage capacity consumption speed, is the average read / write speed of the hard disk of the i-th link node, is the reference hard disk read / write speed, is the device temperature of the i-th link node, is the reference device temperature, is the CPU usage rate weight, is the memory occupancy rate weight, is the hard disk storage capacity consumption speed weight, is the average read / write speed weight of the hard disk, is the device temperature weight, i is the number of each link node, , m is the number of link nodes, and e is the natural constant.

[0077] It should be noted that the CPU usage weight has a value range between 0 and 1. In this embodiment, the CPU usage weight is the CPU usage weight preset in the database, which represents the numerical value of the influence degree of the CPU usage on the hardware operation load index of the link node. When in use, the preset CPU usage weight can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the node CPU usage and the CPU usage weight preset in the database form a mapping set, and the real-time CPU usage is input into the mapping set to obtain the CPU usage weight, and the mapping relationship therein is one-to-one correspondence.

[0078] It should be noted that the memory occupancy weight has a value range between 0 and 1. In this embodiment, the memory occupancy weight is the memory occupancy weight preset in the database, which represents the numerical value of the influence degree of the memory occupancy on the hardware operation load index of the link node. When in use, the preset memory occupancy weight can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the node memory occupancy and the memory occupancy weight preset in the database form a mapping set, and the real-time memory occupancy is input into the mapping set to obtain the memory occupancy weight, and the mapping relationship therein is one-to-one correspondence.

[0079] It should be noted that the hard disk storage capacity consumption speed weight has a value range between 0 and 1. In this embodiment, the hard disk storage capacity consumption speed weight is the hard disk storage capacity consumption speed weight preset in the database, which represents the numerical value of the influence degree of the hard disk storage capacity consumption speed on the hardware operation load index of the link node. When in use, the preset hard disk storage capacity consumption speed weight can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the node hard disk storage capacity consumption speed and the hard disk storage capacity consumption speed weight preset in the database form a mapping set, and the real-time hard disk storage capacity consumption speed is input into the mapping set to obtain the hard disk storage capacity consumption speed weight, and the mapping relationship therein is one-to-one correspondence.

[0080] It should be noted that the average hard disk read / write speed weight has a value range between 0 and 1. In this embodiment, the average hard disk read / write speed weight is the average hard disk read / write speed weight preset in the database, which represents the numerical value of the influence degree of the average hard disk read / write speed on the hardware operation load index of the link node. When in use, the preset average hard disk read / write speed weight can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the node average hard disk read / write speed and the average hard disk read / write speed weight preset in the database form a mapping set, and the real-time average hard disk read / write speed is input into the mapping set to obtain the average hard disk read / write speed weight, and the mapping relationship therein is one-to-one correspondence.

[0081] It should be noted that the device temperature weight value of the device ranges from 0 to 1. In this embodiment, the device temperature weight value of the device is the device temperature weight value preset in the database, which represents the value of the influence degree of the device temperature on the hardware operation load index of the link node. When used, the preset device temperature weight value of the device can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the node device temperature and the device temperature weight value preset in the database form a mapping set, and the real-time device temperature is input into the mapping set to obtain the device temperature weight value of the device, and the mapping relationship therein is one-to-one correspondence.

[0082] It should also be noted that in this embodiment, the hardware operation load index of each link node is obtained by processing the CPU usage rate, memory occupancy rate, hard disk storage capacity consumption speed, hard disk average read and write speed, and device temperature. This is because of the mutual influence among these parameters. For example, when the CPU usage rate of a certain link node is relatively high, it means that the central processing unit of this node is performing a large number of computing tasks, and its processing ability is facing great pressure. If the memory occupancy rate is also relatively high at this time, then the superposition of these two may lead to a serious decline in system performance, because a high memory occupancy rate may cause the CPU to encounter bottlenecks in data exchange and storage, thereby further increasing the hardware operation load. If the hard disk storage capacity consumption speed is relatively low at this time, to a certain extent, it may relieve the overall hardware pressure, because a lower hard disk storage capacity consumption speed means that the system has relatively few read and write operations on the hard disk, and the hard disk can have more resources to respond to the requests of other hardware components. When the hard disk average read and write speed is relatively fast, even if the CPU usage rate and memory occupancy rate are relatively high, the hard disk can quickly respond to the read and write requests of data, provide the required data for the CPU and memory, and thus relieve the overall pressure of hardware operation to a certain extent. On the contrary, if the hard disk average read and write speed is relatively slow, then in the case of high CPU usage rate and memory occupancy rate, it may become the bottleneck of the entire hardware system. If the device temperature rises, it may cause the CPU to downclock or throttle to prevent overheating, thereby resulting in a decrease in CPU usage rate, because the CPU processing ability decreases and it cannot handle the same load. High temperature may also affect the stability and performance of the memory. At the same time, the increase in hard disk temperature may affect its read and write performance, resulting in a decrease in the hard disk average read and write speed. If the system attempts to perform a large number of I / O operations at high temperature, it may accelerate the consumption of hard disk storage capacity and cause a decrease in the hard disk average read and write speed. The hardware operation load index obtained by comprehensively considering these parameters can more comprehensively reflect the actual operation status and pressure-bearing ability of the hardware of this link node. When the hardware operation load index of a certain link node is relatively high, it may be the adverse result of the superposition of these factors, and it is necessary to conduct in-depth analysis and take comprehensive optimization measures to ensure that the hardware of the link node can operate stably and efficiently, improve the reliability and performance of the entire system, and avoid the risk of hardware failure.

[0083] In a specific embodiment, by analyzing the hardware operation load indices of each link node, the hardware operation efficiency and pressure conditions of each key node in the medical alliance chain system can be effectively measured and controlled, providing strong support for the stable operation and optimization and upgrading of the system.

[0084] Based on the data transmission parameters, the data transmission load indices of each link node are analyzed and processed.

[0085] In this embodiment, the specific analysis process of the data transmission load index of each link node is as follows: extract the reference data transmission parameters stored in the database, and the reference data transmission parameters include the reference cumulative number of bytes of data transmission, the reference average data transmission rate, the reference cumulative amount of transmitted data, the reference data packet loss rate, and the reference network interface bandwidth utilization rate.

[0086] Based on the data transmission parameters and the reference data transmission parameters, the data transmission load indices of each link node are analyzed and processed.

[0087] The data transmission load index of each link node is a numerical result of quantifying the data transmission parameters and the reference data transmission parameters, and is used to characterize the data transmission load degree of each link node.

[0088] In a specific embodiment, the specific formula of the data transmission load index of each link node is as follows:

[0089] ,

[0090] where is the data transmission load index of the i-th link node, is the cumulative number of bytes of data transmission of the i-th link node, is the reference cumulative number of bytes of data transmission, is the average data transmission rate of the i-th link node, is the reference average data transmission rate, is the cumulative amount of transmitted data of the i-th link node, is the reference cumulative amount of transmitted data, is the data packet loss rate of the i-th link node, is the reference data packet loss rate, is the network interface bandwidth utilization rate of the i-th link node, is the reference network interface bandwidth utilization rate, is the weight of the cumulative number of bytes of data transmission, is the weight of the average data transmission rate, is the weight of the cumulative amount of transmitted data, is the weight of the data packet loss rate, is the weight of the network interface bandwidth utilization rate, i is the number of each link node, , m is the number of link nodes, and e is the natural constant.

[0091] It should be noted that the weight of the cumulative number of bytes of data transmission ranges from 0 to 1. In this embodiment, the weight of the cumulative number of bytes of data transmission is the weight of the cumulative number of bytes of data transmission preset in the database, which represents the numerical value of the influence degree of the cumulative number of bytes of data transmission on the data transmission load index of the link node. When used, the preset weight of the cumulative number of bytes of data transmission can be directly obtained from the database, and its corresponding relationship can be a preset mapping relationship. For example, the cumulative number of bytes of node data transmission and the preset weight of the cumulative number of bytes of data transmission in the database form a mapping set, and the real-time cumulative number of bytes of data transmission is input into the mapping set to obtain the weight of the cumulative number of bytes of data transmission, and the mapping relationship therein is one-to-one correspondence.

[0092] It should be noted that the weight of the average data transmission rate ranges from 0 to 1. In this embodiment, the weight of the average data transmission rate is the weight of the average data transmission rate preset in the database, which represents the numerical value of the influence degree of the average data transmission rate on the data transmission load index of the link node. When used, the preset weight of the average data transmission rate can be directly obtained from the database, and its corresponding relationship can be a preset mapping relationship. For example, the average data transmission rate of the node and the preset weight of the average data transmission rate in the database form a mapping set, and the real-time average data transmission rate is input into the mapping set to obtain the weight of the average data transmission rate, and the mapping relationship therein is one-to-one correspondence.

[0093] It should be noted that the weight of the cumulative amount of data transmitted ranges from 0 to 1. In this embodiment, the weight of the cumulative amount of data transmitted is the weight of the cumulative amount of data transmitted preset in the database, which represents the numerical value of the influence degree of the cumulative amount of data transmitted on the data transmission load index of the link node. When used, the preset weight of the cumulative amount of data transmitted can be directly obtained from the database, and its corresponding relationship can be a preset mapping relationship. For example, the cumulative amount of data transmitted by the node and the preset weight of the cumulative amount of data transmitted in the database form a mapping set, and the real-time cumulative amount of data transmitted is input into the mapping set to obtain the weight of the cumulative amount of data transmitted, and the mapping relationship therein is one-to-one correspondence.

[0094] It should be noted that the weight of the data packet loss rate ranges from 0 to 1. In this embodiment, the weight of the data packet loss rate is the weight of the data packet loss rate preset in the database, which represents the value of the influence degree of the data packet loss rate on the data transmission load index of the link node. When used, the preset weight of the data packet loss rate can be directly obtained from the database, and its corresponding relationship can be a preset mapping relationship. For example, the node data packet loss rate and the preset weight of the data packet loss rate in the database form a mapping set, and the real-time data packet loss rate is input into the mapping set to obtain the weight of the data packet loss rate, and the mapping relationship therein is one-to-one correspondence.

[0095] It should be noted that the weight of the network interface bandwidth utilization rate ranges from 0 to 1. In this embodiment, the weight of the network interface bandwidth utilization rate is the weight of the network interface bandwidth utilization rate preset in the database, which represents the value of the influence degree of the weight of the network interface bandwidth utilization rate on the data transmission load index of the link node. When used, the preset weight of the network interface bandwidth utilization rate can be directly obtained from the database, and its corresponding relationship can be a preset mapping relationship. For example, the weight of the node network interface bandwidth utilization rate and the preset weight of the network interface bandwidth utilization rate in the database form a mapping set, and the real-time weight of the network interface bandwidth utilization rate is input into the mapping set to obtain the weight of the network interface bandwidth utilization rate, and the mapping relationship therein is one-to-one correspondence.

[0096] It should also be noted that in this embodiment, the data transmission load index of each link node is obtained by processing according to the cumulative number of bytes of data transmission, the average data transmission rate, the cumulative amount of transmitted data, the data packet loss rate and the network interface bandwidth utilization rate, taking into account the mutual influence between these parameters. For example, when the cumulative number of bytes of data transmission of a link node is high, it means that the node has transmitted a large amount of data within a period of time, which shows that the node bears a large load in terms of the scale of the data transmission task. If the average data transmission rate is low at this time, then this indicates that the data transmission efficiency is relatively low, and a large amount of data is accumulated waiting for transmission, which will increase the pressure on the node in data transmission, making it possible for it to face difficulties in processing subsequent data transmission tasks, such as overflow of the data buffer area, backlog of the transmission queue, etc., which may lead to increased delays and instability in data transmission. When the average data transmission rate is high, even if the cumulative number of bytes of data transmission is large, it is possible to complete the data transmission task relatively quickly, thereby alleviating the data transmission to a certain extent. Pressure. If the cumulative amount of transmitted data is large, it means that the node has been performing frequent data transmission activities over a long period of time. This is not only a test of the node's current transmission capacity, but may also pose a challenge to its long-term stability and resource management capabilities. If the data packet loss rate is also high at this time, it means that more data has been lost during the data transmission process. Data packet loss will not only affect the integrity of data transmission, but may also trigger the start of the retransmission mechanism, further increasing the burden of data transmission and the load of the node. An increase in bandwidth utilization means that more data is transmitted through the network interface, so the cumulative number of bytes of data transmission will also increase accordingly. An increase in bandwidth utilization may also cause network congestion, which in turn affects the average rate of data transmission. If the network interface is close to its maximum bandwidth, new data packets may queue up for transmission, which may reduce the average data transmission rate. At the same time, when the network interface bandwidth utilization reaches or approaches the maximum value, new data packets may be discarded due to insufficient bandwidth resources, resulting in an increase in data packet loss rate. The data transmission load index obtained by comprehensively considering these parameters can more comprehensively reflect the actual working status and pressure-bearing ability of the link node in data transmission. When the data transmission load index of a link node is high, it may be the unfavorable result of the superposition of these factors. In-depth analysis and comprehensive optimization measures are needed to ensure that the link node can transmit data stably and efficiently, improve the reliability and performance of the entire network system, and avoid the risk of failure.

[0097] In a specific embodiment, by analyzing the data transmission load index of each link node, the operating pressure faced by the link node in the data transmission process can be accurately reflected, providing an accurate guidance for the reasonable allocation of resources, contributing to the realization of refined network management, providing key data support and decision-making basis for the realization of refined network management of the medical alliance chain, and promoting the digital construction of the medical industry to move towards a higher level.

[0098] According to the hardware operating load index of each link node and the data transmission load index of each link node, the comprehensive load evaluation index value of each link node is obtained through comprehensive analysis and processing.

[0099] The comprehensive load evaluation index value of each link node is a numerical result obtained by quantifying the hardware operating load index of each link node and the data transmission load index of each link node, and is used to characterize the operating load degree of each link node.

[0100] In a specific embodiment, the formula for the comprehensive load evaluation index value of each link node is as follows:

[0101] , where is the comprehensive load evaluation index value of the i-th link node, is the hardware operating load index of the i-th link node, is the data transmission load index of the i-th link node, is the weight value of the hardware operating load index, is the weight value of the data transmission load index, i is the number of each link node, , and m is the number of link nodes.

[0102] It should be noted that the weight value of the hardware operating load index ranges from 0 to 1. In this embodiment, the weight value of the hardware operating load index is the preset weight value of the hardware operating load index in the database, which represents the numerical value of the influence degree of the hardware operating load index on the operating load index of the link node. When in use, the preset weight value of the hardware operating load index can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the node hardware operating load index and the preset weight value of the hardware operating load index in the database form a mapping set, and the real-time hardware operating load index is input into the mapping set to obtain the weight value of the hardware operating load index, where the mapping relationship is one-to-one.

[0103] It should be noted that the value range of the data transmission load exponential weight is between 0 and 1. In this embodiment, the data transmission load exponential weight is the preset data transmission load exponential weight in the database, which represents the value of the influence degree of the data transmission load index on the operation load index of the link node. When used, the preset data transmission load exponential weight can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the node data transmission load index and the preset data transmission load exponential weight in the database form a mapping set, and the real-time data transmission load index is input into the mapping set to obtain the data transmission load exponential weight, and the mapping relationship therein is one-to-one correspondence.

[0104] It should also be noted that in this embodiment, the comprehensive load evaluation index value of each link node is obtained by processing the hardware operation load index and the data transmission load index of each link node, taking into account the mutual influence between these parameters. For example, when the hardware operation load index of a certain link node is relatively high, it means that the hardware device of this node faces greater pressure when processing tasks, the CPU usage rate may be close to saturation, the memory occupancy rate is relatively high, or the read and write of the storage device is frequently close to the limit, etc. If the data transmission load index is relatively low at this time, that is, the data inflow and outflow volume of this node is relatively small, and the frequency and scale of data transmission are within the tolerable range, then to a certain extent, this can relieve the overall operation pressure, because the relatively low data transmission load will not further increase the burden on the hardware, and the hardware can have more spare capacity to handle the current tasks. However, when the data transmission load index is relatively high, it means that this link node needs to frequently receive, process and send a large amount of data. If the hardware operation load index is also at a relatively high level at this time, then this node may get into trouble. For example, a large amount of data requires fast CPU processing and memory caching, but due to the high load operation of the hardware itself, it may lead to a slowdown in data processing speed, a situation where data queues up waiting to be processed, and further cause an increase in data transmission delay. Moreover, if the hardware operation load index is relatively low but the data transmission load index is relatively high, although the hardware has certain potential to handle data transmission tasks, if it is in this state for a long time, it may cause the hardware device to accelerate aging due to continuous high data flow impact, or there may be a single-point bottleneck in some key hardware components. For example, the network interface may overheat or make errors due to a large amount of data transmission, affecting the quality and stability of data transmission. The operation load index of the link node obtained by comprehensively considering these parameters can more comprehensively reflect the actual operation status and pressure-bearing capacity of this node, so as to ensure that the link node can operate stably and efficiently, improve the reliability of the entire consortium chain, and avoid the risk of failure.

[0105] Please refer to Figure 2As shown in the figure, the scatter plot of the relationship between the data transmission load index and the operating load index of the link nodes involved in the embodiments of the present invention. Among them, the abscissa is the data transmission load index of the link nodes, and the ordinate is the operating load index of the link nodes. The multiple scatter points in the figure are the operating load indexes of the link nodes corresponding to the data transmission load indexes involved in this embodiment. It can be clearly seen from the figure that as the data transmission load index increases, the operating load index of the link nodes generally shows an upward trend. This is because when the data transmission load index increases, the operating pressure of the link nodes increases. However, the distribution of the scatter points does not show a strict linear relationship, but there is a certain degree of discreteness. This reflects that in the actual operating environment, in addition to the factor of the data transmission load index, there are many other factors that will affect the operating load index of the link nodes. When the data transmission load index of the link nodes increases, it may mean that the node needs to process more data traffic and more frequent data interaction tasks, which will indeed increase its operating pressure and thus prompt the operating load index to rise. However, due to the influence of factors such as the hardware operating load index, even if the data transmission load index increases, the change in the operating load index may also be limited. For example, if the hardware operating load index is low, indicating that the hardware device of the node has sufficient processing power to handle the increased data transmission tasks, then even if the data transmission load index rises, the operating load index may not increase significantly, and may even remain relatively stable or decrease due to the efficient processing of the hardware.

[0106] In a specific embodiment, by analyzing the comprehensive load evaluation index values of each link node, the operating efficiency and pressure status of each link node in the medical consortium chain system can be effectively evaluated and grasped, providing a solid foundation for the optimization and stable operation of the system. At the same time, it provides a clear direction for the reasonable allocation of resources, helping to achieve the dynamic and precise management of the medical consortium chain system.

[0107] S3. Based on the comprehensive load evaluation index values of the respective link nodes, and comparing with a preset operating load index threshold, thereby generating an operating load evaluation label for each link node.

[0108] In this embodiment, the specific process of generating the operating load evaluation label for each link node is as follows: According to the comprehensive load evaluation index values of each link node and the preset operating load index threshold, generate the operating load evaluation label for each link node by comparison.

[0109] It should be understood that the running load index threshold refers to the key reference value stored in the database for measuring whether the running load status of the link node is within a reasonable range. The running load index threshold is set based on the long-term running monitoring and performance analysis of the medical alliance chain system, combined with the actual needs of medical services and the bearing capacity of system resources. By comparing the comprehensive load evaluation index value of each link node with the running load index threshold, it can be quickly determined whether the running load of the link node is within the normal range.

[0110] The running load evaluation label includes normal running load and abnormal running load.

[0111] If the running load index of a certain link node is lower than or equal to the preset running load index threshold, the running load evaluation label of the link node is defined as normal running load.

[0112] If the running load index of a certain link node is higher than the preset running load index threshold, the running load evaluation label of the link node is defined as abnormal running load.

[0113] In a specific embodiment, by generating the running load evaluation labels of each link node and configuring the node backup resources based on the running load evaluation labels, the convenience and pertinence of management are improved, which helps to conduct long-term performance monitoring and trend analysis. By tracking and recording the changes in the evaluation labels of each link node in different time periods, potential performance problems and trend laws can be discovered, ensuring the sustainable development and stable operation of the medical alliance chain system.

[0114] S4. According to the running load evaluation labels of each link node, the number of redundant backup nodes of each link node is obtained through processing, and the resource configuration of each link node is performed.

[0115] In this embodiment, the specific process of obtaining the number of redundant backup nodes of each link node is as follows: Based on the running load evaluation labels of each link node, if the running load evaluation label of a certain link node is normal running load, the number of redundant backup nodes of the link node is defined as zero; if the running load evaluation label of a certain link node is abnormal running load, the link node is marked as a link node requiring capacity expansion, and thus the link nodes requiring capacity expansion are counted.

[0116] Based on the comprehensive load evaluation index values of each link node, the difference between the running load index of each link node requiring capacity expansion and the preset running load index threshold is extracted and recorded as the capacity expansion reference value. Thus, the capacity expansion reference values of the link nodes requiring capacity expansion are counted and mapped and matched with the number of redundant backup nodes corresponding to each capacity expansion reference value interval defined in the database to obtain the number of redundant backup nodes of each link node requiring capacity expansion.

[0117] In a specific embodiment, by analyzing the number of redundant backup nodes of each link node, the reliability and stability of the system are significantly enhanced. By accurately calculating and configuring the number of redundant backup nodes, when the primary link node fails or is unable to work properly under high load pressure, the redundant backup nodes can be quickly enabled to take over its work, ensuring the uninterrupted transmission and processing of medical data, improving the scalability and adaptability of the system. At the same time, it helps to enhance the security of the entire medical consortium chain. By reasonably distributing the redundant backup nodes, the risk of data loss and leakage can be reduced, providing a strong guarantee for information security.

[0118] It should be noted that the resource configuration of each link node can be completed through the server management console.

[0119] The server management console is the core device for link node resource configuration. It is usually built based on open-source server management software. Through the server management console, the hardware resources of the server nodes can be directly configured and managed. In a specific embodiment, for the configuration of CPU resources, parameters such as the number of CPU cores, main frequency, and power management mode can be adjusted in the console. For example, on a hospital node server in the medical consortium chain, if it is found that the CPU load is too high when processing a large number of medical image data transcoding tasks, some CPU cores on other idle servers can be dynamically allocated to this node server through the console to improve its data processing ability.

[0120] For the configuration of memory resources, memory modules can be added or removed in the console, and the memory allocation strategy can be adjusted, such as setting the memory cache size and the usage method of virtual memory. For example, when a medical research institution node is performing large-scale data analysis and finds that the data processing speed is slow due to insufficient memory, more memory modules can be added or the memory usage method can be optimized through the server management console, and more memory can be allocated to key data processing processes.

[0121] In this embodiment, the method for dynamically configuring security resources of a medical consortium chain further includes dynamically configuring security resources for redundant backup nodes. The specific process is as follows: based on the number of redundant backup nodes of each link node, each redundant backup node is counted.

[0122] During a preset configuration period, each redundant backup node is perceptually monitored to obtain the usage load data of each redundant backup node, which specifically includes the usage access frequency, maximum number of connected people, and average read / write operation rate of each redundant backup node.

[0123] It should be noted that for the described usage access frequency, Wireshark can be used for packet capture, and a filter can be set to only display packets related to the target redundant backup node, and then the access frequency can be calculated based on the number of captured packets and the time span.

[0124] For the described maximum number of connected users, network management software can be used to view the number of users connected to this node, so as to obtain the maximum number of connected users.

[0125] For the described average read / write operation rate, the read / write speed can be tested using the CrystalDiskMark tool, and the average value of the read / write operation rate within a preset period can be calculated to obtain it.

[0126] Extract the redundant backup node reference load data stored in the database, and the redundant backup node reference load data includes reference usage access frequency, reference maximum number of connected users, and reference average read / write operation rate.

[0127] Based on the usage load data of each redundant backup node and the redundant backup node reference load data, analyze and process to obtain the usage load index of each redundant backup node. The usage load index of each redundant backup node is a numerical result obtained by quantifying the usage load data of each redundant backup node and the redundant backup node reference load data, and is used to represent the usage load degree of each redundant backup node.

[0128] According to the usage load index of each redundant backup node, compare it with the redundant backup node usage load index threshold stored in the database. If the usage load index of a certain redundant backup node is less than or equal to the redundant backup node usage load index threshold, no storage resource configuration is performed on this redundant backup node. If the usage load index of a certain redundant backup node is greater than the redundant backup node usage load index threshold, storage resource configuration is performed on this redundant backup node with a preset storage capacity.

[0129] It should be understood that the storage resource configuration for this redundant backup node can execute the preset storage capacity allocation through the storage management interface provided by the computer operating system, and map the allocated storage resources to the redundant backup node through a network storage protocol such as iSCSI. At the same time, install the corresponding storage driver program and configuration file on the redundant backup node, such as the remote management tool SSH, to identify and use the newly allocated storage resources.

[0130] In a specific embodiment, the usage load index of each redundant backup node has the following specific formula:

[0131] , where is the usage load index of the jth redundant backup node, The access frequency used for the j-th redundant backup node The access frequency used for reference The maximum number of connections for the j-th redundant backup node The reference maximum number of connections The average read / write operation rate of the j-th redundant backup node The reference average read / write operation rate The weight of the access frequency used The weight of the maximum number of connections The weight of the average read / write operation rate, where j is the number of each redundant backup node , n is the number of redundant backup nodes, and e is the natural constant

[0132] It should be noted that the weight of the access frequency used ranges from 0 to 1. In this embodiment, the weight of the access frequency used is the preset weight of the access frequency used in the database, which represents the value of the influence degree of the access frequency used on the usage load index of the redundant backup node. When using, the preset weight of the access frequency used can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the access frequency used by the node and the preset weight of the access frequency used in the database form a mapping set, and the real-time access frequency used is input into the mapping set to obtain the weight of the access frequency used, and the mapping relationship therein is one-to-one correspondence

[0133] It should be noted that the weight of the maximum number of connections ranges from 0 to 1. In this embodiment, the weight of the maximum number of connections is the preset weight of the maximum number of connections in the database, which represents the value of the influence degree of the maximum number of connections on the usage load index of the redundant backup node. When using, the preset weight of the maximum number of connections can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the maximum number of connections of the node and the preset weight of the maximum number of connections in the database form a mapping set, and the real-time maximum number of connections is input into the mapping set to obtain the weight of the maximum number of connections, and the mapping relationship therein is one-to-one correspondence

[0134] It should be noted that the weight of the average read / write operation rate ranges from 0 to 1. In this embodiment, the weight of the average read / write operation rate is the preset weight of the average read / write operation rate in the database, which represents the value of the influence degree of the average read / write operation rate on the usage load index of the redundant backup node. When using, the preset weight of the average read / write operation rate can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average read / write operation rate of the node and the preset weight of the average read / write operation rate in the database form a mapping set, and the real-time average read / write operation rate is input into the mapping set to obtain the weight of the average read / write operation rate, and the mapping relationship therein is one-to-one correspondence

[0135] It should also be noted that in this embodiment, the usage load index of each redundant backup node is obtained by processing the usage access frequency, the maximum number of concurrent connections, and the average read / write operation rate of each redundant backup node. This is because of the mutual influence among these parameters. For example, when the usage access frequency of a certain redundant backup node is relatively high, it means that the number of service requests for this node per unit time is relatively large, indicating that this node undertakes relatively frequent tasks in data interaction, and its load pressure is relatively large at the access level. If the maximum number of concurrent connections is relatively low at this time, it means that the number of devices or users establishing connections with this node is relatively small, which alleviates its overall load pressure to a certain extent, because a smaller number of connections means relatively less concurrent occupation of system resources and relatively lower concurrent requirements for data processing and transmission. When the average read / write operation rate is relatively fast, the node can quickly process read / write requests for data. Even if the access frequency and the maximum number of concurrent connections are relatively high, it may still be able to handle them efficiently, thus reducing the perception of load pressure to a certain extent. If the average read / write operation rate is relatively low, then in the face of a relatively high access frequency and a relatively large number of connections, it may lead to data backlog and processing delays, greatly increasing the load pressure on the node. If the usage access frequency of a redundant backup node is relatively high, and at the same time the maximum number of concurrent connections is close to its upper limit, and the average read / write operation rate cannot meet the requirement of fast response, then this node may be at risk of slow response, data transmission interruption, or even system crash. The usage load index of the redundant backup node obtained by comprehensively considering these parameters can more comprehensively reflect the actual working state and load-bearing capacity of this node. When the usage load index of a certain redundant backup node is relatively high, it may be an unfavorable result of the superposition of these factors, and it is necessary to conduct in-depth analysis and take comprehensive optimization measures for resource allocation to ensure that the redundant backup node can operate stably and efficiently, improve the reliability and performance of the entire system, and avoid failure risks.

[0136] Taking the 4 groups of different redundant backup nodes involved in this embodiment as an example, when the average read / write operation rate of these 4 groups of redundant backup nodes is 80 kilobytes per second, the reference usage access frequency is defined as 50 times per minute, the reference maximum number of concurrent connections is defined as 100 people, the reference average read / write operation rate is defined as 100 kilobytes per second, the weight of the usage access frequency is defined as 0.35, the weight of the maximum number of concurrent connections is defined as 0.4, and the weight of the average read / write operation rate is defined as 0.25. Based on different usage access frequencies and maximum numbers of concurrent connections, the usage load index of the redundant backup node obtained through processing is as follows in the table:

[0137] Table 1 Usage load index of redundant backup nodes:

[0138] Group Usage access frequency Maximum number of concurrent connections Redundant backup node usage load index The first group 20 70 1.87 The second group 40 90 2.01 The third group 60 110 2.16 The fourth group 80 130 2.32 ;

[0139] Combined with the above table, it can be seen that the utilization load index of the fourth group of redundant backup nodes is the highest, which indicates that compared with other groups, the operating pressure faced by the fourth group of redundant backup nodes is the most prominent. In specific instances, for redundant backup nodes with relatively high usage access frequency and maximum number of concurrent connections, the complexity of data processing and the pressure on system resource allocation are relatively large, which means that it is necessary to optimize the allocation of its related resources to ensure its stable and efficient operation.

[0140] In a specific embodiment, by analyzing the utilization load index of each redundant backup node, it can clearly indicate which redundant backup nodes are under greater working pressure, providing an accurate basis for the reasonable allocation and dynamic adjustment of resources, contributing to the realization of refined system maintenance and management, and providing a reliable guarantee for the stable operation and data security of the medical consortium chain system.

[0141] In a specific embodiment, by providing a dynamic security resource configuration method for the medical consortium chain, the accurate allocation of resources in the medical consortium chain system can be realized. It can adjust the resource allocation strategy and the enabling mechanism of redundant backup nodes according to the real-time load conditions of each link node, improving the operation efficiency and stability of the entire medical consortium chain system.

[0142] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0143] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can understand and utilize the present invention well. As long as it does not deviate from the structure of the present invention or exceed the scope defined by the present invention, it should fall within the protection scope of the present invention.

Claims

1. A dynamic security resource allocation method for a medical consortium chain, characterized in that: It includes the following steps: S1. Count several nodes in the node network architecture of the medical alliance chain and label them as each link node; S2. Based on a preset dynamic monitoring period, monitor the operating status of each link node within the dynamic monitoring period and analyze the comprehensive load evaluation index value of each link node; S3. Based on the comprehensive load evaluation index value of each link node, compare it with a preset operating load index threshold, and thus generate an operating load evaluation label for each link node; S4. According to the operating load evaluation label of each link node, obtain the number of redundant backup nodes of each link node through processing and perform resource allocation for each link node; It also includes performing dynamic security resource allocation for redundant backup nodes. The specific process is as follows: Based on the number of redundant backup nodes of each link node, count each redundant backup node; Perform sensing monitoring on each redundant backup node in a preset configuration period to obtain the usage load data of each redundant backup node, specifically including the usage access frequency, maximum connection number, and average read / write operation rate of each redundant backup node; Extract the reference load data of redundant backup nodes stored in the database. The reference load data of redundant backup nodes includes reference usage access frequency, reference maximum connection number, and reference average read / write operation rate; Based on the usage load data and reference load data of redundant backup nodes of each redundant backup node, analyze and process to obtain the usage load index of each redundant backup node. The usage load index of each redundant backup node is a numerical result obtained by quantifying the usage load data and reference load data of redundant backup nodes of each redundant backup node and is used to characterize the usage load degree of each redundant backup node; According to the usage load index of each redundant backup node, compare it with the usage load index threshold of redundant backup nodes. If the usage load index of a certain redundant backup node is less than or equal to the usage load index threshold of redundant backup nodes, no storage resource allocation is performed on this redundant backup node. If the usage load index of a certain redundant backup node is greater than the usage load index threshold of redundant backup nodes, storage resource allocation is performed on this redundant backup node with a preset storage capacity; The usage load index of each redundant backup node is specifically calculated by the following formula: ， Among them, is the load index used by the j-th redundant backup node, is the access frequency used by the j-th redundant backup node, is the reference access frequency, the maximum number of connections of the j-th redundant backup node, is the reference maximum number of connections, the average read / write operation rate of the j-th redundant backup node, is the reference average read / write operation rate, is the weight of the access frequency used, is the weight of the maximum number of connections, is the weight of the average read / write operation rate, and j is the number of each redundant backup node, , n is the number of redundant backup nodes, and e is the natural constant.

2. The dynamic security resource allocation method for a medical alliance chain according to claim 1, wherein: The process of specifically monitoring the operating status of each link node is as follows: Within a preset dynamic monitoring period, monitor the operating status of each link node based on a preset dynamic monitoring frequency to obtain the operating status data of each link node. The operating status data of each link node includes hardware performance parameters and data transmission parameters; The hardware performance parameters include the CPU usage rate, memory occupancy rate, hard disk storage capacity consumption speed, hard disk average read / write speed, and device temperature of each link node within the dynamic monitoring period; The data transmission parameters include the cumulative number of bytes transmitted, average data transmission rate, cumulative amount of transmitted data, data packet loss rate, and network interface bandwidth usage rate of each link node within the dynamic monitoring period; 3. The dynamic secure resource allocation method for a medical alliance chain according to claim 2, wherein: The process of specifically analyzing the comprehensive load evaluation index value of each link node is as follows: Based on the hardware performance parameters, analyze and process to obtain the hardware operating load index of each link node; Based on the data transmission parameters, analyze and process to obtain the data transmission load index of each link node; According to the hardware operation load index of each link node and the data transmission load index of each link node, comprehensively analyze and process to obtain the comprehensive load evaluation index value of each link node; The comprehensive load evaluation index value of each link node is a numerical result of quantifying the hardware operation load index of each link node and the data transmission load index of each link node, and is used to characterize the operation load degree of each link node.

4. The method for dynamically and securely configuring resources in a medical alliance chain according to claim 3, wherein: The specific analysis process of the hardware operation load index of each link node is as follows: Extract the reference hardware performance parameters stored in the database. The reference hardware performance parameters include reference CPU usage rate, reference memory occupancy rate, reference consumption speed of hard disk storage capacity, reference hard disk read / write speed, and reference device temperature; According to the hardware performance parameters and the reference hardware performance parameters, analyze and process to obtain the hardware operation load index of each link node; The hardware operation load index of each link node is a numerical result of quantifying the hardware performance parameters and the reference hardware performance parameters, and is used to characterize the hardware operation load status of each link node.

5. The method for dynamically and securely allocating resources in a medical alliance chain according to claim 3, wherein: The specific analysis process of the data transmission load index of each link node is as follows: Extract the reference data transmission parameters stored in the database. The reference data transmission parameters include reference cumulative number of bytes of data transmission, reference average data transmission rate, reference cumulative amount of transmitted data, reference data packet loss rate, and reference network interface bandwidth usage rate; According to the data transmission parameters and the reference data transmission parameters, analyze and process to obtain the data transmission load index of each link node; The data transmission load index of each link node is a numerical result of quantifying the data transmission parameters and the reference data transmission parameters, and is used to characterize the data transmission load degree of each link node.

6. The dynamic secure resource allocation method for a medical alliance chain according to claim 1, characterized in that: The specific process of generating the operation load evaluation label of each link node is as follows: According to the comprehensive load evaluation index value of each link node and the preset operation load index threshold, generate the operation load evaluation label of each link node by comparison; The operation load evaluation label includes normal operation load and abnormal operation load; If the operation load index of a certain link node is lower than or equal to the preset operation load index threshold, then define the operation load evaluation label of the link node as normal operation load; If the operation load index of a certain link node is higher than the preset operation load index threshold, then define the operation load evaluation label of the link node as abnormal operation load.

7. The method for dynamically and securely configuring resources in a medical alliance chain according to claim 6, wherein: The specific process of obtaining the number of redundant backup nodes of each link node is as follows: Based on the operation load evaluation label of each link node, if the operation load evaluation label of a certain link node is normal operation load, then define the number of redundant backup nodes of this link node as zero. If the operation load evaluation label of a certain link node is abnormal operation load, then mark this link node as a link node requiring capacity expansion, and thus count each link node requiring capacity expansion; Based on the comprehensive load evaluation index values of each link node, extract the difference between the operating load index of each demand-expanded link node and the preset operating load index threshold, and record it as the expansion reference value. Thus, count the expansion reference values of each demand-expanded link node, and map and match them with the number of redundant backup nodes corresponding to each expansion reference value interval defined in the database to obtain the number of redundant backup nodes for each demand-expanded link node.

8. The method for dynamically and securely configuring resources in a medical alliance chain according to claim 3, wherein: The specific formula for the comprehensive load evaluation index values of each link node is as follows: ， Among them, is the comprehensive load evaluation index value of the i-th link node, is the hardware operation load index of the i-th link node, is the data transmission load index of the i-th link node, is the weight of the hardware operation load index, is the weight of the data transmission load index, and i is the number of each link node, , and m is the number of link nodes.

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