Hyper-arithmetic power scheduling server and resource management method

Abstract

The invention discloses a hyper-arithmetic scheduling server and a resource management method, and relates to the technical field of communication, the method comprises the following steps: 1, scheduling server data acquisition: acquiring server task requirements and resources acquired by a system; 2, preprocessing and classifying the collected server resource information; 3, performing classified management on effective resources; 4, constructing and updating an original scheduling strategy according to task requirements of workers; 5, monitoring server resources in real time; and 6, protecting the resource information of the scheduling server. According to whether the monitored resources are in the safe state or not, an alarm mechanism is established, related data are recorded, an external environment manager is warned of dangerous situations, the situation of resource leakage is blocked in time, losses caused to people are avoided, and the safety of the computing power scheduling server system is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a supercomputing power scheduling server and a resource management method. Background Art

[0002] The supercomputing power scheduling server is a central server used to manage and coordinate task distribution and resource scheduling in computer clusters. Its main function is to allocate appropriate resources to each group of tasks based on task requirements and system resource conditions, and ensure that tasks can be executed smoothly; the main functions of the supercomputing power scheduling server include task management, resource allocation, resource monitoring, load balancing, and resource recovery, etc. Resource management methods are also one of the important steps in using the supercomputing power scheduling server. It is necessary to scientifically and rationally configure resources and task allocation management to ensure balanced execution of tasks.

[0003] As the enterprise environment gradually shifts to a network computing environment, enterprise-level Web applications are becoming more complex, among which resource management, utilization, and scheduling are the main issues affecting server performance. The network system load information is increasing, and task requests are sent to the server every day. Reasonably improving the load response time and not putting too much pressure on the Web application server is the key to improving the overall performance of the network system. Therefore, it is necessary to invent a supercomputing power scheduling server and resource management method.

[0004] On this basis, the Chinese patent CN116541166A was retrieved and disclosed a supercomputing power scheduling server and resource management method. The present invention continuously optimizes the path through a genetic algorithm, which can greatly reduce its usage limitations and greatly improve the server resource scheduling efficiency. It can model and search for parameters by itself, while improving the efficiency of resource scheduling management.

[0005] However, traditional scheduling servers and resource management methods have limitations. When traditional scheduling server management is centralized, the server system can manage and control a variety of different types of resources, but the scheduling server lacks resource protection measures and cannot monitor whether resource information is in a safe state. If a leak occurs in resource management, the server cannot alert external managers and cannot promptly prevent the theft of information, which can easily cause losses to people. Summary of the invention

[0006] The purpose of the present invention is to provide a supercomputing power scheduling server and resource management method. The device is used to work to solve the limitations of the traditional scheduling server and resource management method mentioned in the above background. When the traditional scheduling server management is centrally controlled, the server system can manage and control a variety of different types of resources, but the scheduling server lacks resource protection measures and cannot monitor whether the resource information is in a safe state. If a leak occurs in resource management, the server cannot warn external managers and cannot promptly prevent the theft of information, which can easily cause losses to people.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A supercomputing power scheduling server and resource management method, comprising the following steps:

[0009] Step 1: Collect data from the scheduling server: collect server task requirements and resources obtained by the system;

[0010] Step 2: Preprocessing and classifying the collected server resource information: Screening the data obtained in step 1, classifying according to the main direction of the information, and implementing information preprocessing to facilitate the subsequent system resource allocation work;

[0011] Step 3: Classification management of effective resources: Classify and manage the information processed in step 2 in a reasonable manner;

[0012] Step 4: Construct and update the original scheduling strategy based on the task requirements of the staff; first collect the operation information of the original scheduling strategy as a sample data set, then calculate the average value of the sample data set, and obtain the standard deviation of the sample data set based on the calculated average value, and then eliminate the data in the sample data set based on the standard deviation; the specific calculation formula of the standard deviation is as follows:

[0013]

[0014]

[0015] in represents the average value of x, x i Represents each data point, v n is the data deviation of the sample data set, s is the standard deviation, if any data x i The deviation v n Satisfy v n >3σ, the data is considered abnormal and will be removed;

[0016] The above formula is used to calculate the standard deviation, which reflects the degree of dispersion of the data set;

[0017] The calculation formula for the standard deviation can be further explained as follows: first, the square of the difference between each data point and the mean is calculated, then these square values ​​are summed, and finally the sum is divided by (i-1) (i is the number of data points) and the square root is taken. This calculation process gives the standard deviation, which indicates the degree of dispersion of the data points relative to the mean.

[0018] Step 5: Real-time monitoring of server resources: Provide a secure office environment for data terminals, control whether the resources input into the server are safe information, prevent harmful information from entering the server, and adopt encryption protection technology for data resources to control employees' operations on data in various ways, effectively preventing employees from actively leaking information;

[0019] Step 6: Resource information protection of the scheduling server: The resource files in the server use mature and advanced encryption algorithms, encryption methods and file system underlying drivers to achieve ultra-high encryption security strength after file encryption and folder encryption, so that files and resources can also be prevented from being deleted, copied and moved, effectively preventing resource information from being stolen.

[0020] Preferably, the management and calculation method for effective server resources in step 3 includes the following:

[0021] (1) Load balancing algorithm. Load balancing algorithm is a commonly used resource scheduling algorithm. It mainly monitors the load in the cloud and then distributes the load to multiple nodes to balance the load and improve the reliability and performance of the system.

[0022] (2) Grayscale release algorithm: The grayscale release algorithm is a strategy that balances release speed and risk. It enables the system to gradually transition from a grayscale state to a fully operational state, ensuring the stability and security of the system.

[0023] (3) Containerized management algorithm: Containerized management algorithm is a technology that packages an application and all its dependencies into a separate application container for easy deployment and management. Through containerized management, applications can be quickly started and stopped, as well as load balancing and resource management can be achieved.

[0024] Preferably, the server resource management technology in step 3 includes the following:

[0025] (1) Autoscaling: Autoscaling is an automated management tool that can automatically adjust the resources of a cloud computing platform according to load conditions. For example, when the load increases, autoscaling can automatically increase the number of cloud instances to meet higher resource requirements.

[0026] (2) Allocation mechanism: Allocation mechanism is a dynamic management technology that can allocate corresponding resources to different applications according to different business needs. For example, high-priority tasks can get more resources to complete faster.

[0027] (3) Resource monitoring: Resource monitoring is a technology for managing cloud computing platform resources. It can monitor the load of the server so as to promptly discover and solve resource bottleneck problems.

[0028] Preferably, the specific steps of updating the scheduling strategy in step 4 include the following:

[0029] The remaining data is standardized, and then each group of processed data is normalized and used as a training set, and then the number of neurons in each neural network layer is determined;

[0030] The training set is input into the input layer of the neural network, and the center vector value is determined to obtain the output layer as a linear combination of the hidden node outputs. Then the least squares recursive method is used to define the energy function of multiple rounds of learning of the convolutional neural network. When the energy function value is less than the target error, the training process ends and the scheduling update model is output. Otherwise, the training continues.

[0031] Preferably, the specific steps of updating the scheduling strategy also include the following: determining the optimal parameters of the scheduling update model, the subsequent staff inputs the demand data into the scheduling update model, the scheduling update model generates a corresponding scheduling strategy after processing, and feeds it back to the staff for review.

[0032] Preferably, the resource information monitoring method of the scheduling server in step 5 includes: sensitive information encryption protection, confidentiality level authority control, multi-dimensional risk monitoring and intelligent early warning mechanism. These methods help the server to reasonably manage internal information through functions such as data encryption, authority control, risk monitoring and early warning.

[0033] (1) Encryption protection of sensitive information: Automatically encrypt important server data through transparent encryption and decryption mode to ensure that encrypted files cannot be opened and used normally when they are out of the company's authorized scope, and will be displayed in garbled code, thereby ensuring that encrypted files are always protected.

[0034] (2) Confidentiality level control: You can set the confidentiality level of the encrypted file yourself, and each terminal has a strict restricted range. The level of confidentiality of the file will directly determine whether the terminal has the permission to open the encrypted file. If it is not within the permission range, the encrypted file cannot be opened.

[0035] (3) Multi-dimensional risk monitoring: The server can monitor risks in the data transmission and storage process, including file transfer, email sending, printing and other channels, and record terminal operation behaviors to facilitate the understanding of the company's overall situation and conduct corresponding audits.

[0036] (4) Intelligent early warning mechanism: The server has a real-time early warning and response mechanism, which can collect statistics and summarize the data it collects, and intelligently display security risks and trends. Once abnormal behavior is found, an alarm can be issued in time, and managers can take measures to prevent risky operations at any time.

[0037] Preferably, the method for protecting the resource information of the scheduling server in step 6 includes:

[0038] (1) Strengthen network security protection: Establish a multi-level network security protection system, including firewalls, intrusion detection systems, encrypted transmission and other technical means to ensure the security of data transmission.

[0039] (2) Strengthen the control of internal data access servers: through identity authentication, permission management and other means, limit the access rights of different users to sensitive data to ensure that data can only be accessed by authorized personnel.

[0040] (3) Establish a data backup and recovery mechanism: Back up important data regularly and establish a rapid recovery mechanism to prevent data loss or accidental damage.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention provides a supercomputing power scheduling server and a resource management method. The scheduling server can allocate appropriate computing resources to each task according to the task requirements and the system resource conditions, and ensure that the task can be executed smoothly. The computing power server is mainly used as a server for computing-intensive tasks. It has strong processing power and computing performance, and can perform complex scientific computing, artificial intelligence, data analysis and other tasks. The supercomputing power scheduling server and the resource management method of the present invention can be applied in multiple fields. By integrating computing power resources of different sources, types and architectures, unified scheduling and operation are achieved to meet the computing power business needs of different application fields.

[0043] 2. The present invention provides a supercomputing power scheduling server and resource management method. The present invention collects the original scheduling strategy operation information as the data set basis, obtains the standard deviation of the sample data set, and then eliminates the data in the sample data set according to the standard deviation. After the remaining data is processed, the information is input into the system, thereby improving the effectiveness of the server resource management scheduling strategy formulation.

[0044] 3. The computing power scheduling server of the present invention adds safety protection measures. The computing power scheduling server can monitor resources, which is an important means to ensure the normal operation of the resource management system and data security by selecting appropriate monitoring tools; and the present invention can establish an alarm mechanism and record relevant data according to whether the monitored resources are in a safe state, warn external environment managers of dangerous situations, and promptly prevent resource leakage from occurring, avoid losses to people, and effectively ensure the security of the computing power scheduling server system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flowchart of the steps of the supercomputing power scheduling server and resource management method. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings.

[0048] Implementation 1:

[0049] Combination Figure 1 The present invention provides a technical solution comprising the following steps:

[0050] Step 1: Collect data from the scheduling server: collect server task requirements and resources obtained by the system;

[0051] Step 2: Preprocess and classify the collected server resource information: Filter the data obtained in step 1, classify according to the main direction of the information, and implement information preprocessing to facilitate the subsequent system resource allocation work;

[0052] Step 3: Classification management of effective resources: Classify and manage the information processed in step 2 in a reasonable manner;

[0053] Step 4: Build and update the original scheduling strategy based on the staff's task requirements; first collect the original scheduling strategy operation information as a sample data set, then calculate the average value of the sample data set, and obtain the standard deviation of the sample data set based on the calculated average value, and then eliminate the data in the sample data set based on the standard deviation; the specific calculation formula for the standard deviation is as follows:

[0054]

[0055]

[0056] in represents the average value of x, x i Represents each data point, v n is the data deviation of the sample data set, s is the standard deviation, if any data x i The deviation v n Satisfy v n >3σ, the data is considered abnormal and will be removed;

[0057] The above formula is used to calculate the standard deviation, which reflects the degree of dispersion of the data set;

[0058] The calculation formula for the standard deviation can be further explained as follows: first, the square of the difference between each data point and the mean is calculated, then these square values ​​are summed, and finally the sum is divided by (i-1) (i is the number of data points) and the square root is taken. This calculation process gives the standard deviation, which indicates the degree of dispersion of the data points relative to the mean.

[0059] Step 5: Real-time monitoring of server resources: Provide a secure office environment for data terminals, control whether the resources input into the server are safe information, prevent harmful information from entering the server, and adopt encryption protection technology for data resources to control employees' operations on data in various ways, effectively preventing employees from actively leaking information;

[0060] Step 6: Resource information protection of the scheduling server: The resource files in the server use mature and advanced encryption algorithms, encryption methods and file system underlying drivers to achieve ultra-high encryption security strength after file encryption and folder encryption, so that files and resources can also be prevented from being deleted, copied and moved, effectively preventing resource information from being stolen.

[0061] The management and calculation method for the effective resources of the server in step 3 includes the following:

[0062] (1) Load balancing algorithm. Load balancing algorithm is a commonly used resource scheduling algorithm. It mainly monitors the load in the cloud and then distributes the load to multiple nodes to balance the load and improve the reliability and performance of the system.

[0063] (2) Grayscale release algorithm: The grayscale release algorithm is a strategy that balances release speed and risk. It enables the system to gradually transition from a grayscale state to a fully operational state, ensuring the stability and security of the system.

[0064] (3) Containerized management algorithm: Containerized management algorithm is a technology that packages an application and all its dependencies into a separate application container for easy deployment and management. Through containerized management, applications can be quickly started and stopped, as well as load balancing and resource management can be achieved.

[0065] The server resource management techniques in step 3 include the following:

[0066] (1) Autoscaling: Autoscaling is an automated management tool that can automatically adjust the resources of a cloud computing platform according to load conditions. For example, when the load increases, autoscaling can automatically increase the number of cloud instances to meet higher resource requirements.

[0067] (2) Allocation mechanism: Allocation mechanism is a dynamic management technology that can allocate corresponding resources to different applications according to different business needs. For example, high-priority tasks can get more resources to complete faster.

[0068] (3) Resource monitoring: Resource monitoring is a technology for managing cloud computing platform resources. It can monitor the load of the server so as to promptly discover and solve resource bottleneck problems.

[0069] The specific steps of updating the scheduling strategy in step 4 include the following:

[0070] The remaining data is standardized, and then each group of processed data is normalized and used as a training set, and then the number of neurons in each neural network layer is determined;

[0071] The training set is input into the input layer of the neural network, and the center vector value is determined to obtain the linear combination of the hidden node outputs in the output layer. Then, the least squares recursive method is used to define the energy function of the convolutional neural network for multiple rounds of learning. When the energy function value is less than the target error, the training process ends and the scheduling update model is output. Otherwise, the training continues.

[0072] The specific steps of scheduling strategy update also include the following: determining the optimal parameters of the scheduling update model, the subsequent staff inputs the demand data into the scheduling update model, the scheduling update model generates the corresponding scheduling strategy after processing, and feeds it back to the staff for review.

[0073] Implementation 2:

[0074] The resource information monitoring methods of the scheduling server in step 5 include: sensitive information encryption protection, confidentiality level permission control, multi-dimensional risk monitoring and intelligent early warning mechanism. These methods help the server to reasonably manage internal information through functions such as data encryption, permission control, risk monitoring and early warning.

[0075] (1) Encryption protection of sensitive information: Automatically encrypt important server data through transparent encryption and decryption mode to ensure that encrypted files cannot be opened and used normally when they are out of the company's authorized scope, and will be displayed in garbled code, thereby ensuring that encrypted files are always protected.

[0076] (2) Confidentiality level control: You can set the confidentiality level of the encrypted file yourself, and each terminal has a strict restricted range. The level of confidentiality of the file will directly determine whether the terminal has the permission to open the encrypted file. If it is not within the permission range, the encrypted file cannot be opened.

[0077] (3) Multi-dimensional risk monitoring: The server can monitor risks in the data transmission and storage process, including file transfer, email sending, printing and other channels, and record terminal operation behaviors to facilitate the understanding of the company's overall situation and conduct corresponding audits.

[0078] (4) Intelligent early warning mechanism: The server has a real-time early warning and response mechanism, which can collect statistics and summarize the data it collects, and intelligently display security risks and trends. Once abnormal behavior is found, an alarm can be issued in time, and managers can take measures to prevent risky operations at any time.

[0079] The security protection method for resource information of the scheduling server in step 6 includes:

[0080] (1) Strengthen network security protection: Establish a multi-level network security protection system, including firewalls, intrusion detection systems, encrypted transmission and other technical means to ensure the security of data transmission.

[0081] (2) Strengthen the control of internal data access servers: through identity authentication, permission management and other means, limit the access rights of different users to sensitive data to ensure that data can only be accessed by authorized personnel.

[0082] (3) Establish a data backup and recovery mechanism: Back up important data regularly and establish a rapid recovery mechanism to prevent data loss or accidental damage.

[0083] In summary, the present invention provides a supercomputing power scheduling server and a resource management method. The scheduling server can allocate appropriate computing resources to each task according to the task requirements and the system resource conditions, and ensure that the task can be executed smoothly. The computing power server is mainly used as a server for computing-intensive tasks. It has powerful processing power and computing performance, and can perform complex scientific computing, artificial intelligence, data analysis and other tasks. The supercomputing power scheduling server and the resource management method of the present invention can be applied in multiple fields. By integrating computing power resources of different sources, types and architectures, unified scheduling and operation are achieved to meet the computing power business needs of different application fields.

[0084] A supercomputing power scheduling server and resource management method of the present invention collects original scheduling strategy operation information as a data set basis, obtains the standard deviation of the sample data set, and then removes data in the sample data set according to the standard deviation, and then processes the remaining data and inputs the information into the system, thereby improving the effective performance of server resource management scheduling strategy formulation;

[0085] The computing power scheduling server of the present invention adds safety protection measures. The computing power scheduling server can monitor resources, which is an important means to ensure the normal operation of the resource management system and data security by selecting appropriate monitoring tools; and the present invention can establish an alarm mechanism and record relevant data according to whether the monitored resources are in a safe state, warn external environment managers of dangerous situations, and promptly block resource leakage to avoid losses to people, thereby effectively ensuring the security of the computing power scheduling server system.

[0086] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0087] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A supercomputing power scheduling server and resource management method, characterized in that: The steps include: Step 1: Collect data from the scheduling server: collect server task requirements and resources obtained by the system; Step 2: Preprocessing and classifying the collected server resource information: Screening the data obtained in step 1, classifying according to the main direction of the information, and implementing information preprocessing to facilitate the subsequent system resource allocation work; Step 3: Classification management of effective resources: Classify and manage the information processed in step 2 in a reasonable manner; Step 4: Build and update the original scheduling strategy based on the staff's task requirements; (1) First, the original scheduling strategy operation information is collected as a sample data set, and then the average value of the sample data set is calculated, and the standard deviation of the sample data set is obtained based on the calculated average value, and then the data in the sample data set is eliminated based on the standard deviation; (2) The specific calculation formula of the standard deviation is as follows: in represents the average value of x, x i Represents each data point, v n is the data deviation of the sample data set, s is the standard deviation, if any data x i The deviation v n Satisfy v n >3σ, the data is considered abnormal and will be removed; The above formula is used to calculate the standard deviation, which reflects the degree of dispersion of the data set; The calculation formula for the standard deviation can be further explained as follows: first, the square of the difference between each data point and the mean is calculated, then these square values ​​are summed, and finally the sum is divided by (i-1) (i is the number of data points) and the square root is taken. This calculation process gives the standard deviation, which indicates the degree of dispersion of the data points relative to the mean. Step 5: Real-time monitoring of server resources: Provide a secure office environment for data terminals, control whether the resources input into the server are safe information, prevent harmful information from entering the server, and adopt encryption protection technology for data resources to control employees' operations on data in various ways, effectively preventing employees from actively leaking information; Step 6: Resource information protection of the scheduling server: The resource files in the server use mature and advanced encryption algorithms, encryption methods and file system underlying drivers to achieve ultra-high encryption security strength after file encryption and folder encryption, so that files and resources can be prevented from being deleted, copied and moved, effectively preventing resource information from being stolen.

2. A supercomputing power scheduling server and resource management method according to claim 1, characterized in that: The management and calculation method for the effective resources of the server in step 3 includes the following: (1) Load balancing algorithm: Load balancing algorithm is a commonly used resource scheduling algorithm. It mainly monitors the load in the cloud and then distributes the load to multiple nodes to balance the load and improve the reliability and performance of the system. (2) Grayscale release algorithm: A grayscale release algorithm is a strategy that balances release speed and risk. It allows the system to gradually transition from a grayscale state to a fully operational state, ensuring the stability and security of the system. (3) Containerized management algorithm: Containerized management algorithm is a technology that packages an application and all its dependencies into a separate application container to enable easy deployment and management. Through containerized management, applications can be quickly started and stopped, as well as load balancing and resource management can be achieved.

3. A supercomputing power scheduling server and resource management method according to claim 2, characterized in that: The server resource management techniques in step 3 include the following: (1) Autoscaling: Autoscaling is an automated management tool that can automatically adjust the resources of a cloud computing platform according to load conditions. For example, when the load increases, autoscaling can automatically increase the number of cloud instances to meet higher resource demands. (2) Allocation mechanism: Allocation mechanism is a dynamic management technology that can allocate corresponding resources to different applications according to different business needs. For example, high-priority tasks can obtain more resources to complete faster. (3) Resource monitoring: Resource monitoring is a technology for managing cloud computing platform resources. It can monitor the load of the server so as to promptly discover and solve resource bottleneck problems.

4. A supercomputing power scheduling server and resource management method according to claim 3, characterized in that: The specific steps of updating the scheduling strategy in step 4 include the following: (3) Standardize the remaining data, then normalize each group of data after processing and use it as a training set, and then determine the number of neurons in each neural network layer; (4) The training set is input into the input layer of the neural network, and the center vector value is determined to obtain the output layer as a linear combination of the hidden node outputs. The least squares recursive method is then used. When the energy function value is less than the target error, the training process ends and the scheduling update model is output. Otherwise, the training continues.

5. A supercomputing power scheduling server and resource management method according to claim 4, characterized in that: The specific steps of updating the scheduling strategy also include the following: (5) determining the optimal parameters of the scheduling update model, and then the staff inputs the demand data into the scheduling update model. The scheduling update model generates a corresponding scheduling strategy after processing and feeds it back to the staff for review.

6. A supercomputing power scheduling server and resource management method according to claim 5, characterized in that: The resource information monitoring method of the scheduling server in step 5 includes: sensitive information encryption protection, confidentiality level authority control, multi-dimensional risk monitoring and intelligent early warning mechanism; these methods help the server to reasonably manage internal information through functions such as data encryption, authority control, risk monitoring and early warning; (1) Encryption protection of sensitive information: Automatically encrypt important server data through transparent encryption and decryption mode to ensure that encrypted files cannot be opened and used normally when they are out of the company's authorized scope, and will be displayed in garbled code, thereby ensuring that encrypted files are always protected; (2) Confidentiality level control: You can set the confidentiality level of encrypted files by yourself. Each terminal has a strict restricted range. The confidentiality level of the file will directly determine whether the terminal has the permission to open the encrypted file. If it is not within the permission range, the encrypted file cannot be opened. (3) Multi-dimensional risk monitoring: The server can monitor risks in the data transmission and storage process, including file transfer, email sending, printing and other channels, and record terminal operation behaviors to facilitate the understanding of the company's overall situation and conduct corresponding audits; (4) Intelligent early warning mechanism: The server is equipped with a real-time early warning and response mechanism, which can collect statistics and summarize the data it collects and intelligently display security risks and trends. Once abnormal behavior is detected, an alarm can be issued in time, and managers can take measures at any time to prevent risky operations.

7. A supercomputing power scheduling server and resource management method according to claim 6, characterized in that: The method for protecting the resource information of the scheduling server in step 6 includes: (1) Strengthen network security protection: Establish a multi-level network security protection system, including firewalls, intrusion detection systems, encrypted transmission and other technical means to ensure the security of data transmission; (2) Strengthen the control of internal data access servers: through identity authentication, permission management and other means, limit the access rights of different users to sensitive data to ensure that data can only be accessed by authorized personnel; (3) Establish a data backup and recovery mechanism: Back up important data regularly and establish a rapid recovery mechanism to prevent data loss or accidental damage.

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