Intelligent central control system based on 5G cloud computing technology

Abstract

The invention relates to the technical field of intelligent control, in particular to an intelligent central control system based on the 5G cloud computing technology. The system comprises a data acquisition unit which is used for acquiring business data transmitted by each interface in computer equipment; the cloud computing platform is used for receiving the data transmitted by the data acquisition unit, storing, processing and managing the data, and finally distributing the data to the terminal application program; and the logic control unit is used for defining and managing flow rules of services and coordinating an interaction process between the data acquisition unit and the cloud computing platform. The introduction of the trend term in the system enables the model to capture the linear trend in the data, and the comprehensive optimization not only improves the resource utilization rate, but also enhances the flexibility and adaptability of the system, so that the system can efficiently operate in a complex and changeable environment, and meets the requirements of different tasks.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent central control system based on 5G cloud computing technology. Background Art

[0002] The intelligent central control system based on 5G cloud computing technology is supported by high-speed, low-latency 5G communication networks and powerful cloud computing platforms, and realizes optimized management and control of complex application scenarios through real-time data collection, efficient computing and processing, and intelligent decision-making. The system utilizes the high bandwidth and low latency characteristics of 5G, combined with the elastic resource scheduling and big data analysis capabilities of cloud computing, and can provide real-time response, efficient operation and intelligent decision-making support for multiple fields such as industrial automation, smart cities, and telemedicine, meet the growing demand for intelligence, and promote digital transformation and efficiency improvement in all walks of life. Existing resource management methods usually only focus on a single goal, such as minimizing resource waste or achieving load balancing, but rarely consider both at the same time. This may lead to uneven resource allocation, overload of some nodes while other nodes are idle, or low resource utilization. Therefore, an intelligent central control system based on 5G cloud computing technology is designed. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent central control system based on 5G cloud computing technology to solve the problem that the existing resource management methods proposed in the above background technology usually only focus on a single goal, such as minimizing resource waste or achieving load balancing, but rarely consider both at the same time. This may lead to uneven resource allocation, some nodes are overloaded while other nodes are idle, or low resource utilization.

[0004] To achieve the above objectives, the present invention provides an intelligent central control system based on 5G cloud computing technology, including: A data collection unit, which is used to collect business data transmitted from various interfaces in the computer device, and the data collection unit supports multiple protocols, and can obtain business data from different types of interfaces and transmit it to the cloud computing platform; A cloud computing platform, which is used to receive data transmitted from a data acquisition unit, store, process and manage the data, and finally distribute the data to terminal applications. In the process of storing, processing and managing data, node performance, network latency, task priority and load balancing are introduced for optimization; Among them, the cloud computing platform includes a data storage module, a resource management module, and a processing and distribution module; A logic control unit is used to define and manage business process rules and coordinate the interaction process between the data acquisition unit and the cloud computing platform.

[0005] As a further improvement of the present technical solution, in the data acquisition unit, business data is acquired from different types of interfaces and transmitted to the cloud computing platform based on a 5G communication module. The 5G communication module can provide high-speed and low-latency wireless communication capabilities, so that the data acquired by the data acquisition unit can be quickly transmitted to the cloud computing platform.

[0006] As a further improvement of this technical solution, in the cloud computing platform: The data storage module is used to store structured and unstructured data and also provides data storage solutions. At the same time, the data storage module also supports data backup and recovery functions, as well as data compression and data encryption; The resource management module is used to monitor the real-time load and resource usage of each computing node and the task requirements of business data, dynamically allocate and schedule computing resources on the cloud computing platform, and then generate data allocation instructions to transmit to the processing and distribution module. The resource management module also supports elastic scaling and can adjust the resource scale according to the actual task requirements; The processing and distribution module is used to receive and verify the data allocation instructions of the resource management module, and allocate the tasks to be processed to the corresponding computing nodes according to the instructions.

[0007] As a further improvement of the technical solution, in the resource management module, the real-time load and resource usage of each node and the task requirements of business data are monitored, and the computing resources on the cloud computing platform are dynamically allocated and scheduled, specifically: S21. Continuously monitor the resource usage of all computing nodes and collect the real-time load and resource usage of each node; S22, generating a task queue, the task queue including the task requirements of the business data to be processed, and sorting the tasks; S23, analyzing the current and historical load conditions of each computing node, predicting the future load trend of the computing node, and then selecting the computing node that is most suitable for executing the current task based on task requirements and resource usage; S24. Generate a data allocation instruction according to the allocation result, specify which data should be transmitted to which computing nodes, and send the data allocation instruction to the processing distribution module.

[0008] As a further improvement of the technical solution, the tasks are sorted in S22 based on the priorities and deadlines of the tasks.

[0009] As a further improvement of the present technical solution, in S23, the current and historical load conditions of each computing node are analyzed to predict the future load trend of the computing node, specifically: ; in, For the current moment The first-level smoothing value of ; is the first-level smoothing coefficient; For the current moment The actual value of For the previous moment The first-level smoothing value of ; For the previous moment Trend items; ; in, For the current moment Trend items; is the trend smoothing coefficient, which ranges from 0 to 1 and indicates the degree of influence of the current trend change on the trend item; Use first-order smoothing and trend items To predict future load : ; in, For predicted future loads; is the time step of prediction; When there is a new actual value When the first-level smoothing value and trend item are repeatedly calculated, the and , and re-predict; Regularly evaluate the error between the forecast result and the actual load, and adjust the smoothing coefficient if the error is large and Optimize prediction results.

[0010] As a further improvement of the technical solution, in S23, the most suitable computing node for executing the current task is selected according to the task requirements and resource usage, specifically: ; in, To minimize resource waste; is the number of computing nodes; For Node The total amount of resources available; is the number of tasks; For the task At the node The amount of resources required on ; .

[0011] As a further improvement of the technical solution, in the cloud computing platform, node performance, network delay, task priority and load balancing are introduced to optimize the process of storing, processing and managing data. The optimization is as follows: ; in, To minimize resource waste after optimization; For Node performance indicators; For the task At the node Data transmission delay on For the task Priority; For the task At the node The amount of resources required on .

[0012] As a further improvement of the present technical solution, if the resources in S23 are insufficient, the computing resources are automatically increased or decreased, the tasks are evenly distributed to multiple computing nodes, and resources are reserved for key tasks.

[0013] As a further improvement of the present technical solution, the logic control unit includes a rule engine, which is used to define and manage business rules. Once the triggering conditions of the defined business rules are met, the rule engine will automatically trigger a preset action.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In this intelligent central control system based on 5G cloud computing technology, the introduction of trend terms enables the model to capture linear trends in the data. This is very effective when processing time series data with obvious upward or downward trends. By considering the sum of squares of resource waste and the maximum resource demand at the same time, this objective function aims to find a balance point that minimizes resource waste and achieves load balancing.

[0015] 2. In the intelligent central control system based on 5G cloud computing technology, it is ensured that the resource usage of each computing node does not exceed the total amount of its available resources, while taking into account the performance of the node, network latency and task priority, so as to improve the speed and response time of task execution. In addition, it can also keep the load of each node relatively balanced and avoid overloading of a single node, thereby improving the stability and reliability of the system. This comprehensive optimization not only improves resource utilization, but also enhances the flexibility and adaptability of the system, enabling the system to operate efficiently in a complex and changing environment, meet the needs of different tasks, and provide a solid foundation for future expansion and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is the overall flow chart of the present invention; Figure 2 It is a flow chart of the pressure control system of the present invention; The meaning of each number in the figure is: 1. Data acquisition unit; 2. Cloud computing platform; 21. Data storage module; 22. Resource management module; 23. Processing and distribution module; 3. Logical control unit. DETAILED DESCRIPTION

[0017] 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. Example

[0018] See also Figure 1-Figure 2 As shown, an intelligent central control system based on 5G cloud computing technology is provided, including a data acquisition unit 1, a cloud computing platform 2 and a logic control unit 3; The data acquisition unit 1 is used to collect business data transmitted from various interfaces in the computer device, and the data acquisition unit 1 supports multiple protocols, and can obtain business data from different types of interfaces and transmit it to the cloud computing platform 2; wherein the multiple protocols include network protocols, such as TCP / IP, UDP and HTTP / HTTPS; also include Internet of Things protocols, such as MQTT and CoAP; also include file transfer protocols, such as FTP, SFTP and SCP.

[0019] In the data acquisition unit 1, business data is obtained from different types of interfaces and transmitted to the cloud computing platform 2 based on the 5G communication module. The 5G communication module can provide high-speed and low-latency wireless communication capabilities, so that the data obtained by the data acquisition unit 1 can be quickly transmitted to the cloud computing platform 2.

[0020] The characteristics of 5G enable low latency even in the case of large amounts of data transmission, which is particularly important for application scenarios with high real-time requirements. In addition, the 5G communication module also supports large-scale device connections, providing a basis for multi-device access in the IoT environment.

[0021] The cloud computing platform 2 is used to receive data transmitted from the data acquisition unit 1, store, process and manage the data, and finally distribute the data to the terminal application. In the process of storing, processing and managing data, node performance, network latency, task priority and load balancing are introduced for optimization.

[0022] The cloud computing platform 2 ensures high availability, scalability and security of the system, supports real-time data analysis and intelligent decision-making, and provides powerful computing power and flexible service support for various application scenarios.

[0023] The cloud computing platform 2 includes a data storage module 21, a resource management module 22 and a processing and distribution module 23; In cloud computing platform 2: The data storage module 21 is used to store structured and unstructured data, and also provides a data storage solution. At the same time, the data storage module 21 also supports data backup and recovery functions, as well as data compression and data encryption to ensure data security and privacy; Structured data, such as table data in relational databases, and unstructured data, such as documents, pictures, and videos, meet the needs of different business scenarios. The data storage solutions provided include object storage services, block storage services, file storage services, distributed file systems, and database services. Among them, object storage services are suitable for storing large amounts of unstructured data, such as pictures, videos, and documents. They can easily process PB-level data, automatically replicate data to multiple geographic locations, improve data reliability and availability, and provide fine-grained permission management, supporting access control based on users, roles, or policies. Block storage services are suitable for applications that require high-performance reading and writing, such as databases, virtual machine disks, etc. They provide high-performance IOPS (input and output operations per second), are suitable for applications with high performance requirements, and can dynamically adjust storage capacity according to demand. File storage services It is suitable for environments that require shared file storage, such as enterprise file systems and development collaboration. It can automatically expand storage space according to the usage of the file system and ensure the high availability of the file system through multi-availability zone deployment. Distributed file systems are suitable for big data processing and analysis, such as Hadoop clusters and machine learning training. They can be designed to process large-scale data sets and provide high-throughput data access. Through the multi-copy mechanism of data blocks, data integrity can be guaranteed even if some nodes fail. Storage capacity and performance can be expanded by adding more nodes. Database services are suitable for storing structured data, supporting complex queries and transaction processing, supporting cross-region replication, ensuring high availability and disaster recovery capabilities of the database, regularly and automatically backing up the database, supporting point-in-time recovery, and providing automatic tuning and performance monitoring tools to help optimize database performance. The resource management module 22 is used to monitor the real-time load and resource usage of each computing node and the task requirements of business data, dynamically allocate and schedule computing resources on the cloud computing platform 2, and then generate data allocation instructions to be transmitted to the processing and distribution module 23. The resource management module 22 also supports elastic scaling and can adjust the resource scale according to the actual task requirements to cope with traffic peaks or valleys, while ensuring the stability and high availability of the system; The resource management module 22 monitors the real-time load and resource usage of each node and the task requirements of business data, and dynamically allocates and schedules computing resources on the cloud computing platform 2, specifically: S21. Continuously monitor the resource usage of all computing nodes and collect the real-time load and resource usage of each node; S22, generating a task queue, the task queue including the task requirements of the business data to be processed, sorting the tasks, and sorting the tasks based on the priority and deadline of the tasks; S23, analyzing the current and historical load conditions of each computing node, predicting the future load trend of the computing node, and then selecting the computing node that is most suitable for executing the current task based on task requirements and resource usage; In S23, the current and historical load conditions of each computing node are analyzed to predict the future load trend of the computing node, specifically: generally and The value range of is between 0 and 1, and Used to control the degree of data smoothing. Used to control the impact of trend changes; Determine the initial first-order smoothing value and trend items . Usually you can Set to first observation ,and It can be set to 0 or estimated based on actual conditions; Collect historical load data for each compute node ,in . These data can be CPU utilization, memory usage, disk I / O operations, etc.

[0024] ; in, For the current moment The first-level smoothing value of ; is the first-level smoothing coefficient; For the current moment The actual value of For the previous moment The first-level smoothing value of ; For the previous moment Trend items; ; in, For the current moment Trend items; is the trend smoothing coefficient, which ranges from 0 to 1 and indicates the degree of influence of the current trend change on the trend item; Use first-order smoothing and trend items To predict future load : ; in, For predicted future loads; is the time step of prediction; When there is a new actual value When the first-level smoothing value and trend item are repeatedly calculated, the and , and re-predict; Regularly evaluate the error between the forecast result and the actual load, and adjust the smoothing coefficient if the error is large and Optimize prediction results.

[0025] Trending Items The introduction of allows the model to capture linear trends in the data. This is very effective when dealing with time series data with obvious upward or downward trends. For example, in resource management, if the workload gradually increases or decreases over time, double exponential smoothing can better predict future loads; First level smoothing value By the current actual value and the smoothed value of the previous moment By performing weighted averaging, random fluctuation noise in the data can be effectively smoothed out. This smoothing effect helps improve the stability of predictions and reduce misjudgments caused by short-term fluctuations.

[0026] In S23, the most suitable computing node for executing the current task is selected according to the task requirements and resource usage, specifically: ; in, To minimize resource waste; is the number of computing nodes; For Node The total amount of resources available; is the number of tasks; For the task At the node The amount of resources required on ; ; The sum of squares of resource waste on all nodes. The goal of this part is to minimize resource waste. Ideally, we want the remaining resources on all nodes to be close to zero, so as to maximize resource utilization; The maximum resource demand on each node. The goal of this part is to make the load of each node as balanced as possible. Ideally, we want the resource demand of each node to be as balanced as possible to avoid some nodes being overloaded while other nodes are idle.

[0027] By considering both the sum of squares of resource waste and the maximum resource demand, this objective function aims to find a balance point that minimizes resource waste and achieves load balancing.

[0028] In the cloud computing platform 2, node performance, network latency, task priority and load balancing are introduced to optimize the data storage, processing and management process. The optimization results are as follows: ; in, To minimize resource waste after optimization; For Node performance indicators; For the task At the node Data transmission delay on For the task Priority; For the task At the node The amount of resources required on .

[0029] A weighted sum that takes into account node performance, network latency, and task priority; Measures the difference between the actual load of each node and the average load. Minimizing this term can keep the load of each node relatively balanced and avoid overloading a node.

[0030] This optimization method can minimize resource waste and ensure that the resource usage of each computing node does not exceed the total amount of its available resources, while taking into account the performance of the node, network latency, and task priority to improve the speed and response time of task execution. In addition, it can keep the load of each node relatively balanced and avoid overloading a single node, thereby improving the stability and reliability of the system. Ultimately, this comprehensive optimization not only improves resource utilization, but also enhances the flexibility and adaptability of the system, enabling the system to operate efficiently in a complex and changing environment, meet the needs of different tasks, and provide a solid foundation for future expansion and optimization.

[0031] S24 , generating a data allocation instruction according to the allocation result, specifying which data should be transmitted to which computing nodes, and sending the data allocation instruction to the processing distribution module 23 .

[0032] If resources are insufficient in S23, computing resources are automatically increased or decreased, tasks are evenly distributed to multiple computing nodes, and resources are reserved for critical tasks.

[0033] The processing distribution module 23 is used to receive and verify the data allocation instructions of the resource management module 22, and allocate the tasks to be processed to the corresponding computing nodes according to the instructions.

[0034] The logic control unit 3 is used to define and manage business rules and coordinate the interaction between various subsystems. The logic control unit 3 is the decision-making center of the entire system. It defines and executes business process rules and coordinates the interaction and collaboration between different subsystems. The logic control unit makes intelligent decisions based on preset strategies or real-time data, such as selecting the optimal data processing path, triggering specific event responses, etc., and can quickly take measures to restore the normal operation of the system when anomalies are encountered. By introducing advanced algorithms and technologies (such as AI / ML), the logic control unit can also continuously learn and optimize its control logic to improve the efficiency and response speed of the overall system.

[0035] The logic control unit 3 includes a rule engine, which is used to define and manage business rules. Once the triggering conditions of the defined business rules are met, the rule engine will automatically trigger the preset actions; for example: when the data acquisition unit 1 detects an abnormal data value, the logic control unit 3 can automatically execute the corresponding alarm method according to the preset rules.

[0036] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An intelligent central control system based on 5G cloud computing technology, characterized in that: include: A data collection unit (1), the data collection unit (1) being used to collect business data transmitted from various interfaces in a computer device, and the data collection unit (1) supporting multiple protocols, being able to obtain business data from different types of interfaces and transmitting the data to a cloud computing platform (2); A cloud computing platform (2), the cloud computing platform (2) is used to receive data transmitted from the data acquisition unit (1), store, process and manage the data, and finally distribute the data to terminal applications, and introduce node performance, network delay, task priority and load balancing into the process of storing, processing and managing the data for optimization; The cloud computing platform (2) includes a data storage module (21), a resource management module (22) and a processing and distribution module (23); A logic control unit (3), wherein the logic control unit (3) is used to define and manage business process rules and coordinate the interaction process between the data collection unit (1) and the cloud computing platform (2).

2. The intelligent central control system based on 5G cloud computing technology according to claim 1 is characterized in that: The data acquisition unit (1) acquires business data from different types of interfaces and transmits the data to the cloud computing platform (2) based on a 5G communication module. The 5G communication module can provide high-speed and low-latency wireless communication capabilities, so that the data acquired by the data acquisition unit (1) can be quickly transmitted to the cloud computing platform (2).

3. The intelligent central control system based on 5G cloud computing technology according to claim 2 is characterized in that: In the cloud computing platform (2): The data storage module (21) is used to store structured and unstructured data and also provides a data storage solution. Meanwhile, the data storage module (21) also supports data backup and recovery functions, as well as data compression and data encryption; The resource management module (22) is used to monitor the real-time load and resource usage of each computing node and the task requirements of business data, dynamically allocate and schedule computing resources on the cloud computing platform (2), and then generate data allocation instructions to be transmitted to the processing and distribution module (23). The resource management module (22) also supports elastic scaling and can adjust the resource scale according to the actual task requirements; The processing distribution module (23) is used to receive and verify the data allocation instructions of the resource management module (22), and allocate the tasks to be processed to the corresponding computing nodes according to the instructions.

4. The intelligent central control system based on 5G cloud computing technology according to claim 3 is characterized in that: The resource management module (22) monitors the real-time load and resource usage of each node and the task requirements of business data, and dynamically allocates and schedules computing resources on the cloud computing platform (2), specifically: S21. Continuously monitor the resource usage of all computing nodes and collect the real-time load and resource usage of each node; S22, generating a task queue, the task queue including the task requirements of the business data to be processed, and sorting the tasks; S23, analyzing the current and historical load conditions of each computing node, predicting the future load trend of the computing node, and then selecting the computing node that is most suitable for executing the current task based on task requirements and resource usage; S24. Generate a data allocation instruction based on the allocation result, specify which data should be transmitted to which computing nodes, and send the data allocation instruction to the processing distribution module (23).

5. The intelligent central control system based on 5G cloud computing technology according to claim 4 is characterized in that: The tasks are sorted in S22 based on the priorities and deadlines of the tasks.

6. The intelligent central control system based on 5G cloud computing technology according to claim 5 is characterized in that: In S23, the current and historical load conditions of each computing node are analyzed to predict the future load trend of the computing node, specifically: ； in, For the current moment The first-level smoothing value of ; is the first-level smoothing coefficient; For the current moment The actual value of For the previous moment The first-level smoothing value of ; For the previous moment Trend items; ； in, For the current moment Trend items; is the trend smoothing coefficient, which ranges from 0 to 1 and indicates the degree of influence of the current trend change on the trend item; Use first-order smoothing and trend items To predict future load : ； in, For predicted future loads; is the time step of prediction; When there is a new actual value When the first-level smoothing value and trend item are repeatedly calculated, the and , and re-predict; Regularly evaluate the error between the forecast result and the actual load, and adjust the smoothing coefficient if the error is large and Optimize prediction results.

7. The intelligent central control system based on 5G cloud computing technology according to claim 6 is characterized in that: In S23, the most suitable computing node for executing the current task is selected according to the task requirements and resource usage, specifically: ; in, To minimize resource waste; is the number of computing nodes; For Node The total amount of resources available; is the number of tasks; For the task At the node The amount of resources required on ; .

8. The intelligent central control system based on 5G cloud computing technology according to claim 7 is characterized in that: In the cloud computing platform (2), node performance, network latency, task priority and load balancing are introduced to optimize the data storage, processing and management process. The optimization is as follows: ; in, To minimize resource waste after optimization; For Node performance indicators; For the task At the node Data transmission delay on For the task Priority; For the task At the node The amount of resources required on .

9. The intelligent central control system based on 5G cloud computing technology according to claim 8, characterized in that: If the resources in S23 are insufficient, computing resources are automatically increased or decreased, tasks are evenly distributed to multiple computing nodes, and resources are reserved for key tasks.

10. The intelligent central control system based on 5G cloud computing technology according to claim 9 is characterized in that: The logic control unit (3) comprises a rule engine, which is used to define and manage business rules. Once the triggering conditions of the defined business rules are met, the rule engine will automatically trigger a preset action.

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