An intelligent central control system based on 5G cloud computing technology

Abstract

The present invention relates to the field of intelligent control technology. Specifically, it relates to an intelligent central control system based on 5G cloud computing technology. It includes: a data acquisition unit for collecting service data transmitted from each interface in a computer device; a cloud computing platform for receiving the data transmitted from the data acquisition unit, storing, processing, and managing the data, and finally distributing the data to terminal application programs; the logic control unit is used to define and manage the process rules of services and coordinate the interaction process with the data acquisition unit and the cloud computing platform. The introduction of trend terms in this system enables the model to capture the linear trends in the data. At the same time, the comprehensive optimization not only improves the resource utilization rate, but also enhances the flexibility and adaptability of the system, enabling the system to operate efficiently in a complex and changeable environment and meet the needs of different tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and specifically, 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 a high-speed and low-latency 5G communication network and a powerful cloud computing platform. Through real-time data collection, efficient computing and processing, and intelligent decision-making, it realizes the optimized management and control of complex application scenarios. This 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 remote medical treatment, meeting the growing intelligent needs, promoting the digital transformation and efficiency improvement of 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, with some nodes overloaded while others 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 in the above background art that 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, with some nodes overloaded while others are idle, or low resource utilization.

[0004] To achieve the above purpose, the present invention aims to provide an intelligent central control system based on 5G cloud computing technology, including:

[0005] A data collection unit, which is used to collect service data transmitted from each interface in a computer device, and the data collection unit supports multiple protocols and can obtain service data from different types of interfaces and transmit it to the cloud computing platform;

[0006] A cloud computing platform, which is used to receive the data transmitted from the data collection unit, store, process, and manage the data, and finally distribute the data to the terminal application program. During the process of storing, processing, and managing the data, node performance, network latency, task priority, and load balancing are introduced for optimization;

[0007] Among them, the cloud computing platform includes a data storage module, a resource management module, and a processing and distribution module;

[0008] A logic control unit, which is used to define and manage the process rules of services and coordinate the interaction process with the data acquisition unit and the cloud computing platform.

[0009] As a further improvement of this technical solution, in the data acquisition unit, business data is obtained 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, enabling the data acquired by the data acquisition unit to be quickly transmitted to the cloud computing platform.

[0010] As a further improvement of this technical solution, in the cloud computing platform:

[0011] 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;

[0012] The resource management module is used to monitor the real-time load conditions and resource usage of each computing node, as well as the task requirements of business data, dynamically allocate and schedule the computing resources on the cloud computing platform, and then generate data allocation instructions for transmission 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;

[0013] The processing and distribution module is used to receive and verify the data allocation instructions from the resource management module, and allocate the tasks to be processed to the corresponding computing nodes according to the instructions.

[0014] As a further improvement of this technical solution, in the resource management module, monitoring the real-time load conditions and resource usage of each node, as well as the task requirements of business data, and dynamically allocating and scheduling the computing resources on the cloud computing platform, specifically:

[0015] S21. Continuously monitor the resource usage of all computing nodes, and collect the real-time load conditions and resource usage of each node;

[0016] S22. Generate a task queue that contains the task requirements of the business data to be processed, and sort the tasks;

[0017] S23. Analyze the current and historical load conditions of each computing node, predict the future load trend of the computing node, and then select the most suitable computing node to execute the current task according to the task requirements and resource usage;

[0018] S24. Generate data allocation instructions according to the allocation results, specify which data should be transmitted to which computing nodes, and send the data allocation instructions to the processing and distribution module.

[0019] As a further improvement of this technical solution, in S22, the tasks are sorted based on the priority and deadline of the tasks.

[0020] As a further improvement of this technical solution, in S23, analyze the current and historical load conditions of each computing node, and predict the future load trend of this computing node. Specifically:

[0021] ;

[0022] Among them, is the first-level smoothed value at the current moment ; is the first-level smoothing coefficient; is the actual value at the current moment ; is the first-level smoothed value at the previous moment ; is the trend item at the previous moment ;

[0023] ;

[0024] Among them, is the trend item at the current moment ; is the trend smoothing coefficient, and its value range is between 0 and 1, indicating the influence degree of the current trend change on the trend item;

[0025] Use the first-level smoothed value and the trend item to predict the future load :

[0026] ;

[0027] Among them, is the predicted future load; is the predicted time step;

[0028] When there is a new actual value , recalculate the first-level smoothed value and the trend item, continuously update and , and re-predict;

[0029] Regularly evaluate the error between the prediction result and the actual load. If the error is large, adjust the smoothing coefficients and to optimize the prediction effect.

[0030] As a further improvement of this technical solution, in S23, select the most suitable computing node to execute the current task according to the task requirements and resource usage conditions. Specifically:

[0031] ;

[0032] Among them, is to minimize resource waste; is the number of computing nodes; is the node total available resources; is the number of tasks; is the task on the node required resource amount; ; .

[0033] As a further improvement of this technical solution, in the cloud computing platform, node performance, network latency, task priority, and load balancing are introduced for optimization during the process of storing, processing, and managing data. After optimization, specifically:

[0034] ;

[0035] Among them, is the optimized minimized resource waste; is the performance index of the node ; is the task on the node data transmission latency; is the task priority; is the task on the node required resource amount; .

[0036] As a further improvement of this 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 critical tasks.

[0037] As a further improvement of this technical solution, the logic control unit includes a rule engine, and the rule engine is used to define and manage business rules. Once the trigger conditions of the defined business rules are met, the rule engine will automatically trigger the preset actions.

[0038] Compared with the prior art, the beneficial effects of the present invention:

[0039] 1. In the intelligent central control system based on 5G cloud computing technology, the introduction of the trend term enables the model to capture the linear trend in the data. This is very effective when dealing with time-series data with obvious upward or downward trends. By considering both the sum of squares of resource waste and the maximum resource demand simultaneously, this objective function aims to find a balance point that not only minimizes resource waste but also achieves load balancing.

[0040] 2. In the intelligent central control system based on 5G cloud computing technology, it ensures that the resource usage of each computing node does not exceed its total available resources, while considering the node's performance, 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, 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

[0041] Figure 1 is the overall flow block diagram of the present invention;

[0042] Figure 2 is the flow block diagram of the pressure control system of the present invention;

[0043] The meanings of the various reference numerals in the figure are as follows:

[0044] 1. Data acquisition unit; 2. Cloud computing platform; 21. Data storage module; 22. Resource management module; 23. Processing and distribution module; 3. Logic control unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. 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. Embodiment

[0046] Please refer to 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;

[0047] The data acquisition unit 1 is used to collect service data transmitted from various interfaces in the computer device, and the data acquisition unit 1 supports multiple protocols and can obtain service data from different types of interfaces and transmit it to the cloud computing platform 2; among them, 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.

[0048] In the data acquisition unit 1, obtaining service data from different types of interfaces and transmitting it to the cloud computing platform 2 is based on the 5G communication module. The 5G communication module can provide high-speed and low-latency wireless communication capabilities, enabling the data obtained by the data acquisition unit 1 to be quickly transmitted to the cloud computing platform 2.

[0049] The characteristics of 5G enable low latency to be maintained even in the case of a large amount 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 Internet of Things environment.

[0050] The cloud computing platform 2 is used to receive the data transmitted from the data acquisition unit 1, store, process, and manage the data, and finally distribute the data to the terminal application program. Node performance, network latency, task priority, and load balancing are introduced for optimization during the process of storing, processing, and managing the data.

[0051] The cloud computing platform 2 ensures the high availability, scalability, and security of the system, supports real-time data analysis and intelligent decision-making, and provides powerful computing capabilities and flexible service support for various application scenarios.

[0052] Among them, the cloud computing platform 2 includes a data storage module 21, a resource management module 22, and a processing and distribution module 23;

[0053] In the cloud computing platform 2:

[0054] The data storage module 21 is used to store structured and unstructured data, 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 the security and privacy of the data;

[0055] Structured data, such as tabular data in relational databases, and unstructured data, such as documents, pictures, videos, etc., thus meeting the requirements of different business scenarios. The data storage solutions provided specifically 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, documents, etc., can easily handle PB-level data, automatically replicate data to multiple geographical locations, improve data reliability and availability, and have 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., provide high-performance IOPS (input / output operations per second), are suitable for applications with high performance requirements, and can dynamically adjust the storage capacity according to requirements. File storage services are suitable for environments that require shared file storage, such as enterprise file systems, development collaboration, etc., can automatically expand the 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, machine learning training, etc., are designed to handle large-scale data sets, provide high-throughput data access, and through the multi-copy mechanism of data blocks, can ensure data integrity even in case of partial node failures, and can expand the storage capacity and performance by adding more nodes. Database services are suitable for storing structured data, support complex queries and transaction processing, support cross-region replication, ensure the high availability and disaster recovery capabilities of the database, automatically back up the database regularly, support point-in-time recovery, and provide automatic tuning and performance monitoring tools to help optimize database performance;

[0056] The resource management module 22 is used to monitor the real-time load conditions and resource usage conditions of each computing node and the task requirements of business data, dynamically allocate and schedule the computing resources on the cloud computing platform 2, and then generate an instruction for data allocation and transmit it 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 requirements of actual tasks to cope with traffic peaks or troughs, while ensuring the stability and high availability of the system;

[0057] In the resource management module 22, monitor the real-time load conditions and resource usage conditions of each node and the task requirements of business data, and dynamically allocate and schedule the computing resources on the cloud computing platform 2. Specifically:

[0058] S21. Continuously monitor the resource usage conditions of all computing nodes, and collect the real-time load conditions and resource usage conditions of each node;

[0059] S22. Generate a task queue, which contains the task requirements of the business data to be processed, sort the tasks, and the tasks are sorted based on the priority and deadline of the tasks;

[0060] S23. Analyze the current and historical load conditions of each computing node, predict the future load trend of the computing node, and then select the most suitable computing node to execute the current task according to the task requirements and resource usage conditions;

[0061] In S23, analyzing the current and historical load conditions of each computing node and predicting the future load trend of the computing node specifically includes:

[0062] Generally and range from 0 to 1, and is used to control the data smoothing degree, is used to control the influence of trend changes;

[0063] Determine the initial first-level smoothing value and the trend item . Generally, can be set as the first observed value , while can be set to 0 or estimated according to the actual situation;

[0064] Collect the historical load data of each computing node , where . These data can be CPU utilization rate, memory usage rate, disk I / O operation count, etc.

[0065] ;

[0066] Among them, is the first-level smoothing value at the current time ; is the first-level smoothing coefficient; is the actual value at the current time ; is the first-level smoothing value at the previous time ; is the trend item at the previous time ;

[0067] ;

[0068] Among them, is the trend item at the current time ; is the trend smoothing coefficient, ranging from 0 to 1, indicating the influence degree of the current trend change on the trend item;

[0069] Use the first-level smoothing value and the trend item to predict the future load :

[0070] ;

[0071] Among them, is the predicted future load; is the predicted time step;

[0072] When there is a new actual value , recalculate the first-level smoothed value and the trend term, and continuously update and , and re-perform the prediction;

[0073] Regularly evaluate the error between the prediction result and the actual load. If the error is large, adjust the smoothing coefficients and to optimize the prediction effect.

[0074] Trend term The introduction of enables the model to capture the linear trend in the data. This is very effective when dealing with time series data with an obvious upward or downward trend. For example, in resource management, if the workload gradually increases or decreases over time, the double exponential smoothing method can better predict the future load;

[0075] First-level smoothed value By taking a weighted average of the current actual value and the smoothed value at the previous moment , it can effectively smooth out the random fluctuation noise in the data. This smoothing effect helps to improve the stability of the prediction and reduce misjudgments caused by short-term fluctuations.

[0076] In S23, according to the task requirements and resource usage conditions, select the most suitable computing node to execute the current task, specifically:

[0077] ;

[0078] Among them, is to minimize resource waste; is the number of computing nodes; is the total available resources of node ; is the number of tasks; is the resource requirement of task on node ; ; ;

[0079] The sum of the squares of resource waste on all nodes. The goal of this part is to minimize resource waste. Ideally, we hope that the remaining resources on all nodes are 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 on each node as balanced as possible. Ideally, we hope that the resource demands on each node are as balanced as possible, avoiding overloading some nodes while other nodes are idle.

[0080] By considering both the sum of the squares of resource waste and the maximum resource demand, this objective function aims to find a balance point that not only minimizes resource waste but also achieves load balancing.

[0081] In Cloud Computing Platform 2, during the process of storing, processing, and managing data, node performance, network latency, task priority, and load balancing are introduced for optimization. After optimization, specifically:

[0082] ;

[0083] Among them, is the minimized resource waste after optimization; is the performance metric of node ; is the data transmission latency of task on node ; is the priority of task ; is the required resource amount of task on node ; .

[0084] The weighted sum of node performance, network latency, and task priority is comprehensively considered;

[0085] Measures the difference between the actual load and the average load of each node. Minimizing this term can keep the load of each node relatively balanced and avoid overloading a certain node.

[0086] This optimization method can minimize resource waste, ensure that the resource usage of each computing node does not exceed its total available resources, and at the same time consider the performance, network latency, and task priority of the nodes to improve the task execution speed and response time. 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. Finally, 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.

[0087] S24. Generate a data allocation instruction according to the allocation result, specifying which data should be transmitted to which computing nodes, and send the data allocation instruction to the processing and distribution module 23.

[0088] If there is insufficient resources in S23, automatically increase or decrease the computing resources, evenly distribute the tasks to multiple computing nodes, and reserve resources for critical tasks at the same time.

[0089] The processing and distribution module 23 is used to receive and verify the data allocation instruction of the resource management module 22, and allocate the tasks to be processed to the corresponding computing nodes according to the instruction.

[0090] The logic control unit 3 is used to define and manage the rules of the service, and coordinate the interaction process between each subsystem; the logic control unit 3 is the decision-making center in the whole system. It defines and executes the service process rules, coordinates the interaction and cooperation between different subsystems. The logic control unit makes intelligent decisions according to the preset policies 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 encountering anomalies. 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.

[0091] The logic control unit 3 includes a rule engine, which is used to define and manage business rules. Once the trigger 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.

[0092] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention, and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

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); In S23, the current and historical load conditions of each computing node are analyzed, 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; The resource management module (22) is used to monitor the real-time load and resource usage of each node and the task requirements of business data, and dynamically allocate and schedule 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; 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; 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); 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 ; 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 ; ; 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) dynamically allocates and schedules computing resources on the cloud computing platform (2) and then generates data allocation instructions which are 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 needs of actual tasks. 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 tasks are sorted in S22 based on the priorities and deadlines of the tasks.

5. The intelligent central control system based on 5G cloud computing technology according to claim 4 is characterized in that: In S23, the future load trend of the computing node is predicted, specifically: 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.

6. The intelligent central control system based on 5G cloud computing technology according to claim 5 is characterized in that: If the resources in S23 are insufficient, computing resources are automatically increased or decreased to evenly distribute tasks to multiple computing nodes, while reserving resources for critical tasks.

7. The intelligent central control system based on 5G cloud computing technology according to claim 6 is characterized in that: 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 a preset action.

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