Intelligent gateway management platform based on cloud computing

Abstract

The invention relates to the field of gateway management, and discloses an intelligent gateway management platform based on cloud computing, which comprises an interface module, the interface module is connected with a plurality of gateways, data of the plurality of gateways are transmitted to a gateway management and control module through the interface module, the interface module is connected with a security protection module, and the security protection module is connected with the gateway management and control module. The interface module is connected with a load balancing and flow management module, the load balancing and flow management module monitors and manages flow in a gateway transmission process, and the load balancing and flow management module is connected with a gateway management and control module. According to the invention, through the load balancing and flow management module, the flow in the gateway transmission process can be monitored and managed in real time, so that the flow can be reasonably distributed during multi-group gateway information transmission in a peak period, the transmission channel blockage is avoided, and the stability and the use performance of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gateway management, and in particular to an intelligent gateway management platform based on cloud computing. Background Art

[0002] In today's digital age, the S domain, as a domain that represents signals and systems in continuous time (analog), plays a vital role in many fields. The continuous-time signal characteristics of the S domain make it irreplaceable in scenarios with extremely high requirements for real-time and stability. For example, in the control of traffic lights in the fields of industrial automation control and intelligent transportation.

[0003] Existing gateway management platforms usually support multiple network connection methods to achieve device access, such as connecting various terminal devices through Ethernet interfaces and establishing a management platform, while also being able to monitor and manage data traffic to a certain extent.

[0004] Considering that during peak usage, the gateway management platform receives multiple sets of different gateway information, and the gateway information flow is too large, it is easy to cause the transmission channel to be blocked, causing the gateway management system to stop or be blocked, and even cause system paralysis and crash, seriously affecting the system's performance and user experience. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an intelligent gateway management platform based on cloud computing, which solves the problem that when the existing gateway management platform receives multiple groups of gateway information and the traffic is too large during peak hours, the transmission channel is easily blocked, and even the system stops, blocks, paralyzes and crashes, seriously affecting the system performance and user experience.

[0006] To achieve the above purpose, the present invention is implemented through the following technical solutions: a cloud computing-based intelligent gateway management platform, including an interface module, the interface module is connected to multiple gateways, and the data of multiple groups of gateways are transmitted to the gateway management and control module through the interface module, the interface module is connected to a security protection module, the security protection module encrypts and protects during the gateway information transmission process, the interface module is connected to a load balancing and traffic management module, the load balancing and traffic management module monitors and manages the traffic during the gateway transmission process, the load balancing and traffic management module is connected to a gateway management and control module, the gateway management and control module receives parameter data from each gateway and is centrally managed by staff, and the specific management content includes: gateway device registration and authentication, gateway device status monitoring, gateway device remote control, the gateway management and control module is connected to a display control module, the display control module displays the specific values ​​of each gateway, which is convenient for staff to watch, and provides an operation platform for staff, the load balancing and traffic management module is connected to an intelligent optimization module, the intelligent optimization module reads the historical management plan data in the load balancing and traffic management module, and analyzes and optimizes according to the data conclusions.

[0007] Preferably, the load balancing and traffic management module includes a traffic parameter acquisition module, which monitors multiple groups of traffic received and transmitted by the interface module, and collects specific traffic size data, and transmits it to the scheduling algorithm module. The traffic parameter acquisition module is connected to a scheduling algorithm module, which receives data collected from the traffic parameter acquisition module, and calculates according to the data into a formula, so as to select a suitable scheduling scheme for selection. The scheduling algorithm module is connected to a load balancing strategy module, and the load balancing strategy module records multiple groups of different scheduling and allocation schemes, which are used for the scheduling algorithm module to substitute the conclusions and select the corresponding scheme for execution. The load balancing strategy module is connected to a traffic allocation module, which reads the final selected scheme and implements and allocates the traffic according to the content of the scheme.

[0008] Preferably, the specific calculation formulas in the scheduling algorithm module include: a weighted polling algorithm flow calculation formula and a minimum connection number algorithm flow calculation formula.

[0009] Preferably, the specific weighted round-robin algorithm flow calculation formula is: Where: W i Represents the weight value of each device or path. The weight can be determined based on device performance, bandwidth capacity, and historical load conditions. T represents the total traffic. F i Represents the current allocated flow of the device or path; n represents the total number of devices or paths.

[0010] Preferably, the minimum connection number algorithm flow calculation formula is: Where: C i Represents the current number of connections for each device or path. ∈ represents a very small positive number to prevent the denominator from being 0.

[0011] Preferably, the specific scheduling and allocation schemes in the load balancing strategy module include: an allocation scheme based on service priority and a dynamic adjustment allocation scheme.

[0012] Preferably, the allocation scheme based on service priority is as follows: different services are divided into three priorities: high, medium and low; high-priority services are given priority to obtain sufficient traffic guarantee, and need to be allocated 50% of the total traffic, then medium-priority services are allocated 30% of the total traffic, and low-priority services are allocated 20% of the total traffic; among services of the same priority, further traffic allocation can be performed based on weighted polling or other algorithms.

[0013] Preferably, the dynamic adjustment allocation scheme is: continuously monitor the load conditions of the equipment and changes in network traffic; when the load of a certain device exceeds a certain threshold (more than 80%), part of its traffic is transferred to the device with lower load. An adjustment coefficient can be set to dynamically adjust the traffic distribution according to the load conditions.

[0014] Preferably, the intelligent optimization module includes a historical data reading module, which extracts and arranges historical adjustment plan contents and adjustment results one by one. The historical data reading module is connected to an AI simulation module, which simulates the current environment according to the adjustment content, and appropriately modifies the plan content to test whether the effect of the optimized adjustment plan is better than the historical plan. When the optimization plan content is greater than the historical plan content, the optimization plan content is determined to be valid and executed. When the historical plan content is greater than the optimization plan content, the optimization plan content is determined to be invalid, and the optimization plan content is readjusted for a second AI simulation. The AI ​​simulation module is connected to an optimization result module, which transmits the adjusted plan to the load balancing strategy module to replace the original historical plan. The optimization result module is connected to the load balancing strategy module.

[0015] An application of an intelligent gateway management platform based on cloud computing, such as the intelligent gateway management platform based on cloud computing as described in claims 1-9, is applied to the field of gateway management, especially the field of industrial automation control and the field of intelligent traffic management.

[0016] The present invention provides a cloud computing-based intelligent gateway management platform, which has the following beneficial effects:

[0017] 1. The present invention can monitor and manage the traffic in the gateway transmission process in real time through the load balancing and traffic management module, wherein the traffic parameter acquisition module accurately collects traffic size data, the scheduling algorithm module selects a suitable scheduling scheme based on the weighted polling algorithm traffic calculation formula and the minimum connection number algorithm traffic calculation formula, the load balancing strategy module provides a distribution scheme based on business priority and a dynamic adjustment distribution scheme, and the traffic distribution module implements specific traffic distribution. This series of measures ensures that when multiple groups of gateway information are transmitted during peak hours, the traffic can be reasonably distributed, avoiding transmission channel blockage and improving the stability and performance of the system.

[0018] 2. The present invention extracts the contents and results of historical adjustment plans through the historical data reading module for sorting, the AI ​​simulation module simulates the current environment and optimizes the plan, and the optimization result module transmits the optimized plan to the load balancing strategy module to replace the original historical plan. This function enables the system to continuously learn and improve, and continuously improve the efficiency and effectiveness of traffic management.

[0019] 3. The present invention provides a scientific basis for the reasonable distribution of traffic by setting calculation formulas. The weighted polling algorithm traffic calculation formula and the minimum connection number algorithm traffic calculation formula can accurately calculate the traffic distribution of each device or path according to different situations, making the traffic distribution more fair and efficient, and combining the distribution scheme based on business priority and the dynamic adjustment distribution scheme to further optimize the traffic distribution strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a system flow chart of the present invention;

[0021] Figure 2 It is a flow chart of the load balancing and traffic management module of the present invention;

[0022] Figure 3 This is a flow chart of the intelligent optimization module of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings of the present invention specification 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.

[0024] Example:

[0025] Please refer to the attached Figure 1 -Attached Figure 3The embodiment of the present invention provides an intelligent gateway management platform based on cloud computing, including an interface module, the interface module is connected to multiple gateways, and the data of multiple groups of gateways are transmitted to the gateway management and control module through the interface module, the interface module is connected to a security protection module, the security protection module encrypts and protects during the gateway information transmission process to prevent the information from being attacked or stolen, and ensure the integrity and security of data transmission, the interface module is connected to a load balancing and traffic management module, the load balancing and traffic management module monitors and manages the traffic during the gateway transmission process to prevent the system from being overloaded or paralyzed during the peak usage stage;

[0026] The load balancing and traffic management module includes a traffic parameter acquisition module, which monitors multiple groups of traffic received and transmitted by the interface module, and collects specific traffic size data, and transmits it to the scheduling algorithm module. The traffic parameter acquisition module is connected to a scheduling algorithm module, which receives data collected from the traffic parameter acquisition module, and calculates according to the data into a formula, so as to select a suitable scheduling scheme for selection, ensure the reasonable allocation of resources, and avoid system overload or paralysis. The scheduling algorithm module is connected to a load balancing strategy module, and the load balancing strategy module records multiple groups of different scheduling and allocation schemes, which are used for the scheduling algorithm module to substitute the conclusions and select the corresponding scheme for execution. The load balancing strategy module is connected to a traffic distribution module, and the traffic distribution module reads the final selected scheme and implements the distribution of traffic according to the content of the scheme;

[0027] The specific calculation formula in the scheduling algorithm module is:

[0028] 1. Weighted round-robin algorithm traffic calculation formula:

[0029]

[0030] Where: W i Represents the weight value of each device or path. The weight can be determined based on device performance, bandwidth capacity, and historical load conditions. T represents the total traffic. F i represents the current allocated flow of the device or path; n represents the total number of devices or paths;

[0031] 2. Minimum connection number algorithm flow calculation formula:

[0032]

[0033] Where: C i Represents the current number of connections for each device or path; ∈ represents a very small positive number to prevent the denominator from being 0;

[0034] The above formula is demonstrated by the following example:

[0035] Allocation scheme based on equipment performance:

[0036] A performance score is set for each device based on performance indicators such as the device's processing power, memory capacity, network bandwidth, etc.

[0037] Traffic is distributed according to the performance score ratio. For example, if the performance score of device A is 80, the performance score of device B is 60, and the total traffic is 100 Mbps, then the traffic allocated to device A is The traffic allocated to device B is 100-57.14=42.86 Mbps.

[0038] The specific scheduling and allocation scheme in the load balancing strategy module is:

[0039] 1. Allocation scheme based on business priority:

[0040] Different services are divided into three priorities: high, medium and low. High-priority services are given priority to obtain sufficient traffic guarantee and need to be allocated 50% of the total traffic, followed by medium-priority services to be allocated 30% of the total traffic and low-priority services to be allocated 20% of the total traffic. For services of the same priority, further traffic allocation can be performed based on weighted polling or other algorithms.

[0041] 2. Dynamically adjust the allocation plan:

[0042] Continuously monitor the load of devices and changes in network traffic; when the load of a device exceeds a certain threshold (more than 80%), part of its traffic will be transferred to the device with lower load. An adjustment coefficient can be set to dynamically adjust traffic distribution according to the load situation;

[0043] Assume that the load of device A is 90%, the load of device B is 40%, and the adjustment coefficient is 0.5. Then part of the flow of device A is transferred to device B, and the amount of the transferred flow is (90% - 80%) × 0.5 of the current flow of device A.

[0044] The load balancing and traffic management module is connected to a gateway management and control module, which receives parameter data from each gateway and is centrally managed by staff. The specific management content includes: gateway device registration and authentication, gateway device status monitoring, and gateway device remote control to ensure the normal operation and efficient operation of the device. The gateway management and control module is connected to a display control module, which displays the specific values ​​of each gateway for the convenience of staff to watch, and provides an operating platform for staff to facilitate the management and control of multiple gateway devices;

[0045] The load balancing and traffic management module is connected to an intelligent optimization module, which reads the historical management plan data in the load balancing and traffic management module, and analyzes and optimizes according to the data conclusions. The intelligent optimization module includes a historical data reading module, which extracts the historical adjustment plan content and adjustment results and arranges them one by one. The historical data reading module is connected to an AI simulation module, which simulates the current environment according to the adjustment content, and appropriately changes the plan content to test whether the effect of the optimized adjustment plan is better than the historical plan. When the optimization plan content is greater than the historical plan content, the optimization plan content is determined to be valid and executed. When the historical plan content is greater than the optimization plan content, the optimization plan content is determined to be invalid, and the optimization plan content is readjusted for a second AI simulation. The AI ​​simulation module is connected to an optimization result module, which transmits the adjusted plan to the load balancing strategy module to replace the original historical plan. The optimization result module is connected to the load balancing strategy module.

[0046] 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 cloud computing-based intelligent gateway management platform, including an interface module, characterized in that: The interface module is connected to multiple gateways, and the data of multiple groups of gateways are transmitted to the gateway management and control module through the interface module. The interface module is connected to a security protection module, which encrypts and protects the gateway information during transmission. The interface module is connected to a load balancing and traffic management module, which monitors and manages the traffic during gateway transmission. The load balancing and traffic management module is connected to a gateway management and control module, which receives parameter data from each gateway and is centrally managed by staff. The specific management content includes: gateway device registration and authentication, gateway device status monitoring, and gateway device remote control. The gateway management and control module is connected to a display control module, which displays the specific values ​​of each gateway for the convenience of staff to watch and provides an operating platform for the staff. The load balancing and traffic management module is connected to an intelligent optimization module, which reads the historical management plan data in the load balancing and traffic management module, and analyzes and optimizes according to the data conclusions.

2. The cloud computing-based intelligent gateway management platform according to claim 1, characterized in that: The load balancing and traffic management module includes a traffic parameter acquisition module, which monitors multiple groups of traffic received and transmitted by the interface module, and collects specific traffic size data, and transmits it to the scheduling algorithm module. The traffic parameter acquisition module is connected to a scheduling algorithm module, which receives data collected from the traffic parameter acquisition module, and calculates according to the data into the formula, so as to select a suitable scheduling scheme for selection. The scheduling algorithm module is connected to a load balancing strategy module, and the load balancing strategy module records multiple groups of different scheduling and allocation schemes, which are used for the scheduling algorithm module to substitute the conclusions and select the corresponding scheme for execution. The load balancing strategy module is connected to a traffic allocation module, which reads the final selected scheme and implements and allocates the traffic according to the content of the scheme.

3. The cloud computing-based intelligent gateway management platform according to claim 2, characterized in that: The specific calculation formulas in the scheduling algorithm module include: a weighted polling algorithm flow calculation formula and a minimum connection number algorithm flow calculation formula.

4. The cloud computing-based intelligent gateway management platform according to claim 3, characterized in that: The specific weighted polling algorithm flow calculation formula is: Where: W i Represents the weight value of each device or path. The weight can be determined based on device performance, bandwidth capacity, and historical load conditions. T represents the total traffic. F i Represents the current allocated flow of the device or path; n represents the total number of devices or paths.

5. The cloud computing-based intelligent gateway management platform according to claim 3, characterized in that: The minimum connection number algorithm flow calculation formula is: Where: C i Represents the current number of connections for each device or path. ∈ represents a very small positive number to prevent the denominator from being 0.

6. The cloud computing-based intelligent gateway management platform according to claim 2, characterized in that: The specific scheduling and allocation schemes in the load balancing strategy module include: an allocation scheme based on service priority and a dynamic adjustment allocation scheme.

7. The cloud computing-based intelligent gateway management platform according to claim 6, characterized in that: The allocation scheme based on service priority is as follows: different services are divided into three priorities: high, medium and low; high-priority services are given priority to obtain sufficient traffic guarantee and need to be allocated 50% of the total traffic, then medium-priority services are allocated 30% of the total traffic, and low-priority services are allocated 20% of the total traffic; among services of the same priority, further traffic allocation can be performed based on weighted polling or other algorithms.

8. The cloud computing-based intelligent gateway management platform according to claim 6, characterized in that: The dynamic adjustment allocation scheme is: continuously monitor the load conditions of the equipment and changes in network traffic; when the load of a device exceeds a certain threshold (more than 80%), part of its traffic will be transferred to the device with lower load. An adjustment coefficient can be set to dynamically adjust traffic distribution according to the load conditions.

9. The cloud computing-based intelligent gateway management platform according to claim 1, characterized in that: The intelligent optimization module includes a historical data reading module, which extracts and arranges historical adjustment plan contents and adjustment results one by one. The historical data reading module is connected to an AI simulation module, which simulates the current environment according to the adjustment content and appropriately modifies the plan content to test whether the effect of the optimized adjustment plan is better than the historical plan. When the optimization plan content is greater than the historical plan content, the optimization plan content is determined to be valid and executed. When the historical plan content is greater than the optimization plan content, the optimization plan content is determined to be invalid, and the optimization plan content is readjusted for a second AI simulation. The AI ​​simulation module is connected to an optimization result module, which transmits the adjusted plan to the load balancing strategy module to replace the original historical plan. The optimization result module is connected to the load balancing strategy module.

10. An application of a cloud computing-based intelligent gateway management platform, such as the cloud computing-based intelligent gateway management platform described in claims 1-9, applied in the field of gateway management, especially in the field of industrial automation control and intelligent traffic management.

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