Multi-aircraft scheduling system and method based on K8S cloud computing power

Abstract

The invention provides a K8S-based cloud computing power multi-aircraft scheduling system, which comprises a Kubernetes resource management module, a task receiving and analyzing module, a load balancing strategy module and a task scheduling module, and is characterized in that the signal end of the Kubernetes resource management module is connected with the signal end of the task receiving module. Through the resource management module, the system can more accurately understand the capability and limitation of each aircraft, so that basic data support is provided for subsequent load balancing and task scheduling, the load balancing module formulates an optimal load balancing strategy according to the current task requirement and the state information of the aircraft, and the task scheduling efficiency is improved in a complex task scene. According to the dynamic switching algorithm, the optimal switching scheme can be quickly calculated according to the current state and task requirements of the aircraft, the task is switched to other available aircrafts from a fault aircraft or a low-efficiency aircraft, and then dynamic adjustment is carried out according to real-time flight data and environment changes.

Description

Technical Field

[0001] The present invention relates to a scheduling system and method, specifically a multi-aircraft scheduling system and method based on K8S cloud computing power, belonging to the technical field of aircraft scheduling system. Background Art

[0002] The multi-aircraft scheduling system is a system specially designed to manage and schedule the flight plans and missions of multiple aircraft. It is a high-tech management platform mainly used to manage and control the flight missions of aircraft. It integrates multiple functions such as flight control, mission planning, real-time monitoring, data acquisition and processing, greatly improving the efficiency and safety of aircraft operations. The multi-aircraft scheduling system integrates the flight plans of multiple aircraft to achieve collaborative management and scheduling, ensuring flight safety and efficiency.

[0003] The existing multi-aircraft scheduling system has relatively simple scheduling algorithms and strategies, which cannot meet the needs of complex tasks. This is because traditional scheduling algorithms, such as First Come First Served (FCFS) and Shortest Job First (SJF), may perform well when processing a single aircraft or simple tasks, but their limitations are fully exposed when facing a complex environment with multiple aircraft and multiple tasks. These algorithms often lack sufficient flexibility and adaptability and cannot be dynamically adjusted according to real-time flight data and environmental changes. Therefore, a multi-aircraft scheduling system and method based on K8S cloud computing power are proposed. Summary of the invention

[0004] In view of this, the present invention provides a multi-aircraft scheduling system and method based on K8S cloud computing power to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0005] The technical solution of the embodiment of the present invention is implemented as follows: a multi-aircraft scheduling system based on K8S cloud computing power, including a Kubernetes resource management module, a task receiving and parsing module, a load balancing strategy module and a task scheduling module, wherein the signal end of the Kubernetes resource management module is connected to the signal end of the task receiving module, and the signal end of the load balancing strategy module is respectively connected to the signal end of the task adjustment module and the signal end of the task receiving and parsing module; The Kubernetes resource management module is used to monitor and manage resources in the Kubernetes cluster, including CPU, memory, storage, and network, and to provide data support for load balancing strategies by understanding resource usage in real time; The task receiving and parsing module is used to receive a task request from an aircraft, and parse and process the task request; The load balancing strategy module is used to design a load balancing strategy and dynamically adjust the task allocation strategy using a dynamic switching algorithm according to the task type, resource requirements and cluster status; The load balancing strategy includes: Weight-based load balancing strategy: assign different weights to nodes according to the node's resource conditions and task requirements; Priority-based load balancing strategy: set different priorities for tasks and schedule tasks according to the priorities; Load balancing strategy based on resource utilization: monitor the resource utilization of nodes in real time and schedule tasks based on the resource utilization; Geographic location-based load balancing strategy: According to the geographical location of the aircraft and mission requirements, the nearest node is selected for task scheduling; Load balancing strategy based on least connections: monitor the current number of active connections of each server and assign new requests to the server with the least current number of connections; Response time-based load balancing: monitors the response time of each server and assigns new requests to the server with the shortest response time; The task scheduling module is used to schedule tasks to designated server nodes according to the current optimal load balancing strategy.

[0006] Further preferably, a monitoring and fault-tolerance mechanism is provided in the task scheduling module for discovering and handling server failures. When a server fails, the task scheduling module redirects the task to other normal servers.

[0007] Further preferably, the dynamic switching algorithm comprises the following steps: Real-time monitoring: collects performance indicators of each server, including CPU usage, memory usage, network bandwidth utilization, response time, and number of active connections; Analyze performance indicators: Conduct in-depth analysis of the collected monitoring data and calculate the changing trends of various performance indicators and their relationships; Strategy evaluation: Based on the analysis results, evaluate the applicability of the current load balancing strategies and present the evaluation results in the form of a report. The evaluation influencing factors include the actual needs of the system, user usage habits, and business importance. Compare the pros and cons of strategies: Compare the pros and cons of various load balancing strategies based on the evaluation report, analyze the performance of each strategy in terms of performance indicators, and the impact on system performance and user experience; Strategy selection: Select the currently optimal load balancing strategy based on the comparison results; Strategy switching: Switch the load balancing system to a new optimal strategy without interrupting services, including updating the configuration of the load balancer and adjusting the weight or priority of the server.

[0008] Further preferably, it also includes a resource reservation module, a resource release module and an execution module; The resource reservation module is used to reserve resources before task scheduling and reserve sufficient resources for key tasks or services in advance. Resource reservation is achieved in a static or dynamic manner. Static reservation is a fixed allocation of a certain amount of resources in the system configuration, and dynamic reservation is to adjust resource allocation according to real-time load and system usage patterns.

[0009] Further preferably, the resource release module is used to release resources when the policy is executed; The execution model is used to execute the load balancing strategy of the optimal strategy.

[0010] Further preferably, it also includes a user interaction module, a fault detection module, a safety module and a monitoring feedback module; The monitoring and feedback module is used to monitor the execution status of the task and provide feedback of the task progress and results to the user.

[0011] Further preferably, the fault detection module is used to detect faults occurring in the system, automatically perform recovery operations, and record log information during system operation.

[0012] Further preferably, the security module is used to perform identity authentication and permission authorization on users, so that unauthorized users cannot access the system.

[0013] Further preferably, the user interaction module is used to provide a user interaction interface, and the user controls the system through the interaction interface.

[0014] A multi-aircraft scheduling method based on K8S cloud computing power includes the following steps: Resource management: monitor and manage resources in the Kubernetes cluster, including CPU, memory, storage, and network; Mission reception and analysis: Receive mission requests from aircraft, and parse and process them; Strategy allocation: Dynamically adjust the task allocation strategy using a dynamic switching algorithm based on task type, resource requirements, and cluster status; Task scheduling: Schedule tasks to designated server nodes based on the current optimal load balancing strategy; Task execution: Execute the load balancing strategy of the optimal strategy.

[0015] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution: The present invention integrates and manages all resources in the system through the resource management module, and can obtain the status information of the aircraft in real time. Through the resource management module, the system can understand the capabilities and limitations of each aircraft more accurately, thereby providing basic data support for subsequent load balancing and task scheduling. The load balancing module formulates the optimal load balancing strategy according to the current task requirements and the status information of the aircraft. In complex mission scenarios, the load balancing strategy module can dynamically adjust the load balancing strategy according to factors such as the performance differences of the aircraft, task priorities, and flight paths to ensure the overall performance and stability of the system. In complex mission scenarios, the dynamic switching algorithm can quickly calculate the optimal switching plan according to the current status of the aircraft and task requirements, and switch the task from a faulty or inefficient aircraft to other available aircraft, and then make dynamic adjustments based on real-time flight data and environmental changes.

[0016] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is a structural diagram of a multi-aircraft scheduling system based on K8S cloud computing power of the present invention; Figure 2 This is a flowchart of the steps of a multi-aircraft scheduling method based on K8S cloud computing power of the present invention. DETAILED DESCRIPTION

[0019] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0020] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0021] like Figure 1-Figure 2As shown, an embodiment of the present invention provides a multi-aircraft scheduling system based on K8S cloud computing power, including a Kubernetes resource management module, a task receiving and parsing module, a load balancing strategy module and a task scheduling module, wherein the signal end of the Kubernetes resource management module is connected to the signal end of the task receiving module, and the signal end of the load balancing strategy module is respectively connected to the signal end of the task adjustment module and the signal end of the task receiving and parsing module; The Kubernetes resource management module is used to monitor and manage resources in the Kubernetes cluster, including CPU, memory, storage, and network. It provides data support for load balancing strategies by understanding resource usage in real time. K8S is Kubernetes. Kubernetes' cloud computing power refers to computing resources managed and scheduled by the Kubernetes platform. These resources are usually provided by cloud computing platforms. As an open source container orchestration and management tool, Kubernetes can automatically deploy, expand and manage containerized applications, thereby effectively utilizing cloud computing resources. The resource management module is responsible for integrating and managing all resources in the system, including aircraft, sensors, communication equipment, etc. This module can obtain aircraft status information in real time, such as location, speed, remaining power, etc., and classify and prioritize aircraft based on this information. Through the resource management module, the system can more accurately understand the capabilities and limitations of each aircraft, thereby providing basic data support for subsequent load balancing and task scheduling; The task receiving and parsing module is used to receive task requests from aircraft, parse and process the task requests, ensure that the task requests can be correctly understood and processed by the system, and lay the foundation for subsequent task scheduling and resource allocation; The load balancing strategy module is used to design load balancing strategies and dynamically adjust task allocation strategies using dynamic switching algorithms according to task types, resource requirements, and cluster status to ensure load balancing and resource optimization. In complex mission scenarios, the load balancing strategy module can dynamically adjust the load balancing strategy based on factors such as aircraft performance differences, mission priorities, and flight paths: When a certain aircraft has better performance and a lower current load, more tasks can be assigned to it; When a certain aircraft has poor performance or a high current load, its task allocation is reduced to ensure the overall performance and stability of the system; Load balancing strategies include: Weight-based load balancing strategy: Different weights are assigned to nodes according to the node's resource conditions and task requirements to achieve reasonable task allocation. The formulation of weight-based load balancing strategy is based on the server's processing power, memory, storage, network bandwidth and other resources, as well as business needs and priorities. The weight value reflects the server's processing power and priority. The server with a higher weight will receive more requests. Priority-based load balancing strategy: Set different priorities for tasks and schedule tasks according to the priorities to ensure that high-priority tasks are processed first. The priority-based load balancing strategy is based on assigning different priorities to traffic or applications according to the priority requirements of different traffic or business applications. High-priority traffic or applications will be given priority to ensure the stability and response speed of key business applications. Load balancing strategy based on resource utilization: monitor the resource utilization of nodes in real time and schedule tasks based on resource utilization to avoid resource overload and waste; Geographic location-based load balancing strategy: According to the geographical location of the aircraft and mission requirements, the nearest node is selected for task scheduling to reduce transmission delay and cost; Load balancing strategy based on least connections: monitors the current number of active connections of each server and assigns new requests to the server with the least current number of connections to ensure that each server can process requests efficiently, reduce response time, make full use of the resources of all servers in the cluster, and avoid resource waste; Response time-based load balancing: monitors the response time of each server and assigns new requests to the server with the shortest response time to ensure that requests are forwarded to the server with the best performance, thereby shortening the response time; The six load balancing strategies, namely weight-based load balancing strategy, priority-based load balancing strategy, resource utilization-based load balancing strategy, geographic location-based load balancing strategy, least connection-based load balancing strategy and response time-based load balancing strategy, are formulated to meet different system requirements and business scenarios to optimize performance, improve service quality and ensure efficient use of resources; Specifically, the weight-based load balancing strategy considers the processing capacity, resources, and business requirements of the server, and assigns a weight value to the server to determine the number of requests it processes; Priority-based policies allocate requests based on traffic or application priority, ensuring that critical services are given priority. Resource utilization-based policies monitor server resource usage in real time and dynamically adjust request allocation to avoid overload and waste; Geolocation-based policies distribute requests to the nearest server based on user location, reducing latency and improving service quality; The least-connection strategy allocates requests to the server with the least number of connections, which is suitable for long-connection services to balance the load. The response time-based strategy measures server response time in real time and assigns requests to the server that is expected to respond the fastest to optimize the user experience. The above strategies can be selected and dynamically adjusted according to system requirements and monitoring data to achieve the best load balancing effect.

[0022] The task scheduling module schedules tasks to designated server nodes according to the current optimal load balancing strategy. Through reasonable task allocation, it ensures that each server or resource in the system can evenly bear the load, avoiding the situation where some servers are overloaded while other servers are idle. Load balancing helps to improve the overall performance and resource utilization of the system, while reducing response time. A monitoring and fault-tolerant mechanism is set up in the task scheduling module to detect and handle server failures. When a server fails, the task scheduling module will redirect tasks to other normal servers to ensure service continuity.

[0023] In one embodiment, the dynamic switching algorithm includes the following steps: Real-time monitoring: Collect performance indicators of each server, including CPU usage, memory usage, network bandwidth utilization, response time, and number of active connections. Monitoring data needs to be collected and analyzed in real time to quickly respond to changes in system performance. Analyze performance indicators: Conduct in-depth analysis of the collected monitoring data and calculate the changing trends of various performance indicators and their relationships; Strategy evaluation: Based on the analysis results, evaluate the applicability of the current load balancing strategies and present the evaluation results in the form of a report. The evaluation factors include the actual needs of the system, user usage habits, and business importance. For example, in scenarios that require fast response and high availability, a load balancing strategy based on response time is more advantageous; when processing a large number of short connection requests, a load balancing strategy based on the least connection is more appropriate. Compare the pros and cons of strategies: Compare the pros and cons of various load balancing strategies based on the evaluation report, analyze the performance of each strategy in terms of performance indicators, and the impact on system performance and user experience; Strategy selection: Based on the comparison results, the optimal load balancing strategy is selected. The selection of the optimal strategy may be based on multiple factors, including minimizing response time, maximizing throughput, optimizing resource utilization, etc. Strategy switching: Switch the load balancing system to a new optimal strategy without interrupting services, including updating the configuration of the load balancer and adjusting the weight or priority of the server; The dynamic switching algorithm is an important mechanism for responding to emergencies and mission changes in multi-aircraft scheduling systems. When aircraft failures, mission priority changes, or flight path adjustments occur in the system, the dynamic switching algorithm can adjust the aircraft's task allocation and flight path in real time to ensure the overall performance and stability of the system. The dynamic switching algorithm can quickly calculate the optimal switching plan based on the current status of the aircraft and mission requirements, and switch the task from a faulty or inefficient aircraft to other available aircraft. At the same time, the algorithm can also consider the correlation between the flight characteristics of the aircraft and the tasks to ensure that the switched tasks can be executed smoothly.

[0024] In the above algorithm, "strategy evaluation" is the key link to ensure the efficient operation of the system, which includes the following steps: 1. Data Collection: Monitoring data: Use monitoring tools to collect performance indicators of the load balancer in real time, including CPU usage, memory utilization, network traffic, response time, and throughput; Task data: record the execution time, resource consumption, and priority of each task; Server status: Get the server's hardware configuration, current load, and processing capacity; 2. Strategy definition: Identify the load balancing strategies used in the current system, including weight-based load balancing, priority-based load balancing, round-robin, and minimum number of connections; Understand how each strategy works and when it can be used; 3. Performance Analysis: Based on the monitoring data, analyze the performance of each strategy under the current load, compare the response time, throughput, and resource utilization under different strategies, and identify performance bottlenecks and potential problems; 4. Strategy suitability assessment: According to the performance analysis results, evaluate the applicability of each strategy in the current system, consider the task type, server configuration, and load change factors, and select the optimal load balancing strategy.

[0025] 5. Strategy adjustment and optimization: Adjust and optimize the load balancing strategy based on the evaluation results; Among them, the main factors affecting the evaluation results include performance indicators and system status indicators. By analyzing the performance indicators and system status indicators, the load balancing strategy can be adjusted and optimized, specifically: The factors affecting performance indicator analysis include response time, throughput, resource utilization, and error rate. The following is a detailed analysis of the factors: Response time: If the response time increases significantly, it indicates that the current strategy does not fully consider the server's processing capacity or network latency when allocating requests; Throughput: A decrease in throughput means that there is a bottleneck in the system, such as an overloaded single server or insufficient network bandwidth; Resource utilization: Unbalanced resource utilization indicates that the strategy fails to effectively distribute the load; Error rate: High error rates are often caused by improper request distribution or server failure due to policies; The influencing factors of system status indicators include server status, network status and task type. The following is a specific analysis of the influencing factors: Server status: The server's hardware configuration, current load, and processing capacity are important for policy adjustment; Network conditions: Network factors such as network delay and bandwidth limitation will affect the effectiveness of load balancing strategies; Task type: Different tasks have different resource requirements, such as compute-intensive tasks and IO-intensive tasks.

[0026] During the operation of the system, the load balancing strategy can be adjusted and optimized according to the evaluation results. The adjustment and optimization of the load balancing strategy is a continuous process, which can be flexibly adjusted according to the evaluation results and actual needs. By continuously optimizing the load balancing strategy, improving resource utilization and network performance, it can ensure that the system maintains high performance and stability in the face of changing load demands; Load balancing strategy adjustment and optimization, including strategy adjustment, resource optimization, network optimization, regular evaluation and adjustment, and emergency response mechanism. Strategy adjustment includes dynamic weight adjustment, algorithm optimization, task analysis and priority processing. Resource optimization includes resource reservation and resource release. Network optimization includes network load balancing and bandwidth management. Regular evaluation and adjustment includes regular monitoring and strategy iteration. Emergency response mechanism includes fault detection and recovery, and disaster recovery backup. The details are as follows: 1. Strategy Adjustment Dynamic weight adjustment: Dynamically adjust the server weight according to the real-time load and performance of the server to ensure that requests can be more evenly distributed to each server; Algorithm optimization: Introduce more advanced load balancing algorithms, including consistent hashing and minimum active connection number algorithms, to adapt to different load scenarios; Task classification and priority processing: Classify tasks and implement different processing strategies based on priority to ensure that high-priority tasks are processed first; 2. Resource optimization Resource reservation: Reserve necessary resources for critical tasks to ensure stable performance under high load conditions; Resource release: timely release of idle or no longer needed resources to improve resource utilization; 3. Network optimization Network load balancing: Implement load balancing at the network level, including using DNS polling and HTTP redirection methods to disperse network traffic; Bandwidth management: Rationally allocate network bandwidth according to business needs to avoid network congestion; 4. Regular evaluation and adjustment Regular monitoring: Establish a regular monitoring mechanism to continuously collect and analyze performance indicators and system status information; Strategy iteration: Continuously optimize the load balancing strategy based on monitoring results to adapt to load changes and system upgrades; 5. Emergency response mechanism: Fault detection and recovery: Establish a rapid response mechanism to promptly detect and handle system faults and ensure that the system can quickly resume normal operation; Disaster recovery and backup: Develop a disaster recovery and backup plan to ensure that you can quickly switch to backup resources when a critical server or network fails.

[0027] In one embodiment, it also includes a resource reservation module, a resource release module and an execution module; The resource reservation module is used to reserve resources before task scheduling, and reserve sufficient resources for key tasks or services in advance. Resource reservation is implemented in a static or dynamic way. Static reservation is to allocate a fixed amount of resources in the system configuration, and dynamic reservation is to adjust resource allocation according to real-time load and system usage mode; the resource release module is used to release resources when the policy is executed; Through reasonable resource reservation and release strategies, we can avoid waste and idleness of resources. When resources are tight, we can give priority to meeting the needs of key tasks or services; when resources are sufficient, we can allocate the remaining resources to other tasks or services. The execution model is used to execute the load balancing strategy of the optimal strategy.

[0028] In one embodiment, it also includes a user interaction module, a fault detection module, a safety module and a monitoring feedback module; The monitoring feedback module is used to monitor the execution status of tasks and provide feedback on task progress and results to users to improve user experience and enable users to understand the task execution status in a timely manner, facilitating subsequent operations and adjustments. The monitoring feedback module uses the following steps when monitoring the aircraft dispatch system: Monitoring target setting: First, clarify the targets monitored by the task monitoring and feedback module, including the status of the aircraft, the progress of task execution, and the utilization of system resources. In a multi-aircraft scheduling system, each aircraft may have different tasks and targets, so the module needs to be able to distinguish and monitor the specific tasks of each aircraft; Data acquisition and collection: collect key status data such as the position, speed, altitude, and attitude of the aircraft in real time through sensors and communication equipment, record the start time, end time, execution phase information of the mission execution, and various parameters and results during the mission execution, monitor the occupancy of system resources such as CPU, memory, and network bandwidth, and ensure that system resources are reasonably allocated and utilized; Data processing and analysis: Use data analysis algorithms and models to process and analyze real-time data, extract key information, including abnormal status of aircraft and bottlenecks in mission execution, and then use machine learning algorithms to predict trends based on historical data and real-time data to identify potential problems and risks in advance; Alarm and reminder: When the aircraft status is abnormal, the mission execution fails, or the system resources are insufficient, the monitoring feedback module automatically triggers the alarm mechanism to remind the user through sound, light, SMS, etc. After the mission is completed, the monitoring feedback module generates a detailed mission feedback report, including the results of the mission execution, the problems encountered, and the solution process. The user evaluates and summarizes the mission execution according to the report; Optimization and adjustment: Based on the monitoring and analysis results, optimize and adjust the aircraft scheduling strategy, task allocation strategy, etc. to improve the overall performance and efficiency of the system. For system vulnerabilities and performance bottlenecks found during the monitoring process, timely upgrade and repair the system to ensure the stability and reliability of the system; Collaborative monitoring of multiple aircraft: In a multi-aircraft scheduling system, the monitoring feedback module realizes information sharing and collaborative operations between aircraft by building a communication network and data sharing mechanism between aircraft. At the same time, the monitoring feedback module can also handle conflicts and competition between multiple aircraft to ensure the overall performance and efficiency of the system.

[0029] In one embodiment, the fault detection module is used to detect faults in the system and automatically perform recovery operations, while recording log information during system operation, thereby improving the reliability and stability of the system and ensuring that tasks can be processed and recovered in a timely manner when faults occur.

[0030] In one embodiment, the security module is used to authenticate the identity of the user and authorize the user to obtain permissions. Unauthorized users cannot access the system to protect the security of system data and resources.

[0031] In one embodiment, the user interaction module is used to provide a user interaction interface, through which the user can control the system to facilitate user operation and management, improve user experience, and reduce the difficulty for users to learn and use the system.

[0032] A multi-aircraft scheduling method based on K8S cloud computing power includes the following steps: Resource management: monitor and manage resources in the Kubernetes cluster, including CPU, memory, storage, and network; Mission reception and analysis: Receive mission requests from aircraft, and parse and process them; Strategy allocation: Dynamically adjust the task allocation strategy using a dynamic switching algorithm based on task type, resource requirements, and cluster status; Task scheduling: Schedule tasks to designated server nodes based on the current optimal load balancing strategy; Task execution: Execute the load balancing strategy of the optimal strategy.

[0033] When the present invention is working: the resources in the Kubernetes cluster, including CPU, memory, storage and network, are monitored and managed through the Kubernetes resource management module; the task request from the aircraft is received through the task receiving and parsing module, and the task request is parsed and processed; the load balancing strategy module dynamically adjusts the task allocation strategy according to the task type, resource demand and cluster status using a dynamic switching algorithm; the load balancing strategy includes a weight-based load balancing strategy, a priority-based load balancing strategy, a resource utilization-based load balancing strategy, a geographic location-based load balancing strategy, a minimum connection-based load balancing strategy and a response time-based load balancing strategy; the task scheduling module schedules the task to the specified server node according to the current optimal load balancing strategy, and then the execution module executes the load balancing strategy of the optimal strategy.

[0034] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-aircraft scheduling system based on K8S cloud computing power, including a Kubernetes resource management module, a task receiving and parsing module, a load balancing strategy module and a task scheduling module, characterized in that: The signal end of the Kubernetes resource management module is connected to the signal end of the task receiving module, and the signal end of the load balancing strategy module is respectively connected to the signal end of the task adjustment module and the signal end of the task receiving and parsing module; The Kubernetes resource management module is used to monitor and manage resources in the Kubernetes cluster, including CPU, memory, storage, and network, and to provide data support for load balancing strategies by understanding resource usage in real time; The task receiving and parsing module is used to receive a task request from an aircraft, and parse and process the task request; The load balancing strategy module is used to design a load balancing strategy and dynamically adjust the task allocation strategy using a dynamic switching algorithm according to the task type, resource requirements and cluster status; The load balancing strategy includes: Weight-based load balancing strategy: assign different weights to nodes according to the node's resource conditions and task requirements; Priority-based load balancing strategy: set different priorities for tasks and schedule tasks according to the priorities; Load balancing strategy based on resource utilization: monitor the resource utilization of nodes in real time and schedule tasks based on the resource utilization; Geographic location-based load balancing strategy: According to the geographical location of the aircraft and mission requirements, the nearest node is selected for task scheduling; Load balancing strategy based on least connections: monitor the current number of active connections of each server and assign new requests to the server with the least current number of connections; Response time-based load balancing: monitors the response time of each server and assigns new requests to the server with the shortest response time; The task scheduling module is used to schedule tasks to designated server nodes according to the current optimal load balancing strategy.

2. The multi-aircraft scheduling system based on K8S cloud computing power according to claim 1 is characterized in that: The task scheduling module is provided with a monitoring and fault-tolerance mechanism for discovering and handling server failures. When a server fails, the task scheduling module redirects the task to other normal servers.

3. The multi-aircraft scheduling system based on K8S cloud computing power according to claim 1 is characterized in that: The dynamic switching algorithm comprises the following steps: Real-time monitoring: collects performance indicators of each server, including CPU usage, memory usage, network bandwidth utilization, response time, and number of active connections; Analyze performance indicators: Conduct in-depth analysis of the collected monitoring data and calculate the changing trends of various performance indicators and their relationships; Strategy evaluation: Based on the analysis results, evaluate the applicability of the current load balancing strategies and present the evaluation results in the form of a report. The evaluation influencing factors include the actual needs of the system, user usage habits, and business importance. Compare the pros and cons of strategies: Compare the pros and cons of various load balancing strategies based on the evaluation report, analyze the performance of each strategy in terms of performance indicators, and the impact on system performance and user experience; Strategy selection: Select the currently optimal load balancing strategy based on the comparison results; Strategy switching: Switch the load balancing system to a new optimal strategy without interrupting services, including updating the configuration of the load balancer and adjusting the weight or priority of the server.

4. The multi-aircraft scheduling system based on K8S cloud computing power according to claim 1 is characterized in that: It also includes a resource reservation module, a resource release module and an execution module; The resource reservation module is used to reserve resources before task scheduling and reserve sufficient resources for key tasks or services in advance. Resource reservation is achieved in a static or dynamic manner. Static reservation is a fixed allocation of a certain amount of resources in the system configuration, and dynamic reservation is to adjust resource allocation according to real-time load and system usage patterns.

5. The multi-aircraft scheduling system based on K8S cloud computing power according to claim 4 is characterized in that: The resource release module is used to release resources when the policy is executed; The execution model is used to execute the load balancing strategy of the optimal strategy.

6. The multi-aircraft scheduling system based on K8S cloud computing power according to claim 1 is characterized in that: It also includes a user interaction module, a fault detection module, a safety module and a monitoring feedback module; The monitoring and feedback module is used to monitor the execution status of the task and provide feedback of the task progress and results to the user.

7. The multi-aircraft scheduling system based on K8S cloud computing power according to claim 6 is characterized by: The fault detection module is used to detect faults in the system, automatically perform recovery operations, and record log information during system operation.

8. The multi-aircraft scheduling system based on K8S cloud computing power according to claim 6 is characterized by: The security module is used to authenticate the identity of the user and authorize the user to access the system.

9. The multi-aircraft scheduling system based on K8S cloud computing power according to claim 6 is characterized by: The user interaction module is used to provide a user interaction interface, through which the user can control the system.

10. A multi-aircraft scheduling method based on K8S cloud computing power, applied to the multi-aircraft scheduling system based on K8S cloud computing power as described in any one of claims 1-9, characterized in that: The following steps are involved: Resource management: monitor and manage resources in the Kubernetes cluster, including CPU, memory, storage, and network; Mission reception and analysis: Receive mission requests from aircraft, and parse and process them; Strategy allocation: Dynamically adjust the task allocation strategy using a dynamic switching algorithm based on task type, resource requirements, and cluster status; Task scheduling: Schedule tasks to designated server nodes based on the current optimal load balancing strategy; Task execution: Execute the load balancing strategy of the optimal strategy.

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