Server communication optimization method and system based on low-altitude economy

Abstract

The present invention relates to the field of low-altitude economy, and in particular to a server communication optimization method and system based on low-altitude economy. The method comprises the following steps: monitoring drone communication relay nodes in the low-altitude field; performing spatial position location calculations on the drone communication relay nodes one by one, and performing distributed relay topology fitting to construct a low-altitude relay communication network; collecting low-altitude real-time interaction information based on the low-altitude relay communication network; performing communication link bandwidth calculation on the low-altitude real-time interaction information, and performing dynamic communication protocol modulation to construct a dynamic low-altitude adaptive communication protocol; identifying relay node real-time communication state parameters based on the low-altitude relay communication network; performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes, and performing optimal channel scheduling decisions to construct an optimal channel scheduling strategy; monitoring the current user communication data of the ground server. The present invention realizes low-latency and high-efficiency server communication optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude economy, and in particular to a server communication optimization method and system based on low-altitude economy. Background Art

[0002] With the rapid development of drone technology and its wide application in various industries, drone communication networks in low-altitude areas have gradually become an important part of modern communication systems. In low-altitude drone communication networks, drones not only undertake tasks such as data collection and real-time transmission, but also provide efficient communication support in complex environments through autonomous flight and collaborative work, especially in the communication between ground servers and drones, which has important application value. However, the flight environment in low-altitude areas is complex, and limited by factors such as flight altitude, network coverage, communication distance, signal interference, etc., traditional communication methods often cannot meet the requirements of real-time, stability and efficiency. Especially in the communication process between drones and ground servers, problems such as limited network bandwidth, high transmission delay, and high packet loss rate often occur, which seriously affect the communication quality and the overall performance of the system.

[0003] Traditional communication optimization methods mostly rely on static network models and manual configuration, and often cannot adapt to the dynamically changing low-altitude environment. As low-altitude UAV communication networks gradually enter the practical application stage, how to achieve efficient and stable communication between ground servers and UAVs in complex flight environments has become a technical problem that needs to be solved urgently. In order to improve the reliability and efficiency of communication, especially in high-density, dynamically changing low-altitude areas, a new type of communication optimization method is urgently needed to ensure the real-time and stability of data transmission between ground servers and UAVs, and solve the problems of bandwidth limitation, interference suppression, network congestion, etc. in traditional communication methods. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a server communication optimization method and system based on low-altitude economy to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a server communication optimization method based on low-altitude economy, comprising the following steps:

[0006] Step S1: monitoring the drone communication relay nodes in the low-altitude area; performing spatial location calculations on the drone communication relay nodes one by one, and performing distributed relay topology fitting to build a low-altitude relay communication network;

[0007] Step S2: collecting low-altitude real-time interaction information based on the low-altitude relay communication network; calculating the communication link bandwidth of the low-altitude real-time interaction information, and performing dynamic communication protocol modulation to construct a dynamic low-altitude adaptive communication protocol;

[0008] Step S3: identifying the real-time communication state parameters of the relay nodes based on the low-altitude relay communication network; performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes, making an optimal channel scheduling decision, and constructing an optimal channel scheduling strategy;

[0009] Step S4: monitoring the current user communication data of the ground server; calculating the server communication load based on the current user communication data, and predicting the short-term communication demand trend to obtain the short-term communication demand trend prediction data for different regions;

[0010] Step S5: Based on the short-term communication demand trend forecast data of different regions, the low-altitude relay communication network is dynamically deployed in the UAV region, and the spatial hierarchical deployment is optimized to construct a dynamic spatial hierarchical deployment strategy;

[0011] Step S6: Make multi-strategy collaborative control decisions on the dynamic spatial hierarchical deployment strategy, the optimal channel scheduling strategy and the dynamic low-altitude adaptive communication protocol, and perform cloud migration and enhancement to obtain a cloud-based low-altitude communication optimization model.

[0012] The present invention realizes the construction of a low-altitude relay communication network through node-by-node spatial position positioning calculation and distributed relay topology fitting, thereby improving the communication coverage and stability. The construction of a low-altitude relay communication network is conducive to realizing the communication connection between drones, laying the foundation for subsequent data transmission and interaction, collecting low-altitude real-time interaction information and calculating the communication link bandwidth, and constructing a dynamic low-altitude adaptive communication protocol, thereby improving communication efficiency and quality. Dynamic communication protocol modulation helps to adjust communication parameters according to real-time needs, adapt to different communication scenarios, provide a more flexible and efficient communication method, identify the real-time communication state parameters of relay nodes and make optimal channel scheduling decisions, thereby optimizing communication channel utilization and Communication quality, the optimal channel scheduling strategy helps to avoid channel interference and congestion, improve communication efficiency and stability, ensure communication quality, and optimize dynamic drone regional deployment and spatial layered deployment based on short-term communication demand trend forecast data, thereby improving the efficiency of communication resource utilization. The dynamic spatial layered deployment strategy helps to adjust drone deployment in real time according to the communication needs of different regions, optimize communication coverage and quality, and multi-strategy collaborative control decision-making and cloud migration enhancement enable the cloud-based low-altitude communication optimization model to be obtained, which improves the overall performance and intelligence level of the communication system. Through cloud migration enhancement, the optimization model responds to changes in communication needs in a timely manner, achieving more flexible and efficient communication resource allocation and management.

[0013] Preferably, step S1 comprises the following steps:

[0014] Step S11: monitoring the UAV communication relay nodes in the low-altitude area;

[0015] Step S12: performing spatial position calculation on each of the drone communication relay nodes to obtain the spatial position coordinates of each relay node;

[0016] Step S13: performing spatial distribution analysis on the spatial position coordinates of each relay node to generate relay node spatial distribution data;

[0017] Step S14: performing inter-node topological connection evolution on the relay node spatial distribution data to obtain the relay node topological logic;

[0018] Step S15: Based on the relay node topology logic, a distributed relay topology is fitted for the drone communication relay nodes to build a low-altitude relay communication network.

[0019] The present invention monitors the UAV communication relay nodes in the low-altitude area, obtains the status and location information of the communication nodes in real time, and provides real-time data support for the optimization of the communication network. Monitoring the UAV communication relay nodes helps to ensure the integrity and stability of the communication network, and timely discovers and handles abnormal situations of the communication nodes. The spatial position positioning calculation of each node one by one accurately obtains the spatial position coordinates of each relay node, and provides accurate basic data for subsequent spatial analysis and topology construction. Obtaining accurate spatial position coordinates helps to optimize the layout of communication nodes and improve the coverage and efficiency of the communication network. By performing spatial distribution analysis on the spatial position coordinates of each relay node, the distribution law and characteristics of the relay nodes in space are understood, and the network is provided with accurate basic data for subsequent spatial analysis and topology construction. It provides a reference for network optimization. Spatial distribution analysis helps to discover the spatial relationship between nodes, optimize the node location layout, and improve the coverage and communication efficiency of the communication network. By evolving the topological connection between nodes on the spatial distribution data of relay nodes, the logical connection relationship between relay nodes is established, laying the foundation for network topology construction. Good topological connection evolution helps to build a stable and efficient communication network structure and improve communication quality and reliability. Distributed relay topology fitting is performed based on the relay node topology logic to build a low-altitude relay communication network, realizing the construction and optimization of the communication network. By building a low-altitude relay communication network, the communication coverage and stability are improved, providing support for subsequent communication data transmission and services.

[0020] Preferably, step S2 comprises the following steps:

[0021] Step S21: performing real-time information interaction with a ground server based on a low-altitude relay communication network to collect low-altitude real-time interaction information;

[0022] Step S22: Calculate the communication link bandwidth of the low-altitude real-time interactive information and identify the low-altitude interactive communication link bandwidth data;

[0023] Step S23: analyzing the UAV-to-ground interaction delay based on the low-altitude interactive communication link bandwidth data, thereby obtaining the UAV-to-ground interaction delay parameter;

[0024] Step S24: Dynamically modulate the communication protocol according to the ground interaction delay parameter to construct a dynamic low-altitude adaptive communication protocol.

[0025] The present invention realizes the collection of low-altitude real-time interactive information by performing real-time information interaction with a ground server based on a low-altitude relay communication network, and provides real-time data support for subsequent communication optimization. Real-time information interaction and collection are helpful to understand the low-altitude communication status and demand changes, and provide a data basis for communication link bandwidth calculation and communication protocol modulation. Communication link bandwidth calculation is performed on low-altitude real-time interactive information, and low-altitude interactive communication link bandwidth data is identified, which provides a basis for communication quality evaluation and optimization. Identifying communication link bandwidth data is helpful to understand communication resource utilization, optimize bandwidth allocation, and improve communication efficiency and quality. Based on the low-altitude interactive communication link bandwidth data, unmanned aerial vehicle ground interaction delay analysis is performed to obtain ground interaction delay parameters, which provides a basis for communication protocol optimization. Analysis of ground interaction delay is helpful to understand communication delay, optimize communication protocol, and improve communication rate and stability. Dynamic communication protocol modulation is performed according to ground interaction delay parameters, and a dynamic low-altitude adaptive communication protocol is constructed to realize dynamic adjustment and optimization of communication parameters. Dynamic communication protocol modulation is helpful to adjust communication parameters according to real-time needs, improve communication adaptability and efficiency, and optimize communication quality and user experience.

[0026] Preferably, the specific steps of step S24 are:

[0027] Perform semantic analysis on low-altitude real-time interactive information and generate semantic features of interactive information;

[0028] Perform information segmentation based on the semantic features of interactive information to generate multiple segments of interactive information;

[0029] Performing lossless data compression encoding on multiple segments of interactive information, thereby obtaining multiple compressed encoding packets;

[0030] Identify the application scenarios of low-altitude real-time interactive information in real time, so as to obtain the application scenarios of the current interactive information;

[0031] Performing data transmission priority analysis on multiple compressed coded packets according to the application scenario of the current interactive information, thereby obtaining the transmission priority of each coded packet;

[0032] Dynamically allocate bandwidth for each coding packet according to the ground interaction delay parameter, thereby generating dynamic bandwidth allocation data for each coding table;

[0033] Dynamically modulating the dynamic bandwidth allocation data of each coding table to generate a low-altitude network dynamic communication protocol;

[0034] Perform interactive information transmission simulation according to the low-altitude network dynamic communication protocol to obtain transmission simulation response data;

[0035] Calculating the data transmission packet loss rate of the transmission simulation response data, thereby generating a simulation protocol transmission packet loss rate;

[0036] Based on the simulated protocol transmission packet loss rate, the low-altitude network dynamic communication protocol is end-to-end adaptively optimized to construct a dynamic low-altitude adaptive communication protocol.

[0037] The present invention generates interactive information semantic features through information semantic analysis, thereby improving the ability to understand and process low-altitude real-time interactive information. Generating interactive information semantic features helps to identify important content in the information and provide a more accurate data basis for subsequent processing. Information segmentation processing based on interactive information semantic features generates multiple segments of interactive information, which helps to improve the flexibility and efficiency of information processing. The generation of multiple segments of interactive information better organizes and manages information content, and provides a better data structure for subsequent data compression and encoding. Lossless data compression encoding of multiple segments of interactive information generates multiple compressed encoding packages, which helps to reduce the amount of data transmission and improve data transmission efficiency. The generation of compressed encoding packages reduces data transmission costs, optimizes data transmission rate, and improves communication efficiency. The application scenario of the current interactive information is identified in real time. Transmission priority analysis is performed in combination with ground interaction delay parameters, which helps to adjust data according to actual conditions. Transmission order and transmission priority analysis optimize the data transmission order according to the application scenario requirements, improve data transmission efficiency and real-time performance, dynamically allocate bandwidth according to transmission priority, generate dynamic bandwidth allocation data, which helps to adjust bandwidth allocation according to real-time needs and improve data transmission efficiency. Dynamic communication protocol modulation generates a low-altitude network dynamic communication protocol based on bandwidth allocation data, optimizes the communication protocol structure, improves communication quality and stability, and calculates the packet loss rate of transmission simulation response data, which helps to evaluate the stability and reliability of data transmission. The packet loss rate calculation helps identify problems in data transmission and provides feedback and improvement directions for subsequent protocol optimization. End-to-end adaptive optimization is performed based on the simulated protocol transmission packet loss rate, and a dynamic low-altitude adaptive communication protocol is constructed to improve the adaptability and stability of the communication system. Adaptive optimization dynamically adjusts the communication protocol according to actual conditions to improve communication quality and user experience.

[0038] Preferably, step S3 specifically comprises the following steps:

[0039] Step S31: identifying the real-time communication status parameters of the relay node based on the low-altitude relay communication network;

[0040] Step S32: performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes to obtain a plurality of communication channel data;

[0041] Step S33: calculating the link quality between nodes for the plurality of communication channel data, thereby generating the communication link quality data between the plurality of nodes;

[0042] Step S34: performing communication quality time series variation analysis on the communication link quality data between the multiple nodes to generate communication quality time series variation data;

[0043] Step S35: Make an optimal channel scheduling decision based on the communication quality time series change data and construct an optimal channel scheduling strategy.

[0044] The present invention provides key network performance indicators by identifying the real-time communication status parameters of relay nodes, helps to monitor and manage the health status of the communication network, the identification of real-time communication status parameters helps to timely discover and solve abnormal situations of communication nodes, improves the stability and reliability of the network, and effectively understands the status and quality of each communication channel by identifying the real-time communication status parameters of relay nodes and identifying communication channels between nodes. The accurate identification of the communication channels of relay nodes helps to optimize the data transmission path, improve communication efficiency and stability, calculate the link quality of multiple communication channels to evaluate the performance of communication links between different nodes, and provide a basis for subsequent decision-making. The calculation of the link quality between nodes helps to identify communication bottlenecks and weaknesses, and provides data support for improving the communication quality. The dynamic change trend of the communication link quality is understood through the time series change analysis, which helps to predict the network performance change and the occurrence of problems. The analysis of the communication quality time series change data helps to adjust the communication strategy in time, improve the adaptability and stability of the communication network, and make the optimal channel scheduling decision based on the communication quality time series change data. The communication strategy is adjusted according to the real-time situation to optimize the communication efficiency. The construction of the optimal channel scheduling strategy helps to improve the success rate and speed of data transmission and optimize the overall performance of the communication network.

[0045] Preferably, the specific steps of step S4 are:

[0046] Step S41: monitoring the current user communication data of the ground server;

[0047] Step S42: Calculating the server communication load on the current user communication data to generate current server communication load data;

[0048] Step S43: performing regional load distribution analysis on the current server communication load data to obtain communication load data of different regions;

[0049] Step S44: Calculating user communication demands for communication load data of different regions, thereby generating communication demand data of different regions;

[0050] Step S45: predicting the trend of short-term communication demand based on the communication demand data of different regions to obtain the short-term communication demand trend prediction data of different regions.

[0051] The present invention monitors the current user communication data of the ground server to understand the user communication behavior and demand in real time, and provides data support for server resource allocation. Real-time monitoring of user communication data helps to optimize server performance, improve data processing efficiency and user experience. By calculating the server communication load, the workload and load of the server are evaluated to help reasonably allocate server resources. Server communication load calculation helps to avoid server overload or resource waste, optimize server performance and stability. Regional load distribution analysis reveals the communication load conditions in different regions, and helps to understand the spatial distribution characteristics of communication needs. By analyzing the regional load distribution, resource allocation is carried out in a targeted manner to improve communication efficiency and service quality. Communication load data in different regions is calculated to quantify user communication needs, providing a scientific basis for resource allocation and planning. User communication demand calculation helps to optimize communication resource configuration, improve user satisfaction and communication efficiency, and predict short-term communication demand trends to help adjust communication resource allocation in advance to cope with changes in future communication needs. Short-term communication demand trend prediction helps to optimize resource utilization and improve system flexibility and adaptability.

[0052] Preferably, the specific steps of step S5 are:

[0053] Step S51: Obtaining meteorological monitoring parameters of the drone area;

[0054] Step S52: performing real-time meteorological characteristic analysis on the meteorological monitoring parameters to obtain regional real-time meteorological characteristics;

[0055] Step S53: evaluating the impact of UAV communication performance according to the real-time meteorological characteristics of the region to obtain communication performance impact data;

[0056] Step S54: performing dynamic regional deployment of drones on the low-altitude relay communication network based on the short-term communication demand trend forecast data of different regions to obtain dynamic regional deployment data;

[0057] Step S55: Perform spatial hierarchical deployment optimization on the dynamic regional deployment data according to the communication performance impact data, and construct a dynamic spatial hierarchical deployment strategy.

[0058] The present invention helps to understand the local meteorological conditions by acquiring the meteorological monitoring parameters of the drone area, and provides basic data for the optimization of the communication system. The meteorological monitoring parameters help predict weather changes and provide a reference basis for the stability and reliability of the communication system. The real-time meteorological feature analysis converts the meteorological monitoring parameters into operable data, which helps to understand the real-time meteorological conditions of the region. Through the meteorological feature analysis, the communication equipment or deployment strategy is adjusted in time to adapt to the communication environment under different meteorological conditions. By evaluating the impact of the weather on the communication performance, the stability and reliability of the drone communication are predicted, and the occurrence of communication failures is reduced. The communication performance impact data helps to optimize the communication link quality, improve the communication efficiency and data transmission speed. The dynamic drone regional deployment flexibly adjusts the position of the drone according to the communication demand trend, optimizes the communication coverage and communication quality, and realizes efficient use of resources through dynamic deployment, meets the communication needs of different regions, and improves the overall performance of the communication system. The spatial layered deployment optimization performs more refined planning of the drone deployment according to the communication performance impact data, improves the stability and coverage of the communication system, optimizes the deployment strategy to reduce communication interference, improves the communication signal quality, and improves the overall performance and efficiency of the communication system.

[0059] Preferably, the specific steps of step S55 are:

[0060] Identify the coordinates of the deployed drone’s location based on dynamic area deployment data;

[0061] Performing meteorological interference analysis on the position coordinates of the deployed UAVs based on regional real-time meteorological characteristics to obtain meteorological interference data for each UAV position;

[0062] Perform optimal flight altitude analysis based on the weather interference data and communication performance impact data of each UAV location to generate the optimal flight altitude for each UAV;

[0063] The UAV movement path is planned based on the position coordinates of the deployed UAV, thereby generating a dynamic deployment movement path;

[0064] The optimal flight altitude and dynamic deployment movement path of each UAV are optimized for spatial hierarchical deployment, and a dynamic spatial hierarchical deployment strategy is constructed.

[0065] The present invention helps to monitor the position of the drone in real time by identifying the position coordinates of the deployed drone, provides accurate position information for subsequent analysis and optimization, realizes timely tracking and management of the deployment of the drone, and ensures the stability and efficiency of the communication system. The meteorological interference analysis can help understand the impact of meteorological conditions on drone communication, identify potential communication interference factors in advance, and adjust communication equipment parameters or drone flight altitude by analyzing meteorological interference to reduce the impact of communication interference on communication performance. The optimal flight altitude analysis determines the optimal flight altitude of each drone according to meteorological interference data and communication performance impact data to optimize communication quality. Determining the optimal flight altitude helps to improve communication coverage and communication efficiency, reduce communication interference, and enhance the stability of the communication system. Through drone mobile path planning, efficient movement of drones in the region is achieved to ensure balanced communication coverage and timely satisfaction of communication needs. Dynamic deployment mobile path generation can dynamically adjust the mobile path of the drone according to real-time conditions, improve the flexibility and adaptability of the communication system, and spatial layered deployment optimization finely adjusts the drone deployment according to the optimal flight altitude and dynamic mobile path to improve the overall performance of the communication system. The dynamic spatial layered deployment strategy helps to reduce communication interference, optimize communication coverage and communication quality, and improve the efficiency and stability of the communication system.

[0066] Preferably, step S6 comprises the following steps:

[0067] Step S61: performing UAV communication coordination optimization on the dynamic space hierarchical deployment strategy to generate a UAV coordination optimization deployment strategy;

[0068] Step S62: Perform multi-strategy collaborative control decisions on the coordinated optimization deployment strategy of the UAV, the optimal channel scheduling strategy, and the dynamic low-altitude adaptive communication protocol to build an intelligent UAV communication optimization strategy;

[0069] Step S63: Perform cloud migration and enhancement on the intelligent UAV communication optimization strategy, thereby obtaining a cloud-based low-altitude communication optimization model.

[0070] The present invention ensures that the communication between drones is not interfered by the coordination and optimization of drone communications, improves the communication efficiency and data transmission quality, optimizes the deployment strategy to make the communication coordination between drones more efficient, avoids communication conflicts and signal interference, and improves the stability of the communication system. The multi-strategy collaborative control decision comprehensively considers factors such as drone coordinated deployment, channel scheduling and communication protocols, optimizes the overall performance of the communication system, and constructs an intelligent drone communication optimization strategy, which helps to improve the adaptability and intelligence level of the communication system and optimize the utilization efficiency of communication resources. Cloud migration strengthens the migration of intelligent drone communication optimization strategies to the cloud, realizes centralized management and optimization of the communication system, and realizes real-time updating and adjustment of the communication optimization model through cloud migration, thereby improving the flexibility and adaptability of the communication system.

[0071] In this specification, a server communication optimization system based on low-altitude economy is provided, which is used to execute the server communication optimization method based on low-altitude economy as described above, including:

[0072] The communication network module is used to monitor the UAV communication relay nodes in the low-altitude area; perform spatial location calculations on the UAV communication relay nodes one by one, and perform distributed relay topology fitting to build a low-altitude relay communication network;

[0073] The protocol modulation module is used to collect low-altitude real-time interactive information based on the low-altitude relay communication network; calculate the communication link bandwidth of the low-altitude real-time interactive information, and perform dynamic communication protocol modulation to build a dynamic low-altitude adaptive communication protocol;

[0074] The optimal channel scheduling module is used to identify the real-time communication state parameters of the relay nodes based on the low-altitude relay communication network; identify the communication channels between nodes based on the real-time communication state parameters of the relay nodes, make optimal channel scheduling decisions, and construct the optimal channel scheduling strategy;

[0075] The demand trend prediction module is used to monitor the current user communication data of the ground server; calculate the server communication load based on the current user communication data, and predict the short-term communication demand trend to obtain the short-term communication demand trend prediction data for different regions;

[0076] Dynamic hierarchical deployment module, which is used to dynamically deploy drones in low-altitude relay communication networks based on short-term communication demand trend forecast data in different regions, optimize spatial hierarchical deployment, and build a dynamic spatial hierarchical deployment strategy;

[0077] The collaborative control module is used to make multi-strategy collaborative control decisions on dynamic spatial layered deployment strategies, optimal channel scheduling strategies, and dynamic low-altitude adaptive communication protocols, and to perform cloud migration and enhancement to obtain a cloud-based low-altitude communication optimization model.

[0078] The present invention helps to track the status of the communication network in a timely manner by real-time monitoring of the UAV communication relay nodes, ensuring the continuity and stability of the communication. The spatial position positioning calculation of each node and the distributed relay topology fitting can build an efficient low-altitude relay communication network, improve the communication coverage and reliability, and collect real-time interactive information based on the low-altitude relay communication network to achieve real-time monitoring and response to the low-altitude communication environment. Dynamic communication protocol modulation helps to adjust the communication protocol according to real-time communication needs, improve communication efficiency and adaptability, identify the real-time communication status parameters of the relay node and perform optimal channel scheduling, which can optimize the communication link quality and improve data transmission efficiency. The optimal channel scheduling strategy is constructed to ensure the effective use of the communication channel, reduce communication interference, improve the performance of the communication system, and monitor the ground The user communication data and predicted short-term communication demand trends of the server are helpful for rationally planning communication resources and improving the quality and efficiency of communication services. The communication demand trend forecast data of different regions provide an important reference for subsequent communication deployment and optimization. Dynamic UAV regional deployment and spatial layered deployment optimization based on communication demand trend forecast data can realize flexible configuration of communication resources and meet the communication needs of different regions. The construction of dynamic spatial layered deployment strategy can help improve communication coverage and communication quality and optimize the performance of the communication system. Multi-strategy collaborative control decision-making comprehensively considers deployment strategy, channel scheduling and communication protocol, optimizes the overall performance of the communication system, and cloud migration strengthens the real-time update and management of communication optimization models, thereby improving the intelligence level and flexibility of the communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic flow chart of the steps of a server communication optimization method based on low-altitude economy of the present invention;

[0080] Figure 2 Detailed implementation flow chart of step S1;

[0081] Figure 3 Detailed implementation flow chart of step S2;

[0082] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0083] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0084] The example of this application provides a server communication optimization method and system based on low-altitude economy. The execution subject of the server communication optimization method and system based on low-altitude economy includes but is not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of audio and image management system, information management system, and cloud data management system.

[0085] See also Figures 1 to 4 The present invention provides a server communication optimization method based on low-altitude economy, and the server communication optimization method based on low-altitude economy comprises the following steps:

[0086] Step S1: monitoring the drone communication relay nodes in the low-altitude area; performing spatial location calculations on the drone communication relay nodes one by one, and performing distributed relay topology fitting to build a low-altitude relay communication network;

[0087] Step S2: collecting low-altitude real-time interaction information based on the low-altitude relay communication network; calculating the communication link bandwidth of the low-altitude real-time interaction information, and performing dynamic communication protocol modulation to construct a dynamic low-altitude adaptive communication protocol;

[0088] Step S3: identifying the real-time communication state parameters of the relay nodes based on the low-altitude relay communication network; performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes, making an optimal channel scheduling decision, and constructing an optimal channel scheduling strategy;

[0089] Step S4: monitoring the current user communication data of the ground server; calculating the server communication load based on the current user communication data, and predicting the short-term communication demand trend to obtain the short-term communication demand trend prediction data for different regions;

[0090] Step S5: Based on the short-term communication demand trend forecast data of different regions, the low-altitude relay communication network is dynamically deployed in the UAV region, and the spatial hierarchical deployment is optimized to construct a dynamic spatial hierarchical deployment strategy;

[0091] Step S6: Make multi-strategy collaborative control decisions on the dynamic spatial hierarchical deployment strategy, the optimal channel scheduling strategy and the dynamic low-altitude adaptive communication protocol, and perform cloud migration and enhancement to obtain a cloud-based low-altitude communication optimization model.

[0092] The present invention realizes the construction of a low-altitude relay communication network through node-by-node spatial position positioning calculation and distributed relay topology fitting, thereby improving the communication coverage and stability. The construction of a low-altitude relay communication network is conducive to realizing the communication connection between drones, laying the foundation for subsequent data transmission and interaction, collecting low-altitude real-time interaction information and calculating the communication link bandwidth, and constructing a dynamic low-altitude adaptive communication protocol, thereby improving communication efficiency and quality. Dynamic communication protocol modulation helps to adjust communication parameters according to real-time needs, adapt to different communication scenarios, provide a more flexible and efficient communication method, identify the real-time communication state parameters of relay nodes and make optimal channel scheduling decisions, thereby optimizing communication channel utilization and Communication quality, the optimal channel scheduling strategy helps to avoid channel interference and congestion, improve communication efficiency and stability, ensure communication quality, and optimize dynamic drone regional deployment and spatial layered deployment based on short-term communication demand trend forecast data, thereby improving the efficiency of communication resource utilization. The dynamic spatial layered deployment strategy helps to adjust drone deployment in real time according to the communication needs of different regions, optimize communication coverage and quality, and multi-strategy collaborative control decision-making and cloud migration enhancement enable the cloud-based low-altitude communication optimization model to be obtained, which improves the overall performance and intelligence level of the communication system. Through cloud migration enhancement, the optimization model responds to changes in communication needs in a timely manner, achieving more flexible and efficient communication resource allocation and management.

[0093] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a server communication optimization method based on low-altitude economy of the present invention. In this example, the steps of the server communication optimization method based on low-altitude economy include:

[0094] Step S1: monitoring the drone communication relay nodes in the low-altitude area; performing spatial location calculations on the drone communication relay nodes one by one, and performing distributed relay topology fitting to build a low-altitude relay communication network;

[0095] In this embodiment, sensors and monitoring equipment are deployed in the low-altitude area, using GPS Positioning systems, radio spectrum analyzers and other communication monitoring tools are used to collect the status data of drone communication relay nodes in real time. UAV monitoring software is installed to record the communication frequency, signal strength, connection status and other information of the nodes in real time. The communication data of each relay node is collected regularly or in real time, including its location, signal strength, bandwidth utilization, delay, etc. All collected data are stored in a central database to ensure the integrity and traceability of the data. Appropriate positioning algorithms are selected, such as triangulation positioning, bilateral positioning or time difference positioning (TDOA) method, to ensure that the spatial position of each relay node can be accurately calculated, and the required input data, including signal arrival time, distance between nodes, etc., are determined. Using the collected signal data, the selected positioning algorithm is used to calculate the three-dimensional spatial coordinates (longitude, latitude, altitude) of each drone communication relay node, and the calculation results are compared with the real-time monitoring data to ensure the accuracy of positioning. The calculated node position is verified and cross-validated using available reference points (such as base stations with known locations). If necessary, the positioning results are corrected to ensure that their accuracy meets the requirements. The topological structure model of the relay nodes is designed to determine the connection relationship and communication between the nodes. The characteristics of the signal link are selected, and the appropriate topology type (such as star, mesh, tree, etc.) is selected. The key factors affecting the topological structure, such as the signal strength, coverage and geographical location of the node, are determined. The collected relay node location and communication quality data are used to perform topological fitting analysis. Graph theory and network topology analysis tools (such as NetworkX and Gephi) are used to build and optimize the relay network topology. According to the communication quality and distance between nodes, the connection between nodes is adjusted to form the optimal topological structure. The connection status of each node is recorded to ensure that the relay topology can effectively support low-altitude communication. The overall architecture of the low-altitude relay communication network is designed, including the layout of relay nodes, channel allocation and data transmission path, etc. The operation mechanism of the network is determined, including strategies such as data forwarding, load balancing and fault recovery. The designed network architecture is applied to the actual environment, the communication parameters and operation mode of the relay nodes are configured, and field tests are conducted to verify the performance and stability of the low-altitude relay communication network to ensure that the network can operate normally. During the operation of the network, the communication status and network performance of the relay nodes are continuously monitored, feedback data is collected, and the topology and configuration of the relay network are continuously optimized based on the monitoring results to improve the communication efficiency and reliability of the network.

[0096] Step S2: collecting low-altitude real-time interaction information based on the low-altitude relay communication network; calculating the communication link bandwidth of the low-altitude real-time interaction information, and performing dynamic communication protocol modulation to construct a dynamic low-altitude adaptive communication protocol;

[0097] In this embodiment, in the low-altitude relay communication network, necessary sensors and communication equipment are deployed to monitor and collect interaction information in real time. These equipment include drones, ground base stations and mobile terminals. The equipment is configured to obtain users' real-time communication needs, network status, channel usage and environmental information (such as weather conditions), ensure that the real-time collected information is transmitted to the central data processing platform through the wireless communication network, use MQTT, CoAP or HTTP and other protocols, store the collected low-altitude real-time interaction information in a database for subsequent analysis and processing, determine the bandwidth indicators to be calculated, including the effective bandwidth, instantaneous bandwidth and available bandwidth of the link, the effective bandwidth usually refers to the bandwidth that can be actually used under specific conditions, collect factors affecting the bandwidth, such as signal quality, interference level, number of users and transmission distance, and select a suitable bandwidth calculation algorithm, such as Shannon's theorem, M / M / 1 Queue model or estimation method based on network traffic to evaluate the actual bandwidth of the link, ensure that the selected algorithm can take into account the dynamically changing network environment, implement bandwidth calculation based on real-time interaction information, evaluate the bandwidth of each communication link, record the calculation results in the database to form a link bandwidth data set for subsequent analysis, design the modulation scheme of the dynamic communication protocol according to different network conditions and bandwidth calculation results, consider using adaptive modulation technology (such as QPSK, 16-QAM, 64-QAM), dynamically adjust the modulation mode according to the link quality, set the threshold and strategy of modulation switching, so as to make flexible adjustments under different conditions, develop a dynamic low-altitude adaptive communication protocol, ensure that it can dynamically adjust the modulation mode according to the real-time bandwidth and link status, test it in an experimental environment, and verify the performance of the protocol under different network conditions, including signal stability, transmission efficiency and delay, apply the tested dynamic low-altitude adaptive communication protocol to the actual low-altitude relay communication network, ensure that each relay node and terminal device can communicate according to the protocol, monitor the network performance after the implementation of the protocol, and make necessary adjustments and optimizations based on feedback.

[0098] Step S3: identifying the real-time communication state parameters of the relay nodes based on the low-altitude relay communication network; performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes, making an optimal channel scheduling decision, and constructing an optimal channel scheduling strategy;

[0099] In this embodiment, monitoring equipment, such as wireless sensors, signal strength detectors, and data loggers, are deployed in the low-altitude relay communication network to monitor the communication status of each relay node, ensure that the equipment can collect parameters such as signal strength, data transmission rate, delay, packet loss rate, etc. in real time, collect communication status data of each relay node regularly or in real time, and transmit it to a central database through a wireless network, store the collected data in a structured format (such as JSON or CSV) to facilitate subsequent analysis, select a suitable channel identification method, such as spectrum analysis, signal detection, and signal strength measurement, identify the communication channel between each relay node, use software defined radio (SDR) technology to perform channel detection to obtain real-time channel information, perform channel identification on each pair of relay nodes, record the communication channel data between them, including frequency, bandwidth, modulation mode, and channel quality, etc., store the channel data in a database to form a communication channel data set to facilitate subsequent analysis, design a channel scheduling strategy, consider the channel quality between nodes, network load, and user needs to ensure channel allocation under efficient conditions, and determine the goals of the scheduling strategy, such as maximizing bandwidth utilization and reducing delay and improve user experience, select appropriate channel scheduling algorithms, such as greedy algorithms, priority queue scheduling, genetic algorithms or reinforcement learning, to achieve dynamic channel scheduling, set the input parameters of the scheduling algorithm, including real-time channel status, node load and historical data, apply the selected scheduling algorithm to make channel scheduling decisions based on real-time communication status parameters and channel identification results, generate the optimal channel scheduling strategy, record the results of the scheduling decisions, ensure that each relay node is assigned to the best channel, and can dynamically adapt to network changes. After the channel scheduling strategy is implemented, continuously monitor network performance, collect feedback data to evaluate the scheduling effect, perform dynamic optimization based on the monitoring results, and adjust the scheduling strategy to cope with network changes and improve communication quality.

[0100] Step S4: monitoring the current user communication data of the ground server; calculating the server communication load based on the current user communication data, and predicting the short-term communication demand trend to obtain the short-term communication demand trend prediction data for different regions;

[0101] In this embodiment, a data acquisition module is deployed on the ground server to monitor the user's communication activities in real time, including login, request processing, data transmission, etc., use network monitoring tools (such as Wireshark, NetFlow) to capture and record communication data, set the data acquisition frequency, record each user's communication data, such as data packet size, transmission rate, connection duration, etc., store the collected data in a database to ensure data integrity and traceability, pre-process the collected user communication data, including removing duplicate data, filling missing values ​​and standardizing data format to ensure data quality, define calculation indicators for communication load, such as the amount of data transmitted per second (bps), the number of user connections, request processing time, etc., select a suitable calculation method, such as the accumulation method or the average method, so as to evaluate the communication load, use the collected user communication data to calculate the server's communication load, generate current server communication load data, record the load calculation results, and perform visual display (such as charts) to help identify time periods and areas with high loads.

[0102] Analyze the load conditions in different time periods, identify load change trends, prepare for short-term communication demand forecasts, integrate server communication load data and user communication data, build a complete data set for forecasting, ensure that the data set contains historical load data, user request patterns and other relevant features, select a suitable forecasting model, such as time series analysis (ARIMA, SARIMA), machine learning models (such as LSTM, random forest) or statistical-based regression models, determine the input variables of the model, including historical communication load, time characteristics (such as hours, days of the week) and other relevant factors, use the selected forecasting model to analyze the current user communication data and load data, generate short-term communication demand trend forecast data, record the forecast results, and generate a visual report for easy understanding and decision-making, evaluate the forecast results, use actual data to compare with the forecast data, calculate the forecast error, and adjust the model parameters or select other models based on the evaluation results to improve the accuracy of the forecast.

[0103] Step S5: Based on the short-term communication demand trend forecast data of different regions, the low-altitude relay communication network is dynamically deployed in the UAV region, and the spatial hierarchical deployment is optimized to construct a dynamic spatial hierarchical deployment strategy;

[0104] In this embodiment, the short-term communication demand trend forecast data obtained from the previous step is analyzed to determine the communication demand intensity and change trend of different regions. The demand data is visualized to highlight high-demand areas and low-demand areas for decision-making. A regional deployment strategy for drones is formulated based on the short-term communication demand of the region, and factors including demand intensity, coverage of the drone, flight altitude and energy consumption are considered. The number and type of drones (such as fixed-wing drones or multi-rotor drones) are determined to ensure that the communication needs of different regions can be met. In the determined high-demand areas, drones are dynamically deployed, and a drone management system or scheduling software is used to implement the deployment plan to ensure that the drones can quickly respond to changing needs and automatically adjust their flight paths and positions. A spatial hierarchical structure is designed based on the communication needs of the region and the technical characteristics of the drone, and factors including regional terrain, communication signal coverage, and The flight altitude of the drone is used to divide each area into different levels (such as high-demand layer, medium-demand layer, and low-demand layer) according to demand, which provides a basis for subsequent optimization. For each spatial level, an optimization strategy is designed to ensure that there are enough drones to cover the high-demand area. The energy efficiency, communication quality, and response speed of the drone are considered to ensure good coordination between deployment plans at different levels. The dynamic spatial hierarchical deployment strategy is calculated using optimization algorithms (such as linear programming, genetic algorithms, or simulated annealing) to ensure optimal deployment under limited resources. The key decisions and results of the optimization process are recorded to ensure that the deployment plan can flexibly respond to changes in demand. After implementing the spatial hierarchical deployment strategy, the communication status and user feedback of the drone are continuously monitored, real-time data is collected, and the deployment and flight strategies of the drone are dynamically adjusted based on the monitoring results to optimize the overall network performance.

[0105] Step S6: Make multi-strategy collaborative control decisions on the dynamic spatial hierarchical deployment strategy, the optimal channel scheduling strategy and the dynamic low-altitude adaptive communication protocol, and perform cloud migration and enhancement to obtain a cloud-based low-altitude communication optimization model.

[0106] In this embodiment, the information of dynamic spatial hierarchical deployment strategy, optimal channel scheduling strategy and dynamic low-altitude adaptive communication protocol is collected and integrated, the input parameters, output results and their mutual relationships of each strategy are determined, so as to carry out effective collaborative control, design a multi-strategy collaborative control model, select a suitable method to achieve coordination between strategies, such as fuzzy logic control, hierarchical analysis method (AHP) or reinforcement learning algorithm, define the input variables of the model, including real-time network status, user needs, environmental changes, etc., and the output variable is the comprehensive decision result of each strategy. Under the guidance of the collaborative control model, the effect of each strategy is comprehensively evaluated, and dynamic adjustments are made to generate the final multi-strategy collaborative control decision, record the decision-making process and results to ensure the transparency and traceability of the decision, and design a cloud system architecture to ensure that it can support the implementation of multi-strategy collaborative control decisions, including data storage, computing resources and network. In terms of network security, select appropriate cloud service platforms (such as AWS, Azure) to build infrastructure to meet the needs of system operation, formulate cloud migration strategies, determine the data types, model parameters and computing tasks that need to be migrated, ensure data consistency and integrity during the migration process, consider the characteristics of the cloud environment, adjust model parameters to adapt to the characteristics of cloud computing resources, migrate the integrated multi-strategy collaborative control decision model to the cloud, use cloud computing resources for reinforcement learning training and model optimization, run the optimization model in the cloud environment, continuously adjust and improve model performance through real-time data feedback, evaluate the cloud low-altitude communication optimization model, test its performance and effects in different environments, including indicators such as response time, resource utilization and user satisfaction, and further optimize and adjust according to the evaluation results to ensure that the model can provide effective support in practical applications.

[0107] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0108] Step S11: monitoring the UAV communication relay nodes in the low-altitude area;

[0109] Step S12: performing spatial position calculation on each of the drone communication relay nodes to obtain the spatial position coordinates of each relay node;

[0110] Step S13: performing spatial distribution analysis on the spatial position coordinates of each relay node to generate relay node spatial distribution data;

[0111] Step S14: performing inter-node topological connection evolution on the relay node spatial distribution data to obtain the relay node topological logic;

[0112] Step S15: Based on the relay node topology logic, a distributed relay topology is fitted for the drone communication relay nodes to build a low-altitude relay communication network.

[0113] In this embodiment, monitoring equipment, such as ground base stations, sensor networks or mobile monitoring platforms, are deployed in the target area to collect data from the UAV communication relay nodes in real time, and appropriate equipment types and technologies (such as RF monitoring, GPS positioning, UAV built-in sensors, etc.) are selected to ensure that the signals of the relay nodes can be captured. A data collection strategy is implemented to monitor the status of the UAV relay nodes regularly or in real time, including signal strength, communication quality and node activity status. The collected data is transmitted to a central database through a wireless network for subsequent analysis. The monitoring equipment is integrated with the data processing system to ensure that the data can be automatically processed and stored. An alarm mechanism is set to monitor the failure or abnormal status of the node in real time to ensure the reliability of the communication link. A suitable positioning method is selected, such as GPS positioning, triangulation, signal strength indication or multi-point positioning algorithm (such as TOA, TDOA, etc.). Environmental factors such as the impact of buildings and trees on signal propagation are considered, and appropriate technologies and algorithms are selected. The deployed positioning equipment is used to calculate the spatial position of the UAV communication relay nodes one by one. According to the monitoring data, the three-dimensional coordinates (X, Y,Z), record the positioning results in the database to form a spatial location data set of relay nodes, verify the positioning results to ensure the accuracy and reliability of the calculation, and organize the spatial location coordinate data of each relay node to form a structured data set by cross-validating the results of different positioning algorithms or comparing them with known coordinates to ensure a unified data format and easy analysis. Use spatial analysis tools (such as GIS software, Python's Geopandas, Matplotlib) to analyze the spatial location of relay nodes, and use cluster analysis (such as K-means, DBSCAN) or spatial distribution models (such as Voronoi Analyze the distribution of relay nodes, identify the clustering areas and coverage of nodes, visualize the analysis results, generate a spatial distribution map of relay nodes, help identify the effective areas and potential blind spots of communication coverage, define the connection rules between relay nodes, such as determining the connection relationship based on factors such as signal strength, distance between nodes or network quality, determine the topological structure type, such as star, ring, mesh, etc., to adapt to different communication needs, implement the topological connection evolution algorithm, generate the topological logic of relay nodes based on spatial distribution data, use graph theory methods or network optimization algorithms (such as minimum spanning tree algorithm) to record the connection status and adjacent node information of each node, form a topological structure data set of relay nodes, verify the generated topological structure to ensure that it can support efficient communication links, make necessary adjustments and optimizations according to the actual network performance and data transmission requirements, select suitable topological fitting methods, such as distributed algorithms, simulated annealing, genetic algorithms, etc., to optimize the connection mode and network performance of relay nodes, implement the construction of distributed relay topology according to the generated relay node topological logic, ensure that each node can operate independently in the network and communicate effectively with other nodes, and use software defined networking (SDN) Technology, dynamically adjust the connection strategy of relay nodes to cope with network changes and load adjustments, evaluate the performance of the constructed relay communication network, including indicators such as delay, bandwidth, and connection stability, use network simulation tools (such as NS-3, OMNeT++) for simulation analysis, and make necessary optimizations based on the evaluation results to ensure the efficiency and stability of the low-altitude relay communication network.

[0114] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0115] Step S21: performing real-time information interaction with a ground server based on a low-altitude relay communication network to collect low-altitude real-time interaction information;

[0116] Step S22: Calculate the communication link bandwidth of the low-altitude real-time interactive information and identify the low-altitude interactive communication link bandwidth data;

[0117] Step S23: analyzing the UAV-to-ground interaction delay based on the low-altitude interactive communication link bandwidth data, thereby obtaining the UAV-to-ground interaction delay parameter;

[0118] Step S24: Dynamically modulate the communication protocol according to the ground interaction delay parameter to construct a dynamic low-altitude adaptive communication protocol.

[0119] In this embodiment, a communication architecture is established between the low-altitude relay communication network and the ground server to ensure the stability of the network and the reliability of data transmission. An appropriate communication protocol (such as MQTT, HTTP, WebSocket) is selected to support real-time data interaction. An information interaction mechanism is designed, including data types (such as sensor data, status information, control instructions) and interaction frequency (for example, every second, every minute). Two-way communication is established between the UAV and the ground server to ensure the upload and download of real-time information. A data acquisition module is deployed on the UAV to collect flight status, environmental information, sensor data, etc. in real time, and the collected data is transmitted to the ground server through the low-altitude relay communication network. , ensure the timeliness and completeness of information, determine the method of calculating link bandwidth, use network throughput test, packet size and transmission time analysis, etc., select appropriate tools or algorithms (such as iperf, Wireshark) for bandwidth testing and analysis, and regularly monitor the bandwidth usage of communication links during data interaction, record bandwidth data for each time period, calculate the bandwidth consumption of each data transmission, identify the maximum available bandwidth and current bandwidth usage of the link, store the calculated bandwidth data in the database, form a data set of bandwidth usage, perform bandwidth analysis, identify bottlenecks and optimization points, provide a basis for subsequent delay analysis, and select a delay analysis method. , such as round-trip time (RTT) measurement, delay distribution analysis, etc., use timestamps to record the sending and receiving times to calculate the delay, set appropriate test conditions, ensure the accuracy of the test results, establish a regular delay measurement mechanism between the drone and the ground server, such as sending a test package every minute, record the sending and receiving time of each test package, calculate the delay of each interaction, analyze the collected delay data, including the average delay, maximum delay and delay change trend, identify the factors that affect the interaction delay based on the analysis results, such as bandwidth fluctuations, network congestion, etc., design a dynamic communication protocol modulation strategy based on the ground interaction delay parameters to optimize data transmission efficiency and delay Delay, adopt adaptive modulation technology (such as QAM, PSK) or delay-based protocol adjustment, determine the protocol parameters and adjustment rules within different delay ranges, ensure that the system can effectively adjust according to real-time data, integrate the designed dynamic communication protocol into the communication system of the UAV and the ground server, ensure that the parameters can be adjusted in real time during the data transmission process, ensure that all UAVs can adapt to the new communication protocol settings through software updates or remote configuration, test the performance of the dynamic low-altitude adaptive communication protocol in a real environment, evaluate its effectiveness and stability under different delay conditions, make necessary optimization according to the test results, and adjust the protocol parameters to improve the overall communication quality and efficiency.

[0120] In this embodiment, the specific steps of step S24 are:

[0121] Perform semantic analysis on low-altitude real-time interactive information and generate semantic features of interactive information;

[0122] Perform information segmentation based on the semantic features of interactive information to generate multiple segments of interactive information;

[0123] Performing lossless data compression encoding on multiple segments of interactive information, thereby obtaining multiple compressed encoding packets;

[0124] Identify the application scenarios of low-altitude real-time interactive information in real time, so as to obtain the application scenarios of the current interactive information;

[0125] Performing data transmission priority analysis on multiple compressed coded packets according to the application scenario of the current interactive information, thereby obtaining the transmission priority of each coded packet;

[0126] Dynamically allocate bandwidth for each coding packet according to the ground interaction delay parameter, thereby generating dynamic bandwidth allocation data for each coding table;

[0127] Dynamically modulating the dynamic bandwidth allocation data of each coding table to generate a low-altitude network dynamic communication protocol;

[0128] Perform interactive information transmission simulation according to the low-altitude network dynamic communication protocol to obtain transmission simulation response data;

[0129] Calculating the data transmission packet loss rate of the transmission simulation response data, thereby generating a simulation protocol transmission packet loss rate;

[0130] Based on the simulated protocol transmission packet loss rate, the low-altitude network dynamic communication protocol is end-to-end adaptively optimized to construct a dynamic low-altitude adaptive communication protocol.

[0131] In this embodiment, low-altitude real-time interactive information is collected to ensure the integrity and accuracy of the data. Data cleaning is performed to remove invalid or redundant information. The information is formatted to ensure that the information structure is unified for subsequent analysis. Appropriate natural language processing (NLP) technologies and algorithms (such as word embedding, syntactic analysis, and named entity recognition) are selected to parse the semantics of interactive information. Existing NLP libraries (such as SpaCy, NLTK, or Hugging Face Transformers) are used for information analysis. The parsed information is converted into a semantic feature vector, and key information, themes, emotions, and other features are recorded to form a "semantic feature of interactive information" data set. According to the semantic features of the interactive information, an information segmentation strategy is designed. Determine the basis for segmentation, such as sentences, themes, timestamps, etc. Set the length or format of each information segment to ensure that the segmented information remains complete and coherent. Use string processing and text analysis tools to segment the interactive information according to the set strategy to generate multiple information segments. Number and mark each generated segment of information for subsequent processing and analysis. Select a suitable lossless compression algorithm (such as Gzip, LZ77, Huffman coding) to process multiple segments of interactive information. Ensure that the selected algorithm can effectively reduce the data size without losing information content. Apply the selected compression algorithm to each information segment to generate multiple compressed code packets. Record the size and compression efficiency of each code packet to ensure data recoverability. Define application scenarios, such as monitoring, navigation, data transmission, etc., and determine the characteristics and requirements of each scenario. Collect contextual information related to the scenario to support the identification process. Use machine learning or pattern recognition technology (such as decision trees, SVM, neural networks) to identify application scenarios for real-time interactive information. Match the identification results with real-time information to obtain the application scenario of the current interactive information. Set the criteria for transmission priority analysis based on the urgency of the application scenario, data importance, and real-time requirements. Determine the priority level (such as high, medium, low) and its corresponding transmission strategy. Evaluate the priority of multiple compressed code packets and generate the transmission priority of each code packet based on the set analysis criteria. Design a dynamic bandwidth allocation model to determine the bandwidth allocation strategy based on the ground interaction delay parameter and transmission priority. Consider bandwidth availability and network conditions to optimize bandwidth utilization. Based on the model calculation results, dynamically allocate bandwidth for each code packet and record the bandwidth allocation data for each code packet. Select the dynamic communication protocol modulation method, such as QAM, PSK, etc., and modulate according to bandwidth allocation and priority.

[0132] Set modulation parameters, such as modulation rate and encoding method, to ensure adaptation to different network conditions. Integrate dynamic modulation parameters into the communication system to realize the construction of dynamic communication protocols for low-altitude networks. Build a low-altitude network model in a simulation environment to simulate the transmission process of interactive information. Use network simulation tools (such as NS-3 and OMNeT++) to simulate communication scenarios. During the simulation, record the response data of the transmission, including indicators such as latency, bandwidth utilization, and data integrity. Set the standard for calculating the packet loss rate, which is usually the ratio of the number of lost packets to the number of sent packets. Extract relevant data from the simulation results for statistics and analysis. Calculate the transmission packet loss rate of the simulation protocol based on the recorded transmission response data and generate a packet loss rate report. Design an adaptive optimization strategy based on the simulated packet loss rate data and adjust the communication protocol parameters to reduce packet loss. Set optimization goals, such as improving the success rate of data transmission and reducing latency. Perform end-to-end adaptive optimization on the dynamic low-altitude adaptive communication protocol to ensure that it can maintain high performance under different network conditions.

[0133] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0134] Step S31: identifying the real-time communication status parameters of the relay node based on the low-altitude relay communication network;

[0135] Step S32: performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes to obtain a plurality of communication channel data;

[0136] Step S33: calculating the link quality between nodes for the plurality of communication channel data, thereby generating the communication link quality data between the plurality of nodes;

[0137] Step S34: performing communication quality time series variation analysis on the communication link quality data between the multiple nodes to generate communication quality time series variation data;

[0138] Step S35: Make an optimal channel scheduling decision based on the communication quality time series change data and construct an optimal channel scheduling strategy.

[0139] In this embodiment, monitoring equipment is deployed in the low-altitude relay communication network to collect communication status parameters of each relay node in real time, such as signal strength, data transmission rate, delay and packet loss rate, etc., and sensors and monitoring software (such as SNMP, NetFlow) are used to perform status monitoring to ensure the real-time and accuracy of the data. The collected communication status parameters are integrated into a central database to form a real-time communication status data set of the relay node, ensuring that the data format is unified to facilitate subsequent analysis and processing, and selecting a suitable channel identification method, including channel detection and analysis algorithms, using spectrum monitoring technology to identify the communication channels between each relay node, using software defined radio (SDR) technology to perform channel detection to obtain real-time channel information, performing channel identification on each pair of relay nodes, recording the communication channel data between them, such as frequency, bandwidth and modulation mode, etc., storing the channel data in a database, generating multiple communication channel data sets, determining the calculation indicators of link quality, such as signal-to-noise ratio (SNR), bit error rate (BER), delay and packet loss rate, etc., and selecting a suitable calculation model or formula to evaluate the link quality, such as using Shannon Theorem calculates the maximum available bandwidth. Based on the acquired communication channel data, calculates the quality of the link between each relay node to generate communication link quality data. Stores the evaluation results in a database to form a link quality data set for subsequent analysis. Sorts the collected link quality data and ensures that the data is arranged in chronological order for timing analysis. Generates a timestamp to mark the collection time of each link quality data. Selects a timing analysis method, such as time series analysis, moving average method, or exponential smoothing method, to analyze the changing trend of link quality. Uses data analysis tools (such as Python's Pandas, SciPy, or R Through the analysis, the time-series change data of link quality are generated, including information such as change trend, periodicity and mutation point. The results are recorded in the database to provide a basis for subsequent scheduling decisions. According to the communication quality time-series change data, the optimal channel scheduling strategy is designed to ensure that the network selects the best channel under different conditions. Factors to be considered include link quality, network load and delay, etc., in order to formulate dynamic scheduling strategies and develop scheduling algorithms. Greedy algorithms, genetic algorithms or reinforcement learning methods are used to dynamically adjust channel allocation according to real-time data. The formulated scheduling strategies are applied to low-altitude relay communication networks, and the channel usage of relay nodes is adjusted in real time. The communication effect after implementation is continuously monitored, the scheduling strategies are evaluated, necessary adjustments and optimizations are made according to the actual effects, and a feedback mechanism is established to continuously improve the channel scheduling strategies according to network status and user needs.

[0140] In this embodiment, step S4 includes the following steps:

[0141] Step S41: monitoring the current user communication data of the ground server;

[0142] Step S42: Calculating the server communication load on the current user communication data to generate current server communication load data;

[0143] Step S43: performing regional load distribution analysis on the current server communication load data to obtain communication load data of different regions;

[0144] Step S44: Calculating user communication demands for communication load data of different regions, thereby generating communication demand data of different regions;

[0145] Step S45: predicting the trend of short-term communication demand based on the communication demand data of different regions to obtain the short-term communication demand trend prediction data of different regions.

[0146] In this embodiment, a data collection module is deployed on the ground server to monitor the user's communication activities in real time, including login, data transmission, request processing, etc., use network monitoring tools (such as Wireshark, NetFlow) to capture and record communication data, set the data collection frequency to ensure real-time performance, record the communication data of each user, such as data packet size, transmission rate, connection time, etc., store the collected data in a database to ensure data integrity and traceability, define the calculation index of the communication load, such as the amount of data transmitted per second (bps), the number of user connections, request processing time, etc., select a suitable calculation method, such as the accumulation method or the average method, so as to evaluate the communication load, calculate the communication load of the server by analyzing the current user communication data in real time, generate the current server communication load data, and record it in the database to provide basic data for subsequent analysis, divide the service area of ​​the ground server into regions according to geographical location, network topology or user distribution, define the boundaries and characteristics of each region for load analysis, calculate the communication load of each region according to the communication data and load data of users in different regions, and use data analysis tools (such as Python's Pandas, R Language) to aggregate and analyze load data to generate regional load distribution data; use visualization tools (such as Matplotlib and Tableau) to visualize communication load data in different regions to help identify high-load areas; design a user communication demand calculation model, considering factors including user activity, historical communication data, and service type; select an appropriate calculation method, such as a model based on statistical analysis or a machine learning model; analyze user communication data in each region to calculate communication demand data in different regions; record communication demand data in each region to form a regional communication demand data set; select a method for short-term communication demand trend prediction, such as time series analysis, linear regression, or machine learning algorithms (such as ARIMA and LSTM); determine the historical data window to be used for trend prediction; use the calculated communication demand data in different regions to apply the selected prediction algorithm to predict short-term communication demand trends; generate short-term communication demand trend prediction data for different regions and store the results in a database; analyze the prediction results to identify potential high-demand periods and regions; adjust resource allocation in a timely manner to meet communication needs; formulate corresponding response strategies based on the prediction results to ensure service quality during high-demand periods.

[0147] In this embodiment, step S5 includes the following steps:

[0148] Step S51: Obtaining meteorological monitoring parameters of the drone area;

[0149] Step S52: performing real-time meteorological characteristic analysis on the meteorological monitoring parameters to obtain regional real-time meteorological characteristics;

[0150] Step S53: evaluating the impact of UAV communication performance according to the real-time meteorological characteristics of the region to obtain communication performance impact data;

[0151] Step S54: performing dynamic regional deployment of drones on the low-altitude relay communication network based on the short-term communication demand trend forecast data of different regions to obtain dynamic regional deployment data;

[0152] Step S55: Perform spatial hierarchical deployment optimization on the dynamic regional deployment data according to the communication performance impact data, and construct a dynamic spatial hierarchical deployment strategy.

[0153] In this embodiment, meteorological monitoring equipment is deployed in the drone activity area, such as a weather station, a sensor network or a drone's own meteorological sensor to monitor parameters such as temperature, humidity, wind speed, and air pressure, to ensure that the equipment can collect data in real time with high accuracy and stability, and the collected meteorological monitoring parameters are transmitted to the central database in real time through a wireless network to ensure the real-time and integrity of the data. The data is stored in an appropriate data format (such as JSON, CSV) for subsequent analysis, and a suitable meteorological feature analysis method is selected, including statistical analysis, machine learning models or rule-based analysis methods, to determine the meteorological features to be extracted, such as temperature change trends, wind speed ranges, humidity fluctuations, etc. The acquired meteorological monitoring parameters are analyzed in real time to calculate the meteorological feature data of the area, and data analysis tools (such as Python) are used to analyze the meteorological monitoring parameters. Pandas, NumPy) to process the data, generate regional real-time meteorological characteristics, record the analysis results in the database, and generate visual reports (such as charts and dashboards) for easy understanding and sharing. Determine the meteorological factors that affect the communication performance of the drone, such as rainfall, wind speed, temperature, etc., set relevant evaluation indicators, design an evaluation model, and consider the relationship between meteorological characteristics and communication performance. For example, use regression analysis or machine learning models to evaluate the impact of regional real-time meteorological characteristics on the communication performance of the drone, calculate communication performance impact data, such as signal attenuation, delay changes, etc., record the evaluation results in the database, and form a communication performance impact report. Use the existing short-term communication demand trend forecast data to analyze the changes in communication demand in different regions in the future time period, select appropriate forecasting methods, such as time series analysis or machine learning algorithms, and generate trend data.

[0154] According to the trend of short-term communication demand and meteorological impact assessment, a dynamic UAV regional deployment strategy is formulated to ensure that enough UAVs are deployed in high-demand areas. Dynamic regional deployment data is generated, and the deployment plan and number of UAVs in each area are recorded. According to the communication performance impact data and dynamic regional deployment data, a spatial layered deployment strategy is designed to ensure that the deployment in different areas is targeted. Factors to be considered include meteorological conditions, communication needs, terrain features, etc. The spatial hierarchy is divided, and the deployment strategy is calculated using optimization algorithms (such as linear programming and genetic algorithms) to ensure optimal deployment under limited resources. The effects of different schemes are evaluated, and the best spatial layered deployment scheme is selected. The optimized dynamic spatial layered deployment strategy is recorded in the database, and a specific execution plan is formulated. In actual operation, the flight path and working mode of the UAV are adjusted according to the deployment strategy to ensure the best communication performance.

[0155] In this embodiment, the specific steps of step S55 are:

[0156] Identify the coordinates of the deployed drone’s location based on dynamic area deployment data;

[0157] Performing meteorological interference analysis on the position coordinates of the deployed UAVs based on regional real-time meteorological characteristics to obtain meteorological interference data for each UAV position;

[0158] Perform optimal flight altitude analysis based on the weather interference data and communication performance impact data of each UAV location to generate the optimal flight altitude for each UAV;

[0159] The UAV movement path is planned based on the position coordinates of the deployed UAV, thereby generating a dynamic deployment movement path;

[0160] The optimal flight altitude and dynamic deployment movement path of each UAV are optimized for spatial hierarchical deployment, and a dynamic spatial hierarchical deployment strategy is constructed.

[0161] In this embodiment, the position coordinates of the deployed drones are extracted from the dynamic area deployment data, including the latitude, longitude and altitude information of each drone, to ensure the integrity and accuracy of the deployment data for subsequent analysis.

[0162] The identified drone position coordinates are stored in the database to form a drone position coordinate data set, ensuring that the data structure is standardized to facilitate subsequent processing and analysis. Real-time meteorological feature data related to the drone position coordinates, such as wind speed, temperature, humidity, and precipitation, are collected, and meteorological data and drone position coordinates are associated for subsequent analysis. A meteorological interference analysis model is designed to determine the meteorological factors that affect the drone communication performance, such as the impact of wind speed on flight stability and the impact of rainfall on signal attenuation. Appropriate analysis methods, such as multivariate regression analysis or machine learning algorithms (such as random forests and support vector machines) are selected to predict meteorological interference. Meteorological interference analysis is performed on each drone position, meteorological interference data is calculated, and the communication conditions at each position are evaluated. The analysis results are used as input to the model. The results are recorded in the database to form a meteorological interference data set. The meteorological interference data and corresponding communication performance impact data (such as signal strength, delay, packet loss rate, etc.) of each UAV position are collected to ensure the comprehensiveness of the data for comprehensive analysis. The optimal flight altitude analysis model is designed to determine the relationship between communication performance and flight altitude. The impact of meteorological factors on flight altitude selection is considered. Appropriate optimization algorithms, such as genetic algorithms and particle swarm optimization, are selected to calculate the optimal flight altitude. Based on the meteorological interference data and communication performance impact data, the optimal flight altitude of each UAV position is calculated to generate the optimal flight altitude of each UAV. The results are recorded in the database to ensure that the flight altitude of each UAV matches its position. Appropriate path planning algorithms, such as A*, are selected. The algorithm, Dijkstra algorithm or graph-based search algorithm is used to plan the movement path of the UAV, considering the flight altitude, meteorological interference, obstacles and other factors of the UAV. According to the current position and target position of the UAV, the selected path planning algorithm is used to generate a dynamic deployment movement path to ensure the effectiveness of the path planning and avoid obstacles and high meteorological interference areas. The generated dynamic movement path is recorded in the database to form a path data set, and optimized and adjusted according to real-time feedback. According to the optimal flight altitude and dynamic movement path of each UAV, a spatial hierarchical deployment strategy is designed to ensure that UAVs at different altitudes effectively cover different areas. Considering meteorological factors, communication requirements and flight stability, the spatial hierarchy is divided, and the spatial hierarchical deployment strategy is calculated using optimization algorithms (such as linear programming and simulated annealing) to ensure the optimal deployment under limited resources. The effects of different schemes are evaluated and the best spatial hierarchical deployment scheme is selected. The optimized dynamic spatial hierarchical deployment strategy is recorded in the database, and a specific execution plan is formulated. In actual operation, the flight path and working mode of the UAV are adjusted according to the deployment strategy to ensure the best communication performance and coverage effect.

[0163] In this embodiment, the specific steps of step S6 are:

[0164] Step S61: performing UAV communication coordination optimization on the dynamic space hierarchical deployment strategy to generate a UAV coordination optimization deployment strategy;

[0165] Step S62: Perform multi-strategy collaborative control decisions on the coordinated optimization deployment strategy of the UAV, the optimal channel scheduling strategy, and the dynamic low-altitude adaptive communication protocol to build an intelligent UAV communication optimization strategy;

[0166] Step S63: Perform cloud migration and enhancement on the intelligent UAV communication optimization strategy, thereby obtaining a cloud-based low-altitude communication optimization model.

[0167] In this embodiment, the goal of coordinated optimization of UAV communication is determined, such as maximizing signal coverage, minimizing communication delay, reducing energy consumption, etc., and relevant data such as the location, flight altitude, channel usage, etc. of the UAV are collected for optimization analysis, and a suitable optimization algorithm is selected, such as genetic algorithm, particle swarm optimization or reinforcement learning, to achieve coordinated optimization between UAVs, and a fitness function is set to evaluate the effects of different coordination strategies. Based on the selected algorithm, the flight path, channel allocation and communication parameters of the UAV are coordinated and optimized, and a coordinated optimization deployment strategy for UAVs is generated. The optimization results are recorded, and it is ensured that the coordinated behavior of each UAV can effectively improve the overall communication performance. The coordinated optimization deployment strategy, optimal channel scheduling strategy and dynamic low-altitude adaptive communication protocol of UAVs are collected and integrated to form a multi-strategy collaborative control framework, the relationship and influence between different strategies are determined, the criteria for collaborative control decisions are defined, an intelligent decision model is designed, and a suitable method (such as fuzzy logic, decision tree, reinforcement learning) is selected to achieve multi-strategy collaborative control decisions, and the input and Output variables, including drone status, environmental changes, communication quality, etc. Under the guidance of the intelligent decision-making model, a comprehensive evaluation of multiple strategies is carried out, and an intelligent drone communication optimization strategy is generated. The decision-making process and results are recorded for subsequent analysis and optimization. The cloud system architecture is designed to ensure that it can support the migration of the low-altitude communication optimization model. Considering factors such as data storage, computing power and network security, appropriate cloud service platforms (such as AWS, Azure) are selected for infrastructure construction, cloud migration strategies are formulated, and the data types, model parameters and computing tasks to be migrated are determined to ensure the consistency and integrity of data during the migration process to avoid data loss or errors. The intelligent drone communication optimization strategy is migrated to the cloud, and reinforcement learning training and model optimization are carried out. The optimization model is run in the cloud environment, and the performance of the model is continuously adjusted and improved through real-time data feedback. The cloud low-altitude communication optimization model is evaluated, and its performance and effects in different environments are tested. Further optimization and adjustment are carried out based on the evaluation results to ensure that the model can provide effective support in practical applications.

[0168] In this embodiment, a server communication optimization system based on low-altitude economy is provided, which is used to execute the server communication optimization method based on low-altitude economy as described above, including:

[0169] The communication network module is used to monitor the UAV communication relay nodes in the low-altitude area; perform spatial location calculations on the UAV communication relay nodes one by one, and perform distributed relay topology fitting to build a low-altitude relay communication network;

[0170] The protocol modulation module is used to collect low-altitude real-time interactive information based on the low-altitude relay communication network; calculate the communication link bandwidth of the low-altitude real-time interactive information, and perform dynamic communication protocol modulation to build a dynamic low-altitude adaptive communication protocol;

[0171] The optimal channel scheduling module is used to identify the real-time communication state parameters of the relay nodes based on the low-altitude relay communication network; identify the communication channels between nodes based on the real-time communication state parameters of the relay nodes, make optimal channel scheduling decisions, and construct the optimal channel scheduling strategy;

[0172] The demand trend prediction module is used to monitor the current user communication data of the ground server; calculate the server communication load based on the current user communication data, and predict the short-term communication demand trend to obtain the short-term communication demand trend prediction data for different regions;

[0173] Dynamic hierarchical deployment module, which is used to dynamically deploy drones in low-altitude relay communication networks based on short-term communication demand trend forecast data in different regions, optimize spatial hierarchical deployment, and build a dynamic spatial hierarchical deployment strategy;

[0174] The collaborative control module is used to make multi-strategy collaborative control decisions on dynamic spatial layered deployment strategies, optimal channel scheduling strategies, and dynamic low-altitude adaptive communication protocols, and to perform cloud migration and enhancement to obtain a cloud-based low-altitude communication optimization model.

[0175] The present invention helps to track the status of the communication network in a timely manner by real-time monitoring of the UAV communication relay nodes, ensuring the continuity and stability of the communication. The spatial position positioning calculation of each node and the distributed relay topology fitting can build an efficient low-altitude relay communication network, improve the communication coverage and reliability, and collect real-time interactive information based on the low-altitude relay communication network to achieve real-time monitoring and response to the low-altitude communication environment. Dynamic communication protocol modulation helps to adjust the communication protocol according to real-time communication needs, improve communication efficiency and adaptability, identify the real-time communication status parameters of the relay node and perform optimal channel scheduling, which can optimize the communication link quality and improve data transmission efficiency. The optimal channel scheduling strategy is constructed to ensure the effective use of the communication channel, reduce communication interference, improve the performance of the communication system, and monitor the ground The user communication data and predicted short-term communication demand trends of the server are helpful for rationally planning communication resources and improving the quality and efficiency of communication services. The communication demand trend forecast data of different regions provide an important reference for subsequent communication deployment and optimization. Dynamic UAV regional deployment and spatial layered deployment optimization based on communication demand trend forecast data can realize flexible configuration of communication resources and meet the communication needs of different regions. The construction of dynamic spatial layered deployment strategy can help improve communication coverage and communication quality and optimize the performance of the communication system. Multi-strategy collaborative control decision-making comprehensively considers deployment strategy, channel scheduling and communication protocol, optimizes the overall performance of the communication system, and cloud migration strengthens the real-time update and management of communication optimization models, thereby improving the intelligence level and flexibility of the communication system.

[0176] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0177] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A server communication optimization method based on low-altitude economy, characterized in that: The following steps are involved: Step S1: monitoring the UAV communication relay nodes in the low-altitude area; performing spatial location calculations on the UAV communication relay nodes one by one, and performing distributed relay topology fitting to build a low-altitude relay communication network; Step S2: collecting low-altitude real-time interaction information based on the low-altitude relay communication network; Calculate the communication link bandwidth for low-altitude real-time interactive information, perform dynamic communication protocol modulation, and build a dynamic low-altitude adaptive communication protocol; Step S3: identifying the real-time communication state parameters of the relay nodes based on the low-altitude relay communication network; performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes, making an optimal channel scheduling decision, and constructing an optimal channel scheduling strategy; Step S4: monitoring the current user communication data of the ground server; calculating the server communication load based on the current user communication data, and predicting the trend of short-term communication demand, to obtain the short-term communication demand trend prediction data of different regions; Step S5: Based on the short-term communication demand trend forecast data of different regions, the low-altitude relay communication network is dynamically deployed in the UAV region, and the spatial hierarchical deployment is optimized to construct a dynamic spatial hierarchical deployment strategy; Step S6: Make multi-strategy collaborative control decisions on the dynamic spatial hierarchical deployment strategy, the optimal channel scheduling strategy and the dynamic low-altitude adaptive communication protocol, and perform cloud migration and enhancement to obtain a cloud-based low-altitude communication optimization model.

2. The server communication optimization method based on low-altitude economy according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: monitoring the UAV communication relay nodes in the low-altitude area; Step S12: performing spatial position calculation on each of the drone communication relay nodes to obtain the spatial position coordinates of each relay node; Step S13: performing spatial distribution analysis on the spatial position coordinates of each relay node to generate relay node spatial distribution data; Step S14: performing inter-node topological connection evolution on the relay node spatial distribution data to obtain the relay node topological logic; Step S15: Based on the relay node topology logic, a distributed relay topology is fitted for the drone communication relay nodes to build a low-altitude relay communication network.

3. The server communication optimization method based on low-altitude economy according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: performing real-time information interaction with a ground server based on a low-altitude relay communication network to collect low-altitude real-time interaction information; Step S22: Calculate the communication link bandwidth of the low-altitude real-time interactive information and identify the low-altitude interactive communication link bandwidth data; Step S23: analyzing the UAV-to-ground interaction delay based on the low-altitude interactive communication link bandwidth data, thereby obtaining the UAV-to-ground interaction delay parameter; Step S24: Dynamically modulate the communication protocol according to the ground interaction delay parameter to construct a dynamic low-altitude adaptive communication protocol.

4. The server communication optimization method based on low-altitude economy according to claim 3 is characterized in that: The specific steps of step S24 are: Perform semantic analysis on low-altitude real-time interactive information and generate semantic features of interactive information; Perform information segmentation based on the semantic features of interactive information to generate multiple segments of interactive information; Performing lossless data compression encoding on multiple segments of interactive information, thereby obtaining multiple compressed encoding packets; Identify the application scenarios of low-altitude real-time interactive information in real time, so as to obtain the application scenarios of the current interactive information; Performing data transmission priority analysis on multiple compressed coded packets according to the application scenario of the current interactive information, thereby obtaining the transmission priority of each coded packet; Dynamically allocate bandwidth for each coding packet according to the ground interaction delay parameter, thereby generating dynamic bandwidth allocation data for each coding table; Dynamically modulating the dynamic bandwidth allocation data of each coding table to generate a low-altitude network dynamic communication protocol; Perform interactive information transmission simulation according to the low-altitude network dynamic communication protocol to obtain transmission simulation response data; Calculating the data transmission packet loss rate of the transmission simulation response data, thereby generating a simulation protocol transmission packet loss rate; Based on the simulated protocol transmission packet loss rate, the low-altitude network dynamic communication protocol is end-to-end adaptively optimized to construct a dynamic low-altitude adaptive communication protocol.

5. The server communication optimization method based on low-altitude economy according to claim 1 is characterized in that: The specific steps of step S3 are: Step S31: identifying the real-time communication status parameters of the relay node based on the low-altitude relay communication network; Step S32: performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes to obtain a plurality of communication channel data; Step S33: calculating the link quality between nodes for the plurality of communication channel data, thereby generating the communication link quality data between the plurality of nodes; Step S34: performing communication quality time series variation analysis on the communication link quality data between the multiple nodes to generate communication quality time series variation data; Step S35: Make an optimal channel scheduling decision based on the communication quality time series change data and construct an optimal channel scheduling strategy.

6. The server communication optimization method based on low-altitude economy according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41: monitoring the current user communication data of the ground server; Step S42: Calculating the server communication load on the current user communication data to generate current server communication load data; Step S43: performing regional load distribution analysis on the current server communication load data to obtain communication load data of different regions; Step S44: Calculating user communication demands for communication load data of different regions, thereby generating communication demand data of different regions; Step S45: predicting the trend of short-term communication demand based on the communication demand data of different regions to obtain the short-term communication demand trend prediction data of different regions.

7. The server communication optimization method based on low-altitude economy according to claim 1 is characterized in that: The specific steps of step S5 are: Step S51: Obtaining meteorological monitoring parameters of the drone area; Step S52: performing real-time meteorological characteristic analysis on the meteorological monitoring parameters to obtain regional real-time meteorological characteristics; Step S53: evaluating the impact of UAV communication performance according to the real-time meteorological characteristics of the region to obtain communication performance impact data; Step S54: performing dynamic regional deployment of drones on the low-altitude relay communication network based on the short-term communication demand trend forecast data of different regions to obtain dynamic regional deployment data; Step S55: Perform spatial hierarchical deployment optimization on the dynamic regional deployment data according to the communication performance impact data, and construct a dynamic spatial hierarchical deployment strategy.

8. The server communication optimization method based on low-altitude economy according to claim 7 is characterized in that: The specific steps of step S55 are: Identify the coordinates of the deployed drone’s location based on dynamic area deployment data; Performing meteorological interference analysis on the position coordinates of the deployed UAVs based on regional real-time meteorological characteristics to obtain meteorological interference data for each UAV position; Perform optimal flight altitude analysis based on the weather interference data and communication performance impact data of each UAV location to generate the optimal flight altitude for each UAV; The UAV movement path is planned based on the position coordinates of the deployed UAV, thereby generating a dynamic deployment movement path; The optimal flight altitude and dynamic deployment movement path of each UAV are optimized for spatial hierarchical deployment, and a dynamic spatial hierarchical deployment strategy is constructed.

9. The server communication optimization method based on low-altitude economy according to claim 1 is characterized in that: The specific steps of step S6 are: Step S61: performing UAV communication coordination optimization on the dynamic space hierarchical deployment strategy to generate a UAV coordination optimization deployment strategy; Step S62: Perform multi-strategy collaborative control decisions on the coordinated optimization deployment strategy of the UAV, the optimal channel scheduling strategy, and the dynamic low-altitude adaptive communication protocol to build an intelligent UAV communication optimization strategy; Step S63: Perform cloud migration and enhancement on the intelligent UAV communication optimization strategy, thereby obtaining a cloud-based low-altitude communication optimization model.

10. A server communication optimization system based on low-altitude economy, characterized in that: The method for optimizing server communication based on low-altitude economy as claimed in claim 1 comprises: The communication network module is used to monitor the UAV communication relay nodes in the low-altitude area; perform spatial location calculations on the UAV communication relay nodes one by one, and perform distributed relay topology fitting to build a low-altitude relay communication network; The protocol modulation module is used to collect low-altitude real-time interactive information based on the low-altitude relay communication network; calculate the communication link bandwidth of the low-altitude real-time interactive information, and perform dynamic communication protocol modulation to build a dynamic low-altitude adaptive communication protocol; The optimal channel scheduling module is used to identify the real-time communication state parameters of the relay nodes based on the low-altitude relay communication network; identify the communication channels between nodes based on the real-time communication state parameters of the relay nodes, make optimal channel scheduling decisions, and construct the optimal channel scheduling strategy; The demand trend prediction module is used to monitor the current user communication data of the ground server; calculate the server communication load based on the current user communication data, and predict the short-term communication demand trend to obtain the short-term communication demand trend prediction data for different regions; Dynamic hierarchical deployment module, which is used to dynamically deploy drones in low-altitude relay communication networks based on short-term communication demand trend forecast data in different regions, optimize spatial hierarchical deployment, and build a dynamic spatial hierarchical deployment strategy; The collaborative control module is used to make multi-strategy collaborative control decisions on dynamic spatial layered deployment strategies, optimal channel scheduling strategies, and dynamic low-altitude adaptive communication protocols, and to perform cloud migration and enhancement to obtain a cloud-based low-altitude communication optimization model.

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