Network communication method and system for low-altitude field

Abstract

The invention relates to the technical field of wireless communication, in particular to a network communication method and system for the low-altitude field, and the method comprises the following steps: S1, analyzing historical communication data through building a linear regression model, predicting a future communication demand, and building a dynamic scene model through a prediction result; s2, establishing a mobile base station and an intelligent antenna, optimizing base station parameters in real time through a deep learning algorithm, and combining network slicing and frequency spectrum sharing technologies; s3, developing a lightweight hybrid protocol, dynamically optimizing protocol parameters by using AI, and introducing an encryption technology and a block chain technology; s4, constructing a virtual test environment and an automatic tool, simulating a real scene through a digital twinning technology, and quickly verifying and optimizing the performance of the communication system; and S5, deploying an AI-driven intelligent monitoring system to realize adaptive maintenance. By optimizing resource allocation, the communication efficiency and safety are improved, and it is ensured that high-quality service can be stably provided in a complex and changeable low-altitude environment.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and particularly to a network communication method and system for the low-altitude area. Background Art

[0002] Network communication in the low-altitude area refers to the technology and method of providing efficient, reliable, and secure communication services for devices (such as drones, low-altitude aircraft, air sensors, etc.) operating in the low-altitude airspace (usually referring to the area below 1000 meters from the ground). It is an important infrastructure to support the development of the low-altitude economy, aiming to meet the communication needs of low-altitude devices in diverse application scenarios. The low-altitude communication network mainly realizes real-time monitoring and data transmission of low-altitude aircraft and their on-board sensors through wireless networking, satellite relay, and mobile communication technologies. This network not only supports various applications such as low-altitude logistics, inspection, security, and rescue, but also meets the low-altitude drone requirements in different fields through an intelligent interconnected low-altitude digital service system.

[0003] Currently, most network communications in the low-altitude area cannot ensure high-quality services and efficient resource utilization in the complex and changeable low-altitude environment, resulting in insufficient network stability and anti-interference ability, thus causing network congestion and delay, and making the user experience not ideal.

[0004] Based on this, the present invention provides a network communication method and system for the low-altitude area to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a network communication method and system for the low-altitude area to solve the problems raised in the related technologies.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] The present invention provides a network communication method and system for the low-altitude area, including the following steps:

[0008] S1: Analyze historical communication data by establishing a linear regression model to predict future communication demands, establish a dynamic scenario model based on the prediction results to simulate communication demands in different environments, select hybrid communication technologies, and design modular devices;

[0009] S2: Establish a mobile base station and intelligent antennas, optimize the base station parameters in real time through deep learning algorithms to improve network performance, dynamically allocate network resources for different applications to ensure service quality, and combine network slicing and spectrum sharing technologies to achieve efficient and anti-interference low-altitude communication coverage;

[0010] S3: Develop a lightweight hybrid protocol, dynamically optimize protocol parameters using AI, and introduce encryption technology and blockchain technology to ensure communication efficiency and security;

[0011] S4: Build a virtual test environment and automation tools, simulate real scenarios through digital twin technology, and quickly verify and optimize the performance of the communication system;

[0012] S5: Deploy an AI-driven intelligent monitoring system to achieve adaptive maintenance, and at the same time design a flexible network architecture to support rapid expansion and future technology upgrades.

[0013] The historical communication data in S1 includes traffic volume, communication duration, communication frequency, geographical location, and device type;

[0014] In S1, the specific steps of using a linear regression model to analyze historical communication data, predict future communication needs, establish a dynamic scenario model through the prediction results, simulate communication needs in different environments, select hybrid communication technologies, and design modular devices are as follows:

[0015] S1.1: First, collect and preprocess the historical communication data of traffic volume, communication duration, communication frequency, geographical location, and device type to improve data quality and prediction accuracy;

[0016] S1.2: Based on the preprocessed historical communication data, construct a linear regression model;

[0017] S1.3: Use the historical communication data to train the linear regression model, and use the trained linear regression model to predict future communication needs;

[0018] S1.4: According to the prediction results of the linear regression model, establish communication demand models in different environments;

[0019] S1.5: Select suitable communication technologies according to different environmental requirements;

[0020] S1.6: According to the selected hybrid communication technology, design modular devices that support multiple communication technologies;

[0021] The specific formula of the linear regression model in S1.2 is:

[0022] y = β0 + β1x1 + β2x2 +... + β n x n +

[0023] Among them, y is the dependent variable, representing traffic volume and communication duration; X1, X2,....., X n are independent variables, representing the historical data characteristics affecting communication needs, such as communication frequency, geographical location, device type, etc.; β0, β1,…, βn is the regression coefficient; ∈ is the error term;

[0024] The specific operation steps in S2 are as follows:

[0025] S2.1: First, deploy mobile base stations and smart antennas at key locations to ensure that the coverage of the base stations and antennas meets the low-altitude communication requirements;

[0026] S2.2: Use deep learning algorithms to optimize the base station parameters in real time;

[0027] S2.3: Dynamically allocate network resources according to the needs of different applications to ensure service quality and avoid network congestion and latency;

[0028] S2.4: Use network slicing technology to provide customized network services for different applications, formulate spectrum sharing strategies, ensure the efficient use of spectrum resources between different users or systems, and reduce interference;

[0029] S2.5: Continuously monitor the network, and continuously adjust and optimize network slicing, spectrum sharing strategies, and base station parameters according to the monitoring results and changes in user needs;

[0030] The specific steps in S3 are as follows:

[0031] S3.1: Clearly define the communication requirements for the design, combine the advantages of lightweight hybrid protocols, determine the functions, data structures, and communication processes, and implement the protocol efficiently and stably;

[0032] S3.2: Collect communication data for AI model training, embed it in the protocol to achieve dynamic parameter adjustment, and improve communication efficiency;

[0033] S3.3: Implement data encryption, select a suitable encryption algorithm, and design a secure key management mechanism to ensure the security of data transmission;

[0034] S3.4: Select a blockchain platform, develop smart contracts, integrate blockchain technology, and achieve distributed storage and verification of data to enhance trust;

[0035] S3.5: Conduct functional, performance, and security tests, optimize the protocol according to the tests and feedback, iterate and update the version, and improve competitiveness;

[0036] S3.6: Deploy the protocol to the target system, conduct system debugging and testing, establish operation and maintenance monitoring, provide technical support and maintenance services, and ensure the stable operation of the system;

[0037] The encryption algorithm in S3.3:

[0038] The specific formulas for encryption and decryption in the RSA encryption algorithm are:

[0039] c = m e mod n

[0040] m = c d mod n

[0041] Wherein, c is the ciphertext; m is the plaintext; e is the public key; d is the private key; n is the modulus;

[0042] The specific steps in S4 are as follows:

[0043] S4.1: Define the specific metrics for the performance of the communication system verified and optimized through digital twin technology, and select a suitable simulation tool;

[0044] S4.2: Build a fully virtualized twin communication system, and set the simulation parameters matching the actual system;

[0045] S4.3: Use the API or scripting language provided by the simulation tool to write an automated test script for executing test tasks, collecting data and analyzing results, and monitor and record the logs in real time for easy discovery and adjustment of problems;

[0046] S4.4: Design multi-scenario tests, simulate the actual network, and analyze the performance metrics and bottlenecks;

[0047] S4.5: According to the test results, adjust the communication system parameters and algorithms to optimize the performance;

[0048] The specific steps in S5 are as follows:

[0049] S5.1: Select devices according to requirements and determine the deployment method of the AI algorithm, install and configure the software to ensure the monitoring effect and achieve adaptive maintenance;

[0050] S5.2: Define the requirements, evaluate the scalability, adopt a distributed and modular design, and select technologies such as virtualization to achieve dynamic resource allocation;

[0051] S5.3: Select software and hardware according to the architecture design, and conduct tests to ensure the normal operation of the network architecture;

[0052] S5.4: Establish a comprehensive monitoring system, monitor and analyze the logs in real time, and optimize and adjust the system performance;

[0053] S5.5: Reserve expansion space when designing the network architecture, pay attention to the development of new technologies, and keep the system advanced.

[0054] On the other hand, the present invention provides a network communication system for the low-altitude field, including communication technology, sensing technology, navigation technology, data processing technology and a security supervision mechanism, characterized in that the communication technology implements the steps of the network communication method for the low-altitude field described in any one of claims 1-9.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] By comprehensively applying advanced technologies such as linear regression prediction, deep learning optimization, hybrid protocol design, and digital twin testing, the present invention solves the requirements for efficient, reliable, and secure communication in the low-altitude domain, and supports dynamic environment adaptation and future technology upgrades. This method ensures the stable provision of high-quality services in the complex and changeable low-altitude environment by optimizing resource allocation, improving communication efficiency and security. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is the overall method flow chart of a network communication method and system for the low-altitude domain of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0059] Embodiment 1:

[0060] Referring to Figure 1 As shown, this embodiment provides a network communication method for the low-altitude domain, including the following steps:

[0061] S1: Analyze historical communication data by establishing a linear regression model to predict future communication requirements. Establish a dynamic scenario model based on the prediction results to simulate communication requirements in different environments, select hybrid communication technologies, and design modular devices;

[0062] S2: Establish a mobile base station and intelligent antennas, and optimize the base station parameters in real time through deep learning algorithms to improve network performance, dynamically allocate network resources for different applications to ensure service quality, and combine network slicing and spectrum sharing technologies to achieve efficient and anti-interference low-altitude communication coverage;

[0063] S3: Develop a lightweight hybrid protocol, use AI to dynamically optimize protocol parameters, and introduce encryption technology and blockchain technology to ensure communication efficiency and security;

[0064] S4: Build a virtual test environment and automation tools, simulate real scenarios through digital twin technology, and quickly verify and optimize the performance of the communication system;

[0065] S5: Deploy an AI-driven intelligent monitoring system to achieve adaptive maintenance. Meanwhile, design a flexible network architecture to support rapid expansion and future technology upgrades.

[0066] The historical communication data in S1 includes traffic volume, communication duration, communication frequency, geographical location, and device type.

[0067] The specific steps of using a linear regression model in S1 to analyze historical communication data, predict future communication demands, establish a dynamic scenario model through the prediction results, simulate communication demands in different environments, select hybrid communication technologies, and design modular devices are as follows:

[0068] S1.1: First, collect and preprocess the historical communication data of traffic volume, communication duration, communication frequency, geographical location, and device type to improve data quality and prediction accuracy;

[0069] S1.2: Based on the preprocessed historical communication data, construct a linear regression model;

[0070] S1.3: Use the historical communication data to train the linear regression model, and use the trained linear regression model to predict future communication demands;

[0071] S1.4: According to the prediction results of the linear regression model, establish communication demand models in different environments;

[0072] S1.5: Select suitable communication technologies according to different environmental requirements;

[0073] S1.6: According to the selected hybrid communication technologies, design modular devices that support multiple communication technologies.

[0074] The specific formula of the linear regression model in S1.2 is:

[0075] y = β0 + β1x1 + β2x2 +... + β n x n +

[0076] where y is the dependent variable, representing traffic volume and communication duration; X1, X2,....., X n are independent variables, representing the historical data characteristics affecting communication demands, such as communication frequency, geographical location, device type, etc.; β0, β1,…, β n are regression coefficients; ∈ is the error term;

[0077] The specific operation steps in S2 are:

[0078] S2.1: First, deploy mobile base stations and smart antennas at key positions to ensure that the coverage of the base stations and antennas meets the low-altitude communication demands;

[0079] S2.2: Use deep learning algorithms to optimize base station parameters in real time;

[0080] S2.3: Dynamically allocate network resources according to the needs of different applications, ensure service quality, and avoid network congestion and latency;

[0081] S2.4: Use network slicing technology to provide customized network services for different applications, formulate spectrum sharing strategies, ensure efficient utilization of spectrum resources between different users or systems, and reduce interference;

[0082] S2.5: Continuously monitor the network, and continuously adjust and optimize network slicing, spectrum sharing strategies, and base station parameters according to the monitoring results and changes in user needs;

[0083] The specific steps in S3 are as follows:

[0084] S3.1: Clearly define the communication requirements, combine the advantages of lightweight hybrid protocols, determine the functions, data structures, and communication processes, and implement the protocol efficiently and stably;

[0085] S3.2: Collect communication data for AI model training, embed the protocol to achieve dynamic parameter adjustment, and improve communication efficiency;

[0086] S3.3: Implement data encryption, select a suitable encryption algorithm, and design a secure key management mechanism to ensure data transmission security;

[0087] S3.4: Select a blockchain platform, develop smart contracts, integrate blockchain technology, achieve distributed storage and verification of data, and enhance trust;

[0088] S3.5: Conduct function, performance, and security tests, optimize the protocol according to the tests and feedback, iterate and update the version, and improve competitiveness;

[0089] S3.6: Deploy the protocol to the target system, conduct system debugging and testing, establish operation and maintenance monitoring, provide technical support and maintenance services, and ensure the stable operation of the system;

[0090] The encryption algorithm in S3.3:

[0091] The specific formulas for encryption and decryption in the RSA encryption algorithm are as follows:

[0092] c = m e mod n

[0093] m = c d mod n

[0094] Among them, c is the ciphertext; m is the plaintext; e is the public key; d is the private key; n is the modulus;

[0095] The specific steps in S4 are as follows:

[0096] S4.1: Identify specific indicators of communication system performance to be verified and optimized through digital twin technology and select appropriate simulation tools;

[0097] S4.2: Build a fully virtualized twin communication system and set simulation parameters that match the actual system;

[0098] S4.3: Use the API or scripting language provided by the simulation tool to write automated test scripts to execute test tasks, collect data, and analyze results. Real-time monitoring and log recording facilitate the discovery and adjustment of problems.

[0099] S4.4: Design multi-scenario tests, simulate actual networks, and analyze performance indicators and bottlenecks;

[0100] S4.5: Adjust communication system parameters and algorithms to optimize performance based on test results;

[0101] The specific steps in S5 are:

[0102] S5.1: Select equipment and decide how to deploy AI algorithms based on demand, install and configure software, ensure monitoring results, and implement adaptive maintenance;

[0103] S5.2: Clarify requirements, evaluate scalability, adopt distributed and modular design, and use virtualization and other technologies to achieve dynamic resource allocation;

[0104] S5.3: Select software and hardware according to the architecture design and conduct tests to ensure the normal operation of the network architecture;

[0105] S5.4: Establish a comprehensive monitoring system to monitor and analyze logs in real time and optimize system performance;

[0106] S5.5: Reserve space for expansion when designing the network architecture, pay attention to the development of new technologies, and keep the system advanced.

[0107] like Figure 1As shown in the figure, this embodiment provides a network communication method for the low-altitude field. The specific method is as follows: First, S1: Analyze historical communication data through establishing a linear regression model to predict future communication demands, establish a dynamic scenario model based on the prediction results to simulate communication demands in different environments, select hybrid communication technologies, and design modular devices; the historical communication data includes traffic volume, communication duration, communication frequency, geographical location, and device type. The specific steps of using the linear regression model to analyze historical communication data, predict future communication demands, establish a dynamic scenario model based on the prediction results to simulate communication demands in different environments, select hybrid communication technologies, and design modular devices are as follows: S1.1: First, collect and preprocess the historical communication data of traffic volume, communication duration, communication frequency, geographical location, and device type to improve data quality and prediction accuracy; it should be added that: the preprocessing includes missing value filling, outlier processing, data normalization, etc. to improve data quality and prediction accuracy. The collection of historical communication data can be from sources such as communication network logs, user behavior records, and device status monitoring. S1.2: Based on the preprocessed historical communication data, construct a linear regression model; S1.3: Use the historical communication data to train the linear regression model, and use the trained linear regression model to predict future communication demands; it should be added that: in the training stage, the goal is to find the best regression coefficients β=(β0,β1,…,βn) such that the error between the model prediction value and the actual value is minimized. This is usually achieved by minimizing the loss function (such as the mean square error). The formula for the mean square error is: where m is the number of training samples; y i is the actual value of the i-th sample; is the predicted value of the i-th sample; After training is completed, the obtained linear regression model can be used to predict future communication demands. S1.4: According to the prediction results of the linear regression model, establish communication demand models in different environments; it should be added that: different environmental factors: such as weather, terrain, electromagnetic environment, etc. S1.5: Select suitable communication technologies according to different environmental demands; S1.6: According to the selected hybrid communication technologies, design modular devices that support multiple communication technologies. It should be added that: hybrid communication technologies: such as Wi-Fi, LTE, 5G, etc. The specific formula of the linear regression model in S1.2 is:

[0108] y = β0 + β1x1 + β2x2 +... + β n x n +

[0109] where y is the dependent variable, representing traffic volume and communication duration; X1, X2,....., X nis the independent variable, representing the historical data characteristics that affect communication requirements, such as communication frequency, geographical location, device type, etc.; β0, β1, …, β n are the regression coefficients; ∈ is the error term. Secondly, S2: Establish mobile base stations and smart antennas, and use deep learning algorithms to optimize base station parameters in real time, improve network performance, dynamically allocate network resources for different applications, ensure service quality, and combine network slicing and spectrum sharing technologies to achieve efficient and anti-interference low-altitude communication coverage; It should be added that: Network slicing can divide the physical network into multiple virtual networks, and each virtual network can be customized and optimized according to the needs of specific applications. This helps to meet the requirements of different applications for network performance, security, and isolation. The specific operation steps in S2 are: S2.1: First, deploy mobile base stations and smart antennas at key locations to ensure that the coverage of the base stations and antennas meets the low-altitude communication requirements; S2.2: Use deep learning algorithms to optimize base station parameters in real time; S2.3: Dynamically allocate network resources according to the needs of different applications to ensure service quality and avoid network congestion and latency; It should be added that: Such as drone inspection, logistics transportation, aerial photography, etc. S2.4: Use network slicing technology to provide customized network services for different applications, formulate spectrum sharing strategies to ensure the efficient use of spectrum resources between different users or systems, and reduce interference; It should be added that: Spectrum sharing strategies include spectrum allocation, access control, interference management, etc., to ensure the efficient use of spectrum resources between different users or systems and reduce interference. S2.5: Continuously monitor the network, and continuously adjust and optimize network slicing, spectrum sharing strategies, and base station parameters according to the monitoring results and changes in user needs. S3: Develop a lightweight hybrid protocol, use AI to dynamically optimize protocol parameters, and introduce encryption technology and blockchain technology to ensure communication efficiency and security; The specific steps in S3 are: S3.1: Clearly define the communication requirements, combine the advantages of the lightweight hybrid protocol, determine the functions, data structures, and communication processes, and implement the protocol efficiently and stably; It should be added that: The hybrid protocol refers to the combination of UDP (low latency) and TCP (reliability). S3.2: Collect communication data for AI model training, embed it in the protocol to achieve dynamic parameter adjustment, and improve communication efficiency; S3.3: Implement data encryption, select a suitable encryption algorithm, and design a secure key management mechanism to ensure the security of data transmission; S3.4: Select a blockchain platform, develop smart contracts, integrate blockchain technology, achieve distributed storage and verification of data, and enhance trust; S3.5: Conduct function, performance, and security tests, optimize the protocol according to the tests and feedback, iterate and update the version, and improve competitiveness; The encryption algorithm in S3.3: The specific formulas for encryption and decryption in the RSA encryption algorithm are:

[0110] c = m e mod n

[0111] m = cd modulo n

[0112] Where c is the ciphertext; m is the plaintext; e is the public key; d is the private key; n is the modulus. S4: Build a virtual test environment and automation tools, and simulate real scenarios through digital twin technology to quickly verify and optimize the performance of the communication system; the specific steps are as follows: S4.1: Define the specific metrics for the performance of the communication system to be verified and optimized through digital twin technology, and select appropriate simulation tools; it should be added that: simulation tools such as MATLAB, ns-3, OPNET, etc., these tools can support the modeling and simulation of wireless communication systems. It should be added that: the specific metrics include throughput, latency, and packet loss rate. S4.2: Build a fully virtualized twin communication system and set the simulation parameters matching the actual system; it should be added that: the parameters of the simulation model such as: base station transmit power, mobile speed of user terminals, channel fading model, etc. S4.3: Use the API or scripting language provided by the simulation tool to write automated test scripts for executing test tasks, collecting data, and analyzing results, and monitor and record logs in real time for easy discovery and adjustment of problems; S4.4: Design multi-scenario tests, it should be added that: multi-scenario tests include urban, suburban, and high-speed mobile. Simulate the actual network and analyze performance metrics and bottlenecks; S4.5: According to the test results, adjust the communication system parameters and algorithms to optimize the performance. Finally, S5: Deploy an AI-driven intelligent monitoring system to achieve adaptive maintenance, and at the same time design a flexible network architecture to support rapid expansion and future technology upgrades. The specific steps are as follows: S5.1: Select devices according to requirements and determine the deployment method of the AI algorithm, install and configure the software to ensure the monitoring effect and achieve adaptive maintenance; S5.2: Define the requirements, evaluate the scalability, adopt a distributed and modular design, and select technologies such as virtualization to achieve dynamic resource allocation; S5.3: Select software and hardware according to the architecture design and conduct tests to ensure the normal operation of the network architecture; S5.4: Establish a comprehensive monitoring system to monitor and analyze logs in real time and optimize and adjust the system performance; S5.5: Reserve expansion space when designing the network architecture, pay attention to the development of new technologies, and keep the system advanced.

[0113] Embodiment 2:

[0114] This embodiment provides a network communication system for the low-altitude field, including communication technology, sensing technology, navigation technology, data processing technology, and a security supervision mechanism. The communication technology implements the steps of any one of the above network communication methods for the low-altitude field.

[0115] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A network communication method for the low-altitude field, characterized in that, It includes the following steps: S1: Analyze historical communication data by establishing a linear regression model to predict future communication demands. Based on the prediction results, establish a dynamic scenario model to simulate communication demands in different environments, select hybrid communication technologies, and design modular devices; S2: Establish mobile base stations and smart antennas, and use deep learning algorithms to optimize base station parameters in real time to improve network performance. Dynamically allocate network resources for different applications to ensure service quality, and combine network slicing and spectrum sharing technologies to achieve efficient and anti-interference low-altitude communication coverage; S3: Develop a lightweight hybrid protocol, use AI to dynamically optimize protocol parameters, and introduce encryption technology and blockchain technology to ensure communication efficiency and security; S4: Build a virtual test environment and automation tools, and use digital twin technology to simulate real scenarios to quickly verify and optimize the performance of the communication system; S5: Deploy an AI-driven intelligent monitoring system to achieve adaptive maintenance, and at the same time design a flexible network architecture to support rapid expansion and future technology upgrades.

2. The network communication method for the low-altitude field according to claim 1, characterized in that, The historical communication data in S1 includes traffic volume, communication duration, communication frequency, geographical location, and device type.

3. A network communication method for the low-altitude field according to claim 1, characterized in that, The specific steps of using the linear regression model in S1 to analyze historical communication data, predict future communication demands, establish a dynamic scenario model based on the prediction results, simulate communication demands in different environments, select hybrid communication technologies, and design modular devices are as follows: S1.1: First, collect and preprocess the historical communication data of traffic volume, communication duration, communication frequency, geographical location, and device type to improve data quality and prediction accuracy; S1.2: Based on the preprocessed historical communication data, construct a linear regression model; S1.3: Use the historical communication data to train the linear regression model, and use the trained linear regression model to predict future communication demands; S1.4: According to the prediction results of the linear regression model, establish a communication demand model for different environments; S1.5: Select suitable communication technologies according to different environmental requirements; S1.6: According to the selected hybrid communication technology, design modular devices that support multiple communication technologies.

4. A network communication method for the low-altitude field according to claim 3, characterized in that, The specific formula of the linear regression model in S1.2 is: y = β0 + β1x1 + β2x2 +... + β n x n + Among them, y is the dependent variable, representing traffic volume and communication duration; X1, X2,....., X n are independent variables, representing historical data characteristics affecting communication demand, such as communication frequency, geographical location, device type, etc.; β0, β1,…, β n are regression coefficients; ∈ is the error term.

5. A network communication method for the low-altitude field according to claim 4, characterized in that, The specific operation steps in S2 are: S2.1: First, deploy mobile base stations and smart antennas at key locations to ensure that the coverage of the base stations and antennas meets the low-altitude communication demands; S2.2: Use deep learning algorithms to optimize the base station parameters in real time; S2.3: Dynamically allocate network resources according to the requirements of different applications to ensure service quality and avoid network congestion and latency; S2.4: Use network slicing technology to provide customized network services for different applications, formulate a spectrum sharing strategy to ensure the efficient use of spectrum resources between different users or systems, and reduce interference; S2.5: Continuously monitor the network, and adjust and optimize the network slicing, spectrum sharing strategy, and base station parameters according to the monitoring results and changes in user demands.

6. A network communication method for the low-altitude field according to claim 1, characterized in that The specific steps in S3 are: S3.1: Clearly define the communication requirements for design, combine the advantages of the lightweight hybrid protocol, determine the functions, data structures, and communication processes, and implement the protocol efficiently and stably; S3.2: Collect communication data for AI model training, embed the protocol to achieve dynamic parameter adjustment, and improve communication efficiency; S3.3: Implement data encryption, select appropriate encryption algorithms, and design secure key management mechanisms to ensure data transmission security; S3.4: Select a blockchain platform, develop smart contracts, integrate blockchain technology, realize distributed data storage and verification, and improve trust; S3.5: Conduct functional, performance and security tests, optimize the protocol based on the tests and feedback, iterate and update the version to improve competitiveness; S3.6: Deploy the protocol to the target system, perform system debugging and testing, establish operation and maintenance monitoring, provide technical support and maintenance services, and ensure stable operation of the system.

7. A network communication method for the low-altitude field according to claim 6, characterized in that The encryption algorithm in S3.3 is: The specific formula for encryption and decryption in the RSA encryption algorithm is: c = m e mod n m = c d mod n Among them, c is the ciphertext; m is the plaintext; e is the public key; d is the private key; and n is the modulus.

8. A network communication method for the low-altitude field according to claim 1, characterized in that, The specific steps in S4 are: S4.1: Identify specific indicators of communication system performance to be verified and optimized through digital twin technology and select appropriate simulation tools; S4.2: Build a fully virtualized twin communication system and set simulation parameters that match the actual system; S4.3: Use the API or scripting language provided by the simulation tool to write automated test scripts to execute test tasks, collect data, and analyze results. Real-time monitoring and log recording facilitate the discovery and adjustment of problems. S4.4: Design multi-scenario tests, simulate actual networks, and analyze performance indicators and bottlenecks; S4.5: Based on the test results, adjust the communication system parameters and algorithms to optimize performance.

9. The network communication method for the low-altitude field according to claim 1, wherein The specific steps in S5 are: S5.1: Select equipment and decide how to deploy AI algorithms based on demand, install and configure software, ensure monitoring results, and implement adaptive maintenance; S5.2: Clarify requirements, evaluate scalability, adopt distributed and modular design, and use virtualization and other technologies to achieve dynamic resource allocation; S5.3: Select software and hardware according to the architecture design and conduct tests to ensure the normal operation of the network architecture; S5.4: Establish a comprehensive monitoring system to monitor and analyze logs in real time and optimize system performance; S5.5: Reserve space for expansion when designing the network architecture, pay attention to the development of new technologies, and keep the system advanced.

10. A network communication system for the low-altitude area, comprising communication technology, sensing technology, navigation technology, data processing technology and a security supervision mechanism, characterized in that, The communication technology implements the steps of the network communication method for low-altitude areas as described in any one of claims 1-9.

Images