Self-adaptive remote communication control system and method based on large model

Abstract

The invention discloses a self-adaptive remote communication control system and method based on a large model, the system comprises a communication data acquisition module, a large model analysis module, a self-adaptive control module, a fault detection and repair module, an interference suppression module, a user interface module and the like, the large model has a multi-layer structure and can accurately predict communication quality, and each module has corresponding functions and performance indexes. For example, the fault detection accuracy is greater than or equal to 95%, an innovative formula is used for channel selection and the like, the system has multiple optimization and guarantee functions, and the precision and reliability of communication control are improved; through accurate prediction of communication quality by a large model and collaborative optimization of multiple modules, efficient fault detection and repair and interference suppression are realized, data security is guaranteed, multiple devices are adapted, user definition is supported, remote communication quality, reliability and user experience are improved, and industry development is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of remote communication control systems, and in particular to a large model-based adaptive remote communication control system and method. Background Art

[0002] With the rapid development of information technology, remote communication is increasingly used in various fields, covering many aspects from mobile communication and video calls in daily life to remote monitoring and intelligent control in the industrial field, as well as data transmission and remote collaboration in scientific research. However, remote communication systems face many complex challenges during operation, which seriously affect the quality and reliability of communication and restrict the further development of related applications.

[0003] In traditional remote communication systems, data collection is often not comprehensive and accurate enough. Only some basic communication parameters such as signal strength and bandwidth utilization are focused on, while factors such as signal spectrum characteristics and channel fading characteristics that have an important impact on communication quality are ignored. This leads to a lack of in-depth understanding of the communication status, making it difficult to accurately predict and respond to potential problems. In addition, the frequency and accuracy of data collection are limited, and it is impossible to accurately reflect the dynamic changes in the communication environment in real time. This makes the system's response and adjustment lag in the face of rapidly changing communication scenarios, affecting the continuity and stability of communication.

[0004] Traditional methods for analyzing and predicting communication quality lack effective models and algorithm support. Most of them rely on simple statistical analysis or judgment based on experience, which makes it difficult to dig out the deep laws and trends hidden in communication data. This makes the prediction of the changing trend of communication quality inaccurate, and it is impossible to take effective optimization measures in advance, resulting in large fluctuations in communication quality, especially in complex electromagnetic environments or network congestion. It is easy to have problems such as signal interruption, increased delay, and data packet loss, which seriously affects user experience and the normal development of business.

[0005] In terms of controlling communication parameters, traditional systems usually adopt fixed or simple rule-based adjustment strategies, which cannot be adaptively optimized according to the real-time communication status. For example, when facing changes in channel conditions, key parameters such as modulation mode and coding rate cannot be adjusted in time, resulting in low communication efficiency or reduced quality. Moreover, in terms of multi-objective optimization, traditional methods have difficulty balancing multiple interrelated quality indicators such as signal strength, signal-to-noise ratio, bandwidth utilization, delay time, etc., and often lose sight of one thing while focusing on another, and cannot achieve the optimal overall communication quality.

[0006] Fault detection and repair are key links in remote communication systems, but traditional methods have many shortcomings. The accuracy of fault detection is low, and some potential fault hazards are difficult to detect in time. They are often not handled until the fault has seriously affected communication. The repair mechanism is also not flexible and efficient enough, usually requiring manual intervention, and the repair time is long, which cannot meet application scenarios with high requirements for communication continuity. At the same time, there is a lack of intelligent classification and targeted processing strategies for faults of different types and severity, which further reduces the efficiency and success rate of fault repair.

[0007] In terms of interference handling, traditional remote communication systems have limited coping capabilities. With the massive increase in wireless communication devices and the increasingly complex electromagnetic environment, various interference signals such as electromagnetic interference and co-frequency interference have seriously affected the quality of communication. Traditional interference suppression methods are ineffective and cannot effectively identify and adaptively suppress these interference signals, resulting in a decrease in the clarity and stability of communication signals, affecting the accurate transmission of data and the reliability of communication.

[0008] In addition, the existing remote communication system also has deficiencies in user interface and user experience. The user interface is often not intuitive and friendly enough, making it difficult for users to understand the detailed information of communication status and quality indicators in real time. The operation is also not convenient enough, and it is cumbersome for users to set system parameters and select control strategies. In addition, it lacks personalized setting functions and cannot meet the needs of different users in different scenarios. At the same time, the system has poor adaptability to different devices, and the display effects and operating experience on various terminal devices such as computers, tablets, and mobile phones are inconsistent, which limits the user's scope of use and flexibility.

[0009] In summary, traditional remote communication control technology can no longer meet the needs of modern society for high-quality, high-efficiency, and high-reliability remote communication. In order to overcome these problems, there is an urgent need for an adaptive remote communication control system and method based on a large model, which can achieve comprehensive and accurate monitoring and adaptive optimization of the communication process by introducing advanced technologies and innovative algorithms, improve communication quality and reliability, and provide more stable and efficient remote communication support for various applications. Summary of the invention

[0010] The present invention proposes a large model-based adaptive remote communication control system and method to solve the problems mentioned in the above-mentioned prior art.

[0011] In order to achieve the above object, the present invention adopts the following technical solution: an adaptive remote communication control system based on a large model, comprising:

[0012] The communication data acquisition module is used to collect various data in the remote communication process in real time, including signal strength, signal-to-noise ratio, bandwidth utilization, data transmission rate, delay time, packet loss rate, signal spectrum characteristics, and channel fading characteristics. The data acquisition frequency is not less than 10 times / second, and the accuracy of data acquisition is within ±0.3%;

[0013] The big model analysis module is connected to the communication data acquisition module and uses a big model based on deep learning to analyze the collected communication data. The big model has a multi-layer neural network structure, containing at least 8 hidden layers, and each hidden layer has no less than 256 neurons. By learning from a large amount of historical communication data, it can accurately predict the changing trend of communication quality, and the prediction accuracy is not less than 85% on the verification set.

[0014] In the communication quality prediction, the prediction formula is used: Q t+1 =σ(W×X t +b), where Q t+1 is the predicted value of communication quality at time t+1, σ is the ReLU activation function, W is the weight matrix, X t is the communication data feature vector at time t, including various collected data and processed features, and b is the bias vector. By continuously training and optimizing W and b, the accuracy of prediction can be improved.

[0015] The adaptive control module formulates adaptive control strategies based on the analysis results of the large model, and makes real-time adjustments to communication parameters, including modulation mode, coding rate, transmission power, bandwidth allocation, and channel selection, to optimize communication quality. It ensures that the signal strength is stable within the set range, the fluctuation range does not exceed ±3dB, the signal-to-noise ratio is increased to no less than 20dB, the bandwidth utilization rate is increased to no less than 80%, the delay time is controlled to no more than 100ms, and the packet loss rate is reduced to no more than 1%.

[0016] In channel selection, the following selection formula based on channel quality assessment is adopted:

[0017] C i =α×S i +β×N i +γ×B i

[0018] Among them C i is the comprehensive evaluation value of the ith channel, S i is the signal strength index of the channel, N i is the signal-to-noise ratio indicator, B i is the bandwidth availability index, α, β, and γ are weight coefficients, which are determined according to actual needs and experience. i Value, select C i The largest channel for communication.

[0019] The fault detection and repair module monitors abnormalities in communication data in real time and can promptly identify communication faults such as signal interruption, severe interference, and equipment failure by establishing a fault diagnosis model. The detection accuracy of common faults is not less than 95%. When a fault is detected, the repair mechanism is automatically started to try to switch to an alternate communication link, adjust communication parameters, and restart related equipment to repair it. The repair success rate is not less than 90%, and a fault alarm is issued in a timely manner, with an alarm response time of no more than 1 second.

[0020] In fault diagnosis, the following fault judgment formula based on probability statistics is used:

[0021]

[0022] Where P(F) is the probability of fault F occurring, n(F) is the number of times fault F is detected within a certain period of time, and n(T) is the total number of detections. When P(F) exceeds the set threshold, fault F is determined to have occurred.

[0023] The interference suppression module is used to identify and suppress various interference signals in the communication process in real time, such as electromagnetic interference and co-frequency interference. By adopting adaptive filtering algorithms and spectrum analysis technology, it can effectively reduce the impact of interference on communication quality and improve signal clarity and stability. The signal-to-noise ratio after interference suppression is improved by no less than 5dB.

[0024] In adaptive filtering, the following filter coefficient update formula is used:

[0025] w n+1 =w n +μ×e n × n

[0026] where w n+1 is the filter coefficient vector of the n+1th iteration, w n is the nth filter coefficient vector, μ is the step size factor, e n is the error signal, x n is the input signal vector. By continuously updating the filter coefficients, the interference signal can be effectively suppressed.

[0027] The user interface module provides users with an intuitive communication status monitoring interface, displaying real-time communication data, quality indicator trend charts, and fault alarm information. Users can set system parameters and select control strategies through the interface. The response time of interface operations does not exceed 0.5 seconds, ensuring the timeliness and smoothness of operations. It also has historical data query and report generation functions, which facilitates users to review and analyze communication conditions.

[0028] The security encryption module ensures the security and confidentiality of communication data. It uses advanced encryption algorithms to encrypt the transmitted data to prevent data theft and tampering. The security of encrypted communication data meets industry standards, and the decryption success rate is no less than 98%.

[0029] In data encryption, the following encryption formula is used:

[0030] E(m,k)=c

[0031] Where E is the encryption function, m is the original data, k is the encryption key, and c is the encrypted data. When decrypting, the original data is restored through the corresponding decryption function D(c, k)=m.

[0032] Furthermore, the large model analysis module has a model self-update function. With the continuous influx of new communication data, the model can be automatically updated and optimized online. After each update, the prediction accuracy of the model on the new data is improved by no less than 3%.

[0033] Furthermore, the adaptive control module supports multi-objective optimization. When adjusting communication parameters, it can simultaneously take into account the optimization of multiple communication quality indicators by establishing a multi-objective optimization function:

[0034] F=w1×Q1+w2×Q2+…+w n ×Q n

[0035] Where F is the optimization objective function, Q1...Q n are the 1st to nth communication quality indicators, w1...w n The corresponding weight coefficient is dynamically adjusted according to actual needs to achieve balanced optimization of different quality indicators.

[0036] Furthermore, the fault detection and repair module has an intelligent fault classification function, which can classify the detected faults according to type and severity, for example, into hardware faults, software faults, minor faults, and major faults, and adopt corresponding repair strategies according to different classifications to improve the efficiency and accuracy of fault repair.

[0037] Furthermore, the user interface module supports multi-device adaptation and can run stably on different types of terminal devices (such as computers, tablets, and mobile phones). The display effect is adaptive to the screen size and resolution of different devices. The interface layout and operation method are optimized according to the characteristics of the device, ensuring that users can easily use the system for remote communication control on various devices.

[0038] Furthermore, a large model-based adaptive remote communication control method comprises the following steps:

[0039] Communication data collection steps: Use specialized communication data collection equipment and technology to collect various data in the remote communication process in real time, including signal strength, signal-to-noise ratio, bandwidth utilization, data transmission rate, delay time, packet loss rate, signal spectrum characteristics, channel fading characteristics, and ensure that the frequency of data collection is not less than 10 times / second and the accuracy of the data is within ±0.3%;

[0040] Big model analysis step: input the collected communication data into a big model based on deep learning for analysis. The big model can mine the potential features and rules in the data and accurately predict the changing trend of communication quality by pre-training a large amount of historical communication data. In the prediction process, an optimized algorithm, such as the stochastic gradient descent algorithm, is used to adjust the model parameters to improve the prediction accuracy. The prediction accuracy on the validation set is not less than 85%;

[0041] Adaptive control decision-making steps: According to the analysis results of the large model, formulate an adaptive control strategy to adjust the communication parameters in real time, including modulation mode, coding rate, transmission power, bandwidth allocation, and channel selection to optimize communication quality. During the adjustment process, by real-time monitoring of changes in communication quality indicators, continuous feedback adjustment of the control strategy is carried out to ensure that the signal strength is stable within the set range, the fluctuation range does not exceed ±3dB, the signal-to-noise ratio is increased to no less than 20dB, the bandwidth utilization rate is increased to no less than 80%, the delay time is controlled to no more than 100ms, and the packet loss rate is reduced to no more than 1%;

[0042] Fault detection and repair steps: Real-time monitoring of abnormal communication data, timely identification of communication faults such as signal interruption, severe interference, and equipment failure by establishing a fault diagnosis model, and applying pattern recognition and data analysis technology. The detection accuracy of common faults is not less than 95%. When a fault is detected, corresponding repair measures are automatically taken, such as switching to backup communication links, adjusting communication parameters, and restarting related equipment. The repair success rate is not less than 90%. At the same time, a fault alarm is issued, and the alarm response time does not exceed 1 second.

[0043] Interference suppression steps: Adaptive filtering algorithm and spectrum analysis technology are used to identify and suppress various interference signals in the communication process, such as electromagnetic interference and co-frequency interference, in real time, reduce the impact of interference on communication quality, and improve signal clarity and stability. The signal-to-noise ratio after interference suppression is improved by no less than 5dB.

[0044] In the interference suppression process, the formula for calculating the interference suppression effect is: I = S before -S after ,

[0045] Where I is the interference suppression amount, S before is the signal-to-noise ratio before interference suppression, Safter is the signal-to-noise ratio after interference suppression.

[0046] User interactive operation steps: Provide users with a visual operation interface, through which users can view communication status, historical data trends, fault alarm information in real time, and can set system parameters and select control strategies. The response time of interface operations does not exceed 0.5 seconds. It also supports historical data query and report generation functions, which is convenient for users to analyze and summarize communication conditions.

[0047] Preferably, before the large model analysis step, a data preprocessing step is also included to clean, denoise and normalize the collected data to remove outliers and noise interference. The data efficiency after data cleaning reaches more than 98%, ensuring the quality and reliability of the data and providing an accurate data basis for the analysis of the large model.

[0048] Furthermore, the adaptive control decision step also includes a control strategy optimization step. According to changes in the communication environment and user needs, the adaptive control strategy is optimized through a reinforcement learning algorithm to improve the adaptability and effectiveness of the control strategy. The convergence time of the strategy optimization does not exceed 5 minutes.

[0049] Furthermore, after the fault detection and repair step, a fault recording and analysis step is also included, in which the faults that occur are recorded in detail, including the fault type, occurrence time, and repair measures information. By analyzing the fault records, the fault patterns are summarized to provide a basis for system improvement and optimization. The accuracy of fault analysis is not less than 85%.

[0050] Furthermore, in the user interaction operation step, users are supported to customize the interface display content and layout. Users can choose to display communication indicators and charts of interest and adjust the color and font settings of the interface according to their preferences and usage habits. The usage rate of customized functions is not less than 70%.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention brings significant improvements in many aspects of remote communication through a series of innovative designs and technical applications.

[0053] By introducing advanced large model analysis modules, using deep learning technology and innovative prediction formulas, we can accurately predict the trend of communication quality changes, with a prediction accuracy of more than 85%, which can provide a basis for system adjustments in advance and effectively avoid the decline of communication quality. The multi-objective optimization function ensures the balanced optimization of multiple key quality indicators such as signal strength, signal-to-noise ratio, and bandwidth utilization in complex communication environments, thereby improving the overall performance of the communication system.

[0054] In terms of fault handling, the fault detection and repair module can quickly and accurately identify and handle various communication faults with its high detection accuracy (not less than 95%), rapid repair mechanism (repair success rate not less than 90%, alarm response time not more than 1 second), and intelligent fault classification function, greatly improving the reliability and stability of the system and reducing the impact of faults on communication services.

[0055] The interference suppression module adopts adaptive filtering algorithm and spectrum analysis technology, combined with the filter coefficient update formula, to effectively suppress a variety of interference signals, so that the signal-to-noise ratio is improved by no less than 5dB, ensuring the clarity and stability of the communication signal and ensuring accurate data transmission.

[0056] The security encryption module uses advanced encryption algorithms, such as, to provide reliable security for communication data, meet industry standard requirements, have a high decryption success rate, prevent data from being stolen and tampered with, and protect user privacy and data security.

[0057] The user interface module provides an intuitive and convenient operating experience, supports multi-device adaptation, can adapt to the screen size and resolution of different terminal devices, and allows users to customize the interface display content and layout, meeting personalized needs and improving users' control over the communication system and operating efficiency.

[0058] In summary, the present invention comprehensively improves the quality, reliability, security and user experience of remote communication, promotes the development and application of remote communication technology, and has important practical significance and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a schematic block diagram of a large model-based adaptive remote communication control system proposed by the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise" and "counterclockwise" indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0062] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0063] Reference Figure 1 :An adaptive remote communication control system based on a large model, comprising:

[0064] The communication data acquisition module plays a vital and basic role in the entire adaptive remote communication control system based on a large model. It is the key data source for the system to achieve precise control and optimization.

[0065] This module is dedicated to comprehensively and accurately collecting various rich data in the remote communication process in real time. Signal strength is a key indicator to measure the strength of communication signals, which directly reflects the propagation ability and reception quality of communication signals. Its collection includes not only the instantaneous strength value of the signal, but also the fluctuation of signal strength at different time points, so that the system can detect the changing trend of signal strength in time and provide a basis for subsequent adjustment decisions.

[0066] The signal-to-noise ratio is one of the important parameters for measuring the quality of communication signals, which reflects the proportional relationship between signal and noise. The module uses a high-precision detection algorithm to accurately obtain signal-to-noise ratio data, helping the system to distinguish between effective signals and interference noise, thereby evaluating the quality of communication and providing key information for optimizing the communication environment.

[0067] Bandwidth utilization reflects the use of bandwidth resources in the communication link. The module monitors bandwidth usage in real time, including the proportion of bandwidth occupied by different applications or data transmission tasks, as well as the idleness of bandwidth. Through accurate collection of bandwidth utilization, the system can reasonably allocate bandwidth resources, ensure priority transmission of important data, and improve communication efficiency.

[0068] The data transmission rate directly determines how fast information is transmitted. The module accurately measures the amount of data transmitted per second. Whether in peak hours or low load conditions, it can accurately record changes in data transmission rates so that the system can adjust the transmission strategy according to actual needs to ensure smooth data transmission.

[0069] Delay time is a key factor affecting the real-time performance of communications. The module uses high-frequency sampling to accurately measure the time delay experienced by data from the sender to the receiver, including network transmission delay, device processing delay and other delays. For applications with high real-time requirements, such as video calls and online games, accurate knowledge of the delay time can enable timely measures to reduce delays and improve user experience.

[0070] The packet loss rate reflects the loss of data during transmission. The module accurately calculates the packet loss rate by comparing and counting the sent and received data. A high packet loss rate may lead to problems such as incomplete data and communication interruption. Therefore, accurate collection of packet loss rate is crucial to timely discover and solve network transmission problems.

[0071] The acquisition of signal spectrum characteristics involves a detailed analysis of the distribution of the signal at different frequencies. The module uses spectrum analysis technology to obtain information such as the frequency composition, bandwidth, and power spectrum density of the signal, helping the system to identify the interfering frequency components in the signal and understand the transmission characteristics of the signal in different frequency bands, providing a basis for frequency selection and interference suppression.

[0072] Channel fading characteristics are important parameters unique to wireless communications. The module collects channel fading characteristics such as amplitude, frequency, and time through real-time monitoring and analysis of wireless channels. Channel fading may cause changes and distortions in signal strength. Understanding channel fading characteristics helps the system take corresponding compensation measures, such as adaptive modulation coding, power control, etc., to ensure the stability of communication quality.

[0073] In order to ensure real-time and accurate perception of the communication status, the data collection frequency is not less than 10 times / second. Such a high collection frequency can capture subtle changes in the communication process and promptly discover potential problems and abnormal conditions. At the same time, the accuracy of data collection is within ±0.3%, which means that the error between the collected data and the actual value is extremely small, ensuring the reliability and effectiveness of the decisions and control measures made by the system based on these data. Through high-precision and high-frequency data acquisition, the communication data acquisition module provides a solid data foundation for the entire adaptive remote communication control system, enabling the system to optimize communication quality more intelligently and accurately, and ensure the stability and efficiency of remote communication;

[0074] As the core analysis component of the adaptive remote communication control system based on the large model, the large model analysis module undertakes the key tasks of mining deep information from massive communication data and predicting the changing trend of communication quality. It is a key link in realizing intelligent adaptive control of the system.

[0075] This module is closely connected to the communication data acquisition module to obtain rich and diverse communication data in real time. The large model based on deep learning used is a complex neural network architecture with powerful computing and learning capabilities. This large model has a multi-layer neural network structure, which contains at least 8 hidden layers, and the number of neurons in each hidden layer is not less than 256. Such a deep and large neural network structure provides the model with sufficient capacity to learn and represent complex patterns and potential laws in communication data.

[0076] During the model training process, it makes full use of a large amount of historical communication data for learning. These historical data cover various communication scenarios, environmental conditions, and multi-dimensional data such as signal strength, signal-to-noise ratio, bandwidth utilization, delay time, and their corresponding communication quality results under network conditions. Through repeated learning and optimization of these massive historical data, the model gradually grasps the inherent correlation and change trends in the data, so that it can accurately predict the future change trends of communication quality.

[0077] In order to ensure the prediction accuracy and reliability of the model, a strict verification and evaluation mechanism is adopted. On the verification set, the prediction accuracy of the model is not less than 85%. This high accuracy is achieved thanks to the model's deep architecture, large-scale data training, and advanced optimization algorithms. During the learning process, the model continuously adjusts the connection weights and biases between neurons to minimize the error between the predicted value and the actual value. Through continuous iterative optimization, the model can gradually adapt to different communication data characteristics and accurately capture subtle signals of changes in communication quality, thereby providing accurate decision-making basis for subsequent adaptive control strategies.

[0078] Specifically, when analyzing communication data, the model can automatically extract high-level features from the data. For example, it can identify characteristic patterns such as periodic changes, trend changes, and abnormal fluctuations from the time series data of signal strength; infer the type and intensity change trend of interference sources from changes in the signal-to-noise ratio; and understand the laws of network load and potential congestion risks from the dynamic changes in bandwidth utilization. The extraction and analysis of these high-level features enables the model to go beyond simple data statistical analysis and gain a deep understanding of the inherent operating mechanism of the communication system, thereby achieving more accurate communication quality predictions.

[0079] In addition, the large model analysis module also has a certain degree of adaptive learning ability. As new communication data continues to flow into the system, the model can update its own parameters and weights online in real time to adapt to the dynamic changes in the communication environment and the emergence of new patterns. This adaptive learning ability ensures that the model always maintains the ability to accurately capture the changing trends of communication quality. Even in the face of complex and changeable actual communication scenarios, it can provide the system with timely and reliable analysis results and prediction information, laying a solid foundation for achieving efficient adaptive remote communication control.

[0080] In the communication quality prediction, the prediction formula is used: Q t+1 =σ(W×X t +b), where Q t+1 is the predicted value of communication quality at time t+1, σ is the ReLU activation function, W is the weight matrix, X t is the communication data feature vector at time t, including various collected data and processed features, and b is the bias vector. By continuously training and optimizing W and b, the accuracy of prediction can be improved.

[0081] The adaptive control module is the key execution link for achieving accurate and dynamic optimization of communication quality in the adaptive remote communication control system based on the large model. It closely relies on the analysis results of the large model and ensures that the communication system always maintains an efficient and stable operating state in various complex environments through intelligent decision-making and real-time adjustment.

[0082] Based on the in-depth analysis results provided by the large model, this module develops highly intelligent adaptive control strategies. These strategies include fine-tuning of multiple key communication parameters to fully optimize communication quality to cope with the ever-changing communication environment and business needs.

[0083] In terms of communication parameter adjustment, the choice of modulation mode directly affects the anti-interference ability and transmission efficiency of the signal during transmission. The module intelligently switches between different modulation modes according to the real-time communication status, for example, from simpler modulation modes (such as BPSK) to ensure basic communication in a low signal-to-noise ratio environment, to complex modulation modes (such as QAM) to increase the data transmission rate when the channel conditions are good. The adjustment of the coding rate focuses on finding a balance between data reliability and transmission efficiency. When the channel quality decreases, the coding rate is reduced to increase redundant information, improve data error correction capabilities, and ensure accurate transmission of information; when the channel quality is good, the coding rate is increased to reduce redundancy and improve transmission efficiency.

[0084] The control of transmission power is of great significance for optimizing communication quality and reducing energy consumption. The adaptive control module dynamically adjusts the transmission power in real time according to factors such as signal strength, distance and interference. While ensuring that the signal strength is stable within the set range (the fluctuation range does not exceed ±3dB), it avoids excessive transmission power causing energy waste and interference to other devices, and prevents too low transmission power from causing communication quality degradation or interruption. Bandwidth allocation is another important control dimension. The module reasonably allocates bandwidth resources according to the priority of different applications and real-time data traffic requirements. For applications with high real-time requirements (such as video conferencing and online games), sufficient bandwidth is allocated first to ensure their smooth operation; at the same time, other non-critical applications are reasonably limited to improve the overall bandwidth utilization rate to no less than 80%.

[0085] Channel selection is one of the key decisions in adaptive control. The module comprehensively considers multiple factors such as the channel's signal quality, interference level, historical stability, etc., and selects the best channel from multiple available channels for communication. By real-time monitoring and evaluating the status of each channel, it switches to a channel with better quality in a timely manner to avoid communication problems caused by channel quality deterioration and ensure the stability and reliability of data transmission.

[0086] The module has clear and strict requirements for the goal of optimizing communication quality. Through precise adjustment of communication parameters, it is committed to ensuring that the signal strength is stable within the set ideal range, and its fluctuation amplitude is strictly controlled to no more than ±3dB, which enables the receiving end to always receive a signal of stable strength, reducing data loss and increased bit error rate caused by signal fluctuations. The signal-to-noise ratio is increased to no less than 20dB, which effectively improves the clarity and anti-interference ability of the signal and ensures the accuracy of data transmission. The bandwidth utilization rate is increased to no less than 80%, giving full play to the efficiency of network bandwidth resources and meeting the data transmission rate requirements of various applications. The delay time is controlled at no more than 100ms, which is crucial for real-time interactive applications, ensuring that users can hardly feel obvious delays during communication and improving user experience. The packet loss rate is reduced to no more than 1%, which greatly reduces data loss and ensures the integrity and reliability of data transmission.

[0087] To achieve these goals, the adaptive control module uses advanced algorithms and real-time feedback mechanisms. It continuously receives real-time data from the communication data acquisition module, evaluates the gap between the current communication quality and the target based on these data, and quickly adjusts the control strategy. At the same time, the module maintains close interaction with the large model analysis module, and makes corresponding adjustments in advance based on the large model's prediction of the trend of changes in the communication environment, achieving forward-looking optimization control, thereby ensuring that the communication system can always operate in the best state and provide users with high-quality, stable and reliable remote communication services.

[0088] In channel selection, the following selection formula based on channel quality assessment is adopted:

[0089] C i =α×S i +β×N i +γ×B i

[0090] Among them C i is the comprehensive evaluation value of the ith channel, S i is the signal strength index of the channel, N i is the signal-to-noise ratio indicator, B i is the bandwidth availability index, α, β, and γ are weight coefficients, which are determined according to actual needs and experience. i Value, select C i The largest channel for communication.

[0091] The fault detection and repair module is an important component of ensuring communication reliability and stability in the adaptive remote communication control system based on the large model. It is like the "health guard" of the system, always guarding the normal operation of the communication link and ensuring that it can respond quickly and effectively when a fault occurs.

[0092] The module always keeps real-time monitoring of communication data, not missing any abnormal situation. It establishes advanced fault diagnosis models and uses a variety of intelligent data analysis technologies and algorithms to conduct in-depth analysis and comparison of various dimensions of communication data. These data include but are not limited to sudden changes in signal strength, sharp drops in signal-to-noise ratio, abnormal fluctuations in bandwidth utilization, sudden drops in data transmission rates, significant increases in delay time, and increases in packet loss rates. Through real-time monitoring and analysis of these data features, the module can promptly and accurately identify various types of communication failures, such as common signal interruptions, severe interference, and equipment failures.

[0093] In terms of fault detection capability, the detection accuracy of common faults is not less than 95%. This high accuracy is due to its precise fault diagnosis model and efficient algorithm. The module can not only accurately judge single fault symptoms, but also identify some complex and potential fault hazards through comprehensive analysis of multiple related data indicators. For example, when the signal strength suddenly drops and is accompanied by an increase in delay time and packet loss rate, the module can quickly determine that it may be due to a failure of a key device or a serious interference in the communication link, rather than simply attributing it to a signal problem.

[0094] Once a fault is detected, the module will automatically start the repair mechanism and adopt a variety of effective repair strategies. Among them, switching to the backup communication link is a common and efficient way. The module is pre-configured with multiple backup links and monitors their status in real time. When the main link fails, it can automatically switch to the backup link in a very short time (usually in milliseconds) to ensure the continuity of communication. At the same time, the module will also try to adjust the communication parameters, such as adjusting the modulation mode, coding rate, transmission power, etc. according to the current channel conditions to adapt to the changing environment and restore normal communication. For some problems that may be caused by short-term equipment failures or software anomalies, the module will automatically restart the relevant equipment and eliminate the fault by reinitializing the equipment. Through these comprehensive repair measures, the repair success rate is not less than 90%, which greatly improves the reliability and availability of the system.

[0095] When a fault occurs, the module will promptly issue a fault alarm to inform relevant managers and maintenance personnel of the fault. The alarm response time does not exceed 1 second, ensuring that the problem can be paid attention to and handled in a timely manner. The alarm information contains detailed key information such as the fault type, occurrence time, possible impact range, etc., to help maintenance personnel quickly locate the problem and take appropriate measures to repair it. In addition, the module will also record the entire process of the fault in detail, including the communication data status before the fault occurs, the repair measures taken, and the effect after the repair, etc., to provide valuable data support for subsequent fault analysis and system optimization. By continuously accumulating and analyzing these fault data, the module can continuously optimize its own fault diagnosis model and repair strategy, further improve the efficiency and accuracy of fault detection and repair, and provide strong guarantees for the stable operation of the remote communication system.

[0096] In fault diagnosis, the following fault judgment formula based on probability statistics is used:

[0097]

[0098] Where P(F) is the probability of fault F occurring, n(F) is the number of times fault F is detected within a certain period of time, and n(T) is the total number of detections. When P(F) exceeds the set threshold, fault F is determined to have occurred.

[0099] The interference suppression module is used to identify and suppress various interference signals in the communication process in real time, such as electromagnetic interference and co-frequency interference. By adopting adaptive filtering algorithms and spectrum analysis technology, it can effectively reduce the impact of interference on communication quality and improve signal clarity and stability. The signal-to-noise ratio after interference suppression is improved by no less than 5dB.

[0100] In adaptive filtering, the following filter coefficient update formula is used:

[0101] w n+1 =w n +μ×e n × n

[0102] where w n+1 is the filter coefficient vector of the n+1th iteration, w n is the nth filter coefficient vector, μ is the step size factor, e n is the error signal, x n is the input signal vector. By continuously updating the filter coefficients, the interference signal can be effectively suppressed.

[0103] The user interface module provides users with an intuitive communication status monitoring interface, displaying real-time communication data, quality indicator trend charts, and fault alarm information. Users can set system parameters and select control strategies through the interface. The response time of interface operations does not exceed 0.5 seconds, ensuring the timeliness and smoothness of operations. It also has historical data query and report generation functions, which facilitates users to review and analyze communication conditions.

[0104] The security encryption module ensures the security and confidentiality of communication data. It uses advanced encryption algorithms to encrypt the transmitted data to prevent data theft and tampering. The security of encrypted communication data meets industry standards, and the decryption success rate is no less than 98%.

[0105] In data encryption, the following encryption formula is used:

[0106] E(m,k)=c

[0107] Where E is the encryption function, m is the original data, k is the encryption key, and c is the encrypted data. When decrypting, the original data is restored through the corresponding decryption function D(c, k)=m.

[0108] In the present invention, the large model analysis module has a model self-update function. With the continuous influx of new communication data, the model can be automatically updated and optimized online. After each update, the prediction accuracy of the model on the new data is improved by no less than 3%.

[0109] In the present invention, the adaptive control module supports multi-objective optimization. When adjusting communication parameters, it can simultaneously take into account the optimization of multiple communication quality indicators by establishing a multi-objective optimization function:

[0110] F=w1×Q1+w2×Q2+…+w n ×Q n

[0111] Where F is the optimization objective function, Q1...Q n are the 1st to nth communication quality indicators, w1...w n The corresponding weight coefficient is dynamically adjusted according to actual needs to achieve balanced optimization of different quality indicators.

[0112] In the large model-based adaptive remote communication control system of the present invention, the intelligent fault classification function of the fault detection and repair module is a highly innovative and practical design, which greatly improves the efficiency and accuracy of fault repair and plays a key role in ensuring the stable operation of the communication system.

[0113] This module can classify the detected faults comprehensively and meticulously. First, it is divided according to the type of fault, clearly distinguishing between hardware faults and software faults. Hardware faults cover problems with various physical components in communication equipment, such as sensor failure, antenna damage, chip failure, power module abnormality, etc. For sensor failure, it may be manifested as inaccurate or completely unable to collect data; antenna damage may cause a significant decrease in signal reception or transmission strength; chip failure may affect the operating stability of the entire device, resulting in data processing errors or device crashes; power module abnormalities may cause unstable power supply to the device, affecting the normal operation of the device. Software faults involve problems with the programs, algorithms, and related configuration files running in the communication system, such as operating system crashes, application errors, driver incompatibility, communication protocol errors, etc. Operating system crashes will prevent the entire device from starting or running normally; application errors may cause specific functions to be unable to be used normally or data processing errors; driver incompatibility may cause hardware devices to not work properly or performance degradation; communication protocol errors may cause data transmission format errors or inability to communicate normally.

[0114] In addition to classification by type, the module will further subdivide the fault according to the severity. Faults are divided into different levels such as minor faults and major faults. Minor faults usually have little impact on communication quality, but if not handled in time, they may gradually worsen. For example, minor data packet loss at a communication node may not have a significant impact on the overall communication for the time being, but if it persists, it may cause data integrity to be damaged or affect applications with high real-time requirements. For such minor faults, the module can adopt a relatively mild repair strategy, such as temporarily adjusting the data transmission path, adding some redundant verification mechanisms, or performing background software repairs and parameter optimization without affecting normal communication. Severe faults will have a greater impact on the communication system and may even cause communication interruption. For example, the complete interruption of the main communication link, hardware damage to key equipment, and severe interference over a large area. For such serious faults, the module will immediately start the emergency repair mechanism to prioritize the restoration of communication. This may include quickly switching to the backup communication link, notifying relevant maintenance personnel to perform on-site hardware repairs, and backing up and restoring the affected data to minimize the impact of the fault on the business.

[0115] Through this intelligent fault classification function, the fault detection and repair module can more accurately adopt corresponding repair strategies for faults of different types and severity. For hardware faults, the module can automatically generate detailed fault reports based on the specific hardware components and fault manifestations, and notify the relevant hardware maintenance personnel to bring the corresponding tools and spare parts for on-site repairs. At the same time, while waiting for repairs, the module will try to mitigate the impact of the fault through some emergency measures, such as adjusting communication parameters to bypass some functions of the faulty hardware, or enabling spare hardware modules (if any). For software faults, the module can automatically attempt to reinstall, upgrade, roll back the software, or repair and reset the incorrect configuration files. For serious software faults, the module will back up important data in time and notify software developers for remote assistance or on-site debugging.

[0116] In the present invention, the user interface module supports multi-device adaptation and can run stably on different types of terminal devices (such as computers, tablets, and mobile phones). The display effect is adaptive to the screen size and resolution of different devices. The interface layout and operation method are optimized according to the characteristics of the device, ensuring that users can conveniently use the system for remote communication control on various devices.

[0117] In the present invention, a large model-based adaptive remote communication control method comprises the following steps:

[0118] Communication data collection steps: Use specialized communication data collection equipment and technology to collect various data in the remote communication process in real time, including signal strength, signal-to-noise ratio, bandwidth utilization, data transmission rate, delay time, packet loss rate, signal spectrum characteristics, channel fading characteristics, and ensure that the frequency of data collection is not less than 10 times / second and the accuracy of the data is within ±0.3%;

[0119] Big model analysis step: input the collected communication data into a big model based on deep learning for analysis. The big model can mine the potential features and rules in the data and accurately predict the changing trend of communication quality by pre-training a large amount of historical communication data. In the prediction process, an optimized algorithm, such as the stochastic gradient descent algorithm, is used to adjust the model parameters to improve the prediction accuracy. The prediction accuracy on the validation set is not less than 85%;

[0120] Adaptive control decision-making steps: According to the analysis results of the large model, formulate an adaptive control strategy to adjust the communication parameters in real time, including modulation mode, coding rate, transmission power, bandwidth allocation, and channel selection to optimize communication quality. During the adjustment process, by real-time monitoring of changes in communication quality indicators, continuous feedback adjustment of the control strategy is carried out to ensure that the signal strength is stable within the set range, the fluctuation range does not exceed ±3dB, the signal-to-noise ratio is increased to no less than 20dB, the bandwidth utilization rate is increased to no less than 80%, the delay time is controlled to no more than 100ms, and the packet loss rate is reduced to no more than 1%;

[0121] Fault detection and repair steps: Real-time monitoring of abnormal communication data, timely identification of communication faults such as signal interruption, severe interference, and equipment failure by establishing a fault diagnosis model, and applying pattern recognition and data analysis technology. The detection accuracy of common faults is not less than 95%. When a fault is detected, corresponding repair measures are automatically taken, such as switching to backup communication links, adjusting communication parameters, and restarting related equipment. The repair success rate is not less than 90%. At the same time, a fault alarm is issued, and the alarm response time does not exceed 1 second.

[0122] Interference suppression steps: Adaptive filtering algorithm and spectrum analysis technology are used to identify and suppress various interference signals in the communication process, such as electromagnetic interference and co-frequency interference, in real time, reduce the impact of interference on communication quality, and improve signal clarity and stability. The signal-to-noise ratio after interference suppression is improved by no less than 5dB.

[0123] In the interference suppression process, the formula for calculating the interference suppression effect is: I = S before -S after ,

[0124] Where I is the interference suppression amount, S before is the signal-to-noise ratio before interference suppression, S after is the signal-to-noise ratio after interference suppression.

[0125] User interactive operation steps: Provide users with a visual operation interface, through which users can view communication status, historical data trends, fault alarm information in real time, and can set system parameters and select control strategies. The response time of interface operations does not exceed 0.5 seconds. It also supports historical data query and report generation functions, which is convenient for users to analyze and summarize communication conditions.

[0126] Preferably, before the large model analysis step, a data preprocessing step is also included to clean, denoise and normalize the collected data to remove outliers and noise interference. The data efficiency after data cleaning reaches more than 98%, ensuring the quality and reliability of the data and providing an accurate data basis for the analysis of the large model.

[0127] In the present invention, the adaptive control decision step also includes a control strategy optimization step. According to changes in the communication environment and user needs, the adaptive control strategy is optimized through a reinforcement learning algorithm to improve the adaptability and effectiveness of the control strategy. The convergence time of the strategy optimization does not exceed 5 minutes.

[0128] In the present invention, after the fault detection and repair steps, a fault recording and analysis step is also included to record the faults in detail, including the fault type, occurrence time, and repair measures. By analyzing the fault records, the fault patterns are summarized to provide a basis for the improvement and optimization of the system. The accuracy of fault analysis is not less than 85%.

[0129] In the present invention, in the user interaction operation step, it supports users to customize the interface display content and layout. Users can choose to display the communication indicators and charts of interest according to their preferences and usage habits, and adjust the color and font settings of the interface. The usage rate of the customized function is not less than 70%.

[0130] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can replace or change the technical solution and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An adaptive remote communication control system based on a large model, characterized in that: include: Communication data acquisition module, used to collect various data in the remote communication process in real time, including signal strength, signal-to-noise ratio, bandwidth utilization, data transmission rate, delay time, packet loss rate, signal spectrum characteristics, and channel fading characteristics; The large model analysis module is connected to the communication data acquisition module and uses a large model based on deep learning to analyze the collected communication data. The large model has a multi-layer neural network structure, including at least 8 hidden layers, and the number of neurons in each hidden layer is not less than 256; In the communication quality prediction, the prediction formula is used: Q t+1 =σ(W×X t +b), where Q t+1 is the predicted value of communication quality at time t+1, σ is the ReLU activation function, W is the weight matrix, X t is the communication data feature vector at time t, including various collected data and processed features, and b is the bias vector; The adaptive control module formulates adaptive control strategies based on the analysis results of the large model and makes real-time adjustments to communication parameters, including modulation mode, coding rate, transmission power, bandwidth allocation, and channel selection; In channel selection, the following selection formula based on channel quality assessment is adopted: C i =α×S i +β×N i +γ×B i Among them C i is the comprehensive evaluation value of the ith channel, S i is the signal strength index of the channel, N i is the signal-to-noise ratio indicator, B i is the bandwidth availability index, α, β, and γ are weight coefficients, which are determined according to actual needs and experience. i Value, select C i The largest channel for communication; Fault detection and repair module, which monitors abnormal conditions of communication data in real time and establishes fault diagnosis models, including signal interruption, severe interference, and equipment failure; In fault diagnosis, the following fault judgment formula based on probability statistics is used: in P ( F ) is the probability of fault F occurring, n( F ) is the number of times the fault F is detected within a certain period of time, n( T ) is the total number of detections. P ( F ) exceeds the set threshold, it is judged that a fault F occurs; Interference suppression module is used to identify and suppress various interference signals in the communication process in real time, including electromagnetic interference and co-frequency interference. By adopting adaptive filtering algorithm and spectrum analysis technology, the signal-to-noise ratio after interference suppression is improved by no less than 5dB; In adaptive filtering, the following filter coefficient update formula is used: In n+1 =in n +μ×e n ×x n where w n+1 is the filter coefficient vector of the n+1th iteration, w n is the nth filter coefficient vector, μ is the step size factor, e n is the error signal, x n is the input signal vector. By continuously updating the filter coefficients, effective suppression of interference signals is achieved; The user interface module provides users with an intuitive communication status monitoring interface that displays real-time communication data, quality indicator trend charts, and fault alarm information; The security encryption module ensures the security and confidentiality of communication data. It uses advanced encryption algorithms to encrypt the transmitted data to prevent data from being stolen and tampered with. In data encryption, the following encryption formula is used: E(m, k) = c Where E is the encryption function, m is the original data, k is the encryption key, and c is the encrypted data. When decrypting, the corresponding decryption function D (c, k )=m restores the original data.

2. According to the large model-based adaptive remote communication control system described in claim 1, the large model analysis module has a model self-update function. With the continuous influx of new communication data, the model can be automatically updated and optimized online. After each update, the prediction accuracy of the model on the new data is improved by no less than 3%.

3. According to the large model-based adaptive remote communication control system of claim 1, the adaptive control module supports multi-objective optimization. When adjusting communication parameters, it can simultaneously take into account the optimization of multiple communication quality indicators, by establishing a multi-objective optimization function: F=w1×Q1+w2×Q2+···+w n ×Q n Where F is the optimization objective function, Q1...Q n is the 1st-nth communication quality indicator, w 1... w n is the corresponding weight coefficient, which is dynamically adjusted according to actual needs to achieve balanced optimization of different quality indicators.

4. According to the large model-based adaptive remote communication control system described in claim 1, the fault detection and repair module has an intelligent fault classification function, which classifies the detected faults according to type and severity, and divides them into hardware faults, software faults, minor faults, and major faults, and adopts corresponding repair strategies according to different classifications to improve the efficiency and accuracy of fault repair.

5. According to the large model-based adaptive remote communication control system described in claim 1, the user interface module supports multi-device adaptation, runs stably on different types of terminal devices, the display effect adapts to the screen size and resolution of different devices, and the interface layout and operation method are optimized according to the characteristics of the device, ensuring that users can conveniently use the system for remote communication control on various devices.

6. A control method for the large model-based adaptive remote communication control system according to claims 1 to 5, characterized in that: The following steps are involved: Communication data collection steps: Use specialized communication data collection equipment and technology to collect various data in the remote communication process in real time, including signal strength, signal-to-noise ratio, bandwidth utilization, data transmission rate, delay time, packet loss rate, signal spectrum characteristics, channel fading characteristics, and ensure that the frequency of data collection is not less than 10 times / second and the accuracy of the data is within ±0.3%; Big model analysis steps: input the collected communication data into a big model based on deep learning for analysis. The big model can mine the potential features and rules in the data and accurately predict the changing trend of communication quality by pre-training a large amount of historical communication data. In the prediction process, the optimization algorithm and stochastic gradient descent algorithm are used to adjust the model parameters to improve the prediction accuracy. The prediction accuracy on the validation set is not less than 85%; Adaptive control decision-making steps: According to the analysis results of the large model, formulate an adaptive control strategy to adjust the communication parameters in real time, including modulation mode, coding rate, transmission power, bandwidth allocation, and channel selection to optimize communication quality. During the adjustment process, by real-time monitoring of changes in communication quality indicators, continuous feedback adjustment of the control strategy is carried out to ensure that the signal strength is stable within the set range, the fluctuation range does not exceed ±3dB, the signal-to-noise ratio is increased to no less than 20dB, the bandwidth utilization rate is increased to no less than 80%, the delay time is controlled to no more than 100ms, and the packet loss rate is reduced to no more than 1%; Fault detection and repair steps: Real-time monitoring of abnormal communication data, timely identification of communication faults such as signal interruption, severe interference, and equipment failure by establishing a fault diagnosis model, and applying pattern recognition and data analysis technology. The detection accuracy of common faults is not less than 95%. When a fault is detected, corresponding repair measures are automatically taken, such as switching to backup communication links, adjusting communication parameters, and restarting related equipment. The repair success rate is not less than 90%. At the same time, a fault alarm is issued, and the alarm response time does not exceed 1 second. Interference suppression steps: Adopt adaptive filtering algorithm and spectrum analysis technology to identify and suppress various interference signals in the communication process in real time, including electromagnetic interference and co-frequency interference, reduce the impact of interference on communication quality, improve signal clarity and stability, and the signal-to-noise ratio after interference suppression is improved by no less than 5dB; In the interference suppression process, the formula for calculating the interference suppression effect is: I = S before -S after , Where I is the interference suppression amount, S before is the signal-to-noise ratio before interference suppression, S after is the signal-to-noise ratio after interference suppression; User interactive operation steps: Provide users with a visual operation interface, through which users can view communication status, historical data trends, fault alarm information in real time, and can set system parameters and select control strategies. The response time of interface operations does not exceed 0.5 seconds. It also supports historical data query and report generation functions, which is convenient for users to analyze and summarize communication conditions.

7. According to the large model-based adaptive remote communication control method of claim 6, before the large model analysis step, it also includes a data preprocessing step, which cleans, denoises and normalizes the collected data to remove outliers and noise interference. The data efficiency after data cleaning reaches more than 98%, ensuring the quality and reliability of the data and providing an accurate data basis for the analysis of the large model.

8. According to the large model-based adaptive remote communication control method described in claim 6, in the adaptive control decision step, it also includes a control strategy optimization step, according to the changes in the communication environment and the needs of users, the adaptive control strategy is optimized through a reinforcement learning algorithm to improve the adaptability and effectiveness of the control strategy, and the convergence time of the strategy optimization does not exceed 5 minutes.

9. According to the large model-based adaptive remote communication control method described in claim 6, after the fault detection and repair step, it also includes a fault recording and analysis step, which records the occurred faults in detail, including the fault type, occurrence time, and repair measures information. By analyzing the fault records, the fault laws are summarized to provide a basis for system improvement and optimization. The accuracy of fault analysis is not less than 85%.

10. According to the large model-based adaptive remote communication control method of claim 6, in the user interaction operation step, it supports users to customize the interface display content and layout. Users can choose to display communication indicators and charts of interest according to their preferences and usage habits, and adjust the color and font settings of the interface. The usage rate of custom functions is not less than 70%.

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