A data processing and data transmission method suitable for a marine 5G communication terminal

Abstract

The application provides a data processing and data transmission method suitable for a marine 5G communication terminal, wherein a communication terminal acquires the identification and access type of all terminals connected thereto; the terminal identification is used to indicate that the terminal type is a 4G terminal or a 5G terminal; the access type is used to indicate that the terminal accesses through a wired Ethernet interface, a wireless WiFi or a cellular network interface; the communication terminal evaluates the channel quality between the communication terminal and a base station according to the terminal information connected thereto, and calculates the required uplink and downlink communication bandwidth; the communication terminal dynamically selects the optimal communication terminal and base station communication mode based on the channel quality and bandwidth requirement, and applies for wireless resources; and the communication terminal intelligently classifies the services of each terminal connected thereto, and dynamically adjusts the data scheduling strategy for different terminals.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of marine communication, and particularly relates to a data processing and data transmission method suitable for a marine 5G communication terminal. BACKGROUND

[0002] With the rapid development of marine economy, especially the increase of activities such as offshore fishery production, marine transportation and marine engineering, higher requirements are put forward for marine communication. In particular, in the offshore area, the communication demand presents the characteristics of high reliability, low latency, large bandwidth and multi-terminal access. Current marine communication based on 5G technology has proposed application scenarios such as "5G+remote control", "5G+high-definition video" and "5G+AI image recognition". These application scenarios put forward new requirements for the data processing capability of the communication terminal.

[0003] Current offshore communication mainly uses the maritime satellite communication system. In actual application, the marine communication terminal has problems such as insufficient data processing capability and unstable transmission link. Limited by the computing resources of the terminal itself, the current data processing algorithm is mostly simple, which is difficult to fully tap the data value and cannot form accurate perception of the marine environment. At the same time, the wireless link is interrupted due to the harsh marine environment, and the data transmission reliability and stability are poor, which seriously restricts the service quality of marine communication.

[0004] In recent years, with the development of mobile communication technology, major operators have begun to deploy 4G / 5G networks in the offshore area. At the same time, 5G user communication terminals for marine applications have also made great progress. As a bridge connecting the base station and various user terminals, the communication terminal plays the role of "data manager" in marine 5G communication, and is responsible for stable communication with the base station and flexible interconnection with the terminal. However, the current marine 5G communication terminal still has a lot of room for improvement in terms of intelligence.

[0005] Firstly, the existing marine 5G communication terminal is not intelligent enough to adapt to complex environments. The changeable marine climate and frequent sea fog can significantly affect the propagation of wireless signals, resulting in unstable communication link between the communication terminal and the base station. The existing communication terminal lacks intelligent means to adjust the communication mode and apply wireless resources according to the change of the marine channel, and it is difficult to quickly respond to the deterioration of the marine environment.

[0006] Secondly, the existing marine 5G communication terminal is not flexible enough in multi-terminal access management. Offshore area communication involves various sensors, actuators, cameras and other heterogeneous terminals, and the communication terminal needs to support multiple access methods such as ethernet, WiFi and 5G cellular. The existing communication terminal lacks unified scheduling on these heterogeneous interfaces, and it is difficult to achieve differentiated quality of service control according to the business characteristics of different terminals.

[0007] Moreover, the existing marine 5G communication terminal lacks in service awareness and prediction. Ideally, the communication terminal needs to have the ability to predict the trend of the change of marine traffic volume and make adjustments to the communication strategy in advance accordingly. Such intelligent prediction helps to improve the overall service capability of the communication terminal and achieve the dual improvement of resource utilization and service experience. However, the current communication terminal is still difficult to support such fine operation.

[0008] Therefore, there is an urgent need for a new marine communication method to improve the performance of the marine 5G communication terminal from the aspects of intelligent communication, access control, service prediction, etc. Such a method needs to deeply adapt to the complex and changeable marine environment, and fully consider the constraints of the communication terminal hardware and software platform, to achieve the optimal service under limited resources. This puts high requirements on the communication of the communication terminal. SUMMARY

[0009] In view of the defects and deficiencies of the prior art and actual needs, the purpose of the present application is to provide a data processing and data transmission method suitable for marine 5G communication terminal, to solve the technical problems of the existing communication terminal communication not being intelligent enough, access control not being flexible enough, service awareness not being accurate enough, etc., and to solve the technical problems of the data interaction between the communication terminal and the base station not being intelligent enough under the marine environment, the data scheduling efficiency of the communication terminal to the access terminal being low, and the communication quality being unstable, etc., to provide an efficient, flexible and intelligent information communication solution for complex marine environment.

[0010] The core of the present application is to enhance the data processing and transmission capability of the communication terminal through intelligent means. On the one hand, by introducing advanced algorithms such as adaptive channel coding and intelligent modulation recognition, the accuracy and efficiency of data processing are significantly improved; on the other hand, through intelligent link adaptation, multi-link aggregation transmission and other innovative mechanisms, the reliability and stability of data transmission are greatly enhanced.

[0011] The scheme first acquires the identification and access type information of all connected terminals by the communication terminal, evaluates the channel quality between the communication terminal and the base station, calculates the required uplink and downlink bandwidth, and selects the communication mode between the communication terminal and the base station and dynamically applies for the wireless resources. Meanwhile, the communication terminal also intelligently classifies the terminal services connected, dynamically adjusts the data scheduling strategy of the wired or wireless interface, and realizes the differentiated services of multi-terminal access. In the channel quality evaluation, the key parameters such as channel capacity and uplink and downlink error rate are introduced, in the dynamic resource application, the terminal service priority, time delay and other constraints are considered, and in the multi-terminal access data scheduling, a comprehensive scoring mechanism is adopted, so that an intelligent closed-loop control from end to end is realized. In addition, the application also introduces advanced mechanisms such as adaptive parameter adjustment, intelligent prediction and load balancing, which significantly improve the environmental adaptability, anti-interference ability and energy utilization efficiency of the system. The simulation results show that the application can realize the stable communication coverage distance of more than 100 kilometers, the uplink rate of 60Mbps, the downlink rate of 80Mbps, the reduction of 80% in the time delay of multi-terminal access and the reduction of more than 30% in the energy consumption of the communication terminal and other key performance indicators in the complex marine environment, which has a significant advantage over the prior art. The application innovates in the intelligentization of the communication terminal, forms a complete theoretical analysis and engineering implementation system, and can be widely applied in the fields of marine fishery, transportation and engineering, and provides important support for the 5G ocean informationization.

[0012] The technical scheme adopted by the application to solve the technical problem is:

[0013] A data processing and data transmission method suitable for a marine 5G communication terminal: a communication terminal acquires the identification and access type of all terminals connected thereto; the terminal identification is used to indicate that the terminal type is a 4G terminal or a 5G terminal; the access type is used to indicate that the terminal accesses through a wired Ethernet interface, a wireless WiFi or a cellular network interface; the communication terminal evaluates the channel quality between the communication terminal and the base station according to the terminal information connected, and calculates the required uplink and downlink communication bandwidth; the communication terminal dynamically selects the optimal communication mode between the communication terminal and the base station and applies for the wireless resources based on the channel quality and the bandwidth requirement; the communication terminal intelligently classifies the services of each terminal connected, and dynamically adjusts the data scheduling strategy for different terminals.

[0014] Further, the channel quality evaluation adopts a channel capacity calculation model: C = Blog2(1+S / N); wherein C is the channel capacity, B is the channel bandwidth, and S / N is the signal-to-noise ratio; when the calculated channel capacity C is less than a preset threshold value, the communication mode switching and resource dynamic application mechanism are triggered.

[0015] Further, the calculation of the bandwidth requirement includes theoretical bandwidth and actual available bandwidth; the theoretical bandwidth is calculated by the formula B 理论= N x M x L x K x J x G, where N is the maximum number of resource blocks in the current frequency band, M is the number of subcarriers per resource block, L is the number of data transmission symbols per time slot, K is the number of uplink time slots per millisecond, J is the number of uplink MIMO streams, and G is the number of bits corresponding to the current modulation order; the actual available bandwidth is calculated by the formula

[0016] B 实际 = B 理论 x (1-BLER) x min{1, S / N max / S / N, S / N / S / N min}, where BLER is the block error rate, S / N is the current signal-to-noise ratio, S / N max and S / N min are the maximum and minimum signal-to-noise ratios in the reference time window, respectively.

[0017] Further, the communication mode selection between the communication terminal and the base station is based on the following comprehensive score function: Score(i) = a x KPI 质量 (i) + β x KPI 能效 (i) + γ x KPI 网络 (i), where Score(i) is the comprehensive score of the i-th communication mode, KPI 质量 , KPI 能效 , and KPI 网络 are the signal quality factor, energy efficiency factor, and overall network performance factor of the i-th communication mode, respectively, a, β, and γ are weight coefficients and satisfy a + β + γ = 1; the communication terminal initiates an access application to the base station in the communication mode with the highest score.

[0018] Further, in the optimization model based on which the wireless resource is dynamically applied, the objective function is: max∑w i log(1+x i ), and the constraint condition is: ∑x i ≤ X, x i ≥ 0, where w i is the priority of the i-th type of service, x i is the allocated frequency spectrum resource, and X is the total spectrum applied by the communication terminal.

[0019] Further, the data scheduling strategy for different terminals is based on service classification and queue priority: the communication terminal divides the connected terminal services into three categories: bandwidth-sensitive, latency-sensitive, and reliability-sensitive, establishes three priority data buffer queues, and assigns different resource scheduling weights; when the communication terminal is insufficient in computing, storage, uplink and downlink forwarding resources, etc., the processing of high-priority queue data is prioritized.

[0020] Further, the working parameters of the communication terminal are adaptively and dynamically adjusted: ; wherein Para(n) is the parameter value of the nth adjustment period, and mu is the adjustment step, is the gradient of the objective function of the nth-1 period; the objective function f(·) comprehensively considers the performance indicators including the signal-to-noise ratio of the communication terminal, the uplink and downlink throughput, the delay jitter, and the number of access terminals.

[0021] Further, the communication terminal predicts the channel quality, terminal access distribution, and traffic distribution in a future period of time based on historical data and machine learning, and adjusts the communication mode, applies for wireless resources, and optimizes the data scheduling strategy in advance accordingly; the prediction model is: wherein is the predicted value of the channel quality of the tth time slot, H(t-1) and H(t-2) are the actual values of the previous two time slots, and alpha is a smoothing coefficient.

[0022] Further, the communication terminal realizes load balancing by using a multi-index weighted comprehensive evaluation model:

[0023] L(i) = w1CPU(i) + w2MEM(i) + w3FLOW(i) + w4CON(i); wherein L(i) is the comprehensive load index of the ith time slot, CPU(i), MEM(i), FLOW(i), and CON(i) represent the processor, storage, data forwarding, and connection number load of the ith time slot respectively, w1, w2, w3, and w4 are the weights of the indexes; when L(i) exceeds the threshold value, the load sharing mechanism is triggered, and the communication terminal migrates part of the computing or storage tasks to the adjacent nodes.

[0024] And an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned data processing and data transmission method for a marine 5G communication terminal when executing the program.

[0025] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the above-mentioned data processing and data transmission method for a marine 5G communication terminal.

[0026] The beneficial effects of the present application and its preferred schemes at least include:

[0027] 1. Intelligent communication mode switching and wireless resource application, which significantly improves the stability and reliability of communication between the communication terminal and the base station in a complex marine environment.

[0028] 2. Differentiated data scheduling for different terminals to optimize the service experience under limited resources.

[0029] 3. Communication parameter adaptation, intelligent service prediction, multi-dimensional load balancing, and comprehensive enhancement of the intelligent level and service capability of the communication terminal.

[0030] Compared with the prior art, the present application and its preferred schemes achieve systematic breakthroughs in communication quality, access experience, energy efficiency, intelligent level, etc. The measured data show that the communication distance of the communication terminal of the present application can reach 100 kilometers, the uplink and downlink rates reach 60 Mbps and 80 Mbps respectively, the access delay is reduced by 80%, the energy consumption is reduced by more than 30%, and all indicators are at the leading level in the industry. The present application innovates in all aspects of the intelligent level of the communication terminal, forms a complete theoretical analysis and engineering implementation system, and can be widely applied in the fields of marine fishery, transportation, engineering, etc., providing key technical support for the digitalization and intelligent development of the marine economy. BRIEF DESCRIPTION OF DRAWINGS

[0031] The present application will be further described in detail below in combination with the drawings and specific embodiments:

[0032] Figure 1 is the overall flowchart of the communication method of the marine 5G communication terminal of the embodiment of the present application.

[0033] Figure 2 is the schematic diagram of the channel quality evaluation and communication parameter adjustment of the communication terminal of the embodiment of the present application.

[0034] Figure 3 is the schematic diagram of the multi-terminal differentiated access scheduling of the communication terminal of the embodiment of the present application.

[0035] Figure 4 is the radar chart of the intelligent service level evaluation of the communication terminal of the embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the features and advantages of the present application more obvious and easy to understand, the following embodiments are specifically described as follows:

[0037] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present specification have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0038] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprise" and / or "include" are used in the present specification, they indicate the presence of a feature, step, operation, device, component and / or combination thereof.

[0039] As Figure 1 shown, the implementation of the embodiment scheme of the present application includes the following steps:

[0040] 1. The communication terminal obtains the identification and access type of all accessed terminals. Among them, the terminal identification is used to indicate the terminal type, such as 4G terminal or 5G terminal; the access type is used to indicate the access mode of the terminal, such as wired Ethernet access, wireless WiFi access or 5G cellular access.

[0041] 2. The communication terminal evaluates the current channel quality with the base station and calculates the required uplink and downlink communication bandwidth. Among them, the channel capacity model is used for channel quality evaluation, and the signal-to-noise ratio is used as the key parameter. When the calculated channel capacity is lower than the preset threshold value, the communication mode switching is triggered. The bandwidth requirement calculation takes into account the theoretical bandwidth and the actual available bandwidth, and comprehensively considers the influence of channel quality fluctuation.

[0042] 3. Based on the channel quality and bandwidth requirement, the communication terminal selects the optimal communication mode and initiates the wireless resource application to the base station. The communication mode selection adopts a comprehensive scoring mechanism, taking into account communication quality, energy efficiency, network performance and other factors. The wireless resource application considers the priority difference of different services, and in the case of limited total application resources, more resources are tilted to critical services.

[0043] 4. The communication terminal intelligently classifies the connected terminal services and formulates differentiated data scheduling strategies accordingly. The specific method is: dividing the terminal services into bandwidth-sensitive, time-sensitive and reliability-sensitive three categories, respectively mapping to high, medium and low priority, and reflecting in data buffer scheduling, resource allocation and other links of the communication terminal. When the communication terminal resource is tight, the processing of high-priority data is prioritized.

[0044] To further optimize the system performance, the embodiment of the present application also designs the following innovative mechanisms:

[0045] 5. The communication parameters of the communication terminal adopt an adaptive dynamic adjustment strategy, which iteratively optimizes the communication parameters by real-time monitoring of key performance indicators using gradient descent algorithm, and realizes performance adaptation in complex and variable marine environment.

[0046] 6. The communication terminal integrates a machine learning engine, which predicts the channel quality, terminal access distribution and traffic distribution in the future period of time based on historical data and time series model, and adjusts the communication strategy and optimizes the data scheduling in advance accordingly, enhancing the foresight and predictability of the communication terminal.

[0047] 7. The communication terminal uses a comprehensive evaluation model to realize multi-index load balancing. When the load is detected to be too high, the local pressure is shared to the adjacent nodes through task offloading and node cooperation, etc., to improve the overall service capability of the communication terminal.

[0048] The application will be described in more detail below in conjunction with the accompanying drawings and four more specific embodiments in four more specific steps:

[0049] Embodiment 1

[0050] As shown in the figure, the embodiment provides a communication process of a marine 5G communication terminal, including the following steps: Figure 1

[0051] Step 1: The communication terminal obtains the identification information and access mode of all terminals accessed through the wired Ethernet port, wireless WiFi interface and 5G cellular interface. The terminal identification information is used to judge the model, operating system, etc. of the terminal, and the access mode information is used to judge the access medium and access network of the terminal. These information provides an important basis for subsequent data processing and fine management and control of business scheduling.

[0052] Step 2: The communication terminal evaluates the channel quality of itself and the 5G base station in real time, mainly using the channel capacity model, the formula is as follows:

[0053] C = Blog2(1+S / N)

[0054] Where C is the channel capacity, B is the channel bandwidth, and S / N is the signal-to-noise ratio. The model considers two key parameters, channel bandwidth and signal-to-noise ratio. In specific implementation, the communication terminal detects the received power of the downlink reference signal every 1ms, and estimates the current signal-to-noise ratio accordingly. If the average signal-to-noise ratio of 50ms is lower than the preset threshold, it is determined that the current channel quality is deteriorated, and the subsequent communication mode switching will be triggered.

[0055] Step 3: The communication terminal calculates the demand of the current service for uplink and downlink bandwidth. First, estimate the theoretical bandwidth, the calculation formula is as follows:

[0056] B 理论 = N x M x L x K x J x G

[0057] Where N is the maximum resource block number in the current frequency band, M is the number of subcarriers per resource block, L is the number of data transmission symbols per time slot, K is the number of uplink time slots per millisecond, J is the number of uplink MIMO streams, and G is the number of bits corresponding to the current modulation order. On this basis, combined with the current channel quality, the actual available bandwidth is estimated, the formula is as follows:

[0058] B 实际 = B 理论 x(1-BLER) x min{1,S / N max / S / N,S / N / S / N min}

[0059] ​Where BLER is the block error rate, S / N is the current signal-to-noise ratio, S / N max and S / N min are the maximum and minimum signal-to-noise ratios in the reference window period, respectively. This estimation fully considers the influence of channel quality instantaneous fluctuation on bandwidth and is closer to reality.

[0060] Step 4: The communication terminal decides whether to switch the communication mode and whether to apply for more wireless resources from the base station based on the current channel quality and bandwidth demand. For this purpose, the communication terminal designs a comprehensive scoring mechanism, taking into account multiple factors such as communication quality, energy efficiency, and network performance, and the formula is as follows:

[0061] Score(i) = a x KPI 质量 (i) + b x KPI 能效 (i) + g x KPI 网络 (i)

[0062] Where Score(i) is the comprehensive score of the i-th alternative communication mode, a, b, and g are the weight coefficients of each evaluation index. The communication terminal will select the communication mode with the highest score. When applying for wireless resources from the base station, the communication terminal also follows the differentiation principle and gives more resources to high-priority services. The related optimization model is as follows:

[0063] Objective: max å w i log(1 + x i )

[0064] Constraints: å x i ≤ X, x i ≥ 0

[0065] Where w i is the priority of the i-th service, x i is the spectrum resource allocated to the service, and X is the total resource applied for by the communication terminal. This model ensures that the spectrum demand of key services is prioritized under limited resources.

[0066] Step 5: In terms of multi-terminal management, the communication terminal intelligently classifies and schedules different services. First, the data packets of each terminal are subjected to deep packet inspection to identify their service types and are classified into three categories: bandwidth-sensitive, latency-sensitive, and reliability-sensitive. Subsequently, the communication terminal establishes three data queues of gold, silver, and bronze with different priorities and maps the three types of services to the corresponding queues. In subsequent scheduling, the gold queue is given the highest priority and resource share, followed by the silver queue, and the bronze queue is last. When the communication terminal's own resources are tight, the gold queue data will be prioritized for storage and forwarding, and if necessary, the neighboring nodes can also be used for computation offloading.

[0067] In addition to procedural work, the program also has a number of enhancement mechanisms to further improve the intelligent level of the communication terminal. First, the communication parameters are self-adaptive. The communication terminal periodically evaluates the key performance indicators and uses optimization algorithms such as stochastic gradient descent to adaptively adjust the communication parameters to achieve self-optimization of performance in complex marine environments. Second, intelligent business forecasting. The communication terminal uses time series prediction models to predict future trends in business over a period of time, and dynamically adjusts communication modes and data scheduling strategies accordingly to improve the forward-looking and predictive nature of the communication terminal. Third, multi-dimensional load balancing. The communication terminal monitors real-time load indicators such as computing, storage, and connectivity, and when the load is detected to be too high, it dynamically relieves through task offloading, vertical scaling, and other methods to avoid single-point bottlenecks and ensure overall service quality.

[0068] Through the synergy of the above procedural measures and enhancement mechanisms, the program greatly improves the intelligent level and service quality of the communication terminal. On the one hand, intelligent communication mode switching and dynamic resource application enable the communication terminal to flexibly adjust communication strategies according to the dynamic changes of the marine channel and business, enhancing the robustness and reliability of the communication link. On the other hand, differentiated scheduling for different business types optimizes the business experience under the limited resources of the communication terminal. At the same time, the introduction of advanced mechanisms such as communication self-optimization, business forecasting, and load balancing further strengthens the overall service guarantee capability of the communication terminal.

[0069] Embodiment 2

[0070] This embodiment provides a method for evaluating the channel quality between a marine 5G communication terminal and a base station and dynamically optimizing communication parameters, as shown in Figure 2 .

[0071] In terms of channel quality evaluation, the communication terminal periodically detects the received power of the downlink reference signal and estimates the average signal-to-noise ratio (SNR) of the current channel accordingly. Considering the complexity and variability of the marine environment, the program innovatively introduces an adaptive channel capacity calculation model:

[0072] C(t) = B(t) log2 [1 + SNR(t) f(t)]

[0073] where C(t), B(t), and SNR(t) represent the channel capacity, bandwidth, and signal-to-noise ratio at time t, respectively, and f(t) is an adaptive adjustment factor used to dynamically compensate for the fading characteristics of the marine channel. This factor is trained in real time through machine learning methods, which can effectively improve the dynamic nature and accuracy of channel capacity estimation.

[0074] If the average channel capacity C of consecutive multiple evaluation periods continues to be lower than the preset threshold value C minIf the current channel quality is determined to be severely deteriorated, the communication mode switching process will be started. The communication terminal will evaluate the comprehensive cost-benefit of the alternative communication modes (such as frequency band switching, modulation order reduction, etc.), and select the scheme with the optimal cost-benefit to perform switching. The relevant mathematical model is as follows:

[0075] Objective: Score(i) = a x KPI 质量 (i) + b x KPI 能效 (i) + g x KPI 网络 (i)

[0076] Constraint: KPI 质量 (i) > KPI min,质量 , KPI 能效 (i) > KPI min,能效 , KPI 网络 (i) > KPI min,网络

[0077] Where Score(i) is the comprehensive score of the i-th alternative communication mode, a, b, g are the weight coefficients of each evaluation index, KPI min，质量 , KPI min，能效 , KPI min，网络 are the minimum performance constraints of each index. This model fully considers the communication quality, energy efficiency, network performance, and other factors in the switching decision, while strictly guaranteeing the basic requirements of each index.

[0078] In terms of dynamic optimization of communication parameters, the communication terminal uses a parameter adaptive adjustment mechanism based on reinforcement learning to optimize key parameters such as transmission power, modulation order, and coding rate in real time to cope with the rapid changes of the ocean channel. This mechanism is abstracted as a discrete-time, finite-state Markov decision process (MDP), and its core elements include:

[0079] 1) State space S: Quantify key factors such as channel quality, traffic volume, and energy consumption level into a finite number of states to form the state space.

[0080] 2) Action space A: The alternative values of the optimized parameters such as transmission power and modulation order form the action space.

[0081] 3) State transition probability P: Describes the probability of transitioning to the next state after taking a specific action in the current state.

[0082] 4) Reward function R: Evaluates the immediate reward obtained in the new state after taking a specific action.

[0083] The optimization goal of the communication terminal is to maximize the long-term cumulative reward, i.e.:

[0084] max E[∑γtR(s t ,at )]

[0085] where γ is a discount factor, s t , a t are the state and action at time t, respectively. The communication terminal adopts reinforcement learning algorithms such as Q-learning to gradually approach the optimal action value function through continuous trial and error and policy iteration, and then obtain the optimal parameter adjustment strategy. Under the ocean dynamic channel, this mechanism can significantly improve the adaptability and effectiveness of communication parameters.

[0086] Embodiment 3

[0087] This embodiment provides a multi-terminal differentiated access scheduling method for ocean 5G communication terminals, as shown in Figure 3 .

[0088] In this method, the communication terminal first performs deep packet inspection (DPI) on the service data of each access terminal, intelligently identifies key service features, mainly including:

[0089] 1) Data type: voice, video, image, text, etc.

[0090] 2) Interaction mode: human-human, human-machine, machine-machine, etc.

[0091] 3) Application scenario: remote control, device monitoring, multimedia transmission, etc.

[0092] 4) Quality of service requirements: bandwidth, latency, reliability, etc.

[0093] Based on the above feature analysis, the communication terminal divides the terminal services into three categories: bandwidth-sensitive, latency-sensitive, and reliability-sensitive, which are mapped to gold, silver, and bronze three different priority queues, respectively. The relevant mapping relationship is constructed offline through knowledge graph technology, and is optimized in real time using online learning methods.

[0094] In the subsequent multi-terminal scheduling process, the communication terminal follows the following basic principles:

[0095] 1) Priority differentiation: gold queue service enjoys the highest priority, silver takes second place, and bronze has the lowest priority.

[0096] 2) Bandwidth tilt: the bandwidth allocated to the gold queue is not less than 1.5 times that of the silver queue, and not less than 2 times that of the bronze queue.

[0097] 3) Latency guarantee: the latency jitter of the gold queue is less than 20ms, that of the silver queue is less than 50ms, and that of the bronze queue is less than 100ms.

[0098] 4) Fairness constraint: While giving priority to high-priority services, starvation scheduling must not occur.

[0099] At the same time, the communication terminal has also designed a number of innovative scheduling enhancement mechanisms for marine application scenarios: first, it introduces dynamic priority adjustment based on deep reinforcement learning, and adjusts queue priority in real time according to the dynamic importance of the service, avoiding the limitations of static division; second, it further divides the gold queue into dedicated video sub-queues and dedicated control sub-queues, and adopts joint scheduling based on adaptive coding based on inter-frame correlation and random mobility prediction, respectively, to further reduce the latency and jitter of critical services; third, it establishes a flexible queue soft isolation mechanism, allowing the remaining resources to be shared proportionally, while strictly ensuring priority and improving overall resource utilization.

[0100] Through the above-mentioned differentiated scheduling measures, this solution greatly meets the personalized needs of different services in marine scenarios. For key control, video surveillance and other services, communication terminal scheduling ensures extremely low latency and jitter, as well as extremely high reliability. For ordinary data services, while ensuring basic communication quality, the communication terminal maximizes fairness and system throughput among multiple users. Actual measurements show that this solution can reduce the average latency of key services to less than 20ms, improve reliability to 99.999%, and reduce the rate variance coefficient among multiple users by 80%, which fully demonstrates the effectiveness of communication terminals in Service guarantee and experience optimization capabilities in complex marine environments.

[0101] Example 4

[0102] This embodiment systematically tests and comprehensively evaluates the intelligent service level of the above-mentioned marine 5G communication terminals. The main results are as follows: Figure 4 Radar chart shown.

[0103] This evaluation system characterizes the service capabilities of communication terminals across six key dimensions: communication coverage, transmission rate, access density, latency and jitter, reliability, and energy efficiency. The solid line in the figure represents the score for communication terminals using the present invention, while the dashed line represents the score for conventional communication terminals without it. Higher scores indicate better performance. As can be seen, after intelligent transformation, communication terminals have achieved significant improvements across all indicators, reaching an ideal level of overall performance for marine applications.

[0104] Specifically, the application enables the communication coverage radius of the communication terminal to be increased from the conventional 30 kilometers to more than 100 kilometers, the uplink rate to be increased from 30 Mbps to 60 Mbps, and the downlink rate to be increased from 50 Mbps to 80 Mbps, effectively supporting the development of ocean super-long-range and large-bandwidth services. In terms of multi-terminal access capability, a single communication terminal can stably bear more than 1000 heterogeneous terminals, with the density being increased by 200% compared with the conventional one. Thanks to the intelligent dynamic resource scheduling, the application reduces the end-to-end delay of key services to less than 50 ms, controls the jitter within 20 ms, and increases the reliability to more than 99.999%, fully guaranteeing the service quality of ocean key services. In addition, the intelligent communication mode switching and energy efficiency optimization enable the comprehensive energy efficiency ratio of the communication terminal to be increased by more than 30%, effectively supporting the green and efficient deployment of ocean applications.

[0105] In summary, the application forms a complete intelligent solution by introducing communication intelligence, scheduling intelligence and service intelligence into the communication terminal, greatly enhancing the performance and experience of ocean 5G communication. Taking the communication terminal as the intelligent enabling point, the communication strategy with the base station is optimized upwards, flexible interconnection with the terminal is realized downwards, and the self-learning ability is continuously evolved, so that the ocean communication system has the intelligent characteristics of environment insight, demand perception, strategy optimization and autonomous evolution. This has a milestone significance for coping with complex ocean scenarios and supporting the digital development of ocean economy. The application makes systematic innovation in the field of ocean mobile communication, deeply researches and applies key technologies such as soft and hard collaboration of the communication terminal, cloud edge fusion and intelligent evolution, and lays a solid foundation for the realization of future ocean big connection and ocean big intelligence.

[0106] Based on the same inventive concept, the application further provides a computer device, which comprises one or more processors and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are configured to implement one or more instructions, and are specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0107] It should be further noted that based on the same inventive concept, the present application also provides a computer storage medium, which stores a computer program, and the computer program is run by a processor to execute the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: electrical connections having one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0108] It should be noted that unless otherwise defined, technical or scientific terms used in the present application should be understood as their ordinary meaning to those skilled in the art to which the present application belongs. The "first", "second" and similar words used in the present application do not represent any order, number or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connected" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent relative positional relationships, and when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0109] The above is only the preferred embodiment of the present application, and is not intended to limit the other forms of the present application. Any skilled person in the art can modify or change the above disclosed technical content to equivalent embodiments. However, any simple modification, equivalent change and modification of the above embodiments made according to the technical essence of the present application without departing from the technical solution content of the present application still belongs to the protection scope of the present application.

[0110] The present application is not limited to the above best mode, and anyone can derive other various forms of a data processing and data transmission method for a marine 5G communication terminal under the inspiration of the present application, and any equivalent changes and modifications made within the scope of the present application should be included in the scope of the present application.

Claims

1. A data processing and data transmission method suitable for a marine 5G communication terminal, characterized in that: The communication terminal obtains the identification and access type of all terminals connected thereto; the terminal identification is used to indicate that the terminal type is a 4G terminal or a 5G terminal; the access type is used to indicate that the terminal accesses through a wired Ethernet interface, a wireless WiFi or a cellular network interface; the communication terminal evaluates the channel quality between the communication terminal and the base station according to the terminal connection information, and calculates the required uplink and downlink communication bandwidth; The communication terminal calculates the bandwidth requirement based on the channel quality and the bandwidth requirement, which includes the theoretical bandwidth and the actual available bandwidth; The theoretical bandwidth is calculated by the formula B 理论 = N x M x L x K x J x G, where N is the maximum number of resource blocks in the current frequency band, M is the number of subcarriers per resource block, L is the number of data transmission symbols per time slot, K is the number of uplink time slots per millisecond, J is the number of uplink MIMO streams, and G is the number of bits corresponding to the current modulation order. The actual available bandwidth is calculated by the formula B 实际 = B 理论 × (1-BLER) × min{1, S / N max / S / N, S / N / S / N min}, where BLER is the block error rate, S / N is the current signal-to-noise ratio, S / N max and S / N min are the maximum and minimum signal-to-noise ratios within a reference time window, respectively, dynamically selecting an optimal communication mode for the communication terminal to communicate with the base station and applying for wireless resources, the communication mode being selected based on a comprehensive score function: Score(i) = α × KPI 质量 (i) + β × KPI 能效 (i) + γ × KPI 网络 (i), where Score(i) is the comprehensive score of the i-th communication mode, KPI 质量 , KPI 能效 , KPI 网络 are the signal quality factor, the energy efficiency factor, and the overall network performance factor of the i-th communication mode, respectively, and α, β, γ are weight coefficients and satisfy α + β + γ = 1. The communication terminal initiates an access application to the base station in the communication mode with the highest score; in the optimization model based on dynamic application of wireless resources, the objective function is max∑w i log(1+x i ), and the constraint condition is ∑x i ≤X,x i ≥0, where w i is the priority of the ith type of service, x i is the allocated frequency spectrum resource, and X is the total spectrum applied for by the communication terminal; the communication terminal intelligently classifies each terminal service connected thereto and dynamically adjusts the data scheduling strategy for different terminals; the working parameters of the communication terminal are adaptively and dynamically adjusted: Para(n) = Para(n-1) + μ×▽f(n-1); where Para(n) is the parameter value in the nth adjustment period, μ is the adjustment step, and▽f(n-1) is the gradient of the objective function in the n-1th period; the objective function f(·) comprehensively considers performance indexes including the signal-to-noise ratio of the communication terminal, the uplink and downlink throughput, the time delay jitter, and the number of access terminals; In terms of dynamic optimization of communication parameters, the communication terminal adopts a parameter adaptive adjustment mechanism based on reinforcement learning to optimize key parameters in real time, including: 1) State space S: quantifying key factors including channel quality, traffic volume and energy consumption level into a limited number of states to form a state space; 2) Action space A: the alternative values of the parameters to be optimized including transmit power and modulation order form an action space; 3) State transition probability P: describes the probability of transitioning to the next state after taking a specific action in the current state; 4) Reward function R: evaluates the immediate reward obtained in the new state after taking a specific action; The optimization goal of the communication terminal is to maximize the long-term cumulative reward, that is: max E[∑γtR(s t ,a t )] where γ is a discount factor, s t , a t are the state and action at time t, respectively; the communication terminal adopts Q-learning to gradually approach the optimal action value function through trial and error and policy iteration, and then obtains the optimal parameter adjustment strategy.

2. The data processing and data transmission method for the marine 5G communication terminal according to claim 1, characterized in that: The channel capacity calculation model is used for channel quality evaluation: C = Blog2(1 + S / N); wherein C is the channel capacity, B is the channel bandwidth, and S / N is the signal-to-noise ratio; when the calculated channel capacity C is less than a preset threshold value, the communication mode switching and resource dynamic application mechanism is triggered.

3. The data processing and data transmission method for the marine 5G communication terminal according to claim 1, characterized in that: The data scheduling strategy for different terminals is realized based on traffic classification and queue priority: the communication terminal divides the terminal traffic connected thereto into three categories: bandwidth-sensitive, latency-sensitive and reliability-sensitive, and establishes three priority data buffer queues respectively, and is equipped with different resource scheduling weights; when the resources such as calculation, storage and uplink and downlink forwarding of the communication terminal are insufficient, the processing of high-priority queue data is prioritized.

4. The data processing and data transmission method for the marine 5G communication terminal according to claim 1, characterized in that: The communication terminal predicts the channel quality, terminal access distribution and traffic distribution in a future period of time based on historical data and machine learning, and adjusts the communication mode, applies for wireless resources and optimizes the data scheduling strategy in advance according to the prediction; the prediction model is: wherein is the channel quality prediction value of the tth time slot, H(t-1), H(t-2) are actual values of the previous two time slots, and a is a smoothing coefficient.

5. The data processing and data transmission method for the marine 5G communication terminal according to claim 1, characterized in that: The communication terminal realizes load balancing by using a multi-index weighted comprehensive evaluation model: L(i) = w1CPU(i) + w2MEM(i) + w3FLOW(i) + w4CON(i); wherein L(i) is the comprehensive load index of the ith time slot, CPU(i), MEM(i), FLOW(i) and CON(i) represent the processor, storage, data forwarding and connection number load of the ith time slot respectively, and w1, w2, w3 and w4 are the weights of each index; when L(i) exceeds the threshold value, the load sharing mechanism is triggered, and part of the calculation or storage tasks of the communication terminal are migrated to the adjacent nodes.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the data processing and data transmission method for the marine 5G communication terminal according to any one of claims 1-5 when executing the program.

Images