Wireless communication transmission efficiency optimization method and system based on data analysis

Abstract

The invention discloses a wireless communication transmission efficiency optimization method and system based on data analysis, and relates to the technical field of wireless communication, and the method comprises the following steps: S1, constructing a local link fluctuation characteristic analysis model; s2, constructing a sudden interference event identification criterion model based on a threshold value; s3, determining whether to execute channel bypass switching or not according to the criterion result in real time; s4, updating the sequence of the service buffer queue in real time; s5, constructing a transmission queue mechanism based on priority; and S6, carrying out self-adaptive fine tuning optimization on the threshold value in the step S2. According to the method, a dynamic optimization system including link fluctuation perception, event identification, channel switching, resource allocation and adaptive feedback is constructed, so that the response speed and the identification precision of the system to burst interference are improved, and the adaptive capacity of a network scheduling strategy to environmental change is enhanced; and finally, the communication transmission efficiency is remarkably improved, and the key service is stably guaranteed.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a method and system for optimizing wireless communication transmission efficiency based on data analysis. Background Technology

[0002] With the continuous evolution of 5G and future 6G wireless communication systems, the dynamics and complexity of network environments are increasing. Especially under conditions of dense multi-user access, high-frequency propagation, and drastic channel transients, communication links face multiple challenges such as sudden interference, resource contention, and bandwidth degradation. To address this, current communication systems typically employ link switching mechanisms and resource allocation strategies based on fixed thresholds. For example, channel switching is triggered by setting a fixed lower limit for signal-to-noise ratio or a bit error rate threshold, or resource scheduling is achieved through static service priority configuration. However, these traditional methods have significant shortcomings: firstly, they lack the ability to model short-term link fluctuations with high sensitivity, making it difficult to identify sudden interference in a timely manner; secondly, resource allocation lacks a real-time feedback adjustment mechanism, making it difficult to cope with abrupt bandwidth changes caused by channel state switching, thus affecting the continuity of critical services.

[0003] For example, in traditional systems, channel switching is often based on long-term average channel quality, failing to fully utilize the high-frequency fluctuation characteristics within a short time window, resulting in interference response delays or misjudgments. Secondly, existing scheduling strategies are mostly statically configured, failing to dynamically optimize based on current service buffer conditions and available channel resources, and lacking a complete "perception-decision-execution-feedback" closed-loop mechanism, leading to a lack of adaptive capabilities in complex environments. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing wireless communication transmission efficiency based on data analysis, thereby solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for optimizing wireless communication transmission efficiency based on data analysis, comprising the following steps: S1. Construct a local link fluctuation characteristic analysis model to capture short-term local characteristic changes in wireless communication links and effectively identify short-term sudden interference. S2. Based on the results of the model output in step S1, construct a sudden interference event identification criterion model based on threshold. S3. Based on the criterion results of the model in step S2, decide in real time whether to perform channel bypass switching; S4. Based on the switching results performed in step S3, update the service buffer queue sorting in real time to address the temporary resource constraints or delays that occur after switching to the backup channel. S5. Based on the service buffer queue, construct a priority-based transmission queue mechanism to optimize the efficiency of wireless communication transmission; S6. After each efficiency optimization cycle, the threshold in step S2 is adaptively fine-tuned based on the current link fluctuations and service transmission quality performance.

[0006] To further optimize this technical solution, in step S1, the local link fluctuation characteristic analysis model collects communication link channel quality parameters by setting a short-term sliding window and analyzes the significance of short-term fluctuations. The local link fluctuation characteristic analysis model is shown below:

[0007] in, An indicator representing the overall degree of short-term link fluctuations; The standard deviation of the signal-to-noise ratio within the window represents channel fluctuation; The average signal-to-noise ratio within the window is used to normalize the fluctuation amplitude. The standard deviation of the interference intensity within the window; This represents the average interference intensity within the window. This represents the number of retransmissions within a short window. The maximum allowed number of retransmissions is preset and used as a normalization benchmark. The model outputs This value reflects the severity of link fluctuations; a higher value indicates more pronounced local fluctuations.

[0008] To further optimize this technical solution, in step S2, the sudden interference event identification criterion model defines the sudden interference identification logic, which is used to quickly and automatically determine whether a sudden interference event has occurred. The criterion model for identifying sudden interference events is shown below:

[0009] in, This is the identifier for triggering interference events; a value of 1 indicates that an interference event has been triggered, while a value of 0 indicates that no interference event has been triggered. The system's preset link fluctuation threshold; Output the model results in step S1; This model is obtained based on real-time computation step S1. The system compares it with the set threshold. If the indicator exceeds the threshold, it is directly determined that a sudden interference event has occurred.

[0010] To further optimize this technical solution, in step S3, the interference event trigger identifier from step S2 is used... Determine whether to perform channel bypass switching and construct a dynamic bypass channel switching decision model; The dynamic bypass channel switching decision model triggers identifiers in real time based on interference events. Adjusting channel switching decisions and selecting backup frequency bands or carriers in advance can help avoid the impact of sudden interference on communication quality and ensure that wireless communication links can respond quickly when encountering sudden interference.

[0011] To further optimize this technical solution, the dynamic bypass channel switching decision model is as follows:

[0012] in, Select an identifier for the currently used channel; This is a preset backup channel identifier; This is the identifier for the currently used main communication channel; Output the model results in step S2; When the output identifier of step S2 When this happens, the system immediately executes a channel switching decision, and the communication link quickly switches to the preset backup channel. If no interference is triggered If so, the current communication channel will continue to be maintained.

[0013] To further optimize this technical solution, in step S4, after switching to the backup channel, the service buffer queue sorting is updated in real time by calculating the current channel resource status and the preset service weight; Based on the currently used channel selection identifier obtained in step S3 Establish a buffer priority adjustment model and output the buffer weight index for each business category. Implement intelligent buffer priority adjustment to ensure the quality of critical business operations.

[0014] To further optimize this technical solution, the buffer priority adjustment model is as follows:

[0015] in, This represents the overall weight value indicating the current business buffer priority. Priority weight for business type; For the current channel Real-time available resource quantity; This represents the maximum theoretical resource capacity of the backup channel; In this model, Services with higher numerical values ​​have higher priority in the sending queue to ensure the quality of service for critical services.

[0016] To further optimize this technical solution, in step S5, a priority-based transmission queue mechanism is used to establish a resource allocation strategy model to optimize the transmission order and resource consumption of data packets. The resource allocation strategy model is shown below:

[0017] in, Resource quotas allocated for current business operations; This represents the total available resource capacity under the current channel. This is the sum of the priority weights of all currently pending service buffer queues; The model calculates the actual resource share of each service in real time based on the service's buffer priority weight, so that services with higher buffer weights can use more resources.

[0018] To further optimize this technical solution, in step S6, when performing adaptive fine-tuning optimization, a threshold dynamic optimization model is constructed to automatically adjust the threshold threshold. The threshold dynamic optimization model is as follows:

[0019] in, This is the threshold for interference triggering after a new optimization cycle; The threshold value triggered by the original interference; This is the threshold adjustment coefficient, used to limit the magnitude of each update and prevent over-adjustment; its value is between 0.01 and 0.2. This is the latest comprehensive index output from step S1; The resource quota for the current service output in step S5; The expected optimal value for allocating service resources reflects the anticipated best transmission quality.

[0020] A data analysis-based wireless communication transmission efficiency optimization system is constructed based on the aforementioned wireless communication transmission efficiency optimization method. This system includes the following functional modules: Local link fluctuation analysis module; Sudden interference event determination module; Dynamic channel switching execution module; Buffer scheduling weight generation module; Resource dynamic allocation and scheduling module; Threshold adaptive optimization module.

[0021] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a wireless communication transmission efficiency optimization method and system based on data analysis as described in the first aspect of the present invention.

[0022] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a data analysis-based wireless communication transmission efficiency optimization method and system as described in the first aspect of the present invention.

[0023] Compared with existing technologies, the present invention provides a method and system for optimizing wireless communication transmission efficiency based on data analysis, which has the following beneficial effects: This data analysis-based wireless communication transmission efficiency optimization method and system, by constructing a dynamic optimization system that includes link fluctuation perception, event identification, channel switching, resource allocation, and adaptive feedback, not only improves the system's response speed and identification accuracy to sudden interference, but also enhances the network scheduling strategy's adaptability to environmental changes. Ultimately, it achieves a significant improvement in communication transmission efficiency and stable guarantee of critical services, and is particularly suitable for next-generation wireless communication scenarios with high dynamics, high density, and multiple concurrent services. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1This is a flowchart illustrating a data analysis-based method for optimizing wireless communication transmission efficiency proposed in this invention. Figure 2 This is a schematic diagram of the module composition of a wireless communication transmission efficiency optimization system based on data analysis proposed in this invention. Detailed Implementation

[0026] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0028] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0029] Example 1: Reference Figure 1 This is the first embodiment of the present invention, which provides a method for optimizing wireless communication transmission efficiency based on data analysis, including the following steps: S1. Construct a local link fluctuation characteristic analysis model to capture short-term local characteristic changes in wireless communication links and effectively identify short-term sudden interference.

[0030] Existing technologies for link interference identification or transmission scheduling often rely on average channel quality (such as average signal-to-noise ratio) within a fixed time window or statistical indicators based on a longer period. These technologies cannot effectively reflect the drastic fluctuations of communication links in a short period of time, resulting in untimely response to sudden interference or a high probability of misjudgment.

[0031] The local link fluctuation characteristic analysis model collects communication link channel quality parameters (including signal-to-noise ratio, interference intensity, and retransmission count) by setting a short-term sliding window, and analyzes the significance of short-term fluctuations. The local link fluctuation characteristic analysis model is shown below:

[0032] in, An indicator representing the overall degree of short-term link fluctuations; The standard deviation of the signal-to-noise ratio within the window represents channel fluctuation; The average signal-to-noise ratio within the window is used to normalize the fluctuation amplitude. The standard deviation of the interference intensity within the window; This represents the average interference intensity within the window. This represents the number of retransmissions within a short window. The maximum allowed number of retransmissions is preset and used as a normalization benchmark. Used to normalize the retransmission pressure on the link.

[0033] The model outputs This value reflects the severity of link fluctuations; a higher value indicates more pronounced local fluctuations.

[0034] In practical applications, the system measures the signal-to-noise ratio within the sliding window at fixed time intervals (e.g., 500ms). Interference intensity sequence and the number of retransmissions within that time period. To conduct statistics.

[0035] This comprehensive volatility indicator The larger the value, the more unstable the current link state. The system uses this value to determine whether sudden interference may occur and passes it as input to the model in step S2. Compared with existing methods that rely on subjective thresholds or average deviations, this model better reflects the dynamics and uncertainties of actual wireless channels and has better recognition capabilities in complex environments.

[0036] S2. Based on the results of the model output in step S1, construct a threshold-based criterion model for identifying sudden interference events.

[0037] In current wireless communication systems, interference detection typically relies on fixed timeout thresholds (e.g., retransmissions exceeding N times) or single performance parameters (e.g., signal-to-noise ratio below a certain value). This single-variable and static threshold-based approach suffers from high false positive rates and an inability to adapt to dynamic changes in different communication scenarios. Especially in environments with multipath fading, frequent obstruction, or sudden interference, existing technologies often fail to accurately determine whether an event is a genuine sudden interference event, easily leading to system failures such as "false handover triggering" or "unresponsive interference."

[0038] The sudden interference event identification criterion model defines the sudden interference identification logic, which is used to quickly and automatically determine whether a sudden interference event has occurred. The criterion model for identifying sudden interference events is shown below:

[0039] in, This is the identifier for triggering interference events; a value of 1 indicates that an interference event has been triggered, while a value of 0 indicates that no interference event has been triggered. The system's preset link fluctuation threshold; Output the model results from step S1.

[0040] Within each scheduling cycle, the system obtains the comprehensive fluctuation index calculated in step S1. and the currently set trigger threshold A comparison is then made. Once the fluctuation amplitude of the current link is found to reach or exceed a preset threshold, it is immediately... A value of 1 indicates that a possible sudden interference event has been identified; otherwise, a value of 0 indicates that the link is in a stable state or the fluctuation is insufficient to trigger the strategy.

[0041] This model can run automatically without external input, triggering an identifier. This status signal is transmitted to step S3, enabling the system to respond instantly. This determination mechanism is highly modular, allowing it to be embedded in base stations or applied to local communication processing chips in terminals. It enables proactive detection and assessment of the health status of communication links, providing strong real-time support for optimizing wireless transmission efficiency.

[0042] This model is obtained based on real-time computation step S1. The system compares it with the set threshold. If the indicator exceeds the threshold, a sudden interference event is directly identified. This criterion is simple and efficient, requiring no complex calculations or human intervention. It not only covers information on channel fluctuations, interference changes, and retransmission frequencies, but also simplifies the judgment process to logical judgment, eliminating the need for complex algorithms or model solutions. It has advantages such as strong real-time performance, simple deployment, and high maintainability.

[0043] S3. Based on the criterion results of the model in step S2, decide in real time whether to perform channel bypass switching.

[0044] When faced with sudden interference, most existing wireless communication systems rely on underlying link automatic retransmission mechanisms (such as ARQ protocols) or pre-set channel reselection strategies. However, these mechanisms often have two major limitations: First, the response is delayed, meaning that the system often only performs channel switching after the user has already perceived severe lag or dropped connection. Second, the switching conditions are too mechanical, usually making decisions based on a single indicator such as signal-to-noise ratio, making it difficult to accurately distinguish between instantaneous channel fluctuations and actual interference events, which can easily lead to unnecessary frequent switching and increase system overhead.

[0045] According to the interference event trigger identifier in step S2 Determine whether to perform channel bypass switching and construct a dynamic bypass channel switching decision model; The dynamic bypass channel switching decision model triggers identifiers in real time based on interference events. Adjusting channel switching decisions and selecting backup frequency bands or carriers in advance can help avoid the impact of sudden interference on communication quality and ensure that wireless communication links can respond quickly when encountering sudden interference.

[0046] The dynamic bypass channel switching decision model is shown below:

[0047] in, Select an identifier (channel index or identifier) ​​for the currently used channel.

[0048] The system identifies a pre-defined backup channel, which is a suboptimal channel determined through historical transmission reliability analysis and regional interference statistics. During operation, the system continuously records the communication stability (e.g., packet loss rate, retransmission count), interference intensity statistics (e.g., interference source density, neighboring cell overlap), and spectrum resource utilization efficiency (e.g., bandwidth occupancy, resource block utilization) of each available channel at different time periods. By weighted analysis and sorting of this historical data, the system dynamically evaluates the most suitable channel as a backup link within the current communication area and sets the channel identifier as [identified channel]. This allows for a rapid switch to the backup channel in the event of performance degradation of the main channel or a sudden interference event, ensuring communication stability and continuity.

[0049] This is the identifier for the currently used main communication channel.

[0050] Output the model results in step S2.

[0051] When the output identifier of step S2 When this occurs, it indicates that the current link is in an unstable state or has been subjected to sudden interference. At this time, the system immediately performs a channel switch, changing the communication carrier or frequency band from the current main channel. Migrate to backup channel This achieves a seamless transition and reduces the risk of service interruption; if no interference is triggered... If the current communication channel is not maintained, the wireless communication link will continue to operate. This ensures a rapid response when encountering sudden interference, effectively avoiding quality degradation caused by communication interruption or severe interference.

[0052] Compared to traditional strategies, this model improves the system's ability to handle unexpected problems through an "identification-response" logical decision chain, exhibiting higher real-time performance, reliability, and application adaptability. The model outputs a channel identifier. It will be directly passed to the next step, and used for subsequent business reordering logic after changes in link resources, so as to achieve close linkage and optimization between processes.

[0053] S4. Based on the switching results executed in step S3, update the service buffer queue sorting in real time to address the temporary resource constraints or delays that occur after switching to the backup channel.

[0054] In traditional wireless communication systems, when a link switches to a backup channel, the system typically continues to use the original service scheduling priority or static buffer order, without making timely adjustments to the instantaneous fluctuations in bandwidth resources and differences in service quality after the channel switch. This fixed strategy can lead to delays, stuttering, or even interruptions in some high-priority services (such as video conferencing and voice calls) after sudden interference switching in highly dynamic environments, especially when backup channel resources are limited.

[0055] After switching to the backup channel, the service buffer queue order is updated in real time by calculating the current channel resource status and the preset service weight; based on the currently used channel selection identifier obtained in step S3... Establish a buffer priority adjustment model and output the buffer weight index for each business category. Implement intelligent buffer priority adjustment to ensure the quality of critical business operations.

[0056] The buffer priority adjustment model is as follows:

[0057] in, The comprehensive weight value represents the current service buffer priority and determines the transmission order of service data.

[0058] Priority weights are assigned to different service types (e.g., 1.0 for video services, 0.8 for voice, and 0.5 for data synchronization). These weights are determined based on the varying sensitivities of different services to communication quality, using a combination of preset strategies and historical service quality feedback data. The system prioritizes analyzing the tolerance of different service types to latency, bandwidth, packet loss, and retransmission in actual operation. For example, real-time video calls are extremely sensitive to bandwidth and latency, thus receiving a high weight (e.g., 1.0); voice communication, while sensitive to latency, has relatively low bandwidth requirements, resulting in a medium weight (e.g., 0.8); and non-real-time services such as data synchronization have higher tolerance and therefore lower weights (e.g., 0.5). These weights are continuously adjusted based on QoS metrics and user experience scores collected during long-term network operation to achieve resource scheduling strategies that better align with actual service needs.

[0059] For the current channel Real-time available resources (such as instantaneous bandwidth).

[0060] This represents the maximum theoretical resource capacity of the backup channel.

[0061] In this model, Services with higher numerical values ​​have higher priority in the sending queue to ensure the quality of service for critical services.

[0062] After the system identifies a sudden interference and performs a channel switch in step S3, step S4 immediately performs a buffer reordering operation on each service queue based on resource real-time performance and service priority. In the model, if the available resources for the current channel are limited, the overall formula value is low, and only critical services with high weights can still obtain higher values. Priority is given to services with higher priority. Conversely, when channel resources are plentiful, lower priority services are prioritized. The value also increases accordingly, giving it a greater chance of being scheduled. The model outputs... This will serve as the input variable for the next step, directly determining the actual bandwidth allocation ratio and achieving logical linkage.

[0063] S5. Based on the service buffer queue, construct a priority-based transmission queue mechanism to optimize the efficiency of wireless communication transmission.

[0064] Currently, most wireless communication systems use static weight mapping or first-come-first-served strategies for resource allocation. For example, the proportional fair scheduling algorithm used in traditional LTE systems mainly relies on the ratio of historical throughput to current rate for resource allocation. This approach is slow to respond to sudden link switching, rapid bandwidth decline, or sudden changes in services, and lacks sensitivity to service types. As a result, high-priority services (such as remote conferencing and real-time interaction) cannot be guaranteed in a timely manner, which seriously affects service continuity and user experience.

[0065] A priority-based transmission queue mechanism is used to establish a resource allocation strategy model to optimize the transmission order and resource consumption of data packets. The resource allocation strategy model is shown below:

[0066] in, Resource quotas (such as bandwidth or channel usage time) allocated for the current service. This represents the total available resource capacity under the current channel. The sum of priority weights for all currently pending service buffer queues is used as a normalization factor.

[0067] During each scheduling cycle, the system first collects the buffer priority weights of all services to be transmitted. Calculate their sum, and then according to their respective... Calculate the resource allocation ratio that each business should receive. The allocation result is directly used for scheduler configuration, such as modulation resource allocation, transmission time slot allocation, and burst control window arrangement.

[0068] The core advantage of this model lies in its ability to implement a real-time scheduling strategy that prioritizes services based on their importance and resource availability, ensuring that high-priority services are not degraded due to channel switching or resource fluctuations. Simultaneously, the model outputs resource quotas... This information is fed into step S6 to enable the system to optimize the interference threshold through feedback, thereby improving the overall network transmission efficiency and service reliability in a closed loop. This mechanism is simple, scalable, and interpretable, facilitating rapid deployment across different wireless protocols and platforms.

[0069] The model calculates the actual resource share of each service in real time based on the service's buffer priority weight, so that services with higher buffer weights can use more resources, thereby improving the efficiency of network resource utilization and the user's perceived experience.

[0070] S6. After each efficiency optimization cycle, the threshold in step S2 is adaptively fine-tuned based on the current link fluctuations and service transmission quality performance.

[0071] In existing wireless communication systems, the trigger thresholds for interference events (such as the lower limit of signal-to-noise ratio and the bit error rate threshold) are often set by network planners based on experience and remain static during system operation. While this strategy may be effective in fixed scenarios, under real-world conditions such as dynamically changing user distribution, diversified service types, and highly fluctuating communication environments, these static thresholds fail to reflect the current network operating status. This can easily lead to excessively high or low interference detection sensitivity, resulting in problems such as false handovers and missed detections, ultimately reducing communication efficiency and stability.

[0072] When performing adaptive fine-tuning optimization, a threshold dynamic optimization model is constructed to automatically adjust the threshold threshold through feedback. The threshold dynamic optimization model is as follows:

[0073] in, This is the threshold for interference triggering after a new optimization cycle; The threshold value triggered by the original interference; This is the threshold adjustment coefficient, used to limit the magnitude of each update and prevent over-adjustment; its value is between 0.01 and 0.2. This is the latest comprehensive index output from step S1; The resource quota for the current service output in step S5; The expected optimal value for allocating service resources reflects the anticipated best transmission quality.

[0074] During system operation, after each complete scheduling cycle (i.e., after resource allocation is completed), the system will update the current status. With current resources Input them into the model together. If the current link fluctuation is significant ( (Higher), while resource allocation performance is relatively low ( If the system experiences significant fluctuations, the model automatically reduces the threshold (increasing system sensitivity); conversely, if the link fluctuations are small or resource scheduling is good, the trigger threshold is appropriately increased to avoid overreaction by the system. This adjustment strategy, driven by the synergy between real-time indicator differences and outcome performance, enables the system to be robust, ensuring that adjustments are both effective and efficient.

[0075] Through the continuous operation of this model, the system will gradually converge to an interference identification state that is more adaptable, has a lower false alarm rate, and is more stable, providing dual support from the data layer and the policy layer for the efficiency optimization of the entire wireless communication link.

[0076] After each optimization cycle, the system adaptively fine-tunes the threshold based on current link fluctuations and service transmission quality performance. When actual resource allocation is low (transmission efficiency decreases) and link fluctuations are significant, the system automatically lowers the threshold to more sensitively respond to interference; conversely, it raises the threshold to reduce unnecessary frequent switching. This achieves dynamic balance and self-optimization of the entire system operation.

[0077] Example 2: Reference Figure 2 This is the second embodiment of the present invention. This embodiment provides a wireless communication transmission efficiency optimization system based on data analysis, constructed based on the wireless communication transmission efficiency optimization method described in Embodiment 1. The system includes the following functional modules: Local link fluctuation analysis module; It is used to monitor and collect key operating parameters in the communication link in real time, including instantaneous signal-to-noise ratio, interference intensity, and retransmission count. Combined with short-time sliding window technology, it dynamically calculates the comprehensive index.

[0078] Sudden interference event determination module; The module receives a comprehensive performance index as input to determine whether a sudden interference event has occurred. It employs a binary decision-making strategy to output an interference event trigger identifier, serving as the core decision-making unit in the system for determining link stability. The decision result directly impacts channel selection and scheduling strategy execution.

[0079] Dynamic channel switching execution module; Based on the interference event trigger identifier, the system automatically selects either the primary or backup channel to quickly switch the current communication link. This module ensures uninterrupted communication through an "on-demand switching" mechanism, making it a key response mechanism in the system for guaranteeing continuous and stable transmission.

[0080] Buffer scheduling weight generation module; After channel switching, the comprehensive weight value of the buffer priority of each service is dynamically calculated based on the actual available resources and service type weights. This module is used to reconstruct the transmission queue priority order, ensuring that high-priority services can still be transmitted with priority under resource-constrained conditions. It is a crucial foundational module for implementing service layering guarantees in the system.

[0081] Resource dynamic allocation and scheduling module; Based on the buffer weights of each service and the available resources on the current channel, dynamic resource allocation is performed for different services. This module ensures efficient scheduling under both resource-constrained and resource-saturated conditions, and is the core scheduling unit for improving bandwidth utilization and user service quality.

[0082] Threshold adaptive optimization module; Based on the current link fluctuations and resource allocation performance, the interference trigger threshold is slightly adjusted in real time, forming a self-learning and self-adjusting mechanism for the system. This module constitutes the core of the system's feedback control, enabling the system to have long-term evolution and environmental adaptability, ensuring that the interference detection sensitivity is always at an optimal level.

[0083] This system is data-driven, with interference detection at its core and channel scheduling and resource allocation as its main execution lines. It also achieves dynamic self-optimization of system parameters through a closed-loop feedback mechanism. The six modules are interconnected and interdependent, constructing a highly responsive, robust, service-aware, and dynamically self-adjustable wireless communication transmission efficiency optimization system, which is particularly suitable for highly dynamic communication scenarios in 5G / 6G environments.

[0084] Example 3: This embodiment also provides a computer device applicable to a data analysis-based wireless communication transmission efficiency optimization method and system, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the data analysis-based wireless communication transmission efficiency optimization method and system proposed in the above embodiment.

[0085] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a wireless communication transmission efficiency optimization method and system based on data analysis as proposed in the above embodiments.

[0086] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0087] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0089] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0090] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the efficiency of wireless communication transmission based on data analysis, characterized in that, The method comprises the following steps: S1, constructing a local link fluctuation feature analysis model to capture short-term local feature changes of a wireless communication link and effectively identify short-term burst interference; S2, constructing a burst interference event identification criterion model based on the results output by the model in step S1; S3, determining whether to perform channel bypass switching in real time according to the criterion results of the model in step S2; S4, updating the service buffer queue order in real time for the temporary resource limitation or delay problem caused after switching to the backup channel based on the switching results in step S3; S5, constructing a priority-based transmission queue mechanism based on the service buffer queue to complete the efficiency optimization of wireless communication transmission; S6, after each efficiency optimization period ends, performing adaptive fine-tuning optimization of the threshold value in step S2 through the current link fluctuation and service transmission quality performance.

2. The wireless communication transmission efficiency optimization method based on data analysis of claim 1, wherein, In step S1, the local link fluctuation feature analysis model collects communication link channel quality parameters by setting a short-time sliding window and analyzes the significance of short-time fluctuation. The local link fluctuation feature analysis model is as follows: In step S2, the burst interference event identification criterion model defines burst interference identification logic for quickly and automatically determining whether a burst interference event occurs currently. an indicator of the degree of integration representing short-term link fluctuations; Snr is the standard deviation of the signal-to-noise ratio within the window, representing channel fluctuations; SNRmean is the mean signal-to-noise ratio within the window, used to normalize the amplitude of fluctuations; is the standard deviation of the interference strength within the window; is the average of the interference strength within the window; is the number of retransmissions for short window; The preset maximum allowed retransmission number is taken as a normalization reference. The model output For reflecting the severity of link fluctuation, the higher the value indicates the more obvious local fluctuation.

3. The method for optimizing the efficiency of wireless communication transmission based on data analysis according to claim 1, characterized in that, The burst interference event identification criterion model is as follows: In step S2, the burst interference event identification criterion model defines burst interference identification logic for quickly and automatically determining whether a burst interference event occurs currently. The dynamic bypass channel switching decision model is as follows: is an interference event trigger identifier, taking the value of 1 to indicate triggering of an interference event, and 0 to indicate no triggering; The preset link fluctuation threshold for the system; is the model output result in step S1; The model compares the index with a set threshold value based on the real-time calculation in step S1 If the index exceeds the threshold value, the system directly determines that a burst interference event has occurred.

4. The wireless communication transmission efficiency optimization method based on data analysis of claim 1, wherein, In step S3, the interference event trigger identifier from step S2 is used as the basis for the process. Determine whether to perform channel bypass switching and construct a dynamic bypass channel switching decision model; Dynamic bypass channel switching decision model triggers according to interference event trigger identifier in real time Adjusting channel switching decision, selecting backup frequency band or carrier in advance, for avoiding the influence of burst interference on communication quality, ensuring that the wireless communication link can respond quickly when encountering burst interference.

5. The wireless communication transmission efficiency optimization method based on data analysis of claim 4, wherein, In step S2, the burst interference event identification criterion model defines burst interference identification logic for quickly and automatically determining whether a burst interference event occurs currently. In step S4, after switching to the backup channel, the service buffer queue order is updated in real time by calculating the current channel resource status and the preset service weight. select an identity for the currently used channel; is a preset standby channel identifier; is the current used communication primary channel identification; is the model output result in step S2; When the output identifier of step S2 identifies the system immediately performs a channel switching decision and the communication link is quickly switched to the pre-arranged channel ; if no interference has been triggered, i.e. , the current communication channel is maintained.

6. The method for optimizing the efficiency of wireless communication transmission based on data analysis according to claim 1, wherein, The buffer priority adjustment model is as follows: Based on the current used channel selection identifier obtained in step S3 , a buffer priority adjustment model is established, and a service class buffer weight index is output , intelligent buffer priority adjustment is implemented to ensure the quality of critical services.

7. The wireless communication transmission efficiency optimization method based on data analysis of claim 6, wherein, In step S4, after switching to the backup channel, the service buffer queue order is updated in real time by calculating the current channel resource status and the preset service weight. In step S5, the priority-based transmission queue mechanism establishes a resource allocation strategy model in the mechanism to optimize the transmission order and resource occupation of data packets. a combined weight value representing the current service buffer priority; is the service type priority weight; for the current channel amount of resources available in real time; is the maximum theoretical resource capacity for the spare channel; In this model, The services with higher values have higher priority in the sending queue, to ensure the quality of service for critical services.

8. The wireless communication transmission efficiency optimization method based on data analysis of claim 1, wherein, The resource allocation strategy model is as follows: In step S5, the priority-based transmission queue mechanism establishes a resource allocation strategy model in the mechanism to optimize the transmission order and resource occupation of data packets. In step S6, when performing adaptive fine-tuning optimization, a threshold value dynamic optimization model is constructed to automatically feedback and optimize the threshold value. a resource quota obtained for the current service; total resource capacity available for the current channel; a sum of priority weight values for all currently buffered queues of traffic to be transmitted; The threshold value dynamic optimization model is as follows:

9. The method for optimizing the efficiency of wireless communication transmission based on data analysis according to claim 1, wherein, In step S6, when performing adaptive fine-tuning optimization, a threshold value dynamic optimization model is constructed to automatically feedback and optimize the threshold value. The system comprises the following functional modules: Local link fluctuation analysis module; Threshold for interference trigger after a new round of optimization period; a threshold value for triggering the original interference; is the threshold adjustment coefficient, used to limit the amplitude of each update, to prevent over-adjustment, and takes a value between 0.01 and 0.2; the latest integrated degree indicator output for step S1; a resource quota for the current service output for step S5; The expected optimal value of the allocation of the business resource reflects the expected optimal transmission quality.

10. A wireless communication transmission efficiency optimization system based on data analysis, constructed based on the wireless communication transmission efficiency optimization method of any one of claims 1-9, characterized in that, Burst interference event determination module; Dynamic channel switching execution module; Buffer scheduling weight generation module; Resource dynamic allocation and scheduling module; Threshold value adaptive optimization module. ​ ​

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