A highly integrated communication method and system combining wide- and narrow-band integration

Abstract

The present invention discloses a highly integrated communication method and system for wide- and narrow-band fusion. The method comprises: obtaining channel state information data from a communication environment, extracting narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and classifying and recording them; constructing a resource allocation model based on the transmission characteristic parameters and capacity parameters, and using a service priority division method to determine the weight relationship between real-time control signals and multimedia data; if the transmission request ratio of real-time control signals exceeds a preset threshold, allocating narrowband channel resources to form a preliminary allocation plan, etc. The method of the present invention achieves the rational allocation and efficient utilization of wideband and narrowband resources by constructing a resource allocation model and adopting a variety of optimization algorithms. While meeting the needs of different types of services, it improves the overall utilization rate of channel capacity and avoids idleness and waste of resources.

Description

Technical Field

[0001] The present invention relates to the field of integrated communication technology, and in particular to a highly integrated wide- and narrowband fusion integrated communication method and system. Background Art

[0002] In the field of industrial automation, advances in communication technology are crucial for improving production efficiency and system reliability. In particular, in highly integrated communication environments, integrated communication approaches that combine broadband and narrowband are considered key to enabling multi-service collaboration and meeting the transmission needs of diverse data types within limited resources. However, research and application in this area still face numerous challenges, and there is an urgent need to overcome the limitations of existing technologies.

[0003] Traditional communication solutions currently struggle to balance the needs of diverse services in converged broadband and narrowband scenarios. Many approaches employ a unified strategy for handling all data types, which can significantly impact resource allocation and transmission efficiency. This often limits system performance when simultaneously processing high-performance, data-intensive services.

[0004] Specifically, the core challenge facing this field lies in how to design reasonable transmission strategies for different bandwidth characteristics and business requirements within limited hardware resources. First, the differences in the characteristics of wideband and narrowband channels make a single processing method difficult to adapt. Narrowband channels are more suitable for low-rate, high-reliability transmission, while wideband channels tend to be more suitable for high-throughput transmission. This difference directly leads to the complexity of resource allocation. With the diversification of business types, such as the coexistence of real-time control signals and multimedia data, the system needs to find a balance between low latency and high throughput. The lack of this balance further exacerbates the difficulty of transmission strategy design, creating a unique communication problem.

[0005] Therefore, how to dynamically adjust the transmission strategy according to bandwidth characteristics and business needs to ensure the reliable transmission of real-time control signals while maximizing the transmission efficiency of multimedia data has become a key issue in the current research on integrated broadband and narrowband communication methods. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to propose a highly integrated wide- and narrowband fusion integrated communication method and system, which can solve at least one of the technical problems mentioned in the background technology.

[0007] According to one aspect of the present invention, a highly integrated wide- and narrow-band integrated communication method is provided, the method comprising:

[0008] Acquire channel state information data from the communication environment, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and classify and record them;

[0009] Building a resource allocation model based on the transmission characteristic parameters and capacity parameters, and using a service priority division method to determine the weight relationship between real-time control signals and multimedia data;

[0010] If the transmission request ratio of the real-time control signal exceeds a preset threshold, narrowband channel resources are allocated to form a preliminary allocation plan;

[0011] Calculating the remaining broadband channel capacity according to the preliminary allocation plan and queuing multimedia data transmission requests using a proportional fairness algorithm;

[0012] Obtain transmission efficiency indicators for real-time monitoring, and adjust the narrowband channel resource allocation ratio if the transmission delay exceeds the preset threshold;

[0013] Calculate the service demand balance deviation value through the time window analysis method. If it exceeds the preset range, recalculate the broadband and narrowband resource allocation ratio;

[0014] Obtain channel status change data and use the time series prediction model to generate resource pre-adjustment parameters for the next cycle;

[0015] Resources are pre-allocated for the next periodic communication environment according to the pre-adjustment parameters, and a final transmission strategy is formed through a cyclic iteration method.

[0016] In the above technical solution, the above method is a highly integrated wide-narrowband fusion communication method aimed at improving communication efficiency and reliability. By obtaining key data such as channel state information, resources are dynamically allocated according to the priority and transmission characteristics of different services, and transmission strategies are optimized with the help of multiple algorithms to ensure efficient, fair and timely service transmission in complex communication environments, and meet the transmission requirements of real-time control signals and multimedia data.

[0017] Channel state information acquisition and parameter extraction: Acquire channel state information data from the communication environment, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and classify and record them.

[0018] Resource Allocation Model Construction and Weighting: A resource allocation model is constructed based on transmission characteristics and capacity parameters. Service prioritization is used to determine the weighting between real-time control signals and multimedia data. This prioritization clarifies the order in which different service types compete for resources, ensuring that latency-sensitive services such as real-time control signals receive priority resources while also properly considering the transmission requirements of multimedia data.

[0019] Initial allocation of narrowband channel resources: If the proportion of real-time control signal transmission requests exceeds a preset threshold, narrowband channel resources are allocated to form a preliminary allocation plan. When real-time control signals have high transmission requirements, narrowband channel resources are prioritized, demonstrating rapid response and support capabilities for critical services. Narrowband channels generally offer good stability and reliability, making them suitable for transmitting control signals with strict latency requirements and relatively small data volumes.

[0020] Broadband channel resource allocation optimization: Based on the preliminary allocation plan, the remaining broadband channel capacity is calculated and a proportional fairness algorithm is used to queue multimedia data transmission requests. While ensuring real-time control signal transmission, the remaining broadband channel capacity is utilized for multimedia data services. This proportional fairness algorithm achieves relatively fair resource allocation across multiple multimedia data transmission requests, improving overall channel resource utilization while balancing the interests of different users.

[0021] Transmission efficiency monitoring and resource adjustment: Transmission efficiency metrics are captured for real-time monitoring. If transmission latency exceeds a preset threshold, the narrowband channel resource allocation ratio is adjusted. Real-time transmission efficiency monitoring promptly identifies transmission anomalies. When transmission latency exceeds the threshold, the narrowband channel resource allocation ratio is adjusted to optimize transmission performance and ensure smooth service delivery. This demonstrates the dynamic adaptability of the communication method and its strict control over transmission quality.

[0022] Business demand balance and resource reallocation: Time window analysis is used to calculate the deviation from the business demand balance. If the deviation exceeds a preset range, the broadband and narrowband resource allocation ratios are recalculated. Time window analysis helps observe the changing trends of business demand over time. When there is a significant deviation from the business demand balance, the resource allocation ratio is recalculated promptly to readjust the investment in broadband and narrowband resources to meet the current actual business needs and avoid resource waste or shortage.

[0023] Resource Pre-adjustment and Transmission Strategy Formulation: Channel state change data is acquired and a time series prediction model is used to generate resource pre-adjustment parameters for the next cycle. Resources are pre-allocated for the next communication cycle based on these pre-adjustment parameters, and a final transmission strategy is formed through an iterative approach. The time series prediction model is used to predict future channel state changes and plan resource allocation in advance, improving the communication system's ability to predict and respond to future changes. The iterative approach continuously optimizes the transmission strategy, making it more comprehensive and adaptable to the actual communication environment.

[0024] By constructing a resource allocation model and employing multiple optimization algorithms, the above method achieves the rational allocation and efficient utilization of broadband and narrowband resources. While meeting the needs of different types of services, it also improves the overall utilization of channel capacity and avoids idle and wasted resources. The weight relationship between real-time control signals and multimedia data is clarified, and the transmission of real-time control signals is prioritized during resource allocation, ensuring the timeliness and reliability of critical services. This is of great significance for some delay-sensitive application scenarios. By monitoring transmission efficiency in real time and reallocating resources based on the balance deviation value of service needs, and using a time series prediction model to plan resource allocation in advance, the communication method can quickly adapt to changes in the communication environment and service needs, and has strong flexibility and robustness.

[0025] In some embodiments, acquiring channel state information data from a communication environment, extracting narrowband channel transmission characteristic parameters and wideband channel capacity parameters, and classifying and recording them includes:

[0026] Acquire channel state information data from the communication environment through sensors and monitoring equipment, pre-process the collected raw data to remove noise and redundancy, and obtain preliminary cleaned channel data;

[0027] For the channel data after preliminary cleaning, the support vector machine algorithm is used to extract the data features, separate the narrowband channel transmission characteristic parameters and the broadband channel capacity parameters, and determine the characteristic distribution of the two types of parameters;

[0028] Based on the results of the feature distribution, the narrowband channel transmission characteristic parameters and the broadband channel capacity parameters are structured and divided. If the feature value exceeds the preset threshold range, it is marked as abnormal data, and the classified parameter set is obtained;

[0029] By converting the storage format of the classified parameter set into a unified database storage format, a standardized parameter data set for subsequent analysis is obtained;

[0030] For the standardized parameter data set, cluster analysis is used to group the parameters of narrowband and broadband channels. If the grouping results show that the distribution of a certain type of parameter deviates from the normal range, it is marked to determine the potential channel anomaly category;

[0031] According to the anomaly categories of the marked channels, the corresponding parameter feature change trends are obtained. By comparing historical data records, the specific impact range and change pattern of the anomaly categories are determined;

[0032] By performing data mapping on the change pattern, it is matched with the pre-established channel state model. If the matching degree is higher than the preset threshold, it is classified as a known problem type to obtain the final channel state classification result.

[0033] In the above technical solution, the above method aims to accurately obtain channel state information from a complex communication environment, and deeply process and analyze the original data, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and simultaneously identify and process abnormal data to provide high-quality, standardized data support for subsequent resource allocation and communication optimization. It is the basic link of the entire integrated communication method and is directly related to the rationality and effectiveness of subsequent communication strategies.

[0034] Channel state information acquisition and data preprocessing: Channel state information is acquired from the communication environment through sensors and monitoring equipment. These devices can perceive various channel characteristics, such as signal strength and noise level, in real time. The collected raw data is preprocessed to remove noise and redundancy to improve data quality and usability. Noise removal can be achieved using filtering algorithms, while redundancy removal reduces data volume and improves subsequent processing efficiency, resulting in preliminarily cleaned channel data.

[0035] Feature extraction and parameter separation: A support vector machine algorithm is used to extract features from the initially cleaned channel data, separating narrowband channel transmission characteristic parameters and wideband channel capacity parameters, and determining the characteristic distributions of these two types of parameters. The support vector machine algorithm has excellent performance in extracting features from high-dimensional data, identifying the most representative features from large amounts of channel data and effectively separating the relevant parameters of narrowband and wideband channels.

[0036] Parameter Classification and Anomaly Marking: Based on the characteristic distribution results, narrowband channel transmission characteristic parameters and broadband channel capacity parameters are structured and divided. This means that these parameters are organized according to specific rules and structures to facilitate subsequent management and use. Furthermore, if the characteristic value exceeds the preset threshold range, it is marked as abnormal data. This is to promptly detect possible channel anomalies. The preset threshold is determined based on statistical analysis of normal channel conditions and empirical judgment to obtain a classified parameter set.

[0037] Data storage format conversion and standardization: Converting the classified parameter sets into a unified database storage format ensures data consistency and compatibility, facilitates subsequent reading and processing by various analysis tools and algorithms, and obtains standardized parameter data sets for subsequent analysis.

[0038] Cluster analysis and channel anomaly identification: Cluster analysis is used to group narrowband and wideband channel parameters within a standardized parameter dataset. Cluster analysis can group similar parameters together, helping to uncover hidden patterns and structures within the data. If the grouping results indicate that the distribution of a particular parameter deviates from the normal range, it is labeled and the potential channel anomaly identified.

[0039] Abnormal parameter feature analysis and impact assessment: Based on the anomaly category of the labeled channel, the corresponding parameter feature change trend is obtained. By comparing historical data records, the specific impact scope and change pattern of the anomaly category are determined. This step, through in-depth analysis of the abnormal parameters, can understand the development process of the anomaly and the potential impact.

[0040] Data mapping and channel status classification: The change pattern is mapped and matched against a pre-established channel status model. If the match exceeds a preset threshold, it is classified as a known problem type, resulting in the final channel status classification result. The pre-established channel status model includes various common channel problem types and their characteristic patterns. Through data mapping and matching, the known problem type corresponding to the current channel state can be quickly and accurately determined.

[0041] This method ensures the quality and reliability of channel state information through preprocessing to remove noise and redundancy, as well as marking and processing abnormal data, providing a solid data foundation for subsequent communication decisions. Leveraging support vector machine algorithms and cluster analysis, it is possible to deeply mine characteristic information from channel data, enabling precise extraction and classification of narrowband and wideband channel parameters, and improving understanding and awareness of channel status. The entire process focuses on identifying and analyzing abnormal data, enabling timely detection of potential channel issues. By comparing with historical data and matching models, it accurately assesses the impact and type of anomalies, providing strong support for the stable operation of the communication system.

[0042] In some embodiments, a resource allocation model is constructed based on the transmission characteristic parameters and the capacity parameters, and a service priority division method is used to determine a weight relationship between real-time control signals and multimedia data, including:

[0043] Initial data features are obtained through transmission feature analysis, and the transmission features are hierarchically processed using preset classification rules to obtain feature stratification results;

[0044] Construct a capacity parameter map based on the feature stratification results, use statistical tools to quantitatively analyze the capacity parameters, and determine the capacity distribution range;

[0045] Match the capacity distribution range with the resource allocation requirements. If the capacity distribution range exceeds the preset threshold, dynamically adjust the resource allocation to determine whether the adjusted allocation plan meets the requirements.

[0046] Obtain the adjusted allocation plan data, perform weight calculation based on the business priority division method, and determine the priority order of the real-time control signal through a preset weighted formula;

[0047] Process the control signal data according to the priority order of the real-time control signal, and use the logic judgment method. If the control signal data fluctuation exceeds the preset range, it will be smoothed to obtain stable signal data;

[0048] Through comparative analysis of stable signal data and multimedia data, the support vector machine algorithm is used to optimize the data weights and determine the final configuration of the weight ratio;

[0049] The output of the resource allocation model is generated based on the final configuration of the weight ratio. The ratio determination result is verified through data verification tools to obtain the final allocation strategy.

[0050] In the above technical solution, the above method is the key link in constructing a resource allocation model and determining the service weight relationship. Through steps such as transmission characteristic analysis, capacity parameter quantification, dynamic adjustment and weight optimization, it ensures that the resource allocation plan not only meets the high-priority requirements of real-time control signals, but also reasonably takes into account the transmission requirements of multimedia data, providing scientific and precise strategic guidance for subsequent communication resource allocation.

[0051] Transmission feature layering: Initial data features are obtained through transmission feature analysis. These features are then layered using pre-set classification rules to produce the resulting feature layering results. This detailed layering of transmission features clearly demonstrates the impact of different layers of features on resource allocation.

[0052] Capacity parameter quantification and range determination: Based on the feature stratification results, a capacity parameter map is constructed. Statistical tools are used to quantitatively analyze the capacity parameters and determine the capacity distribution range. Quantitative analysis makes capacity parameters more operational, and a clear capacity distribution range provides a quantitative basis for resource allocation.

[0053] Dynamic adjustment of resource allocation: The capacity distribution range is matched with the resource allocation requirements. If the capacity distribution range exceeds the preset threshold, the resource allocation is dynamically adjusted to determine whether the adjusted allocation plan meets the requirements.

[0054] Business priority weight calculation: Obtain the adjusted allocation plan data, perform weight calculation based on the business priority division method, and determine the priority order of real-time control signals through a preset weighted formula.

[0055] Control signal data stabilization processing: Control signal data is processed according to the priority order of real-time control signals. Using a logical judgment method, if the control signal data fluctuation exceeds the preset range, it is smoothed to obtain stable signal data. Stabilization processing ensures the reliability of the control signal and reduces the adverse effects of fluctuations.

[0056] Data weight optimization: By comparing and analyzing stable signal data with multimedia data, we use a support vector machine algorithm to optimize data weights and determine the final weight ratio. This optimized weight ratio makes resource allocation more reasonable and balances the needs of different services.

[0057] Resource Allocation Model Output and Verification: The output of the resource allocation model is generated based on the final configuration of the weight ratios. The ratio determination results are verified using data verification tools to obtain the final allocation strategy. The verification process ensures the accuracy and reliability of the output results and ensures the effective implementation of the resource allocation model.

[0058] This method precisely processes data at every step, from transmission characteristic analysis to capacity parameter quantification and dynamic resource allocation adjustments, ensuring resource allocation decisions are based on accurate information. This method can adjust resource allocation in real time based on the matching of capacity distribution with resource demand, making the system highly adaptable and flexible, effectively responding to changes in the communication environment. By combining support vector machine algorithms to optimize data weights, it scientifically and rationally balances the resource requirements of real-time control signals and multimedia data, improving overall communication efficiency.

[0059] In some embodiments, if the transmission request ratio of the real-time control signal exceeds a preset threshold, narrowband channel resources are allocated to form a preliminary allocation plan, including:

[0060] By collecting real-time monitoring data of control signals, the dynamic changes of transmission requests are obtained and the current status of the request ratio is determined;

[0061] If the request ratio exceeds the preset threshold, the narrowband channel resource allocation process is triggered, an initial allocation plan is constructed, and a preliminary channel resource configuration result is obtained;

[0062] Based on the preliminary channel resource configuration results, the support vector machine algorithm is used to evaluate the rationality of resource allocation and determine whether the allocation scheme meets the real-time requirements of signal transmission;

[0063] If the evaluation results show that the allocation plan does not meet the signal transmission requirements, the channel resources are dynamically adjusted, the adjusted resource configuration data is obtained, and a new allocation plan is determined;

[0064] By performing secondary verification on the adjusted resource configuration data and using a pre-established performance evaluation model, it is determined whether the channel resource utilization meets the expected standards;

[0065] If the utilization rate does not meet the expected standard, the narrowband channels are optimized and allocated based on the real-time monitoring data to obtain the final resource allocation plan;

[0066] Based on the final resource allocation plan, the real-time performance of signal transmission is continuously tracked, the updated status of transmission requests is obtained, and the stability of system operation is judged.

[0067] In the above technical solution, the process mainly dynamically monitors the transmission request ratio of real-time control signals. When the request ratio exceeds a preset threshold, a series of evaluation and adjustment steps are carried out to reasonably allocate narrowband channel resources to ensure that the real-time requirements of signal transmission are met, while improving the utilization rate of channel resources, forming a dynamic, efficient and stable resource allocation mechanism.

[0068] Dynamic monitoring of transmission requests: By collecting real-time monitoring data of control signals, we can understand the dynamic changes in transmission requests and determine the current status of the request ratio. This is the trigger condition for the entire resource allocation process. Only by accurately monitoring changes in the transmission request ratio can the subsequent resource allocation process be initiated in a timely manner, ensuring that real-time control signals are processed promptly.

[0069] Narrowband channel resource allocation trigger: If the request ratio exceeds a preset threshold, the narrowband channel resource allocation process is triggered, an initial allocation plan is constructed, and a preliminary channel resource configuration result is obtained. The preset threshold is set based on a comprehensive consideration of real-time control signal transmission requirements and system resource conditions. When the request ratio exceeds this threshold, it indicates that the current resource allocation can no longer meet the demand and resource reallocation is required.

[0070] Resource Allocation Rationality Assessment: Based on the preliminary channel resource configuration results, a support vector machine algorithm is used to evaluate the rationality of resource allocation and determine whether the allocation plan meets the real-time requirements of signal transmission. The support vector machine algorithm is used here to evaluate the rationality of resource allocation plans. By learning from historical data and known reasonable allocation patterns, it can quickly and accurately evaluate new allocation plans.

[0071] Dynamic resource allocation adjustment: If the evaluation results indicate that the allocation plan does not meet signal transmission requirements, dynamic adjustments are made to channel resources, the adjusted resource configuration data is obtained, and a new allocation plan is determined. This dynamic adjustment process requires flexible resource reallocation based on the transmission characteristics of real-time control signals and the current channel resource usage to improve the rationality of resource allocation.

[0072] Resource configuration data verification: This involves secondary verification of the adjusted resource configuration data using a pre-established performance evaluation model to determine whether channel resource utilization meets expected standards. This performance evaluation model comprehensively considers multiple indicators, including resource utilization, transmission latency, and signal quality, to verify that the adjusted resource allocation plan meets expected performance requirements.

[0073] Optimize narrowband channel allocation: If the utilization rate does not meet the expected standard, the narrowband channels are optimized and allocated in combination with real-time monitoring data to obtain the final resource allocation plan.

[0074] Continuous tracking of resource allocation plans: Based on the final resource allocation plan, the real-time performance of signal transmission is continuously tracked, the updated status of transmission requests is obtained, and the stability of system operation is determined. Continuous tracking and monitoring can promptly identify problems in the actual operation of the resource allocation plan, ensuring the long-term stability of the system.

[0075] This method dynamically adjusts resource allocation based on the transmission request ratio of real-time control signals, promptly responding to changes in communication needs and improving system flexibility and adaptability. Through secondary verification and optimized allocation steps, it ensures that channel resource utilization meets the expected standard, avoids resource waste, and improves resource utilization efficiency. Continuous tracking of the final resource allocation plan allows for the timely identification and resolution of potential issues, ensuring stable system operation.

[0076] In some embodiments, calculating the remaining broadband channel capacity according to the preliminary allocation plan and queuing multimedia data transmission requests using a proportional fairness algorithm includes:

[0077] By analyzing the broadband channel data, the current channel usage status and allocated resource data are obtained, and the resource distribution under the preliminary allocation plan is obtained;

[0078] Calculate the remaining capacity of the broadband channel based on the resource distribution under the preliminary allocation plan to determine whether the remaining capacity can meet the subsequent transmission needs;

[0079] If the remaining capacity data is lower than the preset threshold, the priority of multimedia data transmission requests is evaluated to determine which requests need to be delayed;

[0080] A proportional fairness algorithm is used to queue and sort multimedia data transmission requests to obtain a sorted request sequence.

[0081] For the sorted request sequence, resources are redistributed based on the remaining capacity data to determine the resource allocation share for each request;

[0082] Dynamically adjust the broadband channel through resource allocation shares to obtain the adjusted channel resource distribution status;

[0083] According to the adjusted channel resource distribution state, the transmission request of the multimedia data is actually scheduled to obtain the final transmission processing result.

[0084] The above technical solution primarily describes how to efficiently manage remaining broadband channel capacity after initial allocation of narrowband channel resources to meet multimedia data transmission requests. This approach uses a proportional fairness algorithm to queue transmission requests, combined with resource reallocation and dynamic adjustments, to achieve fair and efficient utilization of broadband channel resources, ensuring smooth multimedia data transmission.

[0085] Broadband channel resource distribution analysis: Analyze broadband channel data to obtain current channel usage and allocated resource data, and determine the resource distribution under the preliminary allocation plan. This step provides the necessary basic information for subsequent calculation of remaining capacity. Only by clearly understanding the allocated resources can we accurately determine the remaining resources.

[0086] Remaining capacity calculation and demand assessment: Based on resource distribution, the remaining capacity of the broadband channel is calculated to determine whether the remaining capacity meets subsequent transmission needs. By comparing the remaining capacity with a preset threshold, it can quickly determine whether the current resources are sufficient to support the transmission of subsequent multimedia data.

[0087] Transmission request priority assessment: If the remaining capacity data falls below a preset threshold, the system prioritizes multimedia data transmission requests to determine which requests require delay. This priority assessment helps ensure that high-priority transmission requests are met first when resources are limited, thereby improving overall transmission efficiency and user experience.

[0088] Proportional Fairness Algorithm Sorting: This algorithm uses the proportional fairness algorithm to queue multimedia data transmission requests and obtain a sorted request sequence. The proportional fairness algorithm comprehensively considers transmission efficiency and fairness, achieving a relative balance between latency and service quality for each transmission request.

[0089] Resource reallocation determines resource allocation shares: Based on the sorted request sequence, resources are reallocated based on remaining capacity data to determine the resource allocation share for each request. This step allocates limited resources to each multimedia data transmission request, ensuring that each request receives the appropriate resources on a fair basis.

[0090] Dynamic adjustment of broadband channels: Dynamically adjust broadband channels using resource allocation shares to obtain the adjusted channel resource distribution status. Dynamic adjustment of channel resources improves resource utilization efficiency, ensuring that sufficient resources are reserved for future transmission requests while meeting current transmission needs.

[0091] Actual Scheduling of Transmission Requests and Results: Based on the adjusted channel resource distribution, multimedia data transmission requests are actually scheduled to obtain the final transmission processing results. Actual scheduling is a key step in implementing the resource allocation plan. Proper scheduling ensures efficient and stable multimedia data transmission.

[0092] This method ensures efficient utilization of broadband channel resources by accurately calculating remaining capacity and rationally allocating resources, avoiding waste and idleness. It also employs a proportional fairness algorithm to prioritize transmission requests, balancing the interests of different users and ensuring fair resource allocation. The entire process can flexibly adjust based on dynamic changes in remaining capacity and transmission requests, adapting to the ever-changing demands of the communication environment.

[0093] In some embodiments, a transmission efficiency indicator is obtained for real-time monitoring, and if the transmission delay exceeds a preset threshold, the narrowband channel resource allocation ratio is adjusted, including:

[0094] By deploying a monitoring system, we can obtain transmission efficiency indicator data from the network transmission process, continuously collect transmission delay data, and obtain preliminary delay data records;

[0095] Based on the collected delay data records, a preset threshold is used for comparison. If the transmission delay is detected to exceed the preset threshold, a dynamic adjustment mechanism is triggered to determine the narrowband channel range that needs to be optimized.

[0096] Obtain the current resource allocation ratio data of the narrowband channel, analyze the distribution of the resource ratio for the channel range that exceeds the preset threshold, and obtain the deviation data of the resource allocation;

[0097] Calculate the adjustment range of the narrowband channel resource ratio through the deviation data, use the linear regression model to predict the resource allocation ratio, and determine the adjusted channel ratio parameters;

[0098] Generate resource allocation instructions based on the adjusted channel ratio parameters, dynamically adjust the resource ratio for narrowband channels, and obtain an updated resource configuration state;

[0099] Obtain the updated resource configuration status and continuously monitor transmission efficiency indicators and transmission delay data. If the transmission delay still exceeds the preset threshold, the resource ratio adjustment process is executed repeatedly to determine the final optimization result.

[0100] Through the optimization results, the adjusted transmission efficiency indicators and delay data are recorded, and the data is updated for the monitoring system to obtain real-time network transmission status feedback.

[0101] In the above technical solution, the above method mainly focuses on real-time monitoring of network transmission efficiency, especially the transmission delay indicator, and when the delay exceeds the preset threshold, through a series of analysis and adjustment steps, optimizes the resource allocation ratio of the narrowband channel to ensure the efficiency and stability of transmission, thereby improving communication quality.

[0102] Transmission efficiency indicator collection: By deploying a monitoring system, we can obtain transmission efficiency indicator data from the network transmission process, continuously collect transmission delay data, and form preliminary records.

[0103] Latency data comparison and adjustment triggering: Collected latency data is compared against preset thresholds. If the threshold is exceeded, a dynamic adjustment mechanism is triggered to determine the range of narrowband channels that require optimization. The preset threshold is based on network performance requirements. Exceeding the threshold indicates that the current resource allocation may not meet transmission needs and requires optimization.

[0104] Resource Allocation Ratio Analysis: Obtain current resource allocation ratio data for narrowband channels and analyze the resource ratio distribution for channels exceeding the threshold to obtain deviation data. This step aims to identify areas where resource allocation is unreasonable and provide a basis for subsequent adjustments.

[0105] Adjustment Calculation and Prediction: Based on deviation data, the resource ratio adjustment range is calculated and the adjusted channel ratio parameters are predicted using a linear regression model. Based on historical data and trends, the linear regression model provides forecasting support for resource allocation ratio adjustments, improving the scientific nature and accuracy of adjustments.

[0106] Resource allocation instruction generation and adjustment: Based on the predicted channel ratio parameters, resource allocation instructions are generated, and the narrowband channel resource ratio is dynamically adjusted to obtain the updated resource configuration status. This is a key step in putting the analysis results into practice, optimizing network transmission performance by adjusting resource allocation.

[0107] Continuous Monitoring and Iterative Adjustment: Receive updated resource configuration status and continuously monitor transmission efficiency and latency data. If latency still exceeds the threshold, the adjustment process is repeated until the optimization target is achieved. This continuous monitoring and iterative adjustment ensures stable network performance improvements, maintaining high transmission efficiency even in dynamically changing network environments.

[0108] Optimization Results Recording and Feedback: Recording adjusted transmission efficiency and latency data, updating monitoring system data, and providing real-time feedback on network transmission status. This step completes the entire optimization process. Recording and feedback provide a reference for subsequent optimization and facilitate evaluation of optimization results.

[0109] This method monitors transmission efficiency metrics in real time and, if problems are identified, rapidly adjusts resource allocation to ensure efficient and stable network transmission. Based on collected latency data and resource allocation deviations, a linear regression model generates predictions, providing a scientific basis for resource allocation adjustments and improving decision accuracy. This iterative adjustment process and continuous monitoring establish a continuous improvement mechanism that consistently enhances network transmission performance.

[0110] In some embodiments, the service demand balance deviation value is calculated using a time window analysis method. If it exceeds a preset range, the broadband and narrowband resource allocation ratio is recalculated, including:

[0111] By dividing the time window, we can obtain the trend data of business demand changes from historical data, and use segmented processing to determine the demand fluctuations within each time window;

[0112] According to the demand fluctuations in each time window, the balance deviation value is calculated by applying analytical methods to obtain specific deviation quantitative results;

[0113] If the calculated balance deviation value exceeds the preset range, the deviation monitoring mechanism is triggered to determine whether the conditions for readjustment are met and to process the information in combination with historical resource allocation data;

[0114] Based on the deviation monitoring results, the current allocation ratio data of broadband and narrowband resources is obtained to determine whether there is uneven resource allocation;

[0115] Based on the judgment result of uneven resource allocation, a pre-established optimization model is used to calculate the new allocation ratio and obtain the adjusted resource allocation plan;

[0116] If the adjusted resource allocation plan differs significantly from the current allocation ratio, the rationality of the adjustment plan will be verified through the demand analysis module to determine the final resource allocation strategy;

[0117] Through the final resource allocation strategy, the actual allocation ratio of broadband and narrowband resources is updated to achieve dynamic balance adjustment of business needs.

[0118] In the above technical solution, the method primarily aims to achieve a dynamic, balanced allocation of broadband and narrowband resources. By analyzing the changing trends of service demand, the balance deviation is calculated, and whether the deviation exceeds a preset range determines whether to readjust the broadband and narrowband resource allocation ratio. This process ensures that the communication system allocates resources appropriately based on the dynamic changes in service demand, improving resource utilization and system performance.

[0119] Capturing business demand trend data: By dividing the time window into segments, we capture business demand trend data from historical data. Using a segmented approach, we determine demand fluctuations within each time window. This step clearly demonstrates how business demand changes over time, providing a foundation for subsequent balance deviation calculations.

[0120] Balance Deviation Calculation: Based on demand fluctuations within each time window, analytical methods are applied to calculate the balance deviation value, resulting in a specific quantitative deviation result. Calculating the balance deviation value helps quantify the degree of discrepancy between current resource allocation and business needs.

[0121] Deviation monitoring triggers: If the calculated balance deviation value exceeds the preset range, the deviation monitoring mechanism is triggered to determine whether the conditions for readjustment are met, and the information is processed based on historical resource allocation data. This step enables timely response to abnormal fluctuations in business demand and ensures the rationality of resource allocation.

[0122] Determine resource imbalance: Based on the deviation monitoring results, obtain the current allocation ratio of broadband and narrowband resources to determine whether there is any resource imbalance. This step helps identify problems in the current resource allocation and provides a basis for further adjustments.

[0123] Calculate new allocation ratios: Based on the determination of uneven resource allocation, a pre-established optimization model is used to calculate new allocation ratios and arrive at an adjusted resource allocation plan. The application of the optimization model can provide scientific and reasonable resource allocation recommendations, improving resource allocation efficiency.

[0124] Verify the rationality of the adjustment plan: If the adjusted resource allocation plan differs significantly from the current allocation ratio, the demand analysis module verifies the rationality of the adjustment plan and determines the final resource allocation strategy. This step can avoid unreasonable resource allocation adjustments and ensure the feasibility and effectiveness of the adjustment plan.

[0125] Resource Allocation Strategy Implementation: Through the final resource allocation strategy, the actual allocation ratio of broadband and narrowband resources is updated to achieve a dynamic balance adjustment based on business needs. This step puts the adjustment plan into practice, achieving a reasonable allocation of resources and a dynamic balance between business needs.

[0126] This method can timely adjust broadband and narrowband resource allocation based on dynamic changes in service requirements, improving the flexibility and adaptability of the communication system. By calculating balance deviations and applying optimization models, resources are rationally allocated and utilization is improved. The rationality of adjustment plans is verified to avoid system instability caused by unreasonable adjustments, ensuring stable operation of the communication system.

[0127] In some embodiments, acquiring channel state change data and using a time series prediction model to generate pre-adjustment parameters for resources in the next cycle may include:

[0128] By extracting real-time data from the channel status monitoring equipment, the change data of the channel status is obtained;

[0129] Based on the acquired change data, the time series analysis method is used to determine the trend characteristics of the change data;

[0130] If the trend characteristics of the changing data exceed the preset threshold range, the forecast value for the next period is generated through the forecast model;

[0131] Obtain the next cycle's forecast value output by the forecast model and, combined with historical records of resource allocation, determine the initial demand for resource pre-adjustment.

[0132] By analyzing the initial demand and using the long short-term memory network model, we can obtain the optimal parameters for resource pre-adjustment.

[0133] Determine the final adjustment parameters based on the optimization parameters and the results of the cycle analysis;

[0134] Based on the final adjustment parameters, a preliminary adjustment plan for resource allocation is generated to complete the parameter generation process.

[0135] In the above technical solution, the above method aims to monitor changes in channel status, use time series analysis and prediction models, generate resource pre-adjustment parameters in advance, and realize forward-looking and dynamic management of communication environment resources, thereby improving the timeliness and adaptability of resource allocation and ensuring the efficient operation of the communication system.

[0136] Channel status change data acquisition: extract real-time data from channel status monitoring equipment to obtain channel status change data.

[0137] Determining trend characteristics: Based on the acquired change data, time series analysis is used to determine the trend characteristics of the change data. Time series analysis can reveal the patterns and trends of channel state changes over time and help predict possible future changes.

[0138] Prediction model triggering: If the trend characteristics of the changing data exceed the preset threshold range, the prediction model generates a forecast value for the next cycle. The preset threshold is the standard for determining whether the channel status change is significant. Exceeding the threshold means that resources need to be pre-adjusted to accommodate the upcoming channel change.

[0139] Determine preliminary resource pre-adjustment requirements: Obtain the next cycle's forecast output from the forecast model and, combined with historical resource allocation records, determine preliminary resource pre-adjustment requirements. This step comprehensively considers the forecast results and historical allocations, providing preliminary direction for resource pre-adjustment.

[0140] Optimization Parameter Acquisition: Based on preliminary demand analysis, we use the Long Short-Term Memory (LSTM) network model to derive optimization parameters for resource pre-scaling. The LSTM model can learn long-term dependencies in historical data, providing more accurate optimization parameters for resource pre-scaling.

[0141] Final adjustment parameter determination: Based on the optimized parameters and the results of the periodic analysis, the final adjustment parameters are determined. This step combines the optimized parameters with the periodic variation patterns to further improve the accuracy and rationality of resource pre-adjustment.

[0142] Pre-adjustment plan generation: Based on the final adjustment parameters, a pre-adjustment plan for resource allocation is generated, completing the parameter generation process. The pre-adjustment plan is based on the results of the above analysis and calculations and provides specific guidance for resource allocation in the next cycle.

[0143] This method uses time series prediction and long-short-term memory network models to predict channel state changes in advance, enabling proactive resource adjustments and improving the timeliness and adaptability of resource allocation. Advanced data analysis and forecasting models are used to accurately calculate optimization parameters for resource pre-adjustment, improving the accuracy and efficiency of resource allocation. Analysis combined with historical resource allocation records fully taps into the value of historical data, providing a more comprehensive reference for resource pre-adjustment.

[0144] In some embodiments, pre-allocating resources for the next periodic communication environment according to the pre-adjusted parameters and forming a final transmission strategy through a cyclic iteration method includes:

[0145] Obtain historical data and current status information of the communication environment, and determine the environment change trend and resource demand distribution by analyzing key indicators in the data;

[0146] Based on the environmental change trend and resource demand distribution, the preset parameters are used to perform preliminary resource allocation for the communication environment of the next cycle to obtain an initial allocation plan;

[0147] Based on the initial allocation plan, the resource allocation results are adjusted multiple times using a cyclic iteration method. If the resource occupancy rate of a certain area exceeds the preset threshold, the resources in that area are rescheduled to determine whether the balance condition is met.

[0148] By comparing the resource scheduling results after each iteration, the adjusted resource distribution status is obtained to determine whether there is local resource conflict or redundancy;

[0149] If local resource conflicts or redundancies are detected, the resource scheduling scheme is optimized based on the genetic algorithm to obtain an improved allocation strategy;

[0150] Based on the improved allocation strategy, combined with the transmission mode and final strategy requirements, the communication environment is simulated and verified to determine whether the expected transmission performance is achieved;

[0151] Through simulation verification results, parameter optimization and cycle planning schemes are adjusted to build the final transmission strategy and determine the resource scheduling scheme suitable for the next cycle.

[0152] In the above technical solution, the method primarily uses pre-adjusted parameters, combined with historical data and current state information about the communication environment, to pre-allocate resources for the next communication cycle through iterative and optimization methods, and then formulates the final transmission strategy. This process aims to achieve balanced, efficient, and adaptable resource allocation, ensuring stable operation of the communication system and optimized transmission performance.

[0153] Determine environmental trends and resource demand distribution: Obtain historical data and current status information about the communications environment, analyze key indicators, and determine environmental trends and resource demand distribution. This step provides an important reference for subsequent resource allocation and helps understand the dynamic changes in the communications environment and the distribution of resource demands.

[0154] Initial resource allocation: Based on environmental trends and resource demand distribution, we use preset parameters to perform preliminary resource allocation for the next communication cycle, generating an initial allocation plan. This initial allocation plan is a preliminary plan based on current information and provides a foundation for subsequent optimization and adjustment.

[0155] Iterative Adjustment: Based on the initial allocation plan, resource allocation is repeatedly adjusted using an iterative method. If resource utilization in a particular area exceeds a preset threshold, resources in that area are rescheduled to determine whether balance conditions are met. This iterative method gradually optimizes resource allocation and ensures balanced resource utilization.

[0156] Resource distribution status comparison: Compare the resource scheduling results after each iteration to obtain the adjusted resource distribution status and determine whether there are any local resource conflicts or redundancies. This step helps to promptly identify problems in resource allocation.

[0157] Genetic Algorithm Optimization: If local resource conflicts or redundancies are detected, the resource scheduling solution is optimized using a genetic algorithm to obtain an improved allocation strategy. Genetic algorithms can search for a global optimal solution and effectively resolve local resource conflicts and redundancies.

[0158] Simulation Verification: Based on the improved allocation strategy, combined with the transmission method and final strategy requirements, the communication environment is simulated to verify whether the expected transmission performance is achieved. Simulation verification can evaluate the performance of the resource allocation plan in advance to ensure that it meets the requirements of the communication system.

[0159] Final Transmission Strategy Construction: Based on the simulation verification results, parameter optimization and cycle planning are adjusted to construct the final transmission strategy and determine the resource scheduling plan for the next cycle. This step establishes the verified plan as the final transmission strategy, providing guidance for communications in the next cycle.

[0160] By analyzing historical data and current state information about the communication environment, this method can timely capture changing environmental trends and resource demand distribution, making the resource pre-allocation scheme highly adaptable. Using iterative methods and genetic algorithms to repeatedly adjust and optimize resource allocation results effectively resolves local resource conflicts or redundancies, improving the rationality and efficiency of resource allocation. Performance evaluation of the improved allocation strategy is conducted through simulation verification to ensure that the final transmission strategy achieves the expected transmission performance and mitigate risks in practical applications.

[0161] According to another aspect of the present invention, a highly integrated wide- and narrow-band integrated communication system is provided. Based on the above method, the system includes:

[0162] An acquisition module is used to obtain channel state information data from the communication environment, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and classify and record them;

[0163] A weight module, configured to construct a resource allocation model based on the transmission characteristic parameters and the capacity parameters, and determine a weight relationship between real-time control signals and multimedia data using a service priority division method;

[0164] A first determination module is configured to allocate narrowband channel resources to form a preliminary allocation plan if the transmission request ratio of the real-time control signal exceeds a preset threshold;

[0165] a queuing module, configured to calculate the remaining broadband channel capacity according to the preliminary allocation plan and queue multimedia data transmission requests using a proportional fairness algorithm;

[0166] The second determination module is used to obtain the transmission efficiency index for real-time monitoring and adjust the narrowband channel resource allocation ratio if the transmission delay exceeds a preset threshold;

[0167] The third determination module is used to calculate the service demand balance deviation value through the time window analysis method, and recalculate the broadband and narrowband resource allocation ratio if it exceeds the preset range;

[0168] The prediction module is used to obtain channel state change data and generate the resource pre-adjustment parameters for the next cycle using a time series prediction model;

[0169] The strategy module is used to pre-allocate resources for the next period communication environment according to the pre-adjustment parameters, and form a final transmission strategy through a cyclic iteration method.

[0170] In the above technical solution, in order to better utilize the above method, this application proposes a highly integrated wide-narrowband integrated communication system, in which each module corresponds to each step of the above method. The specific principles have been described above and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0171] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0172] Figure 1 This is a flow chart of an embodiment of a highly integrated wide-narrowband fusion integrated communication method of the present invention;

[0173] Figure 2 It is a structural diagram of an embodiment of a highly integrated wide- and narrow-band fusion integrated communication system of the present invention. DETAILED DESCRIPTION

[0174] The present invention will be described in further detail below with reference to the accompanying drawings and examples. It is particularly noted that the following examples are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. Similarly, the following examples are only some embodiments of the present invention and are not intended to be exhaustive. All other embodiments obtained by those of ordinary skill in the art without creative effort are intended to fall within the scope of protection of the present invention.

[0175] Example 1

[0176] See also Figure 1 , a highly integrated wide- and narrow-band fusion integrated communication method, the method comprising:

[0177] S1. Acquire channel state information data from the communication environment, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and classify and record them;

[0178] In this embodiment, S1, acquiring channel state information data from a communication environment, extracting narrowband channel transmission characteristic parameters and wideband channel capacity parameters, and classifying and recording them, includes:

[0179] S11. Acquire channel state information data from the communication environment through sensors and monitoring equipment, pre-process the collected raw data to remove noise and redundancy, and obtain preliminarily cleaned channel data;

[0180] S12. Using a support vector machine algorithm to extract features from the channel data after preliminary cleaning, separate narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and determine the characteristic distribution of the two types of parameters;

[0181] S13. Based on the characteristic distribution results, narrowband channel transmission characteristic parameters and broadband channel capacity parameters are structurally divided. If the characteristic value exceeds a preset threshold range, it is marked as abnormal data to obtain a classified parameter set.

[0182] S14. Converting the classified parameter set into a unified database storage format to obtain a standardized parameter data set for subsequent analysis.

[0183] S15. Using a cluster analysis method to group the parameters of narrowband channels and broadband channels based on the standardized parameter data set, if the grouping results show that the distribution of a certain type of parameter deviates from the normal range, it is marked to determine the potential channel anomaly category;

[0184] S16. Obtain the corresponding parameter feature change trend based on the marked channel anomaly category, and determine the specific impact range and change pattern of the anomaly category by comparing it with historical data records;

[0185] S17. By performing data mapping on the change pattern, it is matched with the pre-established channel state model. If the matching degree is higher than a preset threshold, it is classified as a known problem type to obtain the final channel state classification result.

[0186] For example, in the process of acquiring channel state information data and extracting related parameters in a communication environment, the channel state information is first collected by using orthogonal frequency division multiplexing technology through a channel estimation module deployed in a base station. It is assumed that the collected channel gain data is in complex form, with an amplitude value ranging from 0.1 to 1.5, and the acquisition frequency is 100 times per second. The data is stored in a matrix form with a size of 100x50, representing 100 time points and 50 subcarriers. Subsequently, when extracting the narrowband channel transmission characteristic parameters, a time domain analysis algorithm is used to calculate the root mean square delay spread of the channel impulse response. It is assumed that the calculation result is 50 nanoseconds, and the time domain data is converted to the frequency domain through Fourier transform to obtain the frequency selective fading parameter. The typical value is 0.3, indicating that the channel has a moderate degree of frequency selective fading. Next, the Shannon formula was used to extract the broadband channel capacity parameters and calculate the channel capacity. Assuming a signal-to-noise ratio of 20 decibels and a bandwidth of 10 MHz, the theoretical capacity was calculated to be approximately 66.7 megabits per second. Combined with the actual modulation and coding scheme, the corrected capacity was 50 megabits per second, resulting in a capacity assessment report. Finally, the above parameters were categorized and recorded. Using a database management system, narrowband parameters such as delay spread and frequency-selective fading were stored in a narrowband characteristic table, while broadband parameters such as theoretical capacity and corrected capacity were stored in a broadband performance table. Index fields were set to facilitate subsequent queries, assuming the database was updated hourly to ensure data timeliness.

[0187] S2. Build a resource allocation model based on the transmission characteristic parameters and capacity parameters, and use a service priority division method to determine the weight relationship between real-time control signals and multimedia data;

[0188] In this embodiment, S2, constructing a resource allocation model based on the transmission characteristic parameters and the capacity parameters, and using a service priority division method to determine the weight relationship between the real-time control signal and the multimedia data, includes:

[0189] S21. Obtaining initial data features through transmission feature analysis, and performing stratification processing on the transmission features using preset classification rules to obtain feature stratification results;

[0190] S22. Construct a capacity parameter map based on the feature stratification results, use statistical tools to quantitatively analyze the capacity parameters, and determine the capacity distribution range;

[0191] S23. Match the capacity distribution range with the resource allocation requirements. If the capacity distribution range exceeds a preset threshold, dynamically adjust the resource allocation to determine whether the adjusted allocation plan meets the requirements.

[0192] S24. Obtain the adjusted allocation plan data, perform weight calculation based on the service priority division method, and determine the priority order of the real-time control signal using a preset weighting formula;

[0193] S25. Processing the control signal data according to the priority order of the real-time control signal, using a logical judgment method, and if the control signal data fluctuation exceeds a preset range, smoothing it to obtain stable signal data;

[0194] S26. By comparing and analyzing the stable signal data and the multimedia data, the support vector machine algorithm is used to optimize the data weights and determine the final configuration of the weight ratio;

[0195] S27. Generate the output result of the resource allocation model according to the final configuration of the weight ratio, verify the ratio determination result through a data verification tool, and obtain the final allocation strategy.

[0196] For example, when constructing a resource allocation model and determining the weighting relationship between real-time control signals and multimedia data, the model is first constructed based on transmission characteristic parameters and capacity parameters. Assume that the transmission characteristic parameters include bandwidth utilization and latency. The capacity parameter is a total bandwidth of 100 Mbps, the latency requirement for real-time control signals is 10 ms, and the latency tolerance for multimedia data is 50 ms. Analysis results in a bandwidth utilization of 80%, meaning an effective bandwidth of 80 Mbps, with the remaining 20% ​​reserved for redundancy. Next, a resource allocation model is constructed using a linear programming algorithm with the objective function of maximizing resource utilization. The constraints are that the bandwidth allocated to real-time control signals must be no less than 20 Mbps, and that to multimedia data must be no less than 40 Mbps, with the remaining 20 Mbps dynamically adjusted. The calculation process is as follows: Let x be the real-time control signal allocation variable and y be the multimedia data allocation variable. The objective function is max(0.6x + 0.4y). The weighting coefficients are initially set based on service importance, with the constraints x ≥ 20, y ≥ 40, and x + y ≤ 80. The optimal solution is x = 20, y = 60. Next, a service prioritization method is used to further determine the weight relationship. Assuming that the real-time control signal has a priority of 9 (maximum 10) and multimedia data has a priority of 6, the calculated weight ratio is 9:6, or 3:2. The objective function is adjusted to max(0.6x + 0.4y), leaving it unchanged. However, resources are reallocated based on the priority ratio. If the burst demand for real-time control signals increases to 30 Mbps, the multimedia data rate is dynamically adjusted to 50 Mbps, ensuring the total does not exceed 80 Mbps. Analysis shows that real-time control signals require a higher priority due to their greater latency sensitivity, while multimedia data can be appropriately compressed to ensure system stability. To form a logical chain, edge computing services are introduced as a related scenario. Assuming that edge nodes require an additional 5 Mbps of bandwidth to process real-time signals, this bandwidth is redistributed from multimedia data. The final result is 25 Mbps for real-time control signals and 55 Mbps for multimedia data, satisfying the total bandwidth constraint and balancing priorities. Through the above algorithm and numerical analysis, the resource allocation model not only meets transmission characteristics and capacity requirements, but also reflects the weight relationship of service priorities, ensuring efficient system operation.

[0197] S3. If the transmission request ratio of the real-time control signal exceeds a preset threshold, narrowband channel resources are allocated to form a preliminary allocation plan;

[0198] In this embodiment, S3, if the transmission request ratio of the real-time control signal exceeds a preset threshold, narrowband channel resources are allocated to form a preliminary allocation plan, including:

[0199] S31. Acquire dynamic changes in transmission requests by collecting real-time monitoring data of control signals and determine the current state of the request ratio;

[0200] S32. If the request ratio exceeds the preset threshold, the narrowband channel resource allocation process is triggered, an initial allocation plan is constructed, and a preliminary channel resource configuration result is obtained;

[0201] S33. Based on the preliminary channel resource configuration results, use a support vector machine algorithm to evaluate the rationality of resource allocation and determine whether the allocation scheme meets the real-time requirements of signal transmission;

[0202] S34. If the evaluation result shows that the allocation plan does not meet the signal transmission requirements, dynamically adjust the channel resources, obtain the adjusted resource configuration data, and determine a new allocation plan;

[0203] S35. Perform secondary verification on the adjusted resource configuration data and use a pre-established performance evaluation model to determine whether the utilization of the channel resources meets the expected standard.

[0204] S36. If the utilization rate does not meet the expected standard, optimize the allocation of narrowband channels based on the real-time monitoring data to obtain a final resource allocation plan;

[0205] S37. Based on the final resource allocation plan, continuously track the real-time performance of signal transmission, obtain the updated status of the transmission request, and determine the stability of the system operation.

[0206] For example, in a real-time control signal transmission scenario, if the system detects that the transmission request ratio exceeds a preset threshold (for example, the threshold is set at 60% and the current request ratio reaches 75%), the system will automatically trigger the resource allocation mechanism. First, the system uses a built-in monitoring module to collect network request data in real time and calculate the request ratio. The specific algorithm is: request ratio = (current number of requests / total bandwidth capacity) × 100%. Assuming the current number of requests is 1500 and the total bandwidth capacity is 2000, the ratio is 75%, exceeding the threshold of 60%, and the system determines that narrowband channel resources need to be allocated. Next, the system enters the resource allocation phase, accessing the resource pool database to query currently available narrowband channel resources. Assuming there are 10 narrowband channels, each with a bandwidth of 2 MHz, and 6 currently idle, the system calculates the priority of each request based on a priority algorithm (priority = request urgency × 0.6 + request duration × 0.4). For example, if a request has an urgency of 8 and a duration of 5, the priority is 8 × 0.6 + 5 × 0.4 = 6.8. The system assigns the highest-priority request to an idle channel, forming a preliminary allocation plan. After allocation, the system analyzes channel occupancy. The current occupancy is 40% (4 channels are already occupied), which is below the safety limit of 80%, making the plan feasible. If the occupancy exceeds the limit, the system will use backup channel resources or adjust the allocation strategy to ensure a balance between resource utilization and request demand. Through the above process, the system automatically completes the entire process from monitoring, calculation to allocation, ensuring the efficient transmission of real-time control signals. At the same time, related services such as data backup requests can be processed as secondary priority. If narrowband resources are insufficient, they will be placed in the broadband channel queue, forming a closed-loop logic for resource scheduling.

[0207] S4. Calculate the remaining broadband channel capacity according to the preliminary allocation plan, and use a proportional fairness algorithm to queue multimedia data transmission requests;

[0208] In this embodiment, S4, calculating the remaining broadband channel capacity according to the preliminary allocation plan and queuing multimedia data transmission requests using a proportional fairness algorithm, includes:

[0209] S41, by analyzing the broadband channel data, obtaining the current channel usage status and allocated resource data, and obtaining the resource distribution under the preliminary allocation plan;

[0210] S42. Calculate the remaining capacity data in the broadband channel based on the resource distribution under the preliminary allocation plan to determine whether the remaining capacity meets subsequent transmission requirements;

[0211] S43, if the remaining capacity data is lower than a preset threshold, priority evaluation is performed on the multimedia data transmission requests to determine which requests need to be delayed;

[0212] S44, using a proportional fairness algorithm to queue and sort the multimedia data transmission requests to obtain a sorted request sequence;

[0213] S45. Redistribute resources based on the sorted request sequence and the remaining capacity data to determine the resource allocation share for each request.

[0214] S46. Dynamically adjust the broadband channel by using the resource allocation share to obtain the adjusted channel resource distribution state;

[0215] S47 . Perform actual scheduling on the multimedia data transmission request according to the adjusted channel resource distribution state to obtain a final transmission processing result.

[0216] For example, when calculating the remaining broadband channel capacity and queuing multimedia data transmission requests using the proportional fairness algorithm, assume the current total broadband channel capacity is 1000 Mbps. The data transmission rates allocated to three users are 300 Mbps, 200 Mbps, and 100 Mbps, respectively. The system automatically calculates the remaining capacity as 1000 - 300 - 200 - 100 = 400 Mbps. Next, for four newly received multimedia data transmission requests, the system records their request rates as 50 Mbps, 100 Mbps, 80 Mbps, and 70 Mbps, respectively. The total request rates add up to 300 Mbps, which is less than the remaining capacity of 400 Mbps, making them eligible for allocation. Using the proportional fairness algorithm, the system first calculates the initial priority of each request. Assuming that priority is inversely proportional to request rate (i.e., priority = 1 / request rate), the resulting priorities for the four requests are 1 / 50 = 0.02, 1 / 100 = 0.01, 1 / 80 = 0.0125, and 1 / 70 = 0.0143, respectively. Based on the principle of proportional fairness, the system prioritizes requests with higher priorities. For example, the first request (50 Mbps), with the highest priority, is allocated 50 Mbps, leaving a remaining capacity of 400 - 50 = 350 Mbps. The fourth request (70 Mbps) is then allocated 70 Mbps, leaving a remaining capacity of 350 - 70 = 280 Mbps. The third request (80 Mbps) is then allocated 80 Mbps, leaving a remaining capacity of 280 - 80 = 200 Mbps. Finally, the second request (100 Mbps) is allocated 100 Mbps, leaving a remaining capacity of 200 - 100 = 100 Mbps. This algorithm ensures fair resource allocation while accounting for differences in request priority. Analysis shows that the proportional fairness algorithm effectively balances the satisfaction of requests when resources are limited, preventing a single request from monopolizing resources. If remaining capacity is insufficient, the system further adjusts the allocation ratio, for example, by reducing the allocation rate based on priority, to ensure that all requests receive some resources. This process is closely related to the efficient transmission of multimedia services, ensuring that services with high real-time requirements, such as video streaming and audio streaming, can still maintain basic service quality when the network is congested.

[0217] S5. Obtain transmission efficiency indicators for real-time monitoring. If the transmission delay exceeds a preset threshold, adjust the narrowband channel resource allocation ratio.

[0218] In this embodiment, S5, obtaining a transmission efficiency indicator for real-time monitoring, and adjusting the narrowband channel resource allocation ratio if the transmission delay exceeds a preset threshold, includes:

[0219] S51. Deploy a monitoring system to obtain transmission efficiency indicator data from the network transmission process, continuously collect transmission delay data, and obtain preliminary delay data records;

[0220] S52. Based on the collected delay data records, a preset threshold is used for comparison. If it is detected that the transmission delay exceeds the preset threshold, a dynamic adjustment mechanism is triggered to determine the narrowband channel range that needs to be optimized.

[0221] S53, obtaining current resource allocation ratio data of narrowband channels, analyzing the distribution of resource ratios for a channel range exceeding a preset threshold, and obtaining resource allocation deviation data;

[0222] S54. Calculate the adjustment range of the narrowband channel resource ratio based on the deviation data, predict the resource allocation ratio using a linear regression model, and determine the adjusted channel ratio parameter;

[0223] S55. Generate a resource allocation instruction based on the adjusted channel ratio parameter, dynamically adjust the resource ratio for the narrowband channel, and obtain an updated resource configuration state;

[0224] S56: Obtain the updated resource configuration status, continuously monitor the transmission efficiency index and transmission delay data, and if the transmission delay still exceeds the preset threshold, cyclically execute the resource ratio adjustment process to determine the final optimization result;

[0225] S57. Based on the optimization results, the adjusted transmission efficiency index and delay data are recorded, and the data of the monitoring system is updated to obtain real-time feedback on the network transmission status.

[0226] For example, in the process of implementing real-time monitoring of transmission efficiency indicators and adjusting the allocation ratio of narrowband channel resources based on transmission delay, the monitoring system deployed on the network node first collects transmission data in real time. Assuming that data is collected once per second, indicators such as transmission delay, throughput, and packet loss rate are recorded. For example, if the current delay is 120ms, the throughput is 2.5Mbps, and the packet loss rate is 0.5%, the monitoring system compares this data with the preset threshold. Assuming the delay threshold is 100ms, if the current delay exceeds the threshold by 20ms, the alarm mechanism is triggered. The system then automatically calls the delay analysis algorithm and uses the weighted moving average method to calculate the average delay of the last 10 seconds. The formula is: Average delay = (current delay × 0.3 + average delay of the previous 9 seconds × 0.7). The result is 110ms, confirming that the persistently high delay is not a momentary fluctuation. The system then enters the resource adjustment phase, analyzing the current narrowband channel resource allocation ratio. Assuming an initial ratio of 60% for data transmission and 40% for control channels, the resource optimization algorithm, based on a linear regression model of latency and throughput, calculates that the adjusted data transmission ratio should be increased to 70% and the control channel ratio should be reduced to 30%. This increases data transmission bandwidth and is expected to reduce latency to 95ms. After the adjustment, the system continues to monitor the results. If the average latency remains above 100ms within 5 minutes, the bandwidth scheduling module is further activated to dynamically allocate backup bandwidth resources. For example, 10% of bandwidth can be allocated from the backup pool to reduce the expected latency to 90ms, ensuring transmission efficiency.

[0227] S6. Calculate the service demand balance deviation value through the time window analysis method. If it exceeds the preset range, recalculate the broadband and narrowband resource allocation ratio;

[0228] In this embodiment, S6, calculating the service demand balance deviation value by a time window analysis method, and recalculating the broadband and narrowband resource allocation ratio if it exceeds a preset range, includes:

[0229] S61. Obtain business demand change trend data from historical data by dividing the time window, and use a segmented processing method to determine the demand fluctuation within each time window;

[0230] S62. Calculate the balance deviation value using analytical methods based on the demand fluctuations within each time window to obtain specific deviation quantification results;

[0231] S63. If the calculated balance deviation value exceeds the preset range, the deviation monitoring mechanism is triggered to determine whether the readjustment conditions are met and to perform information processing in combination with historical resource allocation data;

[0232] S64. Obtain current allocation ratio data of broadband and narrowband resources based on the deviation monitoring results to determine whether there is an uneven resource allocation phenomenon;

[0233] S65. Based on the result of the determination of uneven resource allocation, a pre-established optimization model is used to calculate a new allocation ratio to obtain an adjusted resource allocation plan;

[0234] S66. If the adjusted resource allocation plan differs significantly from the current allocation ratio, the rationality of the adjustment plan is verified through the demand analysis module to determine the final resource allocation strategy;

[0235] S67. Update the actual allocation ratio of broadband and narrowband resources through the final resource allocation strategy to achieve dynamic balance adjustment of business needs.

[0236] For example, in the process of calculating the service demand balance deviation value through the time window analysis method and adjusting the resource allocation ratio, assuming we take a communication network resource allocation scenario as an example, the system first collects hourly service demand data for the past 24 hours. For example, the broadband service demand is an average of 5000Mbps per hour, and the narrowband service demand is an average of 2000Mbps per hour. The time window is set to 6 hours. The system automatically calculates the weighted average of the data in each window, with weights of 0.4 for the recent hour data, 0.3 for the middle hour data, and 0.3 for the distant hour data. The calculated average broadband demand in a certain window is 5100Mbps, and the average narrowband demand is 2100Mbps. The system then calculates the balance deviation value, with the preset ideal ratio of broadband to narrowband resource allocation being 2.5:1. The actual allocation ratio is 3:1, with the current resource allocation ratio being 6000Mbps broadband and 2000Mbps narrowband. The deviation value is calculated using the formula |(3-2.5) / 2.5|*100%=20%, which exceeds the preset deviation range by 10%. The system then triggers a recalculation of the resource allocation ratio. Using a linear adjustment algorithm based on the deviation and average demand, the new ratio is calculated as: current ratio * (1 - deviation percentage * 0.5), or 3 * (1 - 0.2 * 0.5) = 2.7. This results in a 5400 Mbps broadband resource allocation and a 2000 Mbps narrowband resource allocation, approaching the ideal ratio of 2.7:1. The system further analyzes the adjusted resource utilization: broadband utilization is 5100 / 5400 = 94.4%, and narrowband utilization is 2100 / 2000 = 105%, indicating a slight shortage of narrowband resources. The system automatically compares the adjusted results with historical data. If narrowband utilization exceeds 100% for three consecutive time windows, a second fine-tuning is triggered, increasing narrowband resources by 5% (2000 * 1.05 = 2100 Mbps), ultimately balancing the allocation of broadband and narrowband resources. This process relies on algorithms and data analysis to form a closed loop, ensuring dynamic optimization of resource allocation. It is also linked to the growth trend of business demand. For example, if the demand for narrowband services surges due to the addition of new IoT devices, the system can further adjust the time window weights in combination with the prediction model to form a long-term resource plan.

[0237] S7. Acquire channel state change data and use a time series prediction model to generate resource pre-adjustment parameters for the next cycle;

[0238] In this embodiment, S7, obtaining channel state change data and using a time series prediction model to generate resource pre-adjustment parameters for the next period, includes:

[0239] S71. Acquire channel status change data by extracting real-time data from a channel status monitoring device;

[0240] S72. Determine trend characteristics of the acquired change data using a time series analysis method;

[0241] S73. If the trend characteristics of the change data exceed the preset threshold range, a prediction value for the next period is generated through the prediction model;

[0242] S74. Obtain the next cycle forecast value output by the forecast model and, combined with the historical records of resource allocation, determine the initial demand for resource pre-adjustment;

[0243] S75. By analyzing the preliminary demand, the optimization parameters for resource pre-adjustment are obtained using the long short-term memory network model;

[0244] S76. Determine the final adjustment parameters based on the optimization parameters and the results of the cycle analysis;

[0245] S77. Generate a preliminary adjustment plan for resource allocation based on the final adjustment parameters, completing the parameter generation process.

[0246] For example, in the process of obtaining channel state change data and generating resource pre-adjustment parameters for the next period using a time series prediction model, the channel state information collection system deployed at the base station is first used to record key indicators such as channel gain, interference level, and signal-to-noise ratio in real time. It is assumed that the channel gain data collected in the past 24 hours is sampled at one point per hour, for a total of 24 data points, and the specific values ​​are [3.2, 3.5, 3.1, 2.9, 3.0, 3.3, 3.6, 3.4, 3.2, 3.0, 2.8, 2.7, 2.9, 3.1, 3.3, 3.5, 3.7, 3.6, 3.4, 3.2, 3.0, 2.9, 3.1, 3.3]. The data was then analyzed using a time series forecasting model such as the Autoregressive Integrated Moving Average (ARIMA) model. The model parameters were set to (p=2, d=1, q=1). The historical data was then differencing to eliminate trends, and the forecasting model coefficients were calculated. The channel gain for the next hour was predicted to be 3.4. Next, the forecast results were combined with the current resource allocation to analyze the impact of channel gain changes on bandwidth requirements. Assuming the current bandwidth allocation is 50 Mbps, the predicted gain of 3.4 is slightly higher than the current value of 3.3, indicating improved channel conditions and reduced resource redundancy. The calculated bandwidth adjustment coefficient was 0.95, resulting in a pre-adjusted resource parameter of 47.5 Mbps for the next cycle. Furthermore, the forecast results were combined with actual service demand. Assuming the peak bandwidth demand is 45 Mbps, the adjusted value of 47.5 Mbps meets the requirement with a margin, ensuring system stability. Through the above process, a complete logical chain is formed from data collection to model prediction and then to parameter adjustment. If the predicted value deviates significantly from the actual business needs, machine learning algorithms such as LSTM can be further introduced to perform multi-dimensional feature prediction, optimize adjustment accuracy, and ensure dynamic matching of resource allocation and channel status.

[0247] S8. Pre-allocate resources for the next period communication environment according to the pre-adjusted parameters, and form a final transmission strategy through a cyclic iteration method.

[0248] In this embodiment, S8, pre-allocating resources for the next periodic communication environment according to the pre-adjusted parameters, and forming a final transmission strategy through a cyclic iteration method, includes:

[0249] S81. Obtain historical data and current status information of the communication environment, and determine the environment change trend and resource demand distribution by analyzing key indicators in the data;

[0250] S82. Based on the environmental change trend and resource demand distribution, use preset parameters to perform preliminary resource allocation for the communication environment of the next cycle to obtain an initial allocation plan;

[0251] S83. Based on the initial allocation plan, the resource allocation result is adjusted multiple times using a cyclic iteration method. If the resource occupancy rate of a certain area exceeds a preset threshold, the resources of the area are rescheduled to determine whether the balance condition is met.

[0252] S84. Compare the resource scheduling results after each iteration to obtain the adjusted resource distribution state and determine whether there is a local resource conflict or redundancy.

[0253] S85. If local resource conflict or redundancy is detected, the resource scheduling scheme is optimized based on a genetic algorithm to obtain an improved allocation strategy;

[0254] S86. Based on the improved allocation strategy, combined with the transmission mode and final strategy requirements, simulate and verify the communication environment to determine whether the expected transmission performance is achieved.

[0255] S87. Based on the simulation verification results, adjust the parameter optimization and cycle planning schemes, build the final transmission strategy, and determine the resource scheduling scheme suitable for the next cycle.

[0256] For example, in a communication resource allocation scenario, assume we pre-allocate resources for the next communication cycle based on pre-adjusted parameters, and then iterate to form a final transmission strategy. First, the system generates pre-adjusted parameters based on historical data and current network load. For example, the predicted bandwidth demand is 500Mbps, the latency tolerance is 20ms, and the channel quality index is 0.85. Next, the resource pre-allocation module divides the total bandwidth of 1000Mbps into multiple sub-channels based on these parameters. The initial allocation ratio is 60% for high-priority services (i.e., 600Mbps), 30% for medium-priority services (300Mbps), and 10% for low-priority services (100Mbps). Dynamic adjustments are made based on the channel quality index of 0.85. If the index falls below 0.8, the bandwidth for high-priority services is increased by 5%. Next, the system enters an iterative phase, employing a gradient descent-based optimization algorithm. Each iteration calculates resource utilization and user satisfaction scores. Assuming an initial satisfaction score of 75 and a target of 85, the system adjusts bandwidth allocation with each iteration (e.g., increasing bandwidth allocation by 2% for high-priority services and decreasing bandwidth allocation by 1% for medium-priority services). The scores are updated using the formula: satisfaction score = current score + (resource utilization - target utilization) * weight (with the weight set to 0.5). After five iterations, if the satisfaction score reaches 83 and resource utilization remains stable above 90%, the iterations cease, resulting in the final transmission strategy: 620 Mbps allocated to high-priority services, 280 Mbps to medium-priority services, and 100 Mbps to low-priority services. The system also coordinates this strategy with edge computing nodes to analyze the traffic prediction error for the next cycle (assuming a ±5% error). If the error exceeds a 10% threshold, the system triggers allocation of an additional 50 Mbps from the backup resource pool to ensure transmission stability. This process forms a closed-loop logic for resource allocation, from prediction to optimization, ensuring communication efficiency and business continuity.

[0257] Example 2

[0258] See also Figure 2 A highly integrated wide- and narrow-band integrated communication system, based on the method described in one embodiment, comprises:

[0259] An acquisition module is used to obtain channel state information data from the communication environment, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and classify and record them;

[0260] A weight module, configured to construct a resource allocation model based on the transmission characteristic parameters and the capacity parameters, and determine a weight relationship between real-time control signals and multimedia data using a service priority division method;

[0261] A first determination module is configured to allocate narrowband channel resources to form a preliminary allocation plan if the transmission request ratio of the real-time control signal exceeds a preset threshold;

[0262] a queuing module, configured to calculate the remaining broadband channel capacity according to the preliminary allocation plan and queue multimedia data transmission requests using a proportional fairness algorithm;

[0263] The second determination module is used to obtain the transmission efficiency index for real-time monitoring and adjust the narrowband channel resource allocation ratio if the transmission delay exceeds a preset threshold;

[0264] The third determination module is used to calculate the service demand balance deviation value through the time window analysis method, and recalculate the broadband and narrowband resource allocation ratio if it exceeds the preset range;

[0265] The prediction module is used to obtain channel state change data and generate the resource pre-adjustment parameters for the next cycle using a time series prediction model;

[0266] The strategy module is used to pre-allocate resources for the next period communication environment according to the pre-adjustment parameters, and form a final transmission strategy through a cyclic iteration method.

[0267] In the above technical solution, in order to better use the method described in one of the embodiments, the present application proposes a highly integrated wide- and narrowband fusion integrated communication system, in which each module corresponds to each step of the above method. The specific principles have been described above and will not be repeated here.

[0268] The above descriptions are only some embodiments of the present invention and do not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A highly integrated communication method combining wide and narrowband communication, characterized in that: The method comprises: Acquire channel state information data from the communication environment, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and classify and record them; Building a resource allocation model based on the transmission characteristic parameters and capacity parameters, and using a service priority division method to determine the weight relationship between real-time control signals and multimedia data; If the transmission request ratio of the real-time control signal exceeds a preset threshold, narrowband channel resources are allocated to form a preliminary allocation plan; Calculating the remaining broadband channel capacity according to the preliminary allocation plan and queuing multimedia data transmission requests using a proportional fairness algorithm; Obtain transmission efficiency indicators for real-time monitoring, and adjust the narrowband channel resource allocation ratio if the transmission delay exceeds the preset threshold; Calculate the service demand balance deviation value through the time window analysis method. If it exceeds the preset range, recalculate the broadband and narrowband resource allocation ratio; Obtain channel status change data and use the time series prediction model to generate resource pre-adjustment parameters for the next cycle; Pre-allocating resources for the next communication cycle according to the pre-adjusted parameters, and forming a final transmission strategy through a cyclic iteration method; The resource allocation model is constructed based on the transmission characteristic parameters and the capacity parameters, and the weight relationship between the real-time control signal and the multimedia data is determined by using a service priority division method, including: Initial data features are obtained through transmission feature analysis, and the transmission features are hierarchically processed using preset classification rules to obtain feature stratification results; Construct a capacity parameter map based on the feature stratification results, use statistical tools to quantitatively analyze the capacity parameters, and determine the capacity distribution range; Match the capacity distribution range with the resource allocation requirements. If the capacity distribution range exceeds the preset threshold, dynamically adjust the resource allocation to determine whether the adjusted allocation plan meets the requirements. Obtain the adjusted allocation plan data, perform weight calculation based on the business priority division method, and determine the priority order of the real-time control signal through a preset weighted formula; Process the control signal data according to the priority order of the real-time control signal, and use the logic judgment method. If the control signal data fluctuation exceeds the preset range, it will be smoothed to obtain stable signal data; Through comparative analysis of stable signal data and multimedia data, the support vector machine algorithm is used to optimize the data weights and determine the final configuration of the weight ratio; The output of the resource allocation model is generated based on the final configuration of the weight ratio. The ratio determination result is verified through data verification tools to obtain the final allocation strategy.

2. The highly integrated communication method of claim 1, characterized in that: Acquire channel state information data from the communication environment, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and record them in categories, including: Acquire channel state information data from the communication environment through sensors and monitoring equipment, pre-process the collected raw data to remove noise and redundancy, and obtain preliminary cleaned channel data; For the channel data after preliminary cleaning, the support vector machine algorithm is used to extract the data features, separate the narrowband channel transmission characteristic parameters and the broadband channel capacity parameters, and determine the characteristic distribution of the two types of parameters; Based on the results of the feature distribution, the narrowband channel transmission characteristic parameters and the broadband channel capacity parameters are structured and divided. If the feature value exceeds the preset threshold range, it is marked as abnormal data, and the classified parameter set is obtained; By converting the storage format of the classified parameter set into a unified database storage format, a standardized parameter data set for subsequent analysis is obtained; For the standardized parameter data set, cluster analysis is used to group the parameters of narrowband and broadband channels. If the grouping results show that the distribution of a certain type of parameter deviates from the normal range, it is marked to determine the potential channel anomaly category; According to the anomaly categories of the marked channels, the corresponding parameter feature change trends are obtained. By comparing historical data records, the specific impact range and change pattern of the anomaly categories are determined; By performing data mapping on the change pattern, it is matched with the pre-established channel state model. If the matching degree is higher than the preset threshold, it is classified as a known problem type to obtain the final channel state classification result.

3. The highly integrated communication method of wide- and narrow-band fusion according to claim 1, characterized in that: If the transmission request ratio of real-time control signals exceeds a preset threshold, narrowband channel resources are allocated to form a preliminary allocation plan, including: By collecting real-time monitoring data of control signals, the dynamic changes of transmission requests are obtained and the current status of the request ratio is determined; If the request ratio exceeds the preset threshold, the narrowband channel resource allocation process is triggered, an initial allocation plan is constructed, and a preliminary channel resource configuration result is obtained; Based on the preliminary channel resource configuration results, the support vector machine algorithm is used to evaluate the rationality of resource allocation and determine whether the allocation scheme meets the real-time requirements of signal transmission; If the evaluation results show that the allocation plan does not meet the signal transmission requirements, the channel resources are dynamically adjusted, the adjusted resource configuration data is obtained, and a new allocation plan is determined; By performing secondary verification on the adjusted resource configuration data and using a pre-established performance evaluation model, it is determined whether the channel resource utilization meets the expected standards; If the utilization rate does not meet the expected standard, the narrowband channels are optimized and allocated based on the real-time monitoring data to obtain the final resource allocation plan; Based on the final resource allocation plan, the real-time performance of signal transmission is continuously tracked, the updated status of transmission requests is obtained, and the stability of system operation is judged.

4. The highly integrated communication method of claim 1, characterized in that: Calculating the remaining broadband channel capacity according to the preliminary allocation plan and queuing multimedia data transmission requests using a proportional fairness algorithm include: By analyzing the broadband channel data, the current channel usage status and allocated resource data are obtained, and the resource distribution under the preliminary allocation plan is obtained; Calculate the remaining capacity of the broadband channel based on the resource distribution under the preliminary allocation plan to determine whether the remaining capacity can meet the subsequent transmission needs; If the remaining capacity data is lower than the preset threshold, the priority of multimedia data transmission requests is evaluated to determine which requests need to be delayed; A proportional fairness algorithm is used to queue and sort multimedia data transmission requests to obtain a sorted request sequence. For the sorted request sequence, resources are redistributed based on the remaining capacity data to determine the resource allocation share for each request; Dynamically adjust the broadband channel through resource allocation shares to obtain the adjusted channel resource distribution status; According to the adjusted channel resource distribution state, the transmission request of the multimedia data is actually scheduled to obtain the final transmission processing result.

5. The highly integrated communication method of wide- and narrow-band fusion according to claim 1, characterized in that: Acquire transmission efficiency indicators for real-time monitoring. If the transmission delay exceeds the preset threshold, adjust the narrowband channel resource allocation ratio, including: By deploying a monitoring system, we can obtain transmission efficiency indicator data from the network transmission process, continuously collect transmission delay data, and obtain preliminary delay data records; Based on the collected delay data records, a preset threshold is used for comparison. If the transmission delay is detected to exceed the preset threshold, a dynamic adjustment mechanism is triggered to determine the narrowband channel range that needs to be optimized. Obtain the current resource allocation ratio data of the narrowband channel, analyze the distribution of the resource ratio for the channel range that exceeds the preset threshold, and obtain the deviation data of the resource allocation; Calculate the adjustment range of the narrowband channel resource ratio through the deviation data, use the linear regression model to predict the resource allocation ratio, and determine the adjusted channel ratio parameters; Generate resource allocation instructions based on the adjusted channel ratio parameters, dynamically adjust the resource ratio for narrowband channels, and obtain an updated resource configuration state; Obtain the updated resource configuration status and continuously monitor transmission efficiency indicators and transmission delay data. If the transmission delay still exceeds the preset threshold, the resource ratio adjustment process is executed repeatedly to determine the final optimization result. Through the optimization results, the adjusted transmission efficiency indicators and delay data are recorded, and the data is updated for the monitoring system to obtain real-time network transmission status feedback.

6. The highly integrated communication method of claim 1, characterized in that: The service demand balance deviation value is calculated through the time window analysis method. If it exceeds the preset range, the broadband and narrowband resource allocation ratio is recalculated, including: By dividing the time window, we can obtain the trend data of business demand changes from historical data, and use segmented processing to determine the demand fluctuations within each time window; According to the demand fluctuations in each time window, the balance deviation value is calculated by applying analytical methods to obtain specific deviation quantitative results; If the calculated balance deviation value exceeds the preset range, the deviation monitoring mechanism is triggered to determine whether the conditions for readjustment are met and to process the information in combination with historical resource allocation data; Based on the deviation monitoring results, the current allocation ratio data of broadband and narrowband resources is obtained to determine whether there is uneven resource allocation; Based on the judgment result of uneven resource allocation, a pre-established optimization model is used to calculate the new allocation ratio and obtain the adjusted resource allocation plan; If the difference between the adjusted resource allocation plan and the current allocation ratio is greater than the preset value, the rationality of the adjustment plan will be verified through the demand analysis module to determine the final resource allocation strategy; Through the final resource allocation strategy, the actual allocation ratio of broadband and narrowband resources is updated to achieve dynamic balance adjustment of business needs.

7. The highly integrated communication method of claim 1, characterized in that: Acquire channel status change data and use the time series prediction model to generate resource pre-adjustment parameters for the next cycle, including: By extracting real-time data from the channel status monitoring equipment, the change data of the channel status is obtained; Based on the acquired change data, the time series analysis method is used to determine the trend characteristics of the change data; If the trend characteristics of the changing data exceed the preset threshold range, the forecast value for the next period is generated through the forecast model; Obtain the next cycle's forecast value output by the forecast model and, combined with historical records of resource allocation, determine the initial demand for resource pre-adjustment. By analyzing the initial demand and using the long short-term memory network model, we can obtain the optimal parameters for resource pre-adjustment. Determine the final adjustment parameters based on the optimization parameters and the results of the cycle analysis; Based on the final adjustment parameters, a preliminary adjustment plan for resource allocation is generated to complete the parameter generation process.

8. The highly integrated communication method of wide- and narrow-band fusion according to claim 1, characterized in that: Pre-allocating resources for the next cycle communication environment according to the pre-adjusted parameters, and forming a final transmission strategy through a cyclic iteration method, including: Obtain historical data and current status information of the communication environment, and determine the environment change trend and resource demand distribution by analyzing key indicators in the data; Based on the environmental change trend and resource demand distribution, the preset parameters are used to perform preliminary resource allocation for the communication environment of the next cycle to obtain an initial allocation plan; Based on the initial allocation plan, the resource allocation results are adjusted multiple times using a cyclic iteration method. If the resource occupancy rate of a certain area exceeds the preset threshold, the resources in that area are rescheduled to determine whether the balance condition is met. By comparing the resource scheduling results after each iteration, the adjusted resource distribution status is obtained to determine whether there is local resource conflict or redundancy; If local resource conflicts or redundancies are detected, the resource scheduling scheme is optimized based on the genetic algorithm to obtain an improved allocation strategy; Based on the improved allocation strategy, combined with the transmission mode and final strategy requirements, the communication environment is simulated and verified to determine whether the expected transmission performance is achieved; Through simulation verification results, parameter optimization and cycle planning schemes are adjusted to build the final transmission strategy and determine the resource scheduling scheme suitable for the next cycle.

9. A highly integrated broadband and narrowband integrated communication system, characterized in that: Based on the method according to any one of claims 1 to 8, the system comprises: An acquisition module is used to obtain channel state information data from the communication environment, extract narrowband channel transmission characteristic parameters and broadband channel capacity parameters, and classify and record them; A weight module, configured to construct a resource allocation model based on the transmission characteristic parameters and the capacity parameters, and determine a weight relationship between real-time control signals and multimedia data using a service priority division method; A first determination module is configured to allocate narrowband channel resources to form a preliminary allocation plan if the transmission request ratio of the real-time control signal exceeds a preset threshold; a queuing module, configured to calculate the remaining broadband channel capacity according to the preliminary allocation plan and queue multimedia data transmission requests using a proportional fairness algorithm; The second determination module is used to obtain the transmission efficiency index for real-time monitoring and adjust the narrowband channel resource allocation ratio if the transmission delay exceeds a preset threshold; The third determination module is used to calculate the service demand balance deviation value through the time window analysis method, and recalculate the broadband and narrowband resource allocation ratio if it exceeds the preset range; The prediction module is used to obtain channel state change data and generate the resource pre-adjustment parameters for the next cycle using a time series prediction model; The strategy module is used to pre-allocate resources for the next period communication environment according to the pre-adjustment parameters, and form a final transmission strategy through a cyclic iteration method.

Images