Power dual-mode carrier network communication quality improving system and method based on optimization algorithm

Abstract

The invention relates to the technical field of mobile data communication services, and provides a power dual-mode carrier network communication quality improvement system and method based on an optimization algorithm, and the method comprises the steps that a network state monitoring module monitors the condition in a power dual-mode carrier network in real time, and transmits monitoring data to a channel quality evaluation and prediction module; the channel quality evaluation and prediction module evaluates the current channel quality by adopting a machine learning model based on the data provided by the network state monitoring module, and predicts the future channel quality change; the resource allocation optimization module is used for dynamically allocating carrier resources of a reverse link by adopting an optimization algorithm based on a channel quality prediction result and real-time network load information; and the cross-modal carrier fusion module provides an allocation scheme according to the resource allocation optimization module. According to the method, the resource utilization efficiency, the communication stability and the overall performance of the power dual-mode carrier network can be remarkably improved, and main problems existing in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile data communication services, and in particular to a system and method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm. Background Art

[0002] The present invention can improve the resource utilization efficiency of the power dual-mode carrier network by introducing dynamic resource allocation, channel quality prediction and cross-modal carrier fusion technology based on optimization algorithm. First, by real-time monitoring of network load, channel quality and interference information, the system can dynamically adjust the carrier resource allocation to achieve accurate configuration of reverse link resources, thereby optimizing the transmission efficiency and stability of the network. Secondly, the channel quality assessment and prediction module provides a more accurate basis for resource allocation through real-time prediction of channel quality, reducing the impact of channel fluctuations on resource scheduling. Finally, the cross-modal carrier fusion module effectively manages interference between modes and optimizes the allocation of spectrum resources by optimizing resource sharing and switching between TDD and FDD modes. In summary, the present invention improves the communication stability and spectrum resource utilization efficiency of the power dual-mode carrier network through precise resource allocation and channel management methods. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm. Aiming at the problems of uneven resource allocation, large fluctuations in channel quality, and difficulty in effectively managing interference in existing power dual-mode carrier networks, a method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm is proposed. This method introduces an intelligent resource allocation optimization strategy, combines channel quality prediction with historical data learning, and achieves dynamic optimization of resource allocation. Specifically, the present invention solves the following technical problems: first, how to dynamically adjust the allocation of carrier resources according to changes in network load and channel quality; second, how to improve the resource utilization efficiency of the network by performing real-time evaluation and prediction of channel quality; and finally, how to effectively manage interference between carrier modes and ensure the collaborative work of different modes to achieve more efficient use of spectrum resources.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a power dual-mode carrier network communication quality improvement system based on an optimization algorithm, comprising: a network status monitoring module, a channel quality evaluation and prediction module, a resource allocation optimization module and a cross-modal carrier fusion module, wherein: the network status monitoring module monitors the load of each link, channel quality and interference information in the power dual-mode carrier network in real time, and transmits the monitoring data to the channel quality evaluation and prediction module; the channel quality evaluation and prediction module uses a machine learning model to evaluate the current channel quality based on the data provided by the network status monitoring module, and predicts future channel quality changes, providing input for the resource allocation optimization module; the resource allocation optimization module uses an optimization algorithm to dynamically allocate carrier resources of the reverse link based on the channel quality prediction results and real-time network load information; the cross-modal carrier fusion module performs carrier mode selection, spectrum resource allocation and interference management according to the allocation scheme provided by the resource allocation optimization module.

[0006] As a preferred solution of the power dual-mode carrier network communication quality improvement system based on optimization algorithm described in the present invention, the network status monitoring module collects the real-time load, channel quality and interference information of each link, and evaluates the interference intensity between links, and transmits the corresponding data to the channel quality evaluation and prediction module; the channel quality evaluation and prediction module evaluates the channel quality according to the received real-time data and historical data using a model, and predicts the channel quality changes at future times based on the evaluation results, and the prediction results are used as input to the resource allocation optimization module.

[0007] As a preferred solution of the power dual-mode carrier network communication quality improvement system based on optimization algorithm described in the present invention, wherein: the resource allocation optimization module receives the channel quality prediction results and network status data from the channel quality assessment and prediction module, and adopts particle swarm optimization PSO or genetic algorithm GA optimization method to perform carrier resource allocation for the reverse link; the cross-modal carrier fusion module performs carrier mode selection and spectrum resource adjustment according to the carrier allocation scheme provided by the resource allocation optimization module, optimizes the use of spectrum resources and manages interference.

[0008] As a preferred solution of the method for improving the communication quality of the power dual-mode carrier network based on the optimization algorithm described in the present invention, wherein: a) the carrier resources are dynamically allocated based on the intelligent reverse link optimization algorithm, and the intelligent algorithm is evaluated in real time according to the network load, channel quality and interference information, and optimizes the carrier resource allocation of the reverse link;

[0009] b) predicting and evaluating the state of the wireless channel based on a machine learning model, wherein the machine learning model uses historical channel data and real-time network information to predict future changes in channel quality and adjust communication parameters to optimize channel quality;

[0010] c) Based on steps a) and b), cross-modal carrier fusion technology is used to perform dynamic resource scheduling and interference management to coordinate resource allocation between different carrier modes.

[0011] As a preferred solution of the method for improving the communication quality of the power dual-mode carrier network based on the optimization algorithm described in the present invention, wherein: the dynamic allocation of carrier resources based on the intelligent reverse link optimization algorithm includes that in the power dual-mode carrier network, the resource allocation of the reverse link changes dynamically with the changes in the network load, channel quality and interference. Therefore, in order to achieve dynamic optimization, the time factor is introduced to represent the optimization objective function at time t. The objective function needs to weigh the load, channel quality and interference at each time, and consider the relative importance of each factor. The formula is as follows:

[0012]

[0013] in, represents the optimization objective function at time t; w1(t), w2(t), w3(t) represent the weight coefficients that change over time, dynamically adjusting the impact of load, channel quality and interference; L i (t) represents the network load of the i-th link; SNR i (t) represents the signal-to-noise ratio of the i-th link; I i (t) represents the interference amount of the i-th link;

[0014] In order to improve the adaptability of the model, the historical data learning part is introduced to predict future load, channel quality and interference based on the historical network status, so as to adjust the optimization strategy. Considering the impact of time on historical data, historical data is combined through weighted accumulation:

[0015]

[0016] Among them, λ represents the weight coefficient of historical data learning; α(t) represents the coefficient that decays over time; represents the load at the past k moments;

[0017] Consider the specific model of interference calculation and optimize the interference based on the network topology. The calculation of the interference amount needs to consider the path loss of signal propagation, the power and frequency of the interference source, and the network topology. The details are as follows:

[0018]

[0019] Where: I i (t) represents the interference of the ith link at time t; P j (t) represents the transmission power of the jth link; d ij(t) represents the distance between the i-th and j-th links; γ represents the path loss factor, which ranges from 2 to 4; α ij (t) represents the coefficient of the interference between links i and j; f ij (t) represents the interference coefficient corresponding to the frequency; β ij (t) represents the coefficient related to frequency and interference mode; α ij (t) represents the coefficient of the interference between links i and j; f ij (t) represents the frequency-dependent interference coefficient; β ij (t) represents the coefficient corresponding to the frequency and interference pattern;

[0020] In order to achieve the optimal allocation of carrier resources in the reverse link, a combined model of particle swarm optimization PSO and genetic algorithm GA is adopted. In particle swarm optimization PSO, particles represent the carrier allocation strategy of each link, and local search is performed by updating the speed and position of particles, while genetic algorithm GA is used for global search and explores the optimal solution through crossover and mutation operations.

[0021] Particle swarm optimization formula:

[0022] v i (t+1)=ω·v i (t)+c1·(p i -x i (t))+c2·(gx i (t))

[0023] x i (t+1)=x i (t)+v i (t+1)

[0024] Genetic algorithms are used for global optimization:

[0025] p=p+∈

[0026] Among them, v i (t) represents the velocity of the particle, x i (t) represents the position of the particle, c1 and c2 are acceleration constants, ω is the inertia weight, and p i is the optimal position of the particle, g is the global optimal position, p is the individual in the current population, and ∈ is the amount of variation.

[0027] As a preferred solution of the method for improving the communication quality of the power dual-mode carrier network based on the optimization algorithm described in the present invention, wherein: the prediction and evaluation include: the system trains a machine learning model through historical channel data and real-time network information, the historical channel data includes signal strength, bit error rate, signal-to-noise ratio, interference information and network load, and the real-time network information includes current signal strength, bit error rate, link status information, and provides the historical channel data and real-time network information as input to the machine learning model to establish a mapping relationship between channel quality and communication parameters;

[0028] The machine learning model adopts a deep neural network DNN algorithm. The deep neural network uses a multi-layer neural network structure and a large amount of historical and real-time data for training to automatically extract channel quality features and predict future channel states.

[0029] During the prediction process, the machine learning model evaluates the current channel status in real time and predicts the future channel quality. The machine learning model makes predictions based on the input data, calculates the trends of signal attenuation, interference changes, and bit error rate changes, and dynamically adjusts communication parameters, including modulation mode, coding scheme, and power control, based on the predicted channel quality changes.

[0030] In terms of modulation mode selection, the model is adjusted according to the channel quality prediction results. When the channel quality meets the preset conditions, the system selects 16-QAM and 64-QAM modulation modes; when the channel quality does not meet the preset conditions, the system selects BPSK and QPSK modulation modes.

[0031] As a preferred solution of the method for improving the communication quality of the power dual-mode carrier network based on the optimization algorithm described in the present invention, the dynamic resource scheduling and interference management includes that the system dynamically adjusts the selection of the carrier mode and resource allocation according to the real-time network load, channel quality, and user demand data;

[0032] Among them, cross-modal carrier fusion technology includes dynamic switching between different carrier modes according to network load conditions. The carrier modes include time division duplex TDD and frequency division duplex FDD. The TDD mode is used in scenarios where the uplink and downlink traffic changes greatly. The uplink and downlink ratio is optimized by adjusting the time slot allocation. The FDD mode is used in scenarios where the uplink and downlink traffic is balanced and the bandwidth requirement is higher than the preset standard. Resources are allocated for uplink and downlink transmission at the same time. The system decides whether to switch the carrier mode based on channel quality, network load and user demand data.

[0033] As a preferred solution of the method for improving the communication quality of the power dual-mode carrier network based on the optimization algorithm described in the present invention, the cross-modal carrier fusion technology also includes collaborative scheduling of carriers in different frequency bands. The collaborative scheduling is based on network status information, including spectrum utilization, link quality and bandwidth requirements. The system evaluates the resource requirements of each frequency band in real time and dynamically switches between different carrier modes according to the demand.

[0034] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm are implemented.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm.

[0036] Beneficial effects of the present invention: The present invention can effectively improve the communication quality of the electric power dual-mode carrier network by introducing dynamic resource allocation, channel quality prediction and cross-modal carrier fusion technology based on optimization algorithm. First, by real-time monitoring of network load, channel quality and interference information, the system can dynamically adjust the carrier resource allocation to achieve the optimal configuration of reverse link resources, thereby improving the transmission efficiency and stability of the network. Secondly, the channel quality assessment and prediction module provides a more accurate basis for resource allocation through real-time prediction of channel quality, reducing the degradation of communication quality caused by channel fluctuations. Finally, the introduction of the cross-modal carrier fusion module optimizes the resource sharing and switching between TDD and FDD modes, and effectively suppresses interference between modes, thereby improving the utilization rate of spectrum resources and the overall performance of the network. In summary, the present invention can significantly improve the resource utilization efficiency, communication stability and overall performance of the electric power dual-mode carrier network, and solve the main problems existing in the current technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A schematic flow chart of a method for improving communication quality of a power dual-mode carrier network based on an optimization algorithm provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a system for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm, comprising:

[0041] Network status monitoring module, channel quality assessment and prediction module, resource allocation optimization module and cross-modal carrier fusion module, wherein: the network status monitoring module monitors the load of each link, channel quality and interference information in the power dual-mode carrier network in real time, and transmits the monitoring data to the channel quality assessment and prediction module; the channel quality assessment and prediction module uses a machine learning model to assess the current channel quality based on the data provided by the network status monitoring module, and predicts future channel quality changes, providing input for the resource allocation optimization module; the resource allocation optimization module uses an optimization algorithm to dynamically allocate carrier resources of the reverse link based on the channel quality prediction results and real-time network load information; the cross-modal carrier fusion module performs carrier mode selection, spectrum resource allocation and interference management according to the allocation plan provided by the resource allocation optimization module.

[0042] The network status monitoring module collects the real-time load, channel quality and interference information of each link, evaluates the interference intensity between links, and transmits the corresponding data to the channel quality evaluation and prediction module; the channel quality evaluation and prediction module evaluates the channel quality based on the received real-time data and historical data using a model, and predicts the channel quality changes at future times based on the evaluation results. The prediction results are used as input to the resource allocation optimization module.

[0043] The resource allocation optimization module receives the channel quality prediction results and network status data from the channel quality assessment and prediction module, and uses particle swarm optimization PSO or genetic algorithm GA optimization method to perform carrier resource allocation for the reverse link; the cross-modal carrier fusion module performs carrier mode selection and spectrum resource adjustment according to the carrier allocation scheme provided by the resource allocation optimization module, optimizes the use of spectrum resources and manages interference.

[0044] Example 2, reference Figure 1 , is an embodiment of the present invention, and provides a method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm, which is specifically as follows:

[0045] a) dynamically allocate carrier resources based on an intelligent reverse link optimization algorithm, which evaluates and optimizes reverse link carrier resource allocation in real time based on network load, channel quality and interference information;

[0046] b) predicting and evaluating the state of the wireless channel based on a machine learning model, wherein the machine learning model uses historical channel data and real-time network information to predict future changes in channel quality and adjust communication parameters to optimize channel quality;

[0047] c) Based on steps a) and b), cross-modal carrier fusion technology is used to perform dynamic resource scheduling and interference management to coordinate resource allocation between different carrier modes.

[0048] The dynamic allocation of carrier resources based on the intelligent reverse link optimization algorithm includes that in the power dual-mode carrier network, the resource allocation of the reverse link changes dynamically with the changes in network load, channel quality and interference. Therefore, in order to achieve dynamic optimization, the time factor is introduced to represent the optimization objective function at time t. The objective function needs to weigh the load, channel quality and interference at each time, and consider the relative importance of each factor. The formula is as follows:

[0049]

[0050] in, represents the optimization objective function at time t; w1(t), w2(t), w3(t) represent the weight coefficients that change over time, dynamically adjusting the impact of load, channel quality and interference; L i (t) represents the network load of the i-th link; SNR i (t) represents the signal-to-noise ratio of the i-th link; I i (t) represents the interference amount of the i-th link;

[0051] In order to improve the adaptability of the model, the historical data learning part is introduced to predict future load, channel quality and interference based on the historical network status, so as to adjust the optimization strategy. Considering the impact of time on historical data, historical data is combined through weighted accumulation:

[0052]

[0053] Among them, λ represents the weight coefficient of historical data learning; α(t) represents the coefficient that decays over time; represents the load at the past k moments;

[0054] Consider the specific model of interference calculation and optimize the interference based on the network topology. The calculation of the interference amount needs to consider the path loss of signal propagation, the power and frequency of the interference source, and the network topology. The details are as follows:

[0055]

[0056] Where: I i (t) represents the interference of the ith link at time t; P j (t) represents the transmission power of the jth link; d ij (t) represents the distance between the i-th and j-th links; γ represents the path loss factor, which ranges from 2 to 4; α ij (t) represents the coefficient of the interference between links i and j; f ij (t) represents the interference coefficient corresponding to the frequency; β ij (t) represents the coefficient related to frequency and interference mode; α ij (t) represents the coefficient of the interference between links i and j; f ij (t) represents the frequency-dependent interference coefficient; β ij (t) represents the coefficient corresponding to the frequency and interference pattern;

[0057] In order to achieve the optimal allocation of carrier resources in the reverse link, a combined model of particle swarm optimization PSO and genetic algorithm GA is adopted. In particle swarm optimization PSO, particles represent the carrier allocation strategy of each link, and local search is performed by updating the speed and position of particles, while genetic algorithm GA is used for global search and explores the optimal solution through crossover and mutation operations.

[0058] Particle swarm optimization formula:

[0059] v i (t+1)=ω·v i (t)+c1·(p i -x i (t))+c2·(gx i (t))

[0060] x i (t+1)=x i (t)+v i (t+1)

[0061] Genetic algorithms are used for global optimization:

[0062] p=p+∈

[0063] Among them, v i (t) represents the velocity of the particle, x i (t) represents the position of the particle, c1 and c2 are acceleration constants, ω is the inertia weight, and p i is the optimal position of the particle, g is the global optimal position, p is the individual in the current population, and ∈ is the amount of variation.

[0064] The prediction and evaluation includes the system training a machine learning model through historical channel data and real-time network information, wherein the historical channel data includes signal strength, bit error rate, signal-to-noise ratio, interference information and network load, and the real-time network information includes current signal strength, bit error rate, link status information, and providing the historical channel data and real-time network information as input to the machine learning model to establish a mapping relationship between channel quality and communication parameters;

[0065] The machine learning model adopts a deep neural network DNN algorithm. The deep neural network uses a multi-layer neural network structure and a large amount of historical and real-time data for training to automatically extract channel quality features and predict future channel states.

[0066] During the prediction process, the machine learning model evaluates the current channel status in real time and predicts the future channel quality. The machine learning model makes predictions based on the input data, calculates the trends of signal attenuation, interference changes, and bit error rate changes, and dynamically adjusts communication parameters, including modulation mode, coding scheme, and power control, based on the predicted channel quality changes.

[0067] In terms of modulation mode selection, the model is adjusted according to the channel quality prediction results. When the channel quality meets the preset conditions, the system selects 16-QAM and 64-QAM modulation modes; when the channel quality does not meet the preset conditions, the system selects BPSK and QPSK modulation modes.

[0068] The dynamic resource scheduling and interference management includes the system dynamically adjusting the selection of carrier modes and resource allocation according to real-time network load, channel quality, and user demand data;

[0069] Among them, cross-modal carrier fusion technology includes dynamic switching between different carrier modes according to network load conditions. The carrier modes include time division duplex TDD and frequency division duplex FDD. The TDD mode is used in scenarios where the uplink and downlink traffic changes greatly. The uplink and downlink ratio is optimized by adjusting the time slot allocation. The FDD mode is used in scenarios where the uplink and downlink traffic is balanced and the bandwidth requirement is higher than the preset standard. Resources are allocated for uplink and downlink transmission at the same time. The system decides whether to switch the carrier mode based on channel quality, network load and user demand data.

[0070] The cross-modal carrier fusion technology also includes collaborative scheduling of carriers in different frequency bands. The collaborative scheduling is based on network status information, including spectrum utilization, link quality and bandwidth requirements. The system evaluates the resource requirements of each frequency band in real time and dynamically switches between different carrier modes based on the demand.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0072] Example 3

[0073] The third embodiment of the present invention is different from the first two embodiments in that:

[0074] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0075] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A power dual-mode carrier network communication quality improvement system based on an optimization algorithm, characterized by: It includes a network status monitoring module, a channel quality assessment and prediction module, a resource allocation optimization module and a cross-modal carrier fusion module, wherein: the network status monitoring module monitors the load of each link, channel quality and interference information in the power dual-mode carrier network in real time, and transmits the monitoring data to the channel quality assessment and prediction module; the channel quality assessment and prediction module uses a machine learning model to assess the current channel quality based on the data provided by the network status monitoring module, and predicts future channel quality changes, providing input for the resource allocation optimization module; the resource allocation optimization module uses an optimization algorithm to dynamically allocate carrier resources of the reverse link based on the channel quality prediction results and real-time network load information; the cross-modal carrier fusion module performs carrier mode selection, spectrum resource allocation and interference management according to the allocation plan provided by the resource allocation optimization module.

2. The power dual-mode carrier network communication quality improvement system based on the optimization algorithm as claimed in claim 1, characterized in that: The network status monitoring module collects the real-time load, channel quality and interference information of each link, evaluates the interference intensity between links, and transmits the corresponding data to the channel quality evaluation and prediction module; The channel quality assessment and prediction module uses the model to assess the channel quality based on the received real-time data and historical data, and predicts the channel quality changes at future times based on the assessment results. The prediction results serve as the input of the resource allocation optimization module.

3. The power dual-mode carrier network communication quality improvement system based on optimization algorithm as claimed in claim 2, characterized in that: The resource allocation optimization module receives the channel quality prediction results and network status data from the channel quality assessment and prediction module, and uses particle swarm optimization PSO or genetic algorithm GA optimization method to perform carrier resource allocation for the reverse link; the cross-modal carrier fusion module performs carrier mode selection and spectrum resource adjustment according to the carrier allocation scheme provided by the resource allocation optimization module, optimizes the use of spectrum resources and manages interference.

4. A method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm, applied to a system for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm as described in any one of claims 1 to 3, characterized in that: include, a) dynamically allocate carrier resources based on an intelligent reverse link optimization algorithm, which evaluates and optimizes reverse link carrier resource allocation in real time based on network load, channel quality and interference information; b) predicting and evaluating the state of the wireless channel based on a machine learning model, wherein the machine learning model uses historical channel data and real-time network information to predict future changes in channel quality and adjust communication parameters to optimize channel quality; c) Based on steps a) and b), cross-modal carrier fusion technology is used to perform dynamic resource scheduling and interference management to coordinate resource allocation between different carrier modes.

5. The method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm as claimed in claim 4, characterized in that: The dynamic allocation of carrier resources based on the intelligent reverse link optimization algorithm includes that in the power dual-mode carrier network, the resource allocation of the reverse link changes dynamically with the changes in network load, channel quality and interference. Therefore, in order to achieve dynamic optimization, the time factor is introduced to represent the optimization objective function at time t. The objective function needs to weigh the load, channel quality and interference at each time, and consider the relative importance of each factor. The formula is as follows: in, represents the optimization objective function at time t; w1(t), w2(t), w3(t) represent the weight coefficients that change over time, dynamically adjusting the impact of load, channel quality and interference; L i (t) represents the network load of the i-th link; SNR i (t) represents the signal-to-noise ratio of the i-th link; I i (t) represents the interference amount of the i-th link; In order to improve the adaptability of the model, the historical data learning part is introduced to predict future load, channel quality and interference based on the historical network status, so as to adjust the optimization strategy. Considering the impact of time on historical data, historical data is combined through weighted accumulation: Among them, λ represents the weight coefficient of historical data learning; α(t) represents the coefficient that decays over time; represents the load at the past k moments; Consider the specific model of interference calculation and optimize the interference based on the network topology. The calculation of the interference amount needs to consider the path loss of signal propagation, the power and frequency of the interference source, and the network topology. The details are as follows: Where: I i (t) represents the interference of the ith link at time t; P j (t) represents the transmission power of the jth link; d ij (t) represents the distance between the i-th and j-th links; γ represents the path loss factor, which ranges from 2 to 4; α ij (t) represents the coefficient of the interference between links i and j; f ij (t) represents the interference coefficient corresponding to the frequency; β ij (t) represents the coefficient corresponding to the frequency and interference pattern; α ij (t) represents the coefficient of the interference between links i and j; f ij (t) represents the interference coefficient corresponding to the frequency; β ij (t) represents the coefficient corresponding to the frequency and interference pattern; In order to achieve the optimal allocation of carrier resources in the reverse link, a combined model of particle swarm optimization PSO and genetic algorithm GA is adopted. In particle swarm optimization PSO, particles represent the carrier allocation strategy of each link, and local search is performed by updating the speed and position of particles, while genetic algorithm GA is used for global search and explores the optimal solution through crossover and mutation operations. Particle swarm optimization formula: v i (t+1)=ω·v i (t)+c1·(p i -x i (t))+c2·(g-x i (t)) x i (t+1)=x i (t)+v i (t+1) Genetic algorithms are used for global optimization: p=p+∈ Among them, v i (t) represents the velocity of the particle, x i (t) represents the position of the particle, c1 and c2 are acceleration constants, ω is the inertia weight, and p i is the optimal position of the particle, g is the global optimal position, p is the individual in the current population, and ∈ is the amount of variation.

6. The method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm as claimed in claim 5, characterized in that: The prediction and evaluation includes the system training a machine learning model through historical channel data and real-time network information, wherein the historical channel data includes signal strength, bit error rate, signal-to-noise ratio, interference information and network load, and the real-time network information includes current signal strength, bit error rate, link status information, and providing the historical channel data and real-time network information as input to the machine learning model to establish a mapping relationship between channel quality and communication parameters; The machine learning model adopts a deep neural network DNN algorithm. The deep neural network uses a multi-layer neural network structure and a large amount of historical and real-time data for training to automatically extract channel quality features and predict future channel states. During the prediction process, the machine learning model evaluates the current channel status in real time and predicts the future channel quality. The machine learning model makes predictions based on the input data, calculates the trends of signal attenuation, interference changes, and bit error rate changes, and dynamically adjusts communication parameters, including modulation mode, coding scheme, and power control, based on the predicted channel quality changes. In terms of modulation mode selection, the model is adjusted according to the channel quality prediction results. When the channel quality meets the preset conditions, the system selects 16-QAM and 64-QAM modulation modes; when the channel quality does not meet the preset conditions, the system selects BPSK and QPSK modulation modes.

7. The method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm as claimed in claim 6, characterized in that: The dynamic resource scheduling and interference management includes the system dynamically adjusting the selection of carrier modes and resource allocation according to real-time network load, channel quality, and user demand data; Among them, cross-modal carrier fusion technology includes dynamic switching between different carrier modes according to network load conditions. The carrier modes include time division duplex TDD and frequency division duplex FDD. The TDD mode is used in scenarios where the uplink and downlink traffic changes greatly. The uplink and downlink ratio is optimized by adjusting the time slot allocation. The FDD mode is used in scenarios where the uplink and downlink traffic is balanced and the bandwidth requirement is higher than the preset standard. Resources are allocated for uplink and downlink transmission at the same time. The system decides whether to switch the carrier mode based on channel quality, network load and user demand data.

8. The method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm as claimed in claim 7, characterized in that: The cross-modal carrier fusion technology also includes collaborative scheduling of carriers in different frequency bands. The collaborative scheduling is based on network status information, including spectrum utilization, link quality and bandwidth requirements. The system evaluates the resource requirements of each frequency band in real time and dynamically switches between different carrier modes based on the demand.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm as described in any one of claims 4 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for improving the communication quality of a power dual-mode carrier network based on an optimization algorithm as described in any one of claims 4 to 8 are implemented.

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