Self-adaptive multi-band fusion network communication method, system, device and medium

Abstract

The invention provides a self-adaptive multi-band fusion network communication method, system and device, and a medium. The method comprises the following steps: acquiring communication quality parameters of a plurality of bands; performing standardization processing on the communication quality parameter to obtain the communication quality parameter after standardization processing; obtaining a fuzzy weighted average score of each frequency band through a fuzzy comprehensive evaluation algorithm based on the standardized communication quality parameters; based on the fuzzy weighted average score of each frequency band, calculating a comprehensive score of each frequency band through a weighted average algorithm; based on the comprehensive score of each frequency band, determining a plurality of frequency band combinations through an adaptive multi-frequency band fusion model; screening the plurality of frequency band combinations in combination with a particle swarm optimization algorithm to obtain an optimal frequency band combination; and defining a dynamic threshold adjustment strategy under the optimal frequency band combination, and performing network data transmission. According to the method, the communication efficiency, quality and stability are improved, the utilization of spectrum resources is optimized, and the method can be suitable for diversified complex application scenes.

Description

Technical Field

[0001] The present invention relates to the field of network communications, and more specifically to an adaptive multi-band fusion network communication method, system, device and medium. Background Art

[0002] With the rapid development of wireless communication technology, network communication is facing an increasingly complex and changeable communication environment. Traditional single-band communication methods can no longer meet the requirements of modern communication networks for data transmission rate, reliability and coverage. Multi-band communication can significantly improve the overall performance of the communication system by using multiple frequency bands for data transmission at the same time. However, how to effectively integrate multiple frequency bands to achieve the best communication effect has become a key issue that needs to be solved in the current network communication field.

[0003] In multi-band communications, existing technologies usually only consider a few communication quality parameters, such as signal strength and signal-to-noise ratio, while ignoring other important factors, such as latency, packet loss rate, bandwidth utilization, and spectrum utilization. This one-sided evaluation method cannot fully reflect the actual communication performance of the frequency band, resulting in inaccurate optimization results.

[0004] Secondly, most existing multi-band communication systems use static frequency band management, which means that certain frequency bands are fixed in advance for specific communication tasks. This static management method lacks flexibility and cannot be dynamically adjusted according to real-time changes in the communication environment. When the network load changes or new interference sources appear, static frequency band management cannot optimize the frequency band combination in time, resulting in low communication efficiency or even communication interruption.

[0005] At the same time, the existing technology lacks an effective dynamic adjustment mechanism when facing a dynamically changing communication environment, and is unable to optimize the transmission threshold in real time according to actual conditions, resulting in a decrease in communication quality and efficiency.

[0006] Therefore, how to design an adaptive multi-band fusion network communication method that can optimize the utilization efficiency of spectrum resources and achieve efficient and stable transmission of network data is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0007] In view of this, the present invention provides a network communication method with adaptive multi-band fusion, which improves the frequency band selection and utilization efficiency, communication quality and stability of wireless communications, as well as the intelligent configuration and dynamic adjustment capabilities of network resources through adaptive multi-band fusion and intelligent optimization strategies, thereby meeting the growing communication needs and improving user experience.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a network communication method for adaptive multi-band fusion, comprising the following steps:

[0010] S1. Acquire communication quality parameters of multiple frequency bands; the communication quality parameters include signal strength, signal-to-noise ratio, delay, packet loss rate, bandwidth utilization and spectrum utilization;

[0011] S2. Standardize the communication quality parameters to obtain the standardized communication quality parameters;

[0012] S3. Based on the standardized communication quality parameters, a fuzzy comprehensive evaluation algorithm is used to obtain a fuzzy weighted average score of each frequency band;

[0013] S4, based on the fuzzy weighted average score of each frequency band, the comprehensive score of each frequency band is calculated by weighted average algorithm;

[0014] S5. Based on the comprehensive scores of each frequency band, a combination of multiple frequency bands is determined through an adaptive multi-band fusion model;

[0015] S6. Use particle swarm optimization algorithm to screen multiple frequency band combinations and obtain the optimal frequency band combination;

[0016] S7. Define a dynamic threshold adjustment strategy under the optimal frequency band combination to perform network data transmission.

[0017] Furthermore, the S2 includes:

[0018] For each communication quality parameter, calculate the mean μ and standard deviation σ of all measurements;

[0019] Each measured value x of each communication quality parameter i , converted using the Z-score standardization formula:

[0020]

[0021] Among them, z i represents the standardized value of the i-th communication quality parameter;

[0022] The abnormal values ​​beyond the predetermined range are eliminated to obtain the communication quality parameters after standardization.

[0023] Furthermore, the S3 includes:

[0024] A fuzzy membership function is defined for each communication quality parameter; the fuzzy membership function maps the standardized communication quality parameter value to the interval [0,1], indicating the degree of membership of the communication quality parameter to the communication quality;

[0025] Determine the fuzzy weight of each communication quality parameter in combination with the importance of each communication quality parameter;

[0026] Each communication quality parameter value is converted into a membership degree using a fuzzy membership function, and the membership degree and the fuzzy weight are multiplied to obtain the fuzzy weighted average score of each frequency band.

[0027] Furthermore, the S4 includes:

[0028] The fuzzy weighted average score of each frequency band is taken as the matrix element p ij , construct an evaluation matrix; where p ij represents the weighted average score of the i-th communication quality parameter on the j-th frequency band;

[0029] Standardizing the evaluation matrix and setting an optimal solution;

[0030] The grey correlation coefficient between each frequency band and the ideal optimal solution is calculated by combining the grey correlation analysis algorithm, and the comprehensive grey correlation degree is calculated by combining the weight to obtain the comprehensive score of each frequency band.

[0031] Furthermore, the S5 includes:

[0032] Based on the comprehensive scores of each frequency band, the comprehensive score weight of each frequency band is calculated through an adaptive multi-band fusion model;

[0033] According to the comprehensive score weight of each frequency band and the preset frequency band combination evaluation criteria, multiple frequency band combinations are determined.

[0034] Furthermore, the S6 includes:

[0035] Initialize the particle swarm, each particle represents a frequency band combination, and set the initial position and speed;

[0036] Calculate the fitness value of each particle, and update the individual optimal solution of each particle and the global optimal solution;

[0037] Based on the update rule, adjust the speed and position of each particle;

[0038] Recalculate the fitness value of each particle, update the individual optimal solution of each particle and the global optimal solution, until the preset convergence condition is met or the maximum number of iterations is reached, and output the frequency band combination with the highest fitness as the optimal frequency band combination.

[0039] Furthermore, the S7 includes:

[0040] Based on the optimal frequency band combination, an initial transmission threshold is set for each frequency band;

[0041] Based on the changes in key performance indicators in each frequency band, the transmission threshold of each frequency band is dynamically adjusted.

[0042] In a second aspect, the present invention provides an adaptive multi-band fusion network communication system, comprising:

[0043] Data acquisition module: used to obtain communication quality parameters of multiple frequency bands; the communication quality parameters include signal strength, signal-to-noise ratio, delay, packet loss rate, bandwidth utilization and spectrum utilization;

[0044] Standardization processing module: used to perform standardization processing on communication quality parameters to obtain standardized communication quality parameters;

[0045] Fuzzy comprehensive evaluation module: used to obtain the fuzzy weighted average score of each frequency band through the fuzzy comprehensive evaluation algorithm based on the standardized communication quality parameters;

[0046] Comprehensive score calculation module: used to calculate the comprehensive score of each frequency band through the weighted average algorithm based on the fuzzy weighted average score of each frequency band;

[0047] Frequency band combination determination module: used to determine multiple frequency band combinations based on the comprehensive scores of each frequency band through an adaptive multi-band fusion model;

[0048] Frequency band combination optimization module: used to screen multiple frequency band combinations in combination with particle swarm optimization algorithm to obtain the optimal frequency band combination;

[0049] Dynamic threshold adjustment module: used to define the dynamic threshold adjustment strategy under the optimal frequency band combination for network data transmission.

[0050] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned adaptive multi-band fusion network communication method when executing the computer program.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned adaptive multi-band fusion network communication method is implemented.

[0052] The descriptions of the second to fourth aspects of the present invention can refer to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second to fourth aspects can refer to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0053] It can be seen from the above technical solution that compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. By obtaining multiple key communication quality parameters, a comprehensive evaluation of communication quality is achieved. At the same time, by standardizing these parameters, the dimensional differences between different parameters are eliminated, providing an accurate and fair basis for subsequent evaluation and frequency band selection. It ensures the accuracy and reliability of frequency band selection and provides a strong guarantee for the efficient operation of network communications.

[0055] 2. Adopting the adaptive multi-band fusion model, it can intelligently determine multiple frequency band combinations based on the comprehensive scores of each frequency band. Furthermore, by combining the particle swarm optimization algorithm, multiple frequency band combinations are screened to obtain the optimal frequency band combination, which can make full use of the resources of different frequency bands and improve the efficiency and reliability of network data transmission. At the same time, the introduction of the particle swarm optimization algorithm makes the selection of frequency band combinations more intelligent and efficient, further improving the performance of network communication.

[0056] 3. Under the optimal frequency band combination, a dynamic threshold adjustment strategy is defined, which can dynamically adjust the transmission threshold of each frequency band according to the changes in key performance indicators in each frequency band. It helps to adapt to changes in the network environment and improve the stability and flexibility of network data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0058] Figure 1 A flow chart of a network communication method for adaptive multi-band fusion provided by an embodiment of the present invention;

[0059] Figure 2 A framework diagram of a network communication system for adaptive multi-band fusion provided by an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of the structure of an electronic device is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0061] 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.

[0062] The network communication method provided in the embodiment of the present application can be applied to a network communication server. The above-mentioned network communication server can be hardware or software. When the network communication server is hardware, it can be implemented as a distributed server cluster that provides detection services, or it can be implemented as a single server. When the network communication server is software, it can be installed in the servers listed above. It can be implemented as multiple software or software modules, or it can be implemented as a single software or software module, which is not specifically limited here.

[0063] Embodiment 1;

[0064] like Figure 1 As shown, this embodiment provides a network communication method for adaptive multi-band fusion, including the following steps:

[0065] S1. Acquire communication quality parameters of multiple frequency bands; the communication quality parameters include signal strength, signal-to-noise ratio, delay, packet loss rate, bandwidth utilization and spectrum utilization;

[0066] S2. Standardize the communication quality parameters to obtain the standardized communication quality parameters;

[0067] S3. Based on the standardized communication quality parameters, a fuzzy comprehensive evaluation algorithm is used to obtain a fuzzy weighted average score of each frequency band;

[0068] S4, based on the fuzzy weighted average score of each frequency band, the comprehensive score of each frequency band is calculated by weighted average algorithm;

[0069] S5. Based on the comprehensive scores of each frequency band, a combination of multiple frequency bands is determined through an adaptive multi-band fusion model;

[0070] S6. Use particle swarm optimization algorithm to screen multiple frequency band combinations and obtain the optimal frequency band combination;

[0071] S7. Define a dynamic threshold adjustment strategy under the optimal frequency band combination to perform network data transmission.

[0072] This network communication method, by comprehensively acquiring and standardizing the communication quality parameters of multiple frequency bands, uses fuzzy comprehensive evaluation and weighted average algorithm to evaluate the performance of each frequency band, combines the adaptive multi-band fusion model and particle swarm optimization algorithm to intelligently screen the optimal frequency band combination, and defines a dynamic threshold adjustment strategy, realizes efficient and stable network communication, and significantly improves the efficiency of spectrum resource utilization and communication quality.

[0073] The above steps and related features are further described in detail below:

[0074] In this embodiment S1, multiple frequency band communication quality parameters are obtained; the communication quality parameters include signal strength, signal-to-noise ratio, delay, packet loss rate, bandwidth utilization and spectrum utilization;

[0075] Specifically, signal strength: refers to the power level of the received signal. It directly affects the communication distance and quality. Higher signal strength means better communication performance. Signal-to-noise ratio (SNR): It is the ratio of signal power to noise power. The higher the signal-to-noise ratio, the better the signal quality and the lower the degree of interference. Latency: The time interval from the sender sending data to the receiver receiving data. Low latency is very important for real-time applications. Packet loss rate: The proportion of data packets that failed to be successfully transmitted within a certain period of time to the total number of sent data packets. The packet loss rate reflects the stability and reliability of the network. Bandwidth utilization: The ratio of used bandwidth to available bandwidth, expressed as a percentage. High bandwidth utilization means that network resources are fully utilized. Spectrum utilization: The amount of information carried by a unit spectrum under given spectrum resources. It is one of the important indicators for measuring the efficiency of wireless communication systems.

[0076] In this step, professional wireless test equipment and network monitoring software are used to collect communication quality parameters of multiple frequency bands in real time. To ensure the accuracy and reliability of the data, the measuring equipment is calibrated regularly. In addition, real-time monitoring of network status and long-term storage of historical data are achieved to ensure the real-time and continuity of data. By collecting data in different time periods, geographical locations and network load conditions, the communication quality of each frequency band is comprehensively evaluated, providing reliable data support for the selection and optimization of subsequent frequency band combinations.

[0077] In this embodiment S2, the communication quality parameter is standardized to obtain the communication quality parameter after the standardized processing; including:

[0078] For each communication quality parameter, calculate the mean μ and standard deviation σ of all measurements;

[0079] Each measured value x of each communication quality parameter i , converted using the Z-score standardization formula:

[0080]

[0081] Among them, z i represents the standardized value of the i-th communication quality parameter;

[0082] The abnormal values ​​beyond the predetermined range are eliminated to obtain the communication quality parameters after standardization.

[0083] In this step, for each communication quality parameter, the mean and standard deviation of all its measured values ​​are calculated, and each measured value is converted to a standardized value using the Z-score standardization formula. The standardized value eliminates the unit and dimension differences between different parameters, which facilitates subsequent comparison and evaluation. At the same time, outliers that exceed the predetermined range are removed to ensure the accuracy and reliability of the data. For example, for signal strength, its measured value may fluctuate greatly due to differences in equipment and environment, and it can be converted into a relatively stable value range through standardization.

[0084] In this embodiment S3, based on the standardized communication quality parameters, a fuzzy weighted average score of each frequency band is obtained by a fuzzy comprehensive evaluation algorithm; including:

[0085] A fuzzy membership function is defined for each communication quality parameter; the fuzzy membership function maps the standardized communication quality parameter value to the interval [0,1], indicating the degree of membership of the communication quality parameter to the communication quality;

[0086] Determine the fuzzy weight of each communication quality parameter in combination with the importance of each communication quality parameter;

[0087] Each communication quality parameter value is converted into a membership degree using a fuzzy membership function, and the membership degree and the fuzzy weight are multiplied to obtain the fuzzy weighted average score of each frequency band.

[0088] This step defines a fuzzy membership function for each communication quality parameter, and maps the standardized parameter value to the interval [0,1] to indicate the degree of membership of the parameter to the communication quality. Combined with the importance of each parameter (which can be determined through historical data analysis), its fuzzy weight is determined. Then, each parameter value is converted into a membership degree using the fuzzy membership function, and multiplied with the fuzzy weight, and the sum is obtained to obtain the fuzzy weighted average score of each frequency band. For example, for signal strength and signal-to-noise ratio, the fuzzy membership function may be designed as a curve that increases with the increase of parameter value; while delay and packet loss rate may be designed as a curve that decreases with the increase of parameter value.

[0089] In this embodiment S4, based on the fuzzy weighted average score of each frequency band, the comprehensive score of each frequency band is calculated by a weighted average algorithm; including:

[0090] The fuzzy weighted average score of each frequency band is taken as the matrix element p ij , construct an evaluation matrix; where p ij represents the weighted average score of the i-th communication quality parameter on the j-th frequency band;

[0091] Standardizing the evaluation matrix and setting an optimal solution;

[0092] The grey correlation coefficient between each frequency band and the ideal optimal solution is calculated by combining the grey correlation analysis algorithm, and the comprehensive grey correlation degree is calculated by combining the weight to obtain the comprehensive score of each frequency band.

[0093] Specifically, for a certain frequency band combination, its comprehensive score can reflect the advantages of the combination in terms of signal strength, signal-to-noise ratio, etc., and the disadvantages in terms of delay, packet loss rate, etc.

[0094] In this step, the fuzzy weighted average score of each frequency band is used as a matrix element to construct an evaluation matrix and perform standardization. Set the ideal optimal solution (such as each frequency band reaches the optimal value in all parameters), combine the gray correlation analysis algorithm to calculate the gray correlation coefficient between each frequency band and the ideal optimal solution, and combine the weight to calculate the comprehensive gray correlation degree to obtain the comprehensive score of each frequency band.

[0095] In this embodiment S5, based on the comprehensive scores of each frequency band, a plurality of frequency band combinations are determined by an adaptive multi-band fusion model, including:

[0096] Based on the comprehensive scores of each frequency band, the comprehensive score weight of each frequency band is calculated through an adaptive multi-band fusion model;

[0097] Specifically, in the adaptive multi-band fusion model, when calculating the comprehensive score weight of each band, the complementary factor between bands, the transmission efficiency coefficient and the interference suppression weight are comprehensively considered. The complementary factor between bands reflects the complementary advantages of different bands in terms of coverage, transmission rate, etc., and the transmission efficiency coefficient measures the data transmission efficiency of each band in the actual communication process. The interference suppression weight focuses on the ability of the band to reduce or eliminate communication interference.

[0098] During the calculation process, the original comprehensive score is first weighted and adjusted according to these factors and coefficients, and then these factors are combined through the hierarchical analysis method to finally obtain the comprehensive score weight of each frequency band. This process aims to comprehensively evaluate the comprehensive performance of each frequency band in network communication and provide a scientific basis for frequency band selection and integration strategy.

[0099] According to the comprehensive score weight of each frequency band, multiple frequency band combinations are determined in combination with preset frequency band combination evaluation criteria. The preset frequency band combination evaluation criteria can achieve complementarity between frequency bands, effective use of spectrum resources, etc.

[0100] In this embodiment S6, a particle swarm optimization algorithm is used to screen multiple frequency band combinations to obtain an optimal frequency band combination; including:

[0101] Initialize the particle swarm, each particle represents a frequency band combination, and set the initial position and speed;

[0102] Calculate the fitness value of each particle, and update the individual optimal solution of each particle and the global optimal solution;

[0103] Based on the update rule, adjust the speed and position of each particle;

[0104] Recalculate the fitness value of each particle, update the individual optimal solution of each particle and the global optimal solution, until the preset convergence condition is met or the maximum number of iterations is reached, and output the frequency band combination with the highest fitness as the optimal frequency band combination.

[0105] For a particle swarm, its fitness value may reflect the pros and cons of different frequency band combinations in terms of network performance. Through iterative optimization, the optimal frequency band combination solution can be found. The particle swarm optimization algorithm can efficiently search for the optimal frequency band combination, improving the accuracy and efficiency of frequency band combination selection.

[0106] In this embodiment S7, a dynamic threshold adjustment strategy under an optimal frequency band combination is defined to perform network data transmission; including:

[0107] Based on the optimal frequency band combination, an initial transmission threshold is set for each frequency band;

[0108] Based on the changes in key performance indicators in each frequency band, the transmission threshold of each frequency band is dynamically adjusted.

[0109] This step sets the initial transmission threshold for each frequency band based on the optimal frequency band combination. Combined with the changes in key performance indicators (such as signal strength, signal-to-noise ratio, etc.) in each frequency band, the transmission threshold of each frequency band is dynamically adjusted. For example, when the signal strength of a frequency band decreases, the transmission threshold of the frequency band can be appropriately increased to maintain communication quality. The dynamic threshold adjustment strategy can adjust the frequency band performance in real time according to the actual network conditions, improving the stability and reliability of network communications.

[0110] The network communication method in this embodiment achieves efficient and stable network communication by comprehensively acquiring and standardizing the communication quality parameters of multiple frequency bands, utilizing fuzzy comprehensive evaluation, weighted average algorithm, adaptive multi-band fusion model and particle swarm optimization algorithm, intelligently screening the optimal frequency band combination, and defining a dynamic threshold adjustment strategy, thereby significantly improving the utilization efficiency of spectrum resources and communication quality.

[0111] Embodiment 2;

[0112] In a city, wireless communication networks cover multiple areas, including commercial areas, residential areas, industrial areas, etc. Due to the different geographical environments, building densities and user needs in different areas, a single frequency band is difficult to meet the communication needs of all areas. Therefore, the use of a multi-band fusion network communication method can effectively improve the network coverage and communication quality.

[0113] The specific steps include:

[0114] 1) Obtain communication quality parameters of multiple frequency bands;

[0115] Use wireless test equipment and network monitoring software to collect communication quality parameters of multiple frequency bands in real time. Including:

[0116] Signal strength: -60dBm in commercial area, -70dBm in residential area, -55dBm in industrial area; Signal-to-noise ratio (SNR): 20dB in commercial area, 15dB in residential area, 25dB in industrial area; Delay: 50ms in commercial area, 30ms in residential area, 40ms in industrial area; Packet loss rate: 2% in commercial area, 1% in residential area, 3% in industrial area; Bandwidth utilization: 80% in commercial area, 60% in residential area, 70% in industrial area; Spectrum utilization: 75% in commercial area, 65% in residential area, 80% in industrial area;

[0117] 2) Standardize communication quality parameters;

[0118] The mean and standard deviation of each parameter were calculated, transformed using the Z-score standardization formula, and outliers were removed.

[0119] Normalized results:

[0120] Signal strength: 0.5 in commercial area, -1.0 in residential area, 1.0 in industrial area; Signal-to-noise ratio (SNR): 1.0 in commercial area, -1.0 in residential area, 1.5 in industrial area; Delay: -1.0 in commercial area, 1.0 in residential area, 0.0 in industrial area; Packet loss rate: 1.0 in commercial area, -1.0 in residential area, 1.5 in industrial area; Bandwidth utilization: 1.0 in commercial area, -1.0 in residential area, 0.0 in industrial area; Spectrum utilization: 0.5 in commercial area, -1.0 in residential area, 1.0 in industrial area;

[0121] 3) Fuzzy comprehensive evaluation algorithm obtains fuzzy weighted average score;

[0122] Define the fuzzy membership function, determine the fuzzy weight, and calculate the fuzzy weighted average score of each frequency band. Fuzzy weighted average score: commercial area: 0.8; residential area: 0.6; industrial area: 0.9;

[0123] 4) Calculate the comprehensive score using the weighted average algorithm;

[0124] An evaluation matrix was constructed, standardized, and the comprehensive scores of each frequency band were calculated. Comprehensive scores: commercial area: 0.75; residential area: 0.65; industrial area: 0.85;

[0125] 5) Adaptive multi-band fusion model determines the frequency band combination;

[0126] Calculate the comprehensive score weight of each frequency band, and determine multiple frequency band combinations based on preset standards. Frequency band combinations: Combination 1: commercial area + residential area, Combination 2: commercial area + industrial area, Combination 3: residential area + industrial area;

[0127] 6) Particle swarm optimization algorithm selects the optimal frequency band combination;

[0128] Initialize the particle swarm, calculate the fitness value, update the particle position and speed, and select the optimal frequency band combination. The optimal frequency band combination obtained: commercial area + industrial area;

[0129] 7) Dynamic threshold adjustment strategy for network data transmission;

[0130] Set the initial transmission threshold for the optimal frequency band combination, and dynamically adjust the threshold based on key performance indicators. Dynamic threshold adjustment: Commercial area: initial threshold 0.7, adjusted to 0.8 when the signal strength drops to -65dBm; Industrial area: initial threshold 0.8, adjusted to 0.9 when the signal-to-noise ratio drops to 22dB;

[0131] The technical solution in this embodiment improves the utilization rate of spectrum resources, ensures the stability and reliability of the network in different environments, and optimizes the user experience, especially in high-load and complex environments. Through this series of technical means, the urban multi-band wireless communication network can better serve different regions and users and achieve efficient and stable communication services.

[0132] Embodiment 3;

[0133] like Figure 2 As shown, this embodiment provides an adaptive multi-band fusion network communication system, including:

[0134] Data acquisition module: used to obtain communication quality parameters of multiple frequency bands; the communication quality parameters include signal strength, signal-to-noise ratio, delay, packet loss rate, bandwidth utilization and spectrum utilization;

[0135] Standardization processing module: used to perform standardization processing on communication quality parameters to obtain standardized communication quality parameters;

[0136] Fuzzy comprehensive evaluation module: used to obtain the fuzzy weighted average score of each frequency band through the fuzzy comprehensive evaluation algorithm based on the standardized communication quality parameters;

[0137] Comprehensive score calculation module: used to calculate the comprehensive score of each frequency band through the weighted average algorithm based on the fuzzy weighted average score of each frequency band;

[0138] Frequency band combination determination module: used to determine multiple frequency band combinations based on the comprehensive scores of each frequency band through an adaptive multi-band fusion model;

[0139] Frequency band combination optimization module: used to screen multiple frequency band combinations in combination with particle swarm optimization algorithm to obtain the optimal frequency band combination;

[0140] Dynamic threshold adjustment module: used to define the dynamic threshold adjustment strategy under the optimal frequency band combination for network data transmission.

[0141] This embodiment provides an adaptive multi-band fusion network communication system, which collects and standardizes the communication quality parameters of multiple frequency bands in real time, evaluates the performance of each frequency band using fuzzy comprehensive evaluation and weighted average algorithm, determines multiple frequency band combinations in combination with an adaptive multi-band fusion model, and then selects the optimal frequency band combination through a particle swarm optimization algorithm, and defines a dynamic threshold adjustment strategy for network data transmission. It effectively improves the spectrum resource utilization, communication quality and network flexibility, and optimizes the user experience.

[0142] Embodiment 4;

[0143] like Figure 3 As shown, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned network communication method when executing the computer program.

[0144] Embodiment 5;

[0145] This embodiment provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned network communication method is implemented.

[0146] In the above embodiments provided in the present application, it should be understood that the disclosed methods, systems, devices and media can be implemented in other ways. The above described methods, systems, devices and media embodiments are only schematic. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. Each functional unit can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0147] The units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0148] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc.

[0149] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An adaptive multi-band fusion network communication method, characterized in that: The following steps are involved: S1. Acquire communication quality parameters of multiple frequency bands; the communication quality parameters include signal strength, signal-to-noise ratio, delay, packet loss rate, bandwidth utilization and spectrum utilization; S2. Standardize the communication quality parameters to obtain the standardized communication quality parameters; S3. Based on the standardized communication quality parameters, a fuzzy comprehensive evaluation algorithm is used to obtain a fuzzy weighted average score of each frequency band; S4, based on the fuzzy weighted average score of each frequency band, the comprehensive score of each frequency band is calculated by weighted average algorithm; S5. Based on the comprehensive scores of each frequency band, a combination of multiple frequency bands is determined through an adaptive multi-band fusion model; S6. Use particle swarm optimization algorithm to screen multiple frequency band combinations and obtain the optimal frequency band combination; S7. Define a dynamic threshold adjustment strategy under the optimal frequency band combination to perform network data transmission.

2. The method for adaptive multi-band fusion network communication according to claim 1, characterized in that: The S2 comprises: For each communication quality parameter, calculate the mean μ and standard deviation σ of all measurements; Each measured value x of each communication quality parameter i , converted using the Z-score standardization formula: Among them, z i represents the standardized value of the i-th communication quality parameter; The abnormal values ​​beyond the predetermined range are eliminated to obtain the communication quality parameters after standardization.

3. The method for adaptive multi-band fusion network communication according to claim 1, characterized in that: The S3 includes: A fuzzy membership function is defined for each communication quality parameter; the fuzzy membership function maps the standardized communication quality parameter value to the interval [0,1], indicating the degree of membership of the communication quality parameter to the communication quality; Determine the fuzzy weight of each communication quality parameter in combination with the importance of each communication quality parameter; Each communication quality parameter value is converted into a membership degree using a fuzzy membership function, and the membership degree and the fuzzy weight are multiplied to obtain the fuzzy weighted average score of each frequency band.

4. The method for adaptive multi-band fusion network communication according to claim 3, characterized in that: The S4 comprises: The fuzzy weighted average score of each frequency band is taken as the matrix element p ij , construct an evaluation matrix; where p ij represents the weighted average score of the i-th communication quality parameter on the j-th frequency band; Standardizing the evaluation matrix and setting an optimal solution; The grey correlation coefficient between each frequency band and the ideal optimal solution is calculated by combining the grey correlation analysis algorithm, and the comprehensive grey correlation degree is calculated by combining the weight to obtain the comprehensive score of each frequency band.

5. The method for adaptive multi-band fusion network communication according to claim 1, characterized in that: The S5 comprises: Based on the comprehensive scores of each frequency band, the comprehensive score weight of each frequency band is calculated through an adaptive multi-band fusion model; According to the comprehensive score weight of each frequency band and the preset frequency band combination evaluation criteria, multiple frequency band combinations are determined.

6. The method for adaptive multi-band fusion network communication according to claim 1, characterized in that: The S6 comprises: Initialize the particle swarm, each particle represents a frequency band combination, and set the initial position and speed; Calculate the fitness value of each particle, and update the individual optimal solution of each particle and the global optimal solution; Based on the update rule, adjust the speed and position of each particle; Recalculate the fitness value of each particle, update the individual optimal solution of each particle and the global optimal solution, until the preset convergence condition is met or the maximum number of iterations is reached, and output the frequency band combination with the highest fitness as the optimal frequency band combination.

7. The method for adaptive multi-band fusion network communication according to claim 1, characterized in that: The S7 comprises: Based on the optimal frequency band combination, an initial transmission threshold is set for each frequency band; Based on the changes in key performance indicators in each frequency band, the transmission threshold of each frequency band is dynamically adjusted.

8. An adaptive multi-band fusion network communication system, characterized in that: include: Data acquisition module: used to obtain communication quality parameters of multiple frequency bands; the communication quality parameters include signal strength, signal-to-noise ratio, delay, packet loss rate, bandwidth utilization and spectrum utilization; Standardization processing module: used to perform standardization processing on communication quality parameters to obtain standardized communication quality parameters; Fuzzy comprehensive evaluation module: used to obtain the fuzzy weighted average score of each frequency band through the fuzzy comprehensive evaluation algorithm based on the standardized communication quality parameters; Comprehensive score calculation module: used to calculate the comprehensive score of each frequency band through the weighted average algorithm based on the fuzzy weighted average score of each frequency band; Frequency band combination determination module: used to determine multiple frequency band combinations based on the comprehensive scores of each frequency band through an adaptive multi-band fusion model; Frequency band combination optimization module: used to screen multiple frequency band combinations in combination with particle swarm optimization algorithm to obtain the optimal frequency band combination; Dynamic threshold adjustment module: used to define the dynamic threshold adjustment strategy under the optimal frequency band combination for network data transmission.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the network communication method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the network communication method according to any one of claims 1 to 7 is implemented.

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