Multi-source weak signal scene information accurate transceiving method based on radio transmission

Abstract

The invention relates to an accurate information transceiving method for a multi-source weak signal scene based on radio transmission. The method comprises the following steps: acquiring a signal transmitting parameter, a channel state parameter and a receiving quality parameter; constructing a signal characteristic model based on the parameters to represent signal transmission characteristics; analyzing a potential interference relationship among the signals based on the signal feature model to obtain an interference analysis result, and identifying a key interference source; constructing a signal optimization model taking the receiving quality as an optimization target based on the interference analysis result; and solving the signal optimization model to obtain a signal optimization result, and generating a signal transceiving configuration parameter according to the result to realize adaptive adjustment of the parameter. By adopting the method, the information transmission precision, reliability and anti-interference capability in a multi-source weak signal scene can be effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of radio transmission signal processing, and in particular relates to a method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission. Background Technology

[0002] With the development of technology in the field of radio transmission signal processing, precise information transmission and reception technology for multi-source weak signal scenarios has emerged. This technology aims to achieve high-reliability transmission in complex environments by comprehensively considering signal parameters, channel conditions, and reception quality. Its features include multi-parameter fusion modeling, interference relationship analysis, and adaptive optimization algorithms. Traditional technologies typically employ linear processing methods that optimize a single signal parameter or ignore multi-source interference, such as relying solely on transmit power adjustment or receive signal strength threshold judgment. These methods lack in-depth analysis of channel dynamics and potential interference between signals, resulting in insufficient signal interference suppression capabilities, large fluctuations in reception quality, and a tendency to cause information loss or increased bit error rate in multi-source weak signal scenarios, making it difficult to meet the requirements for precise transmission and reception. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission that can solve the above problems.

[0004] Firstly, this application provides a method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission, including:

[0005] Acquire signal transmission parameters, channel state parameters, and reception quality parameters;

[0006] A signal feature model is constructed based on signal transmission parameters, channel state parameters, and reception quality parameters;

[0007] Based on the signal feature model, the potential interference relationship between various signals is analyzed to obtain the interference analysis results;

[0008] Based on the interference analysis results, a signal optimization model with reception quality as the optimization objective is constructed.

[0009] The signal optimization model is solved to obtain the signal optimization results, and the signal transmission and reception configuration parameters are generated based on the signal optimization results.

[0010] In one embodiment, a signal feature model is constructed based on signal transmission parameters, channel state parameters, and reception quality parameters, including:

[0011] Based on signal transmission parameters and channel state parameters, signal transmission characteristics are obtained through feature fusion.

[0012] Based on signal transmission characteristics and combined with reception quality parameters, transmission-quality correlation characteristics are obtained through correlation analysis.

[0013] The transmission-quality correlation features are matrixed to obtain the signal synthesis feature matrix;

[0014] Based on the signal comprehensive feature matrix and combined with the electromagnetic propagation law, the propagation influence coefficient is obtained through analysis and calculation.

[0015] Based on the propagation influence coefficient and the signal comprehensive feature matrix, a signal feature model is constructed using a fitting method.

[0016] In one embodiment, the propagation influence coefficient is obtained by analyzing and calculating based on the signal synthesis feature matrix and the electromagnetic propagation law, including:

[0017] Based on the signal comprehensive feature matrix and combined with the electromagnetic propagation law, a scene propagation attenuation matrix is ​​constructed;

[0018] Based on the scene propagation attenuation matrix, the signal field strength distribution is calculated using the ray tracing simulation method.

[0019] Based on the signal field strength distribution, time series analysis is used to obtain the field strength time series correlation parameters;

[0020] Based on the temporal correlation parameters of field strength, the propagation attenuation weights of different signals are obtained by weighted summation.

[0021] Based on the propagation attenuation weight and combined with channel state parameters, a propagation impact assessment matrix is ​​constructed.

[0022] The propagation impact assessment matrix is ​​processed by the eigenvalue decomposition algorithm to obtain the propagation impact coefficient.

[0023] In one embodiment, based on a signal feature model, the potential interference relationships between signals are analyzed to obtain interference analysis results, including:

[0024] Based on the signal feature model, the principal component analysis algorithm is used to extract the core signal features;

[0025] Based on the core signal characteristics and combined with the propagation influence coefficient, the signal interference correlation degree is calculated through correlation analysis.

[0026] Based on the signal-interference correlation degree and combined with channel state parameters, the interference threshold range is determined;

[0027] Based on the interference threshold range, the interference priority of each signal is determined;

[0028] The interference priority, interference threshold range, signal interference correlation, and core signal characteristics are integrated to form interference analysis results.

[0029] In one embodiment, based on the interference analysis results, a signal optimization model is constructed with reception quality as the optimization objective, including:

[0030] Obtain the signal reception quality requirements, and generate reception quality optimization targets based on the signal reception quality requirements and reception quality parameters;

[0031] Based on the interference threshold range and interference priority obtained from the interference analysis results, interference optimization constraints are generated through interval constraint mapping.

[0032] The signal interference correlation degree and core signal characteristics from the interference analysis results are used as supplementary optimization constraints.

[0033] Based on the quality optimization objective, interference optimization constraints, and supplementary optimization constraints, a non-dominated sorting genetic algorithm is used as the optimization algorithm to construct a signal optimization model.

[0034] In one embodiment, the propagation impact assessment matrix is ​​processed using an eigenvalue decomposition algorithm to obtain the propagation impact coefficient, which is achieved through the following formula:

[0035]

[0036] in, Let N be the propagation influence coefficient of the k-th signal, and N be the total number of signal propagation paths. The propagation attenuation weight of the k-th signal is... Let n be the temporal correlation parameter matrix of the field strength for the nth propagation path. The eigenvector matrix corresponding to the nth path of the k-th signal after eigenvalue decomposition of the propagation impact assessment matrix M is given. The eigenvalue matrix corresponding to the nth propagation path after eigenvalue decomposition of the propagation impact assessment matrix M. Eigenvector matrix The inverse matrix, For the trace operation of a matrix, The temporal correlation parameter matrix of the field strength of the nth path The standard deviation of M is given by M, where M is the total number of field strength time-series correlation parameter matrices. Eigenvalue matrix The Frobenius norm, The maximum value among all signal propagation attenuation weights, where K is the total number of signal paths. The eigenvalue matrix corresponding to the k-th signal The largest eigenvalue, The eigenvalue matrix corresponding to the nth path The largest eigenvalue.

[0037] In one embodiment, the signal optimization model is solved to obtain the signal optimization result, and based on the signal optimization result, signal transmission and reception configuration parameters are generated, including:

[0038] Based on the quality optimization objective, interference optimization constraints, and supplementary optimization constraints of the signal optimization model, the population size, iteration number parameters, and initial population of the non-dominated sorting genetic algorithm are set.

[0039] The individual fitness value is calculated based on the initial population and the reception quality optimization objective;

[0040] Based on individual fitness values ​​and interference optimization constraints, the initial population is optimized through selection, crossover, and mutation operations to obtain an optimized population;

[0041] Based on the optimized population and supplementary optimization constraints, a set of non-dominated solutions is obtained.

[0042] Based on the non-dominated solution set, the crowding degree ranking is used to determine the signal optimization result;

[0043] Based on the signal optimization results, signal transmission parameters, and channel state parameters, and combined with a pre-set transceiver configuration parameter library, signal transceiver configuration parameters are generated.

[0044] Secondly, this application also provides a device for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission, comprising:

[0045] The signal parameter acquisition module is used to acquire signal transmission parameters, channel state parameters, and reception quality parameters;

[0046] The feature model construction module is used to construct a signal feature model based on signal transmission parameters, channel state parameters, and reception quality parameters.

[0047] The interference relationship analysis module is used to analyze the potential interference relationships between signals based on the signal feature model and obtain the interference analysis results.

[0048] The optimization model building module is used to build a signal optimization model with reception quality as the optimization objective based on the interference analysis results.

[0049] The transmit / receive parameter generation module is used to solve the signal optimization model, obtain the signal optimization results, and generate signal transmit / receive configuration parameters based on the signal optimization results.

[0050] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission.

[0051] Fourthly, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission.

[0052] The aforementioned method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission constructs a signal feature model by acquiring signal transmission parameters, channel state parameters, and reception quality parameters. This model comprehensively characterizes the transmission characteristics under multi-source weak signal scenarios, providing a data foundation for interference analysis. Based on the signal feature model, the method analyzes the potential interference relationships between various signals to obtain interference analysis results and accurately identify key interference sources and interference modes. Furthermore, it constructs a signal optimization model with reception quality as the optimization objective. The optimization process directly targets quality improvement, achieving targeted interference suppression. Finally, it solves the signal optimization model to generate signal transmission and reception configuration parameters, enabling adaptive adjustment and dynamic optimization of the parameters. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart of the method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission according to the present invention;

[0055] Figure 2 This is a structural diagram of the information accurate transceiver device for multi-source weak signal scenarios based on radio transmission according to the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] In one embodiment, such as Figure 1As shown, a method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In the implementation environment, the hardware architecture includes terminal devices (such as smartphones, IoT sensors, or dedicated wireless equipment such as broadcast transmitters covering medium wave and FM bands), equipped with a signal transmitter, receiver, processor, and memory to acquire signal transmission parameters (such as power, frequency, and modulation scheme), channel state parameters (such as path loss and noise interference), and reception quality parameters (such as bit error rate and signal-to-noise ratio) in real time. The server deploys computing units and a database to support large-scale signal feature modeling and optimization algorithm processing. Data transmission between the terminal and the server occurs via a network. Application scenarios include: in multi-source weak signal environments, such as densely populated urban communication areas or remote emergency communication scenarios, when the terminal detects a decline in signal quality (e.g., asynchronous or mis-repeated demodulation signal and carrier signal), the terminal collects parameters and transmits them to the server; based on the reception quality requirements, the server runs a signal optimization model, analyzes interference relationships and generates transmit and receive configuration parameters, and sends the optimization results to the terminal. The terminal adaptively adjusts the transmit power, receive sensitivity and synchronization parameters (such as carrier synchronization threshold), thereby improving transmission accuracy and anti-interference capability.

[0058] In this embodiment, the method includes the following steps:

[0059] S01, acquire signal transmission parameters, channel state parameters, and reception quality parameters.

[0060] The signal transmission parameters may include indicators that characterize the signal source, such as transmission power, frequency, demodulation bit error rate, synchronization delay, and modulation method. The channel state parameters may include factors that reflect the dynamic changes in the transmission environment, such as path loss, noise interference, and multipath effects. The reception quality parameters may include metrics that evaluate the performance of the receiver, such as bit error rate, signal-to-noise ratio, and throughput. In practice, these parameters can be collected in real time through the transmitter, receiver, and processor built into the terminal device (such as a smartphone or IoT sensor), or obtained interactively with the server-side database, providing a comprehensive and dynamic data foundation for analysis.

[0061] S02. Based on signal transmission parameters, channel state parameters, and reception quality parameters, a signal characteristic model is constructed.

[0062] Among them, the signal feature model is an abstract representation that comprehensively characterizes the signal transmission characteristics through mathematical and statistical methods, and is used to quantify the transmission behavior in multi-source weak signal scenarios. When constructing the model, the signal transmission parameters and channel state parameters can be combined through feature fusion to generate signal transmission features. The transmission-quality correlation features (such as carrier synchronization error ratio and demodulated signal-to-noise ratio offset) are obtained by combining the reception quality parameters. The correlation features are matrixed to form a signal comprehensive feature matrix. The propagation influence coefficient is calculated based on the electromagnetic propagation law. The above elements are integrated into the signal feature model by using a fitting method.

[0063] S03, based on the signal characteristic model, analyzes the potential interference relationship between various signals and obtains the interference analysis results.

[0064] Among them, potential interference relationships refer to the mutual inhibition or deterioration phenomena that may be caused by frequency overlap, power competition or propagation path intersection between multiple source signals. Interference analysis results can include quantitative indicators such as interference priority, interference threshold range, signal interference correlation degree and core signal characteristics, which are used to accurately identify key interference sources. In practice, core signal characteristics can be extracted from the signal feature model through feature extraction algorithms (such as principal component analysis), and the signal interference correlation degree can be calculated by combining the propagation influence coefficient. The interference threshold range and interference priority are dynamically determined based on the channel state parameters. These elements are integrated to form a comprehensive interference analysis result, which provides data support for the subsequent construction of signal optimization models.

[0065] S04. Based on the interference analysis results, a signal optimization model is constructed with reception quality as the optimization objective.

[0066] The receiver quality optimization objective is dynamically generated based on signal receiver quality requirements and real-time receiver quality parameters, aiming to improve transmission accuracy and reliability. The signal optimization model is an adaptive model that combines the quality objective with interference constraints through a mathematical optimization framework. When constructing this model, the optimization objective can be generated by obtaining the receiver quality requirements, and interference optimization constraints can be generated by interval constraint mapping based on the interference threshold range and interference priority of the interference analysis results. The signal interference correlation degree and core signal characteristics are used as supplementary optimization constraints. An optimization algorithm (such as a non-dominated sorting genetic algorithm) is used to integrate the objective and constraints to form a model, providing a theoretical basis for adaptive adjustment of signal parameters.

[0067] S05, solve the signal optimization model to obtain the signal optimization result, and generate signal transmission and reception configuration parameters based on the signal optimization result.

[0068] The signal optimization result is the optimal configuration scheme in the solution set obtained by the optimization algorithm, which is used to quantify the improvement of transmission performance. The signal transmission and reception configuration parameters include a set of parameters such as transmit power, receive sensitivity or frequency adjustment, which are used to directly guide the real-time operation of terminal equipment. In implementation, the initial parameters (such as population size and number of iterations) of the optimization algorithm (such as non-dominated sorting genetic algorithm) can be set. The fitness value of individuals can be calculated based on the receiver quality optimization target. The initial population can be evolved through selection, crossover and mutation operations to obtain the optimized population. The non-dominated solution set can be screened in combination with the constraint conditions and the signal optimization result can be determined by crowding sorting. Based on the result, signal transmission parameters and channel state parameters, specific configuration parameters (such as medium wave transmit power compensation and frequency modulation frequency offset tolerance) can be generated from the preset transmission and reception configuration parameter library to realize the adaptive adjustment and dynamic optimization of transmission parameters.

[0069] The aforementioned method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission acquires signal transmission parameters, channel state parameters, and reception quality parameters, constructs a signal feature model to comprehensively characterize transmission characteristics, analyzes potential interference relationships to accurately identify interference sources, constructs a signal optimization model with reception quality as the optimization objective to achieve targeted interference suppression, and solves to generate signal transmission and reception configuration parameters. This method can overcome the problems of insufficient signal interference suppression, large fluctuations in reception quality, and information loss caused by the lack of dynamic interference analysis in traditional technologies, and improves the accuracy, reliability, and anti-interference capability of information transmission in multi-source weak signal scenarios.

[0070] In one embodiment, a signal feature model is constructed based on signal transmission parameters, channel state parameters, and reception quality parameters, including:

[0071] S11, based on signal transmission parameters and channel state parameters, signal transmission characteristics are obtained through feature fusion;

[0072] S12, based on signal transmission characteristics and combined with reception quality parameters, transmission-quality correlation characteristics are obtained through correlation analysis;

[0073] S13, perform matrix processing on the transmission-quality correlation features to obtain the signal synthesis feature matrix;

[0074] S14. Based on the signal comprehensive feature matrix and combined with the electromagnetic propagation law, the propagation influence coefficient is obtained through analysis and calculation.

[0075] S15. Based on the propagation influence coefficient and the signal comprehensive feature matrix, a signal feature model is constructed using a fitting method.

[0076] For example, based on signal transmission parameters and channel state parameters, a weighted average fusion method can be used to weight the relevant dimensions of the signal transmission process in the two types of parameters according to preset weights (calibrated experimentally based on the degree of influence of the parameters on transmission characteristics, such as setting the transmission power weight to 0.3 and the path loss weight to 0.4, etc.), or the quantized values ​​of the two types of parameters can be sequentially combined to form a high-dimensional feature vector to obtain the signal transmission characteristics. Based on these signal transmission characteristics, combined with the receiving quality parameters (such as bit error rate, signal-to-noise ratio, synchronization delay, and throughput, etc.), Pearson correlation coefficient analysis or mutual information calculation methods are used to calculate the correlation strength between each dimension of the signal transmission characteristics and the receiving quality parameters. Feature combinations with a correlation strength higher than a preset threshold (such as 0.6) are selected to obtain the transmission-quality correlation characteristics. The transmission-quality correlation characteristics are then matrixed, with the dimensions of the correlation features as the columns of the matrix and the time series samples of the signal transmission or samples from different signal sources as the rows of the matrix. Each sample is then compared with the corresponding correlation. The quantified values ​​of the features are filled into the corresponding positions in the matrix to form a two-dimensional numerical matrix, which is the signal comprehensive feature matrix. Based on this signal comprehensive feature matrix, electromagnetic propagation laws (such as free space propagation formula and logarithmic distance path loss model) can be combined to construct a scene propagation attenuation matrix through the signal frequency and propagation distance related features in the matrix. Ray tracing simulation tools are used to simulate the propagation path of the signal in the current scene, and parameters such as signal field strength and time delay of each path are calculated. Combined with the noise level and multipath effect intensity in the channel state parameters, the propagation influence coefficient of each signal is calculated through the eigenvalue decomposition algorithm. Least squares method or support vector regression algorithm can be used as the fitting method, with the signal comprehensive feature matrix as the input variable, the propagation influence coefficient as the weight factor of the input variable, and the quantified indicators of signal transmission characteristics (such as actual transmission attenuation value and transmission efficiency) as the output variable. The fitting parameters are iteratively optimized to make the mean square error between the predicted value of the model output and the actual observed signal transmission characteristic value less than a preset threshold (e.g., A signal feature model that can accurately characterize the signal transmission characteristics in multi-source weak signal scenarios is constructed.

[0077] In one embodiment, the propagation influence coefficient is obtained by analyzing and calculating based on the signal synthesis feature matrix and the electromagnetic propagation law, including:

[0078] S21, based on the signal comprehensive feature matrix and combined with the electromagnetic propagation law, construct the scene propagation attenuation matrix;

[0079] S22. Based on the scene propagation attenuation matrix, the signal field strength distribution is calculated using the ray tracing simulation method.

[0080] S23, based on the signal field strength distribution, time series analysis is used to obtain the field strength time series correlation parameters;

[0081] S24, based on the field strength time series correlation parameters, the propagation attenuation weights of different signals are calculated by weighted summation;

[0082] S25. Based on the propagation attenuation weight and combined with the channel state parameters, a propagation impact assessment matrix is ​​constructed.

[0083] S26. The propagation impact assessment matrix is ​​processed by the eigenvalue decomposition algorithm to obtain the propagation impact coefficient.

[0084] Specifically, based on the signal frequency, transmission power, propagation path length, and other feature dimensions contained in the signal synthesis feature matrix, and combined with electromagnetic propagation laws such as the free-space propagation formula (Friis formula) and the logarithmic distance path loss model, feature parameters corresponding to different propagation paths for each signal source in the matrix are extracted. These parameters are then substituted into the electromagnetic propagation law formulas to calculate the basic propagation attenuation value of each signal under the corresponding path. Finally, using the signal source number as the row index and the propagation path number as the column index, the basic propagation attenuation values ​​of each signal-path combination are filled into the matrix at the corresponding positions to construct the scene propagation attenuation matrix. Based on this scene propagation attenuation... The attenuation distribution data recorded in the matrix can be simulated using ray tracing simulation tools such as Remcom WirelessInSite. Pre-set scene terrain parameters (e.g., altitude, building distribution), obstacle material parameters (e.g., concrete reflectivity, wood transmittance), and signal wavelength parameters. The simulation tool then simulates the signal's reflection, refraction, and diffraction propagation process within the scene, calculating the signal field strength value at each grid point in the three-dimensional spatial grid (grid precision set to 0.1m × 0.1m × 0.1m), forming the signal field strength distribution data. Based on this signal field strength distribution, at 100Hz... Continuous field strength data for 10 seconds was collected at a sampling frequency to form field strength time series for each grid point. Autocorrelation analysis was used to calculate the autocorrelation coefficient of each time series, and cross-correlation analysis was combined to calculate the cross-correlation coefficients between field strength time series at different grid points. These results were integrated to obtain field strength time series correlation parameters, including the autocorrelation coefficient sequence and the time delay correlation matrix. Based on these parameters, two core indicators were selected: time series stability coefficient and attenuation fluctuation amplitude. Experimental calibration assigned a weight of 0.5 to both the time series stability coefficient and the attenuation fluctuation amplitude for each signal. A weighted summation is performed, and the summation result is normalized to the [0,1] interval to obtain the propagation attenuation weights for different signals. Based on these propagation attenuation weights, a propagation impact assessment matrix with the dimension of the total number of signal paths can be constructed by combining the noise power and multipath effect intensity quantization values ​​in the channel state parameters. The propagation impact assessment matrix is ​​processed using the eigenvalue decomposition algorithm in linear algebra, and the eigenvalues ​​are obtained by solving the characteristic equation of the matrix. The corresponding eigenvectors are obtained by solving the homogeneous linear equation system, resulting in the eigenvector matrix and the eigenvalue matrix. These are then substituted into the preset propagation impact coefficient calculation formula to calculate the propagation impact coefficient of the signal.

[0085] In one embodiment, based on a signal feature model, the potential interference relationships between signals are analyzed to obtain interference analysis results, including:

[0086] S31, based on the signal feature model, uses principal component analysis algorithm to extract core signal features;

[0087] S32, based on the core signal characteristics and combined with the propagation influence coefficient, the signal interference correlation degree is calculated through correlation analysis;

[0088] S33. Based on the signal interference correlation degree and combined with channel state parameters, determine the interference threshold range;

[0089] S34, based on the interference threshold range, determine the interference priority of each signal;

[0090] S35 integrates interference priority, interference threshold range, signal interference correlation, and core signal characteristics to form interference analysis results.

[0091] For example, in implementation, the output data of the signal feature model can be standardized using the PCA module in the Python sklearn library (scaling the data to a mean of 0 and a variance of 1), setting the variance explanation threshold to 85%, calculating the covariance matrix and solving for eigenvalues ​​and eigenvectors using an algorithm, and selecting the top m principal components with a cumulative variance share of 85%. The eigenvectors corresponding to these m principal components are then used as the core signal features. Based on these core signal features, and combined with the propagation influence coefficients of each signal, the signal interference correlation can be calculated using the Pearson correlation coefficient analysis method. The core signal feature vectors of each signal are then compared with their corresponding propagation influence coefficients. The comprehensive feature vector is obtained by multiplying element-wise. Then, the Pearson correlation coefficient between any two comprehensive feature vectors of signals is calculated, and the absolute value of the correlation coefficient is taken as the signal interference correlation degree. Based on the statistical results of the interference correlation degree of all signals, combined with the noise interference intensity in the channel state parameters (quantization value range is [-100dBm, -40dBm]), the interference threshold range is determined. First, the mean μ and standard deviation σ of the interference correlation degree of all signals are calculated. If the channel noise interference intensity is ≤-80dBm (low noise scenario), then the strong interference threshold range is set to [μ+0.3σ, 1], the medium interference threshold range is set to [μ-0.3σ, μ+0.3σ], and the weak interference threshold range is set to [μ-0.3σ, μ+0.3σ]. The threshold range is [0, μ-0.3σ]. If the channel noise interference intensity is > -80dBm (high noise scenario), the strong interference threshold range is adjusted to [μ+0.5σ, 1], the medium interference threshold range is [μ-0.5σ, μ+0.5σ], and the weak interference threshold range is [0, μ-0.5σ]. Based on this interference threshold range, the interference priority of each signal is determined. Signals in the strong interference threshold range are set to level 1 (highest priority), those in the medium interference threshold range are set to level 2, and those in the weak interference threshold range are set to level 3 (lowest priority). Simultaneously, considering the multipath effect intensity in the channel state parameters (quantized by delay spread, in ns), if a signal is located in... If the signal is in a strong interference range with a delay spread > 100 ns, or in a medium interference range with a delay spread > 150 ns, its priority is increased by one level (maximum one level). The interference priorities of each signal (specific identifiers for levels 1-3), the set interference threshold ranges (including specific numerical ranges for strong, medium, and weak interference), the calculated signal-interference correlation, and the extracted core signal features (including eigenvectors of m principal components and their respective variance explanation ratios) are integrated. A one-to-one correspondence is established according to the signal number, forming a structured interference analysis results dataset. This dataset comprehensively covers the key parameters related to interference analysis, providing a complete basis for the subsequent construction of signal optimization models.

[0092] In one embodiment, based on the interference analysis results, a signal optimization model is constructed with reception quality as the optimization objective, including:

[0093] S41, obtain the signal reception quality requirements, and generate the reception quality optimization target based on the signal reception quality requirements and reception quality parameters;

[0094] S42, Based on the interference threshold range and interference priority of the interference analysis results, interference optimization constraints are generated through interval constraint mapping;

[0095] S43, use the signal interference correlation degree and core signal characteristics of the interference analysis results as supplementary optimization constraints;

[0096] S44. Based on the quality optimization objective, interference optimization constraints, and supplementary optimization constraints, a non-dominated sorting genetic algorithm is used as the optimization algorithm to construct a signal optimization model.

[0097] Specifically, signal reception quality requirements can be obtained through a preset configuration interface or an external command interface. These requirements are quantified indicators (such as bit error rate ≤ 1). Using a signal-to-noise ratio (SNR) ≥ 25dB and throughput ≥ 10Mbps as indicators, and combining real-time acquired reception quality parameters (current bit error rate, SNR, and throughput values), a target difference quantization method is used to generate reception quality optimization targets, i.e., the optimization objective function is set as follows:

[0098]

[0099]

[0100]

[0101] ω1, ω2, and ω3 are weighting coefficients (calibrated experimentally to 0.4, 0.3, and 0.3, respectively, satisfying a weight sum of 1), achieving precise matching between the optimization objective and actual quality requirements. Based on the interference threshold ranges (the numerical ranges corresponding to strong, medium, and weak interference) and interference priorities (levels 1-3) in the interference analysis results, interference optimization constraints are generated through interval constraint mapping: For level 1 (highest priority) interference signals, their interference correlation is constrained to be ≤ the lower limit of the strong interference threshold range, and the corresponding signal transmission power adjustment range is ≤ ±10%; for level 2 (medium priority) interference signals, their interference correlation is constrained to be within the medium interference threshold range, and the transmission power adjustment range is ≤ ±15%; for level 3 (lowest priority) interference signals, their interference correlation is constrained to be ≥ the upper limit of the weak interference threshold range, and the transmission power adjustment range is ≤ ±20%. Simultaneously, the above constraints are transformed into mathematical constraints recognizable by the model through inequalities; the signal interference correlation degree and core signal features in the interference analysis results are used as supplementary optimization constraints: when the interference correlation degree between any two signals is >0.8, their frequency interval is constrained to be ≥5MHz; the signal transmission parameters (such as modulation order and coding rate) corresponding to the first three principal components (cumulative variance explanation ratio ≥85%) in the core signal features are constrained to have an adjustment range not exceeding ±15% of the initial value, and the directional deviation of the principal component feature vector is ≤10°; based on the above-determined reception quality optimization objective, interference optimization constraints, and supplementary optimization constraints, the non-dominated sorting genetic algorithm (NSGA-II) can be used as the optimization algorithm to construct the signal optimization model, forming a signal optimization model with "optimization objective-constraint conditions-algorithm framework" as the core.

[0102] In one embodiment, S51, the propagation impact assessment matrix is ​​processed by an eigenvalue decomposition algorithm to obtain the propagation impact coefficient, which is achieved by the following formula:

[0103]

[0104] in, Let N be the propagation influence coefficient of the k-th signal, and N be the total number of signal propagation paths. The propagation attenuation weight of the k-th signal is... Let n be the temporal correlation parameter matrix of the field strength for the nth propagation path. The eigenvector matrix corresponding to the nth path of the k-th signal after eigenvalue decomposition of the propagation impact assessment matrix M is given. The eigenvalue matrix corresponding to the nth propagation path after eigenvalue decomposition of the propagation impact assessment matrix M. Eigenvector matrix The inverse matrix, For the trace operation of a matrix, The temporal correlation parameter matrix of the field strength of the nth path The standard deviation of M is given by M, where M is the total number of field strength time-series correlation parameter matrices. Eigenvalue matrix The Frobenius norm, The maximum value among all signal propagation attenuation weights, where K is the total number of signal paths. The eigenvalue matrix corresponding to the k-th signal The largest eigenvalue, The eigenvalue matrix corresponding to the nth path The largest eigenvalue.

[0105] For example, matrix operations and numerical calculations can be implemented using MATLAB or Python's NumPy or SciPy libraries: For the k-th signal, traverse all signal propagation paths (n from 1 to N) to calculate the cumulative terms of the numerator, and then calculate the field strength time-series correlation parameter matrix for the n-th propagation path. The feature vector matrix corresponding to the nth path of the kth signal. The eigenvalue matrix of the nth propagation path and eigenvector matrix inverse matrix according to" → → → Perform matrix multiplication in the order of "", and then perform trace operation on the result. That is, sum the elements of the main diagonal of the matrix; then, sum the result of the trace operation with the propagation attenuation weight of the k-th signal. Multiply by the time series correlation parameter matrix of the field strength of the nth path. Standard deviation Divide this product by the sum of the standard deviations of all field strength time-series correlation parameter matrices (M in total) (i.e., m from 1 to M). The summation is performed to obtain the component value corresponding to the nth path. The summation of all n (1 to N) component values ​​yields the total summation result in the numerator. The eigenvalue matrix of the nth path is then calculated. Frobenius norm That is, for the matrix The sum of squares of all elements is taken as the square root, and then this norm is multiplied by the maximum value among all signal propagation attenuation weights. Multiply, and simultaneously calculate the eigenvalue matrix corresponding to the k-th signal. The sum of the largest eigenvalue λk,max (the maximum value extracted from the main diagonal elements of matrix Λn) and the largest eigenvalues ​​of the eigenvalue matrix Λn corresponding to all paths. (n from 1 to N) The ratio of the sums is obtained by multiplying the two results above to get the denominator; the propagation influence coefficient of the k-th signal is obtained by dividing the total sum of the numerators by the denominator. By sequentially traversing all signals (k from 1 to K) according to the above process, the propagation influence coefficients of all signal paths can be calculated.

[0106] In one embodiment, the signal optimization model is solved to obtain the signal optimization result, and based on the signal optimization result, signal transmission and reception configuration parameters are generated, including:

[0107] S61, Based on the quality optimization objective, interference optimization constraints, and supplementary optimization constraints of the signal optimization model, set the population size, iteration number parameters, and initial population of the non-dominated sorting genetic algorithm;

[0108] S62, based on the initial population and the target of receiving quality optimization, the individual fitness value is calculated;

[0109] S63, based on individual fitness values ​​and interference optimization constraints, optimize the initial population through selection, crossover, and mutation operations to obtain an optimized population;

[0110] S64. Based on the optimized population and supplementary optimization constraints, the set of non-dominated solutions is obtained by screening.

[0111] S65, based on the non-dominated solution set, the crowding degree ranking is used to determine the signal optimization result;

[0112] S66 generates signal transmission and reception configuration parameters based on signal optimization results, signal transmission parameters, and channel state parameters, combined with a preset transmission and reception configuration parameter library.

[0113] Specifically, based on the clearly defined receiver quality optimization objective, interference optimization constraints, and supplementary optimization constraints of the signal optimization model, the core parameters of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) can be configured. The population size is set to 100 (experiments have verified that this size balances solution efficiency and solution diversity), and the number of iterations is set to 50 (to ensure sufficient algorithm convergence). The initial population is constructed using a random generation method, with the generation range strictly limited to the physically feasible range of signal transmission and reception parameters (e.g., transmit power 5-30dBm, frequency 2.4-2.5GHz, modulation order QPSK-64QAM). After generation, it needs to undergo preliminary screening under interference optimization constraints to remove invalid individuals whose transmit power adjustment range exceeds the limit or whose interference correlation does not meet the threshold requirements, ensuring that the initial population satisfies the basic constraints. Based on the signal transmission and reception parameter combination corresponding to each individual in the initial population, the receiver quality optimization objective function (i.e., the previously constructed comprehensive optimization function for bit error rate, signal-to-noise ratio, and throughput) is substituted into the value of each individual, and the fitness value is obtained through numerical calculation. The smaller the value, the closer the parameter combination is to the optimization objective. During the calculation, it is necessary to simultaneously verify whether the individual meets the interference optimization constraints. Individuals that do not meet the constraints can be assigned a penalty fitness value (50% higher than the maximum fitness value of the population). Based on the individual fitness value and the interference optimization constraints, optimization operations are performed. The selection operation adopts the tournament selection method (the tournament size is set to 3, and 3 individuals are randomly selected to select the individual with the smallest fitness value to enter the next generation). The crossover operation adopts the single-point crossover method (randomly select 1 crossover point in the parameter vector, exchange the parameter fragments after the crossover point of two parent individuals, and set the crossover probability to 0.8). The mutation operation adopts the Gaussian mutation method (add Gaussian perturbation to each parameter of the individual with a mutation probability of 0.05, and control the perturbation amplitude within ±5% of the parameter feasible range). After each operation, it is necessary to re-verify whether the individual meets the interference optimization constraints, remove invalid individuals, and maintain the population size by supplementing with randomly generated valid individuals. After 50 iterations, the optimized population is obtained. Based on the optimized population and the supplemented optimization constraints (signal interference correlation > 0).A secondary screening process is performed (e.g., frequency interval ≥ 5MHz, core signal feature adjustment amplitude constraints, etc.) to verify whether each individual in the optimization population meets the supplementary constraints. All individuals that meet the constraints are retained, and a non-dominated solution set (i.e., a solution set where no other individual is superior to this individual in all reception quality indicators) is selected using the non-dominated solution set. A crowding degree ranking method is then applied to the non-dominated solution set to calculate the crowding distance of each solution in the target space (a larger crowding distance indicates sparser surrounding solutions and better diversity). The top 10 solutions with the largest crowding distances are selected as candidate optimization results. Combined with the priority of reception quality requirements (e.g., bit error rate is the highest priority), the solution with the optimal bit error rate and other indicators that meet the requirements is selected as the signal optimization result. Based on this signal optimization result (specific parameter optimization direction and adjustment amplitude), the original... Signal transmission parameters and real-time channel state parameters can be accessed from a pre-defined transceiver configuration parameter library (this database stores experimentally verified optimal transceiver parameter combinations under different channel states and optimization objectives, including complete parameter items such as transmit power, frequency, modulation scheme, coding rate, and receiver sensitivity threshold). Specific transceiver configuration parameters corresponding to the optimization result can be obtained through parameter mapping and matching. For example, if the optimization result requires a 10% increase in transmit power and a 20MHz frequency shift, combined with the current channel state of low noise and weak multipath effects, a complete configuration parameter set of 22dBm transmit power, 2450MHz frequency, 16QAM modulation scheme, 0.75 coding rate, and -95dBm receiver sensitivity threshold can be obtained from the parameter library. This parameter set can be directly used to guide the adjustment of hardware parameters of the signal transceiver equipment, achieving adaptive optimization.

[0114] The aforementioned method for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission acquires signal transmission parameters, channel state parameters, and reception quality parameters. Through feature fusion, matrix processing, and calculation of the propagation influence coefficient based on electromagnetic propagation laws, a signal feature model is constructed to accurately characterize the transmission characteristics of multi-source weak signals. Principal component analysis is used to extract core features, and the signal interference correlation is calculated using the propagation influence coefficient to dynamically determine the interference threshold range and priority, comprehensively analyzing the potential interference relationships between signals. An optimization objective is generated based on reception quality requirements, integrating interference constraints and supplementary constraints. A non-dominated sorting genetic algorithm is used to construct a signal optimization model. The optimized result is obtained through population iterative optimization and non-dominated solution screening, and adaptive transmission and reception configuration parameters are generated by matching a preset parameter library. This method overcomes the limitations of traditional single-parameter optimization and neglecting multi-source dynamic interference. Through multi-parameter fusion modeling, accurate interference identification, and targeted optimization, it achieves dynamic adaptive adjustment of signal transmission and reception parameters, effectively improving interference suppression capabilities in multi-source weak signal scenarios, reducing information loss and bit error rate, stabilizing reception quality, and significantly improving the accuracy, reliability, and anti-interference performance of information transmission, meeting the needs of accurate transmission and reception in complex environments.

[0115] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0116] Based on the same inventive concept, this application also provides an apparatus for accurately transmitting and receiving information in multi-source weak signal scenarios based on radio transmission, used to implement the aforementioned method for accurate transmission and reception of information in multi-source weak signal scenarios based on radio transmission. The solution provided by this apparatus is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the apparatus for accurately transmitting and receiving information in multi-source weak signal scenarios based on radio transmission provided below can be found in the limitations of the method for accurately transmitting and receiving information in multi-source weak signal scenarios based on radio transmission described above, and will not be repeated here.

[0117] In one exemplary embodiment, such as Figure 2 As shown, a device for accurate information transmission and reception in multi-source weak signal scenarios based on radio transmission is provided, comprising:

[0118] The signal parameter acquisition module 101 is used to acquire signal transmission parameters, channel state parameters, and reception quality parameters.

[0119] The feature model construction module 102 is used to construct a signal feature model based on signal transmission parameters, channel state parameters, and reception quality parameters;

[0120] The interference relationship analysis module 103 is used to analyze the potential interference relationship between signals based on the signal feature model and obtain the interference analysis results.

[0121] The optimization model building module 104 is used to build a signal optimization model with reception quality as the optimization objective based on the interference analysis results.

[0122] The transmit / receive parameter generation module 105 is used to solve the signal optimization model, obtain the signal optimization result, and generate signal transmit / receive configuration parameters based on the signal optimization result.

[0123] In one embodiment, the feature model building module 102 is further configured to:

[0124] Based on signal transmission parameters and channel state parameters, signal transmission characteristics are obtained through feature fusion.

[0125] Based on signal transmission characteristics and combined with reception quality parameters, transmission-quality correlation characteristics are obtained through correlation analysis.

[0126] The transmission-quality correlation features are matrixed to obtain the signal synthesis feature matrix;

[0127] Based on the signal comprehensive feature matrix and combined with the electromagnetic propagation law, the propagation influence coefficient is obtained through analysis and calculation.

[0128] Based on the propagation influence coefficient and the signal comprehensive feature matrix, a signal feature model is constructed using a fitting method.

[0129] In one embodiment, the feature model building module 102 is further configured to:

[0130] Based on the signal comprehensive feature matrix and combined with the electromagnetic propagation law, a scene propagation attenuation matrix is ​​constructed;

[0131] Based on the scene propagation attenuation matrix, the signal field strength distribution is calculated using the ray tracing simulation method.

[0132] Based on the signal field strength distribution, time series analysis is used to obtain the field strength time series correlation parameters;

[0133] Based on the temporal correlation parameters of field strength, the propagation attenuation weights of different signals are obtained by weighted summation.

[0134] Based on the propagation attenuation weight and combined with channel state parameters, a propagation impact assessment matrix is ​​constructed.

[0135] The propagation impact assessment matrix is ​​processed by the eigenvalue decomposition algorithm to obtain the propagation impact coefficient.

[0136] In one embodiment, the interference relationship analysis module 103 is further configured to:

[0137] Based on the signal feature model, the principal component analysis algorithm is used to extract the core signal features;

[0138] Based on the core signal characteristics and combined with the propagation influence coefficient, the signal interference correlation degree is calculated through correlation analysis.

[0139] Based on the signal-interference correlation degree and combined with channel state parameters, the interference threshold range is determined;

[0140] Based on the interference threshold range, the interference priority of each signal is determined;

[0141] The interference priority, interference threshold range, signal interference correlation, and core signal characteristics are integrated to form interference analysis results.

[0142] In one embodiment, the optimization model building module 104 is further configured to:

[0143] Obtain the signal reception quality requirements, and generate reception quality optimization targets based on the signal reception quality requirements and reception quality parameters;

[0144] Based on the interference threshold range and interference priority obtained from the interference analysis results, interference optimization constraints are generated through interval constraint mapping.

[0145] The signal interference correlation degree and core signal characteristics from the interference analysis results are used as supplementary optimization constraints.

[0146] Based on the quality optimization objective, interference optimization constraints, and supplementary optimization constraints, a non-dominated sorting genetic algorithm is used as the optimization algorithm to construct a signal optimization model.

[0147] In one embodiment, the feature model construction module 102 is further configured to process the propagation impact assessment matrix using an eigenvalue decomposition algorithm to obtain the propagation impact coefficients using the following formula:

[0148]

[0149] in, Let N be the propagation influence coefficient of the k-th signal, and N be the total number of signal propagation paths. The propagation attenuation weight of the k-th signal is... Let n be the temporal correlation parameter matrix of the field strength for the nth propagation path. The eigenvector matrix corresponding to the nth path of the k-th signal after eigenvalue decomposition of the propagation impact assessment matrix M is given. The eigenvalue matrix corresponding to the nth propagation path after eigenvalue decomposition of the propagation impact assessment matrix M. Eigenvector matrix The inverse matrix, For the trace operation of a matrix, The temporal correlation parameter matrix of the field strength of the nth path The standard deviation of M is given by M, where M is the total number of field strength time-series correlation parameter matrices. Eigenvalue matrix The Frobenius norm, The maximum value among all signal propagation attenuation weights, where K is the total number of signal paths. The eigenvalue matrix corresponding to the k-th signal The largest eigenvalue, The eigenvalue matrix corresponding to the nth path The largest eigenvalue.

[0150] In one embodiment, the transmit / receive parameter generation module 105 is further configured to:

[0151] Based on the quality optimization objective, interference optimization constraints, and supplementary optimization constraints of the signal optimization model, the population size, iteration number parameters, and initial population of the non-dominated sorting genetic algorithm are set.

[0152] The individual fitness value is calculated based on the initial population and the reception quality optimization objective;

[0153] Based on individual fitness values ​​and interference optimization constraints, the initial population is optimized through selection, crossover, and mutation operations to obtain an optimized population;

[0154] Based on the optimized population and supplementary optimization constraints, a set of non-dominated solutions is obtained.

[0155] Based on the non-dominated solution set, the crowding degree ranking is used to determine the signal optimization result;

[0156] Based on the signal optimization results, signal transmission parameters, and channel state parameters, and combined with a pre-set transceiver configuration parameter library, signal transceiver configuration parameters are generated.

[0157] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method for accurate information transmission and reception in a multi-source weak signal scenario based on radio transmission as described above.

[0158] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0159] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0160] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. An information accurate transmitting and receiving method for a multi-source weak signal scene based on radio transmission, characterized in that, The method comprises: acquiring signal transmission parameters, channel state parameters and reception quality parameters; constructing a signal feature model based on the signal transmission parameters, the channel state parameters and the reception quality parameters; analyzing potential interference relationships between signals based on the signal feature model to obtain an interference analysis result; constructing a signal optimization model with reception quality as an optimization objective based on the interference analysis result; solving the signal optimization model to obtain a signal optimization result, and generating signal transceiving configuration parameters according to the signal optimization result.

2. The method of claim 1, wherein, The signal feature model is constructed based on the signal transmission parameters, the channel state parameters and the reception quality parameters, comprising: signal transmission features are obtained through feature fusion based on the signal transmission parameters and the channel state parameters; transmission-quality correlation features are obtained through correlation analysis based on the signal transmission features and in combination with the reception quality parameters; a signal comprehensive feature matrix is obtained through matrix processing of the transmission-quality correlation features; a propagation influence coefficient is obtained through analysis and calculation based on the signal comprehensive feature matrix and in combination with electromagnetic propagation laws; the signal feature model is constructed based on the propagation influence coefficient and the signal comprehensive feature matrix through a fitting method.

3. The method of claim 2, wherein, The propagation influence coefficient is obtained through analysis and calculation based on the signal comprehensive feature matrix and in combination with electromagnetic propagation laws, comprising: a scene propagation attenuation matrix is constructed based on the signal comprehensive feature matrix and in combination with electromagnetic propagation laws; signal field strength distribution is calculated through a ray tracing simulation method based on the scene propagation attenuation matrix; field strength time sequence correlation parameters are obtained through time sequence analysis based on the signal field strength distribution; propagation attenuation weights of different signals are calculated through weighted summation based on the field strength time sequence correlation parameters; a propagation influence evaluation matrix is constructed based on the propagation attenuation weights and in combination with the channel state parameters; the propagation influence coefficient is obtained through eigenvalue decomposition algorithm processing of the propagation influence evaluation matrix.

4. The method of claim 2, wherein, The potential interference relationships between signals are analyzed based on the signal feature model to obtain an interference analysis result, comprising: core signal features are extracted through a principal component analysis algorithm based on the signal feature model; signal interference correlation degrees are calculated through correlation analysis based on the core signal features and in combination with the propagation influence coefficient; interference threshold intervals are determined based on the signal interference correlation degrees and in combination with the channel state parameters; interference priorities of the signals are determined based on the interference threshold intervals; the interference analysis result is formed by integrating the interference priorities, the interference threshold intervals, the signal interference correlation degrees and the core signal features.

5. The method of claim 4, wherein, The signal optimization model with reception quality as an optimization objective is constructed based on the interference analysis result, comprising: reception quality optimization objectives are generated based on signal reception quality requirements and the reception quality parameters; interference optimization constraint conditions are generated through interval constraint mapping based on the interference threshold intervals and the interference priorities of the interference analysis result; The signal interference correlation degree of the interference analysis result and the core signal feature are associated as a supplementary optimization constraint basis; Based on the quality optimization target, the interference optimization constraint condition and the supplementary optimization constraint basis, a non-dominated sorting genetic algorithm is used as an optimization algorithm to construct the signal optimization model.

6. The method of claim 3, wherein, The propagation influence evaluation matrix is processed by the eigenvalue decomposition algorithm to obtain the propagation influence coefficient, which is realized by the following formula: wherein, is the propagation impact coefficient of the kth signal, and N is the total number of signal propagation paths, is the propagation attenuation weight of the kth signal, is the field strength time correlation parameter matrix of the nth propagation path, is the eigenvector matrix corresponding to the kth signal and the nth path after eigenvalue decomposition of the propagation impact evaluation matrix M, is the eigenvalue matrix corresponding to the nth propagation path after eigenvalue decomposition of the propagation impact evaluation matrix M, is the inverse matrix of the eigenvector matrix , is the trace operation of the matrix, is the standard deviation of the field strength time correlation parameter matrix of the nth path , and M is the total number of field strength time correlation parameter matrices, is the Frobenius norm of the eigenvalue matrix , is the maximum value among all signal propagation attenuation weights, and K is the total number of signals, is the maximum eigenvalue of the eigenvalue matrix corresponding to the kth signal, is the maximum eigenvalue of the eigenvalue matrix corresponding to the nth path.

7. The method of claim 5, wherein, The signal optimization model is solved to obtain a signal optimization result, and a signal transceiving configuration parameter is generated according to the signal optimization result, including: Based on the quality optimization target, the interference optimization constraint condition and the supplementary optimization constraint basis of the signal optimization model, the population size, the iteration number parameter and the initial population of the non-dominated sorting genetic algorithm are set; Based on the initial population and the received quality optimization target, an individual fitness value is calculated; Based on the individual fitness value and the interference optimization constraint condition, the initial population is optimized by selection, crossover and mutation operations to obtain an optimized population; Based on the optimized population and the supplementary optimization constraint basis, a non-dominated solution set is screened; Based on the non-dominated solution set, the signal optimization result is determined by using crowding degree sorting; Based on the signal optimization result, the signal transmission parameter and the channel state parameter, the signal transceiving configuration parameter is generated in combination with a preset transceiving configuration parameter library.

8. An information accurate transmitting and receiving device for a multi-source weak signal scene based on radio transmission, characterized in that, The device comprises: A signal parameter acquisition module is configured to acquire a signal transmission parameter, a channel state parameter and a received quality parameter; A feature model construction module is configured to construct a signal feature model based on the signal transmission parameter, the channel state parameter and the received quality parameter; An interference relationship analysis module is configured to analyze potential interference relationships between signals based on the signal feature model to obtain an interference analysis result; An optimization model construction module is configured to construct a signal optimization model with received quality as an optimization target based on the interference analysis result; A transceiving parameter generation module is configured to solve the signal optimization model to obtain a signal optimization result, and generate a signal transceiving configuration parameter according to the signal optimization result. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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