Method, system and apparatus for dynamic enhancement processing of wireless communication signals

Abstract

This invention discloses a method, system, and device for dynamic enhancement processing of wireless communication signals, relating to the field of wireless communication signal enhancement technology. The method includes: deploying N signal monitoring nodes and collecting parameters from N positioning signal nodes; performing feature extraction and evaluation to output the environmental levels of the N positioning signal nodes; constructing a communication signal enhancement strategy library to obtain multi-level positioning signal matching enhancement strategies; performing dynamic enhancement analysis on the N positioning signal node parameters to determine N dynamic signal enhancement strategy parameters; and performing signal collaborative enhancement processing using the N dynamic signal enhancement strategy parameters. This invention solves the technical problems of existing technologies, such as weak adaptive enhancement capability, poor anti-interference, low positioning accuracy, and inability to achieve multi-node collaborative optimization, thus realizing collaborative dynamic enhancement of wireless signals and improving signal stability, anti-interference capability, and positioning accuracy.

Description

Technical Field

[0001] This invention relates to the field of wireless communication signal enhancement technology, and more specifically to a method, system, and device for dynamic enhancement processing of wireless communication signals. Background Technology

[0002] When current wireless communication and positioning technologies are applied in complex scenarios, the signals are easily affected by environmental interference, resulting in insufficient stability. Traditional signal enhancement methods are mostly single and fixed processing modes, lacking the ability to adaptively perceive and dynamically adjust to the real-time communication environment. At the same time, it is difficult to achieve collaborative optimization and unified scheduling among multiple monitoring nodes, resulting in poor signal anti-interference performance and low positioning accuracy, which cannot meet the requirements of high-precision and high-reliability wireless communication and positioning.

[0003] Existing technologies suffer from technical problems such as weak adaptive enhancement capability of wireless communication signals, poor anti-interference, low positioning accuracy, and inability to achieve multi-node collaborative optimization. Summary of the Invention

[0004] This application provides a method, system, and device for dynamic enhancement processing of wireless communication signals, which addresses the technical problems in the prior art such as weak adaptive enhancement capability, poor anti-interference, low positioning accuracy, and inability to achieve multi-node collaborative optimization of wireless communication signals.

[0005] The first aspect of this application provides a method for dynamic enhancement processing of wireless communication signals, the method comprising:

[0006] N signal monitoring nodes are deployed in a wireless communication system. These nodes collect N positioning signal node parameters in real time and transmit them to a central processing unit via an RS485 bus. The central processing unit then calls a communication signal environment assessment channel to perform feature extraction and evaluation on the N positioning signal node parameters, outputting N positioning signal node environment levels. A communication signal enhancement strategy library is constructed, and this library is used to match the environment levels of the N positioning signal nodes with communication scenario requirements, resulting in multi-level positioning signal matching enhancement strategies. Based on these multi-level strategies, the N positioning signal node parameters are dynamically enhanced and analyzed to determine N dynamic signal enhancement strategy parameters. Finally, these parameters are used for coordinated signal enhancement processing.

[0007] A second aspect of this application provides a dynamic enhancement processing system for wireless communication signals, the system comprising:

[0008] The system includes a positioning signal node parameter acquisition module, which deploys N signal monitoring nodes in the wireless communication system to acquire N positioning signal node parameters in real time through these N monitoring nodes and transmits them to the central processing unit via an RS485 bus. A positioning signal node environment level output module is used to call a communication signal environment evaluation channel through the central processing unit, perform feature extraction and evaluation on the N positioning signal node parameters based on the communication signal environment evaluation channel, and output N positioning signal node environment levels. A communication signal enhancement strategy library construction module is used to construct a communication signal enhancement strategy library, and use this library to match the environment levels of the N positioning signal nodes with communication scenario requirements to obtain multi-level positioning signal matching enhancement strategies. A signal collaborative enhancement processing module is used to dynamically enhance and analyze the N positioning signal node parameters based on the multi-level positioning signal matching enhancement strategies, determine N dynamic signal enhancement strategy parameters, and perform signal collaborative enhancement processing using these N dynamic signal enhancement strategy parameters.

[0009] A third aspect of this application provides an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the dynamic enhancement processing method for wireless communication signals provided in this application.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] N signal monitoring nodes are deployed in a wireless communication system. An RS485 bus is activated to transmit the parameters of these N positioning signal nodes to a central processing unit. A communication signal environment assessment channel is invoked to extract and evaluate the features of the N positioning signal node parameters, outputting the environment levels of the N positioning signal nodes. A communication signal enhancement strategy library is constructed, and this library is used to match the environment levels of the N positioning signal nodes with communication scenario requirements, resulting in multi-level positioning signal matching enhancement strategies. Dynamic enhancement analysis is performed to determine the parameters of the N dynamic signal enhancement strategies, and signal collaborative enhancement processing is conducted using these parameters. This achieves collaborative dynamic enhancement of wireless signals, improving signal stability, anti-interference capability, and positioning accuracy. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic flowchart of a dynamic enhancement processing method for wireless communication signals provided in an embodiment of this application.

[0014] Figure 2 This is a schematic diagram of a dynamic enhancement processing system for wireless communication signals provided in an embodiment of this application.

[0015] Figure 3 This is a schematic diagram of the structure of an electronic device provided in this application.

[0016] Explanation of reference numerals in the attached figures: 10 for location signal node parameter acquisition module, 20 for location signal node environment level output module, 30 for communication signal enhancement strategy library construction module, 40 for signal collaborative enhancement processing module, 21 for processor, 22 for memory, 23 for input device, and 24 for output device. Detailed Implementation

[0017] This application provides a method, system, and device for dynamic enhancement processing of wireless communication signals, which addresses the technical problems in the prior art such as weak adaptive enhancement capability, poor anti-interference, low positioning accuracy, and inability to achieve multi-node collaborative optimization of wireless communication signals.

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Example 1, as Figure 1 As shown, this application provides a dynamic enhancement processing method for wireless communication signals, the method comprising:

[0020] Step S100: Deploy N signal monitoring nodes in the wireless communication system, collect N positioning signal node parameters in real time through the N signal monitoring nodes, and activate the RS485 bus to transmit the N positioning signal node parameters to the central processing unit.

[0021] Specifically, the communication monitoring target of the wireless communication system is first obtained. This target includes the monitoring area attributes, positioning accuracy requirements, signal type, and monitoring parameter type. Based on the monitoring area attributes, a 3D model is completed and a monitoring area map model is generated. Key locations are marked on the model to obtain a set of key locations in the monitoring area. Then, according to the positioning accuracy requirements, a coverage layout analysis is performed on the set of key locations to determine N communication monitoring nodes. Based on the signal type and monitoring parameter type, the corresponding monitoring equipment is selected and configured, and the N signal monitoring nodes are deployed in the wireless communication system. Subsequently, the positioning signal node parameters are collected in real time through the deployed N signal monitoring nodes. After the collection is completed, the RS485 bus is activated to stably transmit the collected N positioning signal node parameters to the central processing unit.

[0022] Step S200: The central processing unit calls the communication signal environment assessment channel, performs feature extraction and assessment on the parameters of the N positioning signal nodes based on the communication signal environment assessment channel, and outputs the environment level of the N positioning signal nodes.

[0023] Specifically, the central processing unit calls a pre-built communication signal environment assessment channel, which is composed of a signal feature extraction sub-channel and a signal environment assessment sub-channel connected in series. First, the signal feature extraction sub-channel preprocesses and extracts features from the parameters of N positioning signal nodes, outputting a related signal feature dataset. Then, the signal environment assessment sub-channel conducts multi-dimensional assessment and analysis of the related signal feature dataset according to a signal environment assessment index system that includes signal strength, signal-to-noise ratio, time delay, and frequency domain categories. Combined with the preset signal environment level classification standard, the level label of the signal environment of each node is completed, and finally, the positioning signal node environment level corresponding to each of the N positioning signal nodes is output.

[0024] Step S300: Construct a communication signal enhancement strategy library, and use the communication signal enhancement strategy library to perform strategy matching on the environmental level and communication scenario requirements of the N positioning signal nodes to obtain a multi-level positioning signal matching enhancement strategy.

[0025] Specifically, firstly, relying on various application scenarios and technologies for wireless communication signal enhancement, a communication signal enhancement strategy library is built, which includes adaptation schemes for different signal environment levels and strategies for addressing the needs of different communication scenarios. This strategy library covers multi-level enhancement strategies that match signal environment indicators such as signal strength, signal-to-noise ratio, latency, and frequency domain. Then, the environment levels of the N output positioning signal nodes and the actual wireless communication scenario requirements are used as matching input conditions. The communication signal enhancement strategy library is called to perform precise strategy retrieval and adaptation matching. Based on the differences in the environment levels of each node and the positioning accuracy, signal type, and other requirements of the communication scenario, a corresponding enhancement strategy is matched for each positioning signal node. Finally, a multi-level positioning signal matching enhancement strategy that adapts to N nodes is obtained.

[0026] Step S400: Based on the multi-level positioning signal matching and enhancement strategy, dynamically enhance and analyze the parameters of the N positioning signal nodes to determine the N signal dynamic enhancement strategy parameters, and perform signal collaborative enhancement processing through the N signal dynamic enhancement strategy parameters.

[0027] Specifically, based on the target location of the wireless communication system's communication signals, a corresponding signal enhancement fitness function is constructed. Then, based on the obtained multi-level positioning signal matching enhancement strategy, targeted dynamic enhancement analysis is performed on the parameters of N positioning signal nodes to obtain the signal enhancement strategy parameter thresholds for each node. Subsequently, each parameter threshold is divided into a corresponding signal enhancement strategy parameter interval set, and an intermediate value set of the intervals is obtained. The intermediate value set is simulated and evaluated using the signal enhancement fitness function to obtain an interval fitness set. Based on this fitness set, the parameter interval set is optimized and iteratively divided until a preset termination condition is reached, thus determining N dynamic signal enhancement strategy parameters. Finally, based on these N dynamic signal enhancement strategy parameters, the signals of each positioning signal node are synchronously enhanced to obtain N virtual signals for node positioning enhancement. Based on the triangulation algorithm, collaborative positioning processing and dynamic closed-loop optimization are performed on these virtual signals to complete the collaborative enhancement processing of the entire wireless communication signal.

[0028] In one possible implementation, step S100 further includes:

[0029] Step S110: Obtain the communication monitoring target of the wireless communication system. The communication monitoring target includes monitoring area attributes, positioning accuracy requirements, signal type, and monitoring parameter type.

[0030] Step S120: Perform 3D modeling based on the monitoring area attributes to generate a monitoring area map model, and mark key locations on the monitoring area map model to obtain a set of key locations in the monitoring area.

[0031] Step S130: Perform coverage layout analysis on the key location set of the monitoring area according to the positioning accuracy requirements, and determine N communication monitoring nodes.

[0032] Step S140: Select and configure monitoring equipment for the N communication monitoring nodes based on the signal type and monitoring parameter type, and deploy N signal monitoring nodes.

[0033] Specifically, for the wireless communication system to be optimized, the core requirements for signal monitoring and positioning are first clarified, and the corresponding communication monitoring targets of the system are fully acquired. These communication monitoring targets include four core elements: the monitoring area attribute represents the basic information such as the spatial geography and environmental characteristics of the monitoring range; the positioning accuracy requirement is the system's preset requirements for signal positioning error, accuracy, and other indicators; the signal type clarifies the types of wireless communication signals to be monitored in the system; and the monitoring parameter type defines the signal-related monitoring indicators to be collected, providing a complete and accurate basis for the subsequent deployment of signal monitoring nodes.

[0034] Based on the acquired monitoring area attributes, key information such as geographic spatial features, building layout and structure, and signal propagation environment within the area is extracted. 3D modeling technology is used to digitally reconstruct the monitoring area, generating a monitoring area map model that highly matches the actual scene. Subsequently, in combination with the actual needs of wireless communication signal monitoring, key locations such as signal attenuation points, signal intersection points, area boundary points, and core monitoring points are accurately marked and located in the 3D map model. All marked key locations are integrated and collected to form a standardized set of key locations in the monitoring area.

[0035] Using the preset positioning accuracy requirement as the core quantitative judgment standard, a signal propagation simulation model adapted to the area is first built by combining the propagation characteristics, attenuation law and actual geographical environment of the monitoring area of ​​wireless communication signals. Then, based on this model, a targeted coverage layout analysis is carried out on the key location set of the monitoring area. The effective signal monitoring radius, signal cross coverage range and positioning error value of the key location are calculated one by one. At the same time, the coverage blind spot investigation and supplementation analysis are carried out on the connecting area between key locations. Through multiple rounds of simulation and comparison of the coverage integrity and positioning accuracy of different deployment schemes, redundant deployment points are eliminated and weak points are supplemented. Finally, the specific deployment locations of N communication monitoring nodes that can achieve full coverage of the key location set of the monitoring area without blind spots and whose monitoring positioning accuracy of all points meets the preset requirements are determined.

[0036] Based on the established signal type and monitoring parameter type of the wireless communication system, targeted monitoring equipment selection and parameter configuration are carried out for N communication monitoring nodes with determined deployment locations. Priority is given to selecting receiving and acquisition modules suitable for the corresponding signal type, and matching sensing and detection elements that are compatible with the monitoring parameter type. At the same time, the core parameters of the equipment, such as sampling frequency, detection range, and signal resolution accuracy, are adjusted according to the signal environment of the monitoring area to ensure that the performance indicators of each node device are highly matched with the monitoring requirements. After completing the equipment assembly and parameter calibration of all nodes, the N signal monitoring nodes are deployed in the monitoring area according to the determined deployment locations to ensure that each node can stably and accurately carry out real-time acquisition of positioning signal node parameters.

[0037] In one possible implementation, step S200 further includes:

[0038] Step S210: Construct a signal environment evaluation index system, which includes signal strength, signal-to-noise ratio, time delay, and frequency domain categories.

[0039] Step S220: Collect historical datasets of wireless communication signals, perform feature extraction and analysis on the historical datasets of wireless communication signals, and construct a signal feature extraction sub-channel.

[0040] Step S230: Obtain the associated signal feature dataset output by the signal feature extraction sub-channel, evaluate and train the associated signal feature dataset according to the signal environment evaluation index system, and generate the signal environment evaluation sub-channel.

[0041] Step S240: Connect the signal feature extraction subchannel and the signal environment assessment subchannel in series to build a communication signal environment assessment channel and store it in the central processing unit.

[0042] Specifically, in order to meet the comprehensive assessment needs of the wireless communication signal environment, a multi-dimensional signal environment assessment index system is systematically constructed. This system clearly covers four core index categories: signal strength indexes are used to quantitatively characterize the propagation power and received amplitude of communication signals; signal-to-noise ratio indexes are used to assess the ratio of effective signal to interference noise; delay indexes are used to monitor the delay duration and jitter during signal transmission; and frequency domain indexes are used to analyze the distribution characteristics and spectral purity of signals in the frequency dimension. Through the collaborative construction of multi-dimensional indexes, a standardized and systematic basis for signal environment assessment and judgment is formed.

[0043] First, a comprehensive dataset of historical wireless communication signals generated by the wireless communication system under different propagation environments and working scenarios is collected. This dataset includes raw monitoring data from multiple dimensions, such as signal strength, signal-to-noise ratio, transmission delay, and frequency domain distribution, forming a multi-scenario, multi-dimensional basic data sample. Then, a systematic feature extraction and analysis is performed on this historical dataset. Preprocessing operations such as data cleaning, outlier removal, missing value completion, and data normalization are completed to filter out valid and well-organized signal data samples. Based on the indicators for subsequent signal environment assessment, core related features concerning signal strength, signal-to-noise ratio, delay, and frequency domain indicators are extracted from the preprocessed data. Simultaneously, redundant features are removed through feature dimensionality reduction and feature selection, retaining high-value and highly relevant signal feature dimensions, thus achieving accurate feature extraction and optimization. Finally, based on the established preprocessing process, feature extraction rules, dimensionality optimization methods, and corresponding algorithm logic, a standardized and modular signal feature extraction sub-channel is built to achieve automated and accurate feature extraction output from the input communication signal data.

[0044] The associated signal feature dataset, output from the signal feature extraction sub-channel, is retrieved. This dataset contains high-value core feature data that has undergone preprocessing and feature selection, and aligns with the signal environment assessment index system. Subsequently, using a pre-constructed signal environment assessment index system encompassing signal strength, signal-to-noise ratio, time delay, and frequency domain categories as the quantification standard and classification basis, systematic evaluation training is conducted on the associated signal feature dataset to generate a signal environment assessment sub-channel. Specifically, the feature data is first classified and labeled according to the quantification thresholds of each dimension of the index system, and the dataset is divided into training, validation, and test sets to construct a standardized evaluation training sample set. Then, a deep neural network algorithm is used to build the basic structure of the evaluation model. This network uses four major feature categories—signal strength, signal-to-noise ratio, time delay, and frequency domain—as input layer nodes, and sets 3-5 hidden layers to complete the nonlinear mapping of features, taking the signal environment as the basis. The level is the output layer node. The model is iteratively trained using the backpropagation algorithm combined with gradient descent. By continuously adjusting the network weights and biases, the error between the model's output environment level judgment and the labeled value is reduced to within a preset threshold. After the model is trained, dynamic weights are assigned to the four categories of evaluation indicators. Differentiated weight values ​​are assigned to each indicator based on the degree of influence of each indicator on the signal environment under different communication scenarios. Then, the output results of the deep neural network are optimized a second time through a weighted decision fusion algorithm to integrate the evaluation results of each dimension of indicators to improve the accuracy of the judgment. Finally, the trained and optimized deep neural network evaluation model, dynamic weight allocation rules and weighted decision fusion algorithm are modularly integrated and encapsulated to form a signal environment evaluation sub-channel that can realize end-to-end processing. This sub-channel can automatically receive signal feature data, complete multi-dimensional indicator evaluation, and output accurate signal environment level results.

[0045] The signal feature extraction subchannel and the signal environment assessment subchannel are standardized and connected in series to build an integrated communication signal environment assessment channel. First, the input port of the signal data is connected to the input end of the signal feature extraction subchannel. Then, the feature data output end of the signal feature extraction subchannel is connected to the feature input end of the signal environment assessment subchannel. Fixed data transmission protocols and format conversion rules are set to ensure that the associated signal feature dataset output by the preceding subchannel can be automatically and losslessly transmitted to the subsequent subchannel for evaluation and analysis, forming an end-to-end processing link from raw signal feature extraction to signal environment level output. After the channel is built, the entire link is tested to verify the smoothness of data transmission, the accuracy of feature processing, and the effectiveness of environment assessment. After all test indicators meet the preset requirements, the communication signal environment assessment channel is modularly packaged and stored in the designated storage area of ​​the central processing unit as a callable program module. Dedicated channel call instructions and parameter interfaces are configured so that the central processing unit can quickly call the channel according to actual monitoring needs to complete feature extraction and environmental level assessment of the location signal node parameters.

[0046] In one possible implementation, step S220 further includes:

[0047] Step S221: Perform preprocessing steps on the historical dataset of wireless communication signals according to the communication signal application standard to generate a signal preprocessing channel.

[0048] Step S222: Obtain the available signal historical dataset output by the signal preprocessing channel, and simultaneously perform feature association on the available signal historical dataset according to the signal environment evaluation index system, and select the signal association feature type set.

[0049] Step S223: Perform feature extraction algorithm parsing on the signal association feature type set to obtain the signal association feature type extraction algorithm channel.

[0050] Step S224: Perform a cascaded union of the signal preprocessing channel and the signal association feature type extraction algorithm channel to construct a signal feature extraction sub-channel.

[0051] Specifically, strictly adhering to the industry-standard requirements for signal data processing in communication signal application, a comprehensive and standardized preprocessing process is analyzed for the collected historical wireless communication signal dataset. The core preprocessing steps are clearly defined, encompassing key operations such as formatting the raw data, filtering and removing invalid data, identifying and correcting outliers, interpolating and completing missing values, and data normalization and standardization. Based on communication signal application standards, the execution thresholds, operational logic, processing order, and verification standards for each step are determined. Each preprocessing step is solidified into a standardized, automatically executable processing link according to the signal data processing sequence. Simultaneously, data input / output interfaces and processing status feedback mechanisms are configured. Ultimately, a signal preprocessing channel is generated that can perform standardized and automated preprocessing of the original historical wireless communication signal dataset, ensuring the validity, regularity, and consistency of the output data.

[0052] The system retrieves the available historical signal dataset after the signal preprocessing channel has completed the full-process standardization process. This dataset has undergone format normalization, outlier removal, missing value completion, and normalization, ensuring data validity and regularity. Simultaneously, using a pre-constructed signal environment assessment index system comprising four core dimensions—signal strength, signal-to-noise ratio, transmission delay, and frequency domain—as the sole basis for correlation, precise feature correlation analysis is conducted on the available historical signal dataset. The system matches the correspondence between each signal feature in the dataset and the four categories of assessment indicators, selecting feature dimensions highly correlated with assessment indicators such as signal strength, signal-to-noise ratio, transmission delay, and frequency domain distribution, effectively characterizing the signal environment state. Redundant features irrelevant to the environment assessment are removed. The selected high-value feature dimensions are then categorized, integrated, and standardized, ultimately determining and generating a set of signal correlation feature types suitable for signal environment assessment requirements.

[0053] First, the feature type set is categorized and sorted, distinguishing different feature categories such as numerical features (signal strength), ratio-to-noise ratio (SNR), time-series features (delay), and spectral features (frequency domain). Then, based on the extraction requirements and data characteristics of each type of feature, the optimal extraction algorithm is matched for each type of feature. For example, time-domain statistical methods are used for time-domain features, Fourier transform methods are used for frequency-domain features, and feature quantization extraction methods are used for numerical features. At the same time, the core execution parameters, operation logic, data input and output formats, and accuracy judgment criteria of each algorithm are clarified. Subsequently, the extraction algorithms corresponding to each type of feature are modularly integrated according to the classification logic of the feature type set. The execution timing and data interaction rules of the algorithms are set, and a standardized processing link is built to accurately match the feature category and automatically complete the target feature extraction. Finally, a signal correlation feature type extraction algorithm channel is obtained. This channel can selectively call the corresponding algorithm to complete the accurate extraction of each correlation feature based on the input available signal historical data, ensuring the efficiency and accuracy of feature extraction.

[0054] The signal preprocessing channel and the signal association feature type extraction algorithm channel are standardized and combined. First, the standardized data output end of the signal preprocessing channel is seamlessly connected with the compliant data input end of the signal association feature type extraction algorithm channel, unifying the data transmission format, interface protocol, and triggering mechanism of both ends. This ensures that the preprocessed usable historical signal dataset can be automatically and losslessly transmitted to the feature extraction algorithm channel. At the same time, intermediate data verification nodes are configured in the link to perform secondary verification of the validity of the preprocessed data, preventing invalid data from entering the extraction stage. Then, according to the execution sequence of feature extraction, the algorithm channel is set to call rules for different types of features, ensuring that various signal association features can be accurately matched with the corresponding extraction algorithms for processing. Finally, the two channels are integrated into an end-to-end integrated processing link to complete the construction of the signal feature extraction sub-channel. This sub-channel can realize fully automated and standardized processing from the input of raw wireless communication signal historical data to the output of high-value signal association feature datasets after preprocessing and feature extraction, providing accurate and well-organized feature data support for subsequent signal environment assessment.

[0055] In one possible implementation, step S230 further includes:

[0056] Step S231: Associate and divide the associated signal feature dataset according to the signal environment evaluation index system to obtain the signal association index feature dataset.

[0057] Step S232: Construct a signal environment level classification standard, evaluate and classify the signal-related index feature dataset based on the signal environment level classification standard, and obtain a signal-related index feature sample set.

[0058] Step S233: Use a deep neural network to train the signal correlation index feature sample set for index evaluation and generate a signal environment index evaluator.

[0059] Step S234: Perform dynamic weight allocation and weighted decision fusion on the signal environment index evaluator to generate a signal environment evaluation sub-channel.

[0060] Specifically, based on the established signal environment assessment index system, the associated signal feature dataset is matched and associated in four major index dimensions: signal strength, signal-to-noise ratio, time delay, and frequency domain. Each feature data is classified according to its physical meaning and index type, and then assigned to the corresponding index feature subset. This process aggregates and regularizes features of the same dimension, eliminates mismatched and irrelevant redundant data, and finally forms a signal association index feature dataset that is clearly classified, dimensionally unified, and corresponds one-to-one with the assessment index.

[0061] Based on the actual transmission characteristics, positioning accuracy requirements, and signal interference of wireless communication signals within the monitoring area, a signal environment level classification standard is constructed, which includes multiple environmental levels, corresponding quantization thresholds, and judgment rules. Then, according to this classification standard, the signal environment is evaluated and classified dimension by dimension and sample by sample for the signal correlation index feature dataset that has been classified and diverted. Each data point is labeled with a corresponding environmental level label. After the labeled dataset is formatted and the samples are standardized, a labeled signal correlation index feature sample set that can be directly used for model training is formed.

[0062] A deep neural network (DNN) is used to systematically train a signal environment index evaluator on a sample set of signal-related index features labeled with environmental level tags. The structure and usage of the DNN are as follows: The DNN adopts a multi-layer fully connected architecture. The number of nodes in the input layer perfectly matches the feature dimensions of the signal-related index sample set, corresponding to all related features of the four major indexes: signal strength, signal-to-noise ratio, time delay, and frequency domain, ensuring comprehensive reception of various feature data. The hidden layer has 3-5 layers, with the number of nodes in each layer decreasing progressively from the input layer to the hidden layer and then to the output layer. The ReLU activation function is used to achieve non-linear mapping of features, effectively uncovering the complex correlation between different index features and signal environment levels. A Dropout layer is introduced to suppress model overfitting and improve the model's generalization ability. The number of nodes in the output layer is consistent with the preset number of signal environment levels. The Softmax activation function is used to output the probability values ​​of each environment level, achieving multi-class classification of environment levels. In practice, the signal correlation index feature sample set is first divided into a training set, a validation set, and a test set in a 7:2:1 ratio. The training set is then input into a deep neural network, and the cross-entropy loss function is used as the loss criterion for model training. Gradient descent is used to optimize the model parameters, and the weights and biases of each layer are continuously adjusted through backpropagation. After each training round, the model evaluation accuracy is verified using the validation set. If the accuracy does not reach the preset threshold, iterative training continues until the evaluation error of the model on the validation set is reduced to the preset range and converges and stabilizes. Finally, the performance of the trained model is verified using the test set to check its accuracy and stability in determining the environmental level. After verification, the trained and parameter-fixed deep neural network is encapsulated into an independent signal environmental index evaluator. This evaluator can receive input signal correlation index feature data and automatically output the corresponding signal environmental level determination result.

[0063] Based on the characteristics of different monitoring scenarios and signal environment changes, the four categories of indicators output by the signal environment indicator evaluator—signal strength, signal-to-noise ratio, time delay, and frequency domain—are dynamically weighted. The weight coefficients are adaptively adjusted according to the degree of influence of each indicator on the current environmental assessment. Then, a weighted decision fusion algorithm is used to normalize and weight the independent evaluation results of the four categories of indicators and make a comprehensive decision to obtain a unified and stable comprehensive signal environment assessment result. Subsequently, the dynamic weight allocation rules, weighted decision fusion logic, and the trained signal environment indicator evaluator are integrated and encapsulated to form a complete processing link from feature input, indicator evaluation, weight optimization to comprehensive output. Finally, a signal environment assessment sub-channel with direct call and adaptive evaluation capabilities is constructed.

[0064] In one possible implementation, step S400 further includes:

[0065] Step S410: Locate the target based on the communication signal and construct a fitness function for signal enhancement effect.

[0066] Step S420: Based on the multi-level positioning signal matching enhancement strategy, dynamically enhance and analyze the parameters of the N positioning signal nodes to obtain the threshold values ​​of the N signal enhancement strategy parameters.

[0067] Step S430: Use the signal enhancement effect fitness function to perform optimization analysis on the threshold values ​​of the N signal enhancement strategy parameters to determine the N dynamic signal enhancement strategy parameters.

[0068] Specifically, based on the goal of high-precision positioning of communication signals, a weighted multi-objective optimization method is used to construct a fitness function for signal enhancement effect. The signal strength enhancement rate, signal-to-noise ratio improvement value, transmission delay jitter reduction, and frequency domain interference suppression ratio are used as four-dimensional optimization variables. Through normalization processing, each index is unified to the same numerical range. Then, dynamic weighting coefficients are assigned according to the degree of influence of each index on positioning accuracy. A linear weighted summation method is used to integrate the multi-dimensional indexes into a single-dimensional fitness value, forming a fitness function that can be directly calculated and used to evaluate the quality of signal enhancement effect. The larger the function output value, the better the enhancement effect of the corresponding parameter meets the positioning requirements.

[0069] Based on a pre-defined multi-level positioning signal matching and enhancement strategy, and combined with the environmental level results output by the signal environment assessment sub-channel, the node parameters uploaded by N positioning signal nodes are dynamically enhanced and analyzed node by node and dimension by dimension. For four types of indicators, namely signal strength, signal-to-noise ratio, time delay, and frequency domain interference, feasible value ranges for enhancement parameters such as gain adjustment, filter coefficient, power compensation, and interference suppression are determined. In combination with the current signal environment constraints and positioning accuracy requirements, upper and lower limit constraints and rationality checks are performed on each enhancement parameter of each node. Finally, a set of signal enhancement strategy parameter thresholds that meet the requirements of environmental adaptation and strategy matching are generated for each node.

[0070] The signal enhancement effect fitness function is used to perform optimization analysis on the threshold parameters of N signal enhancement strategies. First, the threshold parameters of N signal enhancement strategies are divided into N signal enhancement strategy parameter interval sets, and the intermediate value sets of N signal enhancement strategy intervals corresponding to the N signal enhancement strategy parameter interval sets are calculated. Then, the signal enhancement effect fitness function is used to simulate and evaluate each of the intermediate value sets of N signal enhancement strategy intervals one by one, and the fitness sets of N signal enhancement strategy intervals corresponding to each interval are obtained. Subsequently, based on the numerical value of the fitness sets of N signal enhancement strategy intervals, interval optimization, iterative subdivision, and stepwise optimization approximation operations are performed on the N signal enhancement strategy parameter interval sets, continuously shrinking the interval where the optimal parameter is located until the preset accuracy termination condition or iteration termination condition is met. Finally, a set of parameters that makes the fitness optimal is determined for each node, thereby determining the N signal dynamic enhancement strategy parameters.

[0071] In one possible implementation, step S430 further includes:

[0072] Step S431: Divide the N signal enhancement strategy parameter thresholds into N signal enhancement strategy parameter interval sets respectively, and obtain the N signal enhancement strategy interval intermediate value set of the N signal enhancement strategy parameter interval sets.

[0073] Step S432: Use the signal enhancement effect fitness function to simulate and evaluate the intermediate value set of the N signal enhancement strategy intervals to obtain the fitness set of the N signal enhancement strategy intervals.

[0074] Step S433: Based on the fitness set of the N signal enhancement strategy intervals, perform interval optimization and iterative division to find the optimal approximation of the N signal enhancement strategy parameter intervals until the preset termination condition is met, and determine the N signal dynamic enhancement strategy parameters.

[0075] Specifically, for each signal enhancement strategy, the upper and lower limits of each enhancement parameter are ordered to form N sets of signal enhancement strategy parameter intervals that correspond to different nodes and contain the value ranges of multiple enhancement parameters. Then, the intermediate value is calculated for each dimension of the parameter interval in each parameter interval set, and the intermediate values ​​of each parameter are combined into a set of intermediate parameters for the corresponding node. Finally, N sets of intermediate values ​​of signal enhancement strategy intervals are obtained that correspond one-to-one with the N sets of signal enhancement strategy parameter intervals.

[0076] The intermediate parameters of each set of intermediate values ​​in the N signal enhancement strategy intervals are sequentially substituted into the pre-constructed fitness function of the signal enhancement effect. Simulation calculations and quantitative evaluations are performed according to the comprehensive optimization objectives of signal strength improvement, signal-to-noise ratio improvement, time delay jitter suppression, and frequency domain interference suppression. The corresponding fitness values ​​are calculated for each set. The fitness values ​​corresponding to all intermediate parameter sets of each node are classified and integrated to form N signal enhancement strategy interval fitness sets that correspond one-to-one with the N signal enhancement strategy parameter interval sets.

[0077] Based on the fitness values ​​of the fitness sets of N signal enhancement strategy intervals, intervals with better fitness are selected. Interval optimization is performed on the N signal enhancement strategy parameter interval sets, retaining high-fitness intervals and discarding low-fitness intervals. Then, a new round of subdivision and interval division is performed on the optimized high-fitness intervals. The iterative optimization process of calculating intermediate values, evaluating fitness, and optimizing intervals is repeated to make the parameter values ​​continuously approach the optimal solution. When the number of iterations reaches a preset value or the parameter interval shrinks to the termination condition that meets the accuracy requirements, the optimal parameter value obtained by the final convergence is taken as the optimal parameter of the corresponding node, thereby determining the parameters of the N signal dynamic enhancement strategies.

[0078] In one possible implementation, step S400 further includes:

[0079] Step S440: Synchronously enhance the positioning signal using the N signal dynamic enhancement strategy parameters to obtain N node positioning enhancement virtual signals.

[0080] Step S450: Perform collaborative positioning processing and dynamic closed-loop optimization on the virtual signals of the N nodes based on the triangulation algorithm.

[0081] Specifically, based on the determined N signal dynamic enhancement strategy parameters, synchronous enhancement operations are performed on the original positioning signals currently collected by each monitoring node. Signal gain adjustment, interference filtering, power compensation, time delay correction, and frequency domain normalization are completed according to the parameters, so that the signals of each node can be adaptively enhanced and optimized under a unified timing, and finally N node positioning enhancement virtual signals with high signal-to-noise ratio, low interference, and strong stability are generated.

[0082] Based on the triangulation algorithm, the Time Difference of Arrival (TDOA) or Signal Strength Index (RSSI) of the virtual signals from N nodes is used as the observation value to construct a multi-node distance constraint equation system. The position coordinates of the target are obtained by solving the least squares method, thus completing the collaborative positioning process. The positioning result is then compared with the preset accuracy index, and the error value is fed back to the signal enhancement parameter optimization stage in real time. This forms a dynamic closed-loop optimization mechanism of positioning solution - error calculation - parameter correction - re-enhancement - re-positioning. The signal enhancement strategy and positioning solution process are iteratively corrected repeatedly until the positioning error converges to the preset range, thus achieving high-precision and stable target positioning.

[0083] Example 2, based on the same inventive concept as the dynamic enhancement processing method for wireless communication signals in the foregoing examples, such as... Figure 2 As shown, this application provides a dynamic enhancement processing system for wireless communication signals. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0084] The positioning signal node parameter acquisition module 10 is used to deploy N signal monitoring nodes in a wireless communication system, acquire N positioning signal node parameters in real time through the N signal monitoring nodes, and activate the RS485 bus to transmit the N positioning signal node parameters to the central processing unit.

[0085] The positioning signal node environment level output module 20 is used to call the communication signal environment evaluation channel through the central processing unit, perform feature extraction and evaluation on the parameters of the N positioning signal nodes based on the communication signal environment evaluation channel, and output the environment level of the N positioning signal nodes.

[0086] The communication signal enhancement strategy library construction module 30 is used to construct a communication signal enhancement strategy library. The communication signal enhancement strategy library is used to perform strategy matching on the environmental level and communication scenario requirements of the N positioning signal nodes to obtain a multi-level positioning signal matching enhancement strategy.

[0087] The signal collaborative enhancement processing module 40 is used to perform dynamic enhancement analysis on the parameters of the N positioning signal nodes based on the multi-level positioning signal matching enhancement strategy, determine the N signal dynamic enhancement strategy parameters, and perform signal collaborative enhancement processing through the N signal dynamic enhancement strategy parameters.

[0088] Furthermore, the system is also used to implement the following functions:

[0089] The communication monitoring targets of the wireless communication system are obtained, including monitoring area attributes, positioning accuracy requirements, signal type, and monitoring parameter type. Based on the monitoring area attributes, a 3D model is created to generate a monitoring area map model. Key locations are marked on the monitoring area map model to obtain a set of key locations in the monitoring area. According to the positioning accuracy requirements, a coverage layout analysis is performed on the set of key locations in the monitoring area to determine N communication monitoring nodes. Based on the signal type and monitoring parameter type, monitoring equipment is selected and configured for the N communication monitoring nodes, and N signal monitoring nodes are deployed.

[0090] Furthermore, the system is also used to implement the following functions:

[0091] A signal environment assessment index system is constructed, which includes signal strength, signal-to-noise ratio, time delay, and frequency domain categories. Historical datasets of wireless communication signals are collected, and feature extraction and analysis are performed on these datasets to construct a signal feature extraction sub-channel. The associated signal feature datasets output by the signal feature extraction sub-channels are obtained, and the associated signal feature datasets are evaluated and trained according to the signal environment assessment index system to generate a signal environment assessment sub-channel. The signal feature extraction sub-channel and the signal environment assessment sub-channel are then connected in series to construct a communication signal environment assessment channel, which is then stored in the central processing unit.

[0092] Furthermore, the system is also used to implement the following functions:

[0093] The historical dataset of wireless communication signals is parsed according to the communication signal application standard to generate a signal preprocessing channel; the available historical dataset of signals output by the signal preprocessing channel is obtained, and the available historical dataset of signals is associated with features according to the signal environment evaluation index system to select a set of signal associated feature types; the set of signal associated feature types is parsed using a feature extraction algorithm to obtain a signal associated feature type extraction algorithm channel; the signal preprocessing channel and the signal associated feature type extraction algorithm channel are cascaded and merged to construct a signal feature extraction sub-channel.

[0094] Furthermore, the system is also used to implement the following functions:

[0095] The associated signal feature dataset is correlated and split according to the aforementioned signal environment assessment index system to obtain a signal-related index feature dataset. A signal environment level classification standard is constructed, and the signal-related index feature dataset is evaluated, classified, and identified based on the aforementioned standard to obtain a signal-related index feature sample set. A deep neural network is used to train the signal-related index feature sample set for index evaluation to generate a signal environment index evaluator. The signal environment index evaluator is then dynamically weighted, weighted, and fused to generate a signal environment assessment sub-channel.

[0096] Furthermore, the system is also used to implement the following functions:

[0097] Based on the target location of the communication signal, a fitness function for signal enhancement effect is constructed; based on the multi-level positioning signal matching enhancement strategy, the parameters of the N positioning signal nodes are dynamically enhanced and analyzed to obtain N signal enhancement strategy parameter thresholds; the fitness function for signal enhancement effect is used to perform optimization analysis on the N signal enhancement strategy parameter thresholds to determine N dynamic signal enhancement strategy parameters.

[0098] Furthermore, the system is also used to implement the following functions:

[0099] The threshold values ​​of the N signal enhancement strategy parameters are divided into N signal enhancement strategy parameter interval sets, and the intermediate value sets of the N signal enhancement strategy intervals are obtained. The fitness function of the signal enhancement effect is used to simulate and evaluate the intermediate value sets of the N signal enhancement strategy intervals to obtain N signal enhancement strategy interval fitness sets. Based on the fitness sets of the N signal enhancement strategy intervals, the N signal enhancement strategy parameter interval sets are optimized by interval selection and iterative division until a preset termination condition is met, thus determining the N dynamic signal enhancement strategy parameters.

[0100] Furthermore, the system is also used to implement the following functions:

[0101] The positioning signal is synchronously enhanced using the N signal dynamic enhancement strategy parameters to obtain N node positioning enhancement virtual signals; the N node positioning enhancement virtual signals are then used for collaborative positioning processing and dynamic closed-loop optimization based on the triangulation algorithm.

[0102] Example 3, Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention for the dynamic enhancement processing method of wireless communication signals, showing a block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. Figure 3As shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 3 Taking a processor 21 as an example, the processor 21, memory 22, input device 23, and output device 24 in an electronic device can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0103] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Specific embodiments of this specification have been described above. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0104] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0105] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for dynamic enhancement processing of wireless communication signals, characterized in that, The method includes: In a wireless communication system, N signal monitoring nodes are deployed. The parameters of N positioning signal nodes are collected in real time through the N signal monitoring nodes, and the RS485 bus is activated to transmit the parameters of the N positioning signal nodes to the central processing unit. The central processing unit calls the communication signal environment assessment channel, performs feature extraction and assessment on the parameters of the N positioning signal nodes based on the communication signal environment assessment channel, and outputs the environment level of the N positioning signal nodes. A communication signal enhancement strategy library is constructed, and the communication signal enhancement strategy library is used to perform strategy matching on the environmental level and communication scenario requirements of the N positioning signal nodes to obtain a multi-level positioning signal matching enhancement strategy. Based on the multi-level positioning signal matching and enhancement strategy, the parameters of the N positioning signal nodes are dynamically enhanced and analyzed to determine the N signal dynamic enhancement strategy parameters, and signal collaborative enhancement processing is performed through the N signal dynamic enhancement strategy parameters. Among them, N signal dynamic enhancement strategy parameters are determined, including: Based on the communication signal, locate the target and construct a fitness function for signal enhancement effect; Based on the multi-level positioning signal matching and enhancement strategy, the parameters of the N positioning signal nodes are dynamically enhanced and analyzed to obtain the threshold values ​​of the N signal enhancement strategy parameters. The signal enhancement effect fitness function is used to perform optimization analysis on the threshold values ​​of the N signal enhancement strategy parameters to determine the N dynamic signal enhancement strategy parameters. The signal enhancement effect fitness function is used to perform optimization analysis on the threshold values ​​of the N signal enhancement strategy parameters to determine the N dynamic signal enhancement strategy parameters, including: The threshold values ​​of the N signal enhancement strategy parameters are divided into N signal enhancement strategy parameter interval sets, and the intermediate value set of the N signal enhancement strategy intervals of the N signal enhancement strategy parameter interval sets is obtained. The signal enhancement effect fitness function is used to simulate and evaluate the intermediate value set of the N signal enhancement strategy intervals, thereby obtaining the N signal enhancement strategy interval fitness sets; Based on the fitness set of the N signal enhancement strategy intervals, the N signal enhancement strategy parameter intervals are optimized and iteratively divided until a preset termination condition is met, thereby determining the N signal dynamic enhancement strategy parameters.

2. The dynamic enhancement processing method for wireless communication signals as described in claim 1, characterized in that, Deploy N signal monitoring nodes in a wireless communication system, including: The communication monitoring target of the wireless communication system is obtained, and the communication monitoring target includes monitoring area attributes, positioning accuracy requirements, signal type and monitoring parameter type; Based on the attributes of the monitoring area, a three-dimensional model is created to generate a map model of the monitoring area. Key locations are marked on the map model of the monitoring area to obtain a set of key locations of the monitoring area. Based on the positioning accuracy requirements, a coverage layout analysis is performed on the key location set of the monitoring area to determine N communication monitoring nodes; Based on the signal type and monitoring parameter type, the monitoring equipment for the N communication monitoring nodes is selected and configured, and N signal monitoring nodes are deployed.

3. The dynamic enhancement processing method for wireless communication signals as described in claim 1, characterized in that, The central processing unit invokes the communication signal environment assessment channel, including: A signal environment assessment index system is constructed, which includes signal strength, signal-to-noise ratio, time delay, and frequency domain categories. Collect historical datasets of wireless communication signals, perform feature extraction and analysis on the historical datasets of wireless communication signals, and construct a signal feature extraction sub-channel; Obtain the associated signal feature dataset output by the signal feature extraction sub-channel, evaluate and train the associated signal feature dataset according to the signal environment evaluation index system, and generate the signal environment evaluation sub-channel; The signal feature extraction subchannel and the signal environment assessment subchannel are connected in series to form a communication signal environment assessment channel, which is then stored in the central processing unit.

4. The dynamic enhancement processing method for wireless communication signals as described in claim 3, characterized in that, Constructing signal feature extraction sub-channels, including: The historical dataset of wireless communication signals is parsed according to the communication signal application standards to generate a signal preprocessing channel. Obtain the available signal historical dataset output by the signal preprocessing channel, and simultaneously perform feature association on the available signal historical dataset according to the signal environment evaluation index system, and select a signal association feature type set; The signal association feature type set is parsed using a feature extraction algorithm to obtain the signal association feature type extraction algorithm channel; The signal preprocessing channel and the signal association feature type extraction algorithm channel are cascaded and combined to construct a signal feature extraction sub-channel.

5. The dynamic enhancement processing method for wireless communication signals as described in claim 3, characterized in that, Generate a signal environment assessment sub-channel, including: The associated signal feature dataset is correlated and split according to the aforementioned signal environment assessment index system to obtain a signal correlation index feature dataset. A signal environment level classification standard is constructed, and the signal-related index feature dataset is evaluated, classified, and identified based on the signal environment level classification standard to obtain a signal-related index feature sample set. A deep neural network is used to train the signal-related index feature sample set for index evaluation, thereby generating a signal environment index evaluator. The signal environment index evaluator is dynamically weighted and weighted decision fusion is performed to generate a signal environment evaluation sub-channel.

6. The dynamic enhancement processing method for wireless communication signals as described in claim 1, characterized in that, Signal co-enhancement processing is performed using the N signal dynamic enhancement strategy parameters, including: The positioning signal is synchronously enhanced by the N signal dynamic enhancement strategy parameters to obtain N node positioning enhancement virtual signals; Based on the triangulation algorithm, the virtual signals of the N nodes are used for collaborative positioning processing and dynamic closed-loop optimization.

7. A dynamic enhancement processing system for wireless communication signals, characterized in that, The system is used to implement the dynamic enhancement processing method for wireless communication signals according to any one of claims 1-6, the system comprising: The positioning signal node parameter acquisition module is used to deploy N signal monitoring nodes in a wireless communication system, acquire N positioning signal node parameters in real time through the N signal monitoring nodes, and activate the RS485 bus to transmit the N positioning signal node parameters to the central processing unit. The positioning signal node environment level output module is used to call the communication signal environment evaluation channel through the central processing unit, perform feature extraction and evaluation on the parameters of the N positioning signal nodes based on the communication signal environment evaluation channel, and output the environment level of the N positioning signal nodes. The communication signal enhancement strategy library construction module is used to construct a communication signal enhancement strategy library. The communication signal enhancement strategy library is used to perform strategy matching on the environmental level and communication scenario requirements of the N positioning signal nodes to obtain a multi-level positioning signal matching enhancement strategy. The signal collaborative enhancement processing module is used to dynamically enhance and analyze the parameters of the N positioning signal nodes based on the multi-level positioning signal matching enhancement strategy, determine the N signal dynamic enhancement strategy parameters, and perform signal collaborative enhancement processing through the N signal dynamic enhancement strategy parameters.

8. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is used to execute the dynamic enhancement processing method for wireless communication signals as described in any one of claims 1 to 6.

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