Wireless bridge signal transmission optimization method and system based on multi-dimensional interference traceability

Abstract

The invention relates to the technical field of wireless signal optimization, and discloses a wireless bridge signal transmission optimization method and system based on multi-dimensional interference traceability, and the method comprises the steps: collecting an original signal and operation environment data of a wireless bridge, the operation environment data comprising frequency spectrum data, obstacle distribution data and profile data of real-time signal intensity; identifying a signal interference component of the wireless network, and identifying a signal shielding area of the wireless bridge so as to analyze signal weakening characteristics of the signal shielding area to the wireless bridge, and constructing a signal quality vector of the wireless bridge; constructing an interference characteristic matrix of the wireless network bridge to perform interference tracing on the wireless network bridge to obtain interference factors; and constructing a candidate transmission path set of the wireless network bridge, calculating the transmission stability of each path to select an optimized transmission path, and executing signal transmission of the wireless network bridge by using the optimized transmission path. According to the invention, the stability of wireless bridge signal optimization can be improved.

Description

Technical Field

[0001] This invention relates to a method and system for optimizing wireless bridge signal transmission based on multi-dimensional interference tracing, belonging to the field of wireless signal optimization technology. Background Technology

[0002] As a core device for long-distance wireless data transmission, wireless bridges are increasingly widely used in fields such as smart security monitoring, industrial park data interconnection, and network coverage in remote areas. The stability and efficiency of their signal transmission directly determine the operational quality of various business systems. In practical application scenarios, wireless bridges often face complex electromagnetic interference, including electromagnetic radiation from industrial equipment, signal conflicts from wireless devices on the same frequency band, and multipath effects caused by terrain obstruction. These interferences can cause signal packet loss, increased latency, and bandwidth fluctuations, seriously affecting the reliability of data transmission and even leading to the interruption of critical services.

[0003] Currently, optimization of wireless bridge signal transmission often employs rather "aggressive" adjustment strategies. For example, when the wireless bridge signal is poor, the solution is to directly increase the transmission power or switch to a fixed frequency band. While such methods can temporarily solve the signal problem, they cannot address the interference problem at its root. Excessive upgrades to hardware parameters can lead to energy waste and signal redundancy, often only alleviating surface interference and resulting in unstable signal transmission optimization effects. Summary of the Invention

[0004] This invention provides a method and system for optimizing wireless bridge signal transmission based on multi-dimensional interference tracing, the main purpose of which is to improve the stability of wireless bridge signal optimization.

[0005] To achieve the above objectives, the present invention provides a wireless bridge signal transmission optimization method based on multi-dimensional interference tracing, comprising: The raw signal and operating environment data of the wireless bridge are collected. The operating environment data includes frequency spectrum data, obstacle distribution data, and real-time signal strength profile data. Using the frequency spectrum data, the signal interference components of the wireless bridge are identified. Based on the obstacle distribution data, the signal blocking area of ​​the wireless bridge is identified, and the signal attenuation characteristics of the signal blocking area on the wireless bridge are analyzed. Based on the signal attenuation characteristics and the profile data, the signal quality vector of the wireless bridge is constructed. Using the original signal, the signal interference components, and the signal quality vector, an interference feature matrix of the wireless bridge is constructed. Using the interference feature matrix, interference sources of the wireless bridge are traced to obtain interference factors. The interference factors include the spatial location, frequency band distribution, and intensity variation of the interference source. Based on the interference factors, a candidate transmission path set for the wireless bridge is constructed, the transmission stability of each path in the candidate transmission path set is calculated, and based on the transmission stability, an optimized transmission path for the wireless bridge is selected from the candidate transmission path set. The optimized transmission path is then used to perform signal transmission to the wireless bridge.

[0006] Optionally, the transmission stability of each path in the candidate transmission path set is calculated, including: Identify the path feature parameters of each path in the candidate transmission path set, and perform parameter normalization on the path feature parameters to obtain standard parameters; The intensity variation pattern of interference factors in each path of the candidate transmission path set is queried, and the standard parameters are adaptively weighted according to the intensity variation pattern to obtain a weighted path feature vector; The transmission stability of each path in the candidate transmission path set is calculated using the weighted path feature vector.

[0007] Optionally, the transmission stability of each path in the candidate transmission path set is calculated, including: Using the weighted path feature vector, the link stability of each path in the candidate transmission path set is calculated; Based on the link stability, the path stability of each path in the candidate transmission path set is calculated; The frequency-distance coupling factor of the interference source corresponding to the wireless bridge is analyzed, and the path stability is weighted and adjusted according to the frequency-distance coupling factor to obtain the transmission stability.

[0008] Optionally, analyzing the signal attenuation characteristics of the signal blocking area on the wireless bridge includes: Identify the spatial point cloud data of the signal obstruction area, and use the spatial point cloud data to construct a three-dimensional model of the obstruction in the communication environment corresponding to the wireless bridge; Using the aforementioned occlusion 3D model, the direct propagation path and diffraction path of the transmitted signal corresponding to the wireless bridge are identified; Calculate the free space loss of the direct propagation path and the additional loss of the diffraction path, and use the free space loss and the additional loss to construct a signal attenuation characteristic map of the signal blocking region; The signal attenuation characteristics of the wireless bridge are identified using the signal attenuation characteristic map.

[0009] Optionally, the interference source of the wireless bridge can be traced using the interference feature matrix to obtain the interference factors, including: The interference feature matrix is ​​then processed into a grid to obtain a feature grid; Extract the mean signal strength, peak interference power, and signal quality quantization value of each grid cell in the feature grid to obtain the grid feature set; Anomaly detection is performed on the grid feature set to construct an anomaly spatiotemporal distribution map of the wireless bridge based on the anomaly detection results. Based on the aforementioned abnormal spatiotemporal distribution map, the spatial orientation, frequency band distribution, and intensity variation patterns of the interference sources of the wireless bridge are analyzed to identify the interference factors.

[0010] Optionally, using the original signal, the signal interference components, and the signal quality vector, an interference feature matrix of the wireless bridge is constructed, including: The characteristic parameters of the original signal are identified, and the characteristic parameters are arranged in a time series to generate a feature sequence of the original signal. The characteristic parameters include instantaneous signal-to-noise ratio, phase jitter value, packet loss rate, and adjacent channel leakage ratio. The signal interference components are subjected to data structuring processing to obtain an interference feature sequence; The original signal feature sequence, the interference feature sequence, and the signal quality vector are aligned in time and space to obtain a three-dimensional aligned dataset. Using the three-dimensional aligned dataset, an interference feature matrix of the wireless bridge is constructed.

[0011] Optionally, based on the signal attenuation characteristics and the profile data, a signal quality vector for the wireless bridge is constructed, including: The maximum, minimum, and average signal strength of the real-time signal corresponding to the wireless bridge are identified using the profile data. Based on the maximum and minimum signal strength values, the signal strength fluctuation range of the wireless bridge is calculated. Utilizing the aforementioned signal attenuation characteristics, the signal attenuation coefficient of the wireless bridge is analyzed; Based on the average signal strength, the signal strength fluctuation range, and the signal attenuation coefficient, the signal quality vector of the wireless bridge is constructed.

[0012] Optionally, using the frequency spectrum data, identifying the signal interference components of the wireless bridge includes: The frequency spectrum data is subjected to a short-time Fourier transform to obtain a time-frequency spectrum. The narrowband interference component, background noise interference component, and impulse interference component of the wireless bridge are identified using the time-spectrum diagram. The signal interference components are obtained by analyzing the center frequency and bandwidth of the narrowband interference component, the average power spectral density of the background noise interference component, and the occurrence time, frequency and peak energy of the pulse interference component.

[0013] Optionally, based on the interference factors, a candidate transmission path set for the wireless bridge is constructed, including: Based on the aforementioned interference factors, the interference avoidance area of ​​the wireless bridge is analyzed, and the basic path range of the wireless bridge is determined according to the interference avoidance area. Based on the basic path range and signal attenuation characteristics, obstacles to signal transmission are screened for the wireless bridge to obtain a set of feasible paths; Interference adaptability verification is performed on each path in the feasible path set, and the feasible path set is filtered according to the verification results of the interference adaptability verification to obtain a candidate transmission path set.

[0014] To address the aforementioned problems, this invention also provides a wireless bridge signal transmission optimization system based on multi-dimensional interference tracing, the system comprising: The signal acquisition module is used to acquire the raw signal and operating environment data of the wireless bridge. The operating environment data includes frequency spectrum data, obstacle distribution data, and real-time signal strength profile data. The signal quality analysis module is used to identify the signal interference components of the wireless bridge using the frequency spectrum data, identify the signal blocking area of ​​the wireless bridge based on the obstacle distribution data, analyze the signal attenuation characteristics of the signal blocking area on the wireless bridge, and construct the signal quality vector of the wireless bridge based on the signal attenuation characteristics and the profile data. The interference tracing module is used to construct the interference feature matrix of the wireless bridge using the original signal, the signal interference components and the signal quality vector, and to use the interference feature matrix to trace the interference source of the wireless bridge to obtain the interference factors. The interference factors include the spatial location, frequency band distribution and intensity variation law of the interference source. The signal transmission optimization module is used to construct a set of candidate transmission paths for the wireless bridge based on the interference factors, calculate the transmission stability of each path in the set of candidate transmission paths, select an optimized transmission path for the wireless bridge from the set of candidate transmission paths based on the transmission stability, and use the optimized transmission path to perform signal transmission to the wireless bridge.

[0015] This invention first captures the "original state" and "external influencing factors" of wireless bridge signal transmission, providing "source data support" for all subsequent optimization stages. Next, it uses frequency spectrum data to identify signal interference components, then analyzes signal attenuation characteristics based on obstacle distribution data. Finally, it combines attenuation characteristics with profile data to construct a signal quality vector, decomposing the signal transmission problem from the two dimensions of "interference" and "attenuation," and achieving quantitative description. Then, it constructs an interference feature matrix: the instantaneous signal-to-noise ratio, phase jitter, and other features of the original signal are sorted by time to generate a signal feature sequence. The interference components are then structured using a "type-parameter-value" approach to obtain the interference feature sequence. These two features are then aligned with the signal quality vector along the "time-space" dimension to form a three-dimensional dataset. This dataset is then filled into a matrix with "time nodes as rows and feature parameters as columns," thus accurately pinpointing the source of interference, moving from "knowing there is interference" to "knowing where the interference is, what frequency it is, and how it changes." Furthermore, this invention first constructs candidate signal transmission paths, then calculates link stability (interference resistance of local sub-links) and path stability (weighted average stability level of the entire path), and finally adjusts the transmission stability using a frequency-distance coupling factor (combining frequency difference and distance). Based on this stability, a path is selected, thereby ensuring that the selected path can avoid interference to the greatest extent, reduce attenuation, and guarantee transmission stability. Therefore, this invention can improve the stability of wireless bridge signal optimization. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a wireless bridge signal transmission optimization method based on multi-dimensional interference tracing, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a module for implementing the wireless bridge signal transmission optimization method based on multi-dimensional interference tracing, as provided in an embodiment of the present invention.

[0017] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for optimizing wireless bridge signal transmission based on multi-dimensional interference tracing. The executing entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a wireless bridge signal transmission optimization method based on multi-dimensional interference tracing according to an embodiment of the present invention. In this embodiment, the wireless bridge signal transmission optimization method based on multi-dimensional interference tracing includes: S1. Collect the raw signal and operating environment data of the wireless bridge. The operating environment data includes frequency spectrum data, obstacle distribution data, and real-time signal strength profile data.

[0021] This invention, through the collection of raw signal and operating environment data from wireless bridges, can comprehensively grasp the original state of signal transmission and external environmental influencing factors, thereby providing users with source data support for signal optimization.

[0022] The wireless bridge refers to a device used to realize wireless data transmission between two or more networks (such as a campus LAN or an outdoor monitoring network). It is usually composed of a transmitter and a receiver and has functions such as signal modulation and demodulation and channel adaptation. The raw signal refers to the unprocessed electrical signal sequence in the transmission channel directly captured by the wireless bridge receiver through the signal acquisition module. The frequency spectrum data refers to the frequency distribution of the wireless bridge transmission channel in the frequency band (such as 2.4GHz or 5.8GHz), which can reflect information such as signal power and interference signal distribution range at different frequency points. The obstacle distribution data refers to physical obstacles on the transmission path between the wireless bridge transmitter and receiver that may block or weaken the signal. The real-time signal strength profile data refers to the sequence of signal strength values ​​(such as RSSI) continuously measured and recorded over a period of time as a function of time or spatial location.

[0023] In practice, a signal acquisition module (such as an RF signal acquisition card) can be deployed at the receiving end of the wireless bridge to capture the original electrical signals in the transmission channel in real time. At the same time, a spectrum analyzer is used to scan the channel frequency band to obtain frequency spectrum data, a laser rangefinder is used in conjunction with environmental mapping to determine obstacle distribution data, and signal strength is collected at 5-10m intervals along the transmission path to generate profile data. The data are then integrated to obtain the operating environment data.

[0024] S2. Using the frequency spectrum data, identify the signal interference components of the wireless bridge; based on the obstacle distribution data, identify the signal blocking area of ​​the wireless bridge; analyze the signal attenuation characteristics of the signal blocking area on the wireless bridge; and construct the signal quality vector of the wireless bridge based on the signal attenuation characteristics and the profile data.

[0025] By utilizing the frequency spectrum data, this invention can accurately locate the interference signal components (such as co-channel interference and adjacent channel interference) present in the wireless bridge transmission, and clarify the frequency range and intensity of the interference.

[0026] The signal interference components refer to various abnormal signal components in the wireless bridge transmission channel that can negatively affect the signal transmission quality, mainly including co-channel interference, adjacent channel interference, and burst pulse interference.

[0027] As an embodiment of the present invention, identifying the signal interference components of the wireless bridge using the frequency spectrum data includes: The frequency spectrum data is subjected to a short-time Fourier transform to obtain a time-frequency spectrum. The narrowband interference component, background noise interference component, and impulse interference component of the wireless bridge are identified using the time-spectrum diagram. The signal interference components are obtained by analyzing the center frequency and bandwidth of the narrowband interference component, the average power spectral density of the background noise interference component, and the occurrence time, frequency and peak energy of the pulse interference component.

[0028] The narrowband interference component refers to the interference signal component that has a narrow bandwidth (usually ≤2MHz), stable power, and continuous existence within the operating frequency band of the wireless bridge. The background noise interference component refers to the broadband interference component that covers the entire operating frequency band, formed by environmental electromagnetic radiation (such as power grid clutter and leakage signals from electronic devices) within the transmission channel of the wireless bridge. The pulse interference component refers to the transient interference signal component that suddenly appears within the operating frequency band of the wireless bridge, has a very short duration (usually ≤10ms), and a sudden increase in power (usually 20-40dB higher than the background noise). It is mostly generated by the switching action of equipment (such as microwave oven starting and motor starting and stopping) and will intermittently impact signal transmission.

[0029] In detail, when identifying signal interference components, different types can be distinguished by setting thresholds: narrowband interference is a continuous signal with power exceeding the normal signal by 10dB and bandwidth ≤2MHz; background noise interference is a broadband signal with power fluctuation ≤3dBm; pulse interference is a transient signal with power surge ≥20dBm and duration ≤10ms; background noise is obtained by calculating the mean power spectral density within a 1MHz bandwidth; pulse interference is obtained by recording the occurrence time, corresponding frequency point, and peak energy through time-domain peak detection.

[0030] Furthermore, this embodiment of the invention identifies signal obstruction areas of the wireless bridge based on the obstacle distribution data, and analyzes the signal attenuation characteristics of the signal obstruction areas on the wireless bridge to pinpoint areas that obstruct the signal in the transmission path, and clarifies the degree of signal attenuation by different obstacles.

[0031] As an embodiment of the present invention, analyzing the signal attenuation characteristics of the signal blocking area on the wireless bridge includes: Identify the spatial point cloud data of the signal obstruction area, and use the spatial point cloud data to construct a three-dimensional model of the obstruction in the communication environment corresponding to the wireless bridge; Using the aforementioned occlusion 3D model, the direct propagation path and diffraction path of the transmitted signal corresponding to the wireless bridge are identified; Calculate the free space loss of the direct propagation path and the additional loss of the diffraction path, and use the free space loss and the additional loss to construct a signal attenuation characteristic map of the signal blocking region; The signal attenuation characteristics of the wireless bridge are identified using the signal attenuation characteristic map.

[0032] The direct propagation path refers to the signal propagation path from the signal transmitter to the signal receiver without any obstacles. The diffraction path refers to the path where the signal of the wireless bridge bypasses the edge of the obstacle (such as a wall corner or device edge) and continues to propagate to the receiver. The free space loss refers to the energy attenuation of the wireless bridge signal in the direct propagation path caused solely by the diffusion of the signal in free space. The additional loss refers to the extra energy attenuation of the wireless bridge signal in the diffraction path caused by the diffraction phenomenon caused by bypassing obstacles. The signal attenuation characteristic diagram refers to a chart that can intuitively reflect the degree of signal attenuation in different areas.

[0033] In detail, point cloud data can be obtained by scanning the communication environment using lidar, and then a 3D model of the occlusion can be constructed using CAD tools. Ray tracing can be used to identify direct and diffracted paths. The free space loss calculation formula is as follows: ; in, Indicates free space loss. Indicates the path length. Indicates the signal wavelength; Diffraction loss is calculated using the Knife-Edge model, and the calculation formula is as follows: ; This indicates the difference between the obstacle height and the ray height. This indicates the path length from the obstacle to the wireless bridge transmitter. This indicates the path length from the obstacle to the receiver of the wireless bridge. Indicates the signal wavelength; After superimposing the free space loss and diffraction loss to obtain the total path loss, the total loss value of each grid area is marked according to a 1m×1m grid to form a loss value distribution map, which is the signal attenuation characteristic map. When using the signal attenuation characteristic map to identify attenuation characteristics, the loss value of each grid in the map can be extracted, and the average loss, loss standard deviation and loss gradient of the obstructed area (such as the grid where the concrete wall is located) can be statistically analyzed. The material and thickness of the corresponding obstacle can be associated to form signal attenuation characteristic data of "location-material-loss value".

[0034] Furthermore, this embodiment of the invention, by constructing a signal quality vector for the wireless bridge based on the signal attenuation characteristics and the profile data, combines the attenuation characteristics (such as attenuation) of the signal obstruction area with the real-time signal strength profile data along the path, transforming it into a quantified signal quality vector. This visually presents the signal quality level at each location along the transmission path, providing a quantitative basis for subsequent identification of weak signal areas. For example, in an outdoor bridge scenario, by combining the 20dBm attenuation characteristic of the tree-obstructed area with the corresponding -75dBm strength value in the profile data, a vector containing "location-attenuation-real-time strength" can be constructed, clearly locating a weak signal quality segment at 100-120m.

[0035] The signal quality vector refers to a multi-dimensional data vector constructed based on the profile data and signal attenuation characteristics of the real-time signal of the wireless bridge, used to quantify the signal quality status. As an embodiment of the present invention, based on the signal attenuation characteristics and the profile data, a signal quality vector of the wireless bridge is constructed, including: The maximum, minimum, and average signal strength of the real-time signal corresponding to the wireless bridge are identified using the profile data. Based on the maximum and minimum signal strength values, the signal strength fluctuation range of the wireless bridge is calculated. Utilizing the aforementioned signal attenuation characteristics, the signal attenuation coefficient of the wireless bridge is analyzed; Based on the average signal strength, the signal strength fluctuation range, and the signal attenuation coefficient, the signal quality vector of the wireless bridge is constructed.

[0036] In detail, 100 sets of signal strength data can be continuously collected at 100ms intervals from the profile data. The maximum value (e.g., -45dBm) and minimum value (e.g., -65dBm) are then extracted, and the average value (e.g., -55dBm) is calculated using an arithmetic mean. The fluctuation range (maximum value minus minimum value) is also calculated. The total loss value of three sampling points at different distances (e.g., 10m, 20m, 30m) is extracted from the signal attenuation characteristic map, and the signal attenuation coefficient (e.g., 1.2dB / m) is calculated using linear fitting. A signal quality vector is constructed in the order of "[average signal strength, signal strength fluctuation range, signal attenuation coefficient]". For example, the signal quality vector of a factory network bridge can be represented as [-55dBm, 20dBm, 1.2dB / m], clearly reflecting the core characteristics of the signal.

[0037] S3. Using the original signal, the signal interference components, and the signal quality vector, construct the interference feature matrix of the wireless bridge. Using the interference feature matrix, trace the interference source of the wireless bridge to obtain the interference factors. The interference factors include the spatial location, frequency band distribution, and intensity variation law of the interference source.

[0038] This invention, by utilizing the original signal, the signal interference components, and the signal quality vector, constructs an interference feature matrix for the wireless bridge. This integrates the transmission state information of the original signal, the interference characteristics of the signal interference components, and the path quality data of the signal quality vector to form a structured interference feature matrix, thereby achieving a multi-dimensional quantitative description of the wireless bridge transmission environment.

[0039] The interference feature matrix refers to a two-dimensional matrix constructed by aligning the original signal feature sequence, interference feature sequence, and signal quality vector based on the wireless bridge in a time-space dimension. It is used to comprehensively integrate and quantify the original signal characteristics and interference status at different time and space nodes.

[0040] As an embodiment of the present invention, the interference feature matrix of the wireless bridge is constructed using the original signal, the signal interference components, and the signal quality vector, including: The characteristic parameters of the original signal are identified, and the characteristic parameters are arranged in a time series to generate a feature sequence of the original signal. The characteristic parameters include instantaneous signal-to-noise ratio, phase jitter value, packet loss rate, and adjacent channel leakage ratio. The signal interference components are subjected to data structuring processing to obtain an interference feature sequence; The original signal feature sequence, the interference feature sequence, and the signal quality vector are aligned in time and space to obtain a three-dimensional aligned dataset. Using the three-dimensional aligned dataset, an interference feature matrix of the wireless bridge is constructed.

[0041] Wherein, the instantaneous signal-to-noise ratio refers to the ratio of the useful signal power to the noise power of the original signal at a certain instant in the scenario of original signal analysis of the wireless bridge; the phase jitter value refers to the random fluctuation deviation of the phase of the original signal of the wireless bridge over time; the packet loss rate refers to the ratio of the number of data packets that are not successfully received by the receiver to the total number of data packets sent by the transmitter during the data transmission process of the wireless bridge; and the adjacent channel leakage ratio refers to the ratio of the power of the original signal of the wireless bridge in its operating frequency band (main frequency band) to the power leaked into the adjacent frequency band (adjacent frequency band).

[0042] In detail, the characteristic parameters of the original signal can be extracted first: instantaneous signal-to-noise ratio, phase jitter, packet loss rate, and adjacent channel leakage. Then, the original signal characteristic sequence is generated by sorting the data according to the acquisition time. The signal interference components are organized according to "interference type-core parameter-value" (e.g., "narrowband interference-center frequency-2.437GHz") to generate an interference characteristic sequence. Based on the original signal acquisition time (500ms per node), the three types of data are aligned according to the time-space dimension (e.g., a signal parameter corresponding to a specific spatial grid at a certain time node) to form a three-dimensional aligned dataset. The data is filled in (0 is filled if there is no data) with the time node as the row and the signal, interference, and quality parameters as the columns to construct an interference characteristic matrix. For example, the value of the 36th row (corresponding to the 3rd minute) and the 5th column (narrowband interference peak power) of the matrix can be -40dBm.

[0043] Furthermore, this embodiment of the invention utilizes the interference feature matrix to trace the interference source of the wireless bridge, accurately pinpointing the interference source and key characteristics affecting the wireless bridge's transmission, clarifying the essential cause of the interference, and providing a targeted basis for targeted interference. For example, in the wireless monitoring network of a coking plant, by analyzing the interference feature matrix, the interference source can be traced and identified as a large converter in the northwest corner of the plant area. The interference is mainly concentrated at the 2.412 GHz frequency point, and the intensity peaks every 15 minutes with the equipment's operating cycle, thus clarifying the physical location and spectral pattern of the interference.

[0044] The interference factors include the spatial location, frequency band distribution, and intensity variation pattern of the interference source. Furthermore, the frequency band distribution refers to the range of wireless communication frequencies occupied or affected by the interference source, which is a key parameter for clarifying the distribution characteristics of the interference signal in the spectral dimension. The intensity variation pattern refers to the fluctuation range, duration, and trend of the interference power of the interference source at different time points, which is the core content reflecting the dynamic characteristics of the interference source intensity.

[0045] As an embodiment of the present invention, the interference feature matrix is ​​used to trace the source of interference in the wireless bridge to obtain the interference factors, including: The interference feature matrix is ​​then processed into a grid to obtain a feature grid; Extract the mean signal strength, peak interference power, and signal quality quantization value of each grid cell in the feature grid to obtain the grid feature set; Anomaly detection is performed on the grid feature set to construct an anomaly spatiotemporal distribution map of the wireless bridge based on the anomaly detection results. Based on the aforementioned abnormal spatiotemporal distribution map, the spatial orientation, frequency band distribution, and intensity variation patterns of the interference sources of the wireless bridge are analyzed to identify the interference factors.

[0046] The aforementioned abnormal spatiotemporal distribution map refers to a visual map that simultaneously embodies three-dimensional information of "time-space-abnormal state" after the grid feature set is processed by the interference feature matrix and abnormal grids are screened out by outlier detection (such as the 3σ criterion).

[0047] In detail, the interference feature matrix is ​​divided into feature grids based on 1-minute time units and 5m×5m spatial units (e.g., a 10-minute×50m×50m area can be divided into 10×100 grids). The mean signal strength, peak interference power, and signal quality quantization value (calculated by weighting the signal-to-noise ratio and packet loss rate, with a maximum score of 100) of each grid are extracted to form a grid feature set. Anomaly detection is performed using the 3σ criterion (e.g., a mean signal quality quantization value of 75 points and a standard deviation of 8 points, with values ​​exceeding 51-99 points considered abnormal). Abnormal grids are marked and an abnormal spatiotemporal distribution map is drawn (red indicates abnormality, blue indicates normality). When analyzing interference factors, the concentrated area of ​​abnormal grids (e.g., the equipment area on the west side of the factory) is the spatial location of the interference source, the interference frequency band corresponding to the abnormal grids (e.g., 2.430-2.432GHz) is the frequency band distribution, and the fluctuation of the peak interference power over time (e.g., -42 to -38dBm) is the intensity variation pattern. Finally, these are integrated into the interference factors.

[0048] S4. Based on the interference factors, construct a candidate transmission path set for the wireless bridge, calculate the transmission stability of each path in the candidate transmission path set, select an optimized transmission path for the wireless bridge from the candidate transmission path set based on the transmission stability, and use the optimized transmission path to perform signal transmission to the wireless bridge.

[0049] This invention, by constructing a candidate transmission path set for the wireless bridge based on the interference factors, can plan multiple potential transmission paths that avoid areas with strong interference and high obstruction, thereby providing diverse options for subsequent selection of the optimal path. For example, in a factory setting, based on the location and frequency band of the machine tool interference source, three paths that bypass the interference source and have less obstruction can be constructed to avoid the lack of alternative solutions when a single path is interfered with.

[0050] As an embodiment of the present invention, constructing a candidate transmission path set for the wireless bridge based on the interference factors includes: Based on the aforementioned interference factors, the interference avoidance area of ​​the wireless bridge is analyzed, and the basic path range of the wireless bridge is determined according to the interference avoidance area. Based on the basic path range and signal attenuation characteristics, obstacles to signal transmission are screened for the wireless bridge to obtain a set of feasible paths; Interference adaptability verification is performed on each path in the feasible path set, and the feasible path set is filtered according to the verification results of the interference adaptability verification to obtain a candidate transmission path set.

[0051] Specifically, based on the location of the interference source and the maximum interference radius among the interference factors, an interference avoidance area (such as a circular area centered on the interference source with a radius of 1.5 times the interference radius) can be defined. Using the straight line connecting the transmitter and receiver of the bridge as a reference, road segments falling into the avoidance area are eliminated to determine the basic path range. Obstacle attenuation values ​​within the basic range are extracted from the signal attenuation characteristic data, and a total attenuation threshold of ≤40dB is set. Paths exceeding the threshold are eliminated to obtain a set of feasible paths. When verifying the interference adaptability of each path in the set of feasible paths, verification conditions can be set first, such as the minimum distance between the feasible path and the interference source being ≥15m and the signal frequency band overlap being ≤10%. Paths that meet the conditions are candidate transmission paths.

[0052] Furthermore, by calculating the transmission stability of each path in the candidate transmission path set, this embodiment of the invention can provide objective data for judging the merits of the paths, avoiding the bias of selecting paths based solely on subjective experience, and thus ensuring that paths with strong anti-interference and low attenuation can be accurately selected in the future.

[0053] The transmission stability refers to an indicator used to quantify the ability of a path to ensure reliable transmission of wireless bridge signals under interference conditions.

[0054] As an embodiment of the present invention, calculating the transmission stability of each path in the candidate transmission path set includes: Identify the path feature parameters of each path in the candidate transmission path set, and perform parameter normalization on the path feature parameters to obtain standard parameters; The intensity variation pattern of interference factors in each path of the candidate transmission path set is queried, and the standard parameters are adaptively weighted according to the intensity variation pattern to obtain a weighted path feature vector; The transmission stability of each path in the candidate transmission path set is calculated using the weighted path feature vector.

[0055] Specifically, the standard parameters can first identify the path characteristic parameters of each path in the candidate transmission path set. Core parameters include the total path attenuation (calculated based on signal weakening characteristics, e.g., path 1 has a total attenuation of 35dB, path 2 has a total attenuation of 40dB in a factory area), the minimum distance between the path and the interference source (e.g., path 1 is 20m from the interference source, path 2 is 18m from the interference source), and the signal frequency band overlap (e.g., path 1 has 5% overlap with the interference frequency band, path 2 has 8% overlap). Then, the parameters are processed using a Min-Max normalization algorithm. When adaptively weighting the standard parameters, the intensity variation pattern of the interference factors corresponding to each path can be queried first, such as the intensity fluctuation range of the interference source in the factory area. For interference ranging from 42 to -38 dBm and lasting for 3 minutes (belonging to "intermittent medium-strong interference"), the parameter weighting rules under different interference intensities are first set: in the intermittent medium-strong interference scenario, "minimum distance between path and interference source" has the highest weight (0.4), followed by "total attenuation value" (0.3), and "signal frequency band overlap" has the lowest weight (0.3); then the standard parameters of each path are multiplied by the corresponding weight to obtain the weighted parameter values, which are arranged in the order of "[weighted total attenuation value, weighted distance, weighted frequency band overlap]" to form a weighted path feature vector.

[0056] Preferably, the step of calculating the transmission stability of each path in the candidate transmission path set using the weighted path feature vector includes: Using the weighted path feature vector, the link stability of each path in the candidate transmission path set is calculated; Based on the link stability, the path stability of each path in the candidate transmission path set is calculated; The frequency-distance coupling factor of the interference source corresponding to the wireless bridge is analyzed, and the path stability is weighted and adjusted according to the frequency-distance coupling factor to obtain the transmission stability.

[0057] The link stability refers to the parameter quantifying the anti-interference and anti-attenuation capabilities of local sub-links (such as segmented paths divided into 10m intervals) in the candidate transmission path of a wireless bridge, obtained by linear weighted summation based on weighted path characteristic parameters (total attenuation coefficient, interference source distance adaptation value, and frequency band isolation). It primarily reflects the basic transmission reliability of the sub-links. The path stability refers to the parameter quantifying the overall transmission stability level of the "entire path" by weighted average calculation based on the link stability of all local sub-links of the candidate transmission path, combined with the length weight of each sub-link (the longer the sub-link, the greater its impact on the overall path). It primarily reflects the overall transmission reliability of the entire path.

[0058] In practical implementation, transmission stability can be determined by first identifying the core factors affecting the frequency-distance coupling factor: first, the difference between the center frequency of the interference source and the operating frequency of the wireless bridge (the greater the difference, the stronger the resistance to frequency interference); second, the minimum distance between the path and the interference source (the greater the distance, the stronger the resistance to spatial interference). Then, considering the characteristics of the scenario (such as a multi-device interference environment in an industrial park), the frequency difference value is measured using a signal analyzer, and the distance value is obtained using a positioning tool. A comprehensive evaluation is then performed according to the principle that "distance has a higher weight than frequency difference" (e.g., distance weight 0.6, frequency difference weight 0.4) to obtain the frequency-distance coupling factor (the larger the value, the lower the coupling degree and the stronger the resistance to interference). Finally, this factor is used to adjust the path stability with weights: a high coupling factor increases the path stability (e.g., path stability 0.9, coupling factor 1.2, adjusted to 1.0), while a low coupling factor decreases the path stability (e.g., path stability 0.9, coupling factor 0.8, adjusted to 0.72). The final result is the transmission stability.

[0059] Furthermore, as another embodiment of the present invention, the formula for calculating link stability is as follows: ; in, Indicates link stability. This represents the average signal strength of link i. This represents the variance of the signal strength of link i. This represents the physical length of link i. This represents the historical average packet loss rate of link i. These are weighting coefficients that are dynamically adjusted based on the intensity variation patterns of the aforementioned interfering factors.

[0060] It should be noted that the formula for calculating link stability integrates core characteristics such as the link's average signal strength, signal strength variance, physical length, and historical average packet loss rate to comprehensively evaluate the link's stability. Each characteristic reflects the link's fundamental signal quality, signal fluctuation, the impact of transmission distance on attenuation, and historical transmission reliability. Furthermore, the weights of each characteristic are dynamically adjusted based on the variation in interference intensity, making the evaluation results more closely reflect the actual stability of the link under real-world interference conditions. Through comprehensive consideration of multi-dimensional characteristics and dynamic weight adjustment, this formula can more accurately quantify the link's stability capability, reflecting both the signal's inherent quality and fluctuation characteristics, as well as the impact of transmission distance and historical transmission performance. It also adapts to the differences in interference intensity scenarios, and the final output link stability value can effectively distinguish the superiority or inferiority of different links, providing a reliable basis for subsequent path stability calculations and optimized path selection.

[0061] Furthermore, as another embodiment of the present invention, the formula for calculating path stability is as follows: ; in, This represents the path stability of path p, where n represents the total number of links in path p. Indicates link stability. This represents the historical average continuous connectivity time of path p.

[0062] It should be noted that the core of the path stability calculation formula is to comprehensively consider the "individual link stability foundation" and "overall historical connectivity performance" of the path: First, the stability of each link within the path is considered, using the stability of a single link as the basis for evaluating path stability; then, the path's historical average continuous connectivity time is incorporated, because the stability of individual links cannot fully represent the overall continuous transmission capability of the path, while historical connectivity time reflects whether the path is prone to interruptions in long-term operation. Combining the two comprehensively covers the current link status and historical operating characteristics of the path, thus arriving at a comprehensive judgment on the degree of path stability. This calculation method avoids the limitations of relying solely on link stability and ignoring the overall continuous transmission capability of the path, and also makes up for the shortcomings of only looking at historical connectivity time without considering the current link status, making the assessment of path stability more closely aligned with actual transmission scenarios. The path stability results obtained in this way can more accurately distinguish the overall reliability of different paths. For example, a path with moderate individual link stability but a long historical continuous connectivity time may have better overall stability performance than a path with high link stability but frequent interruptions, providing a more comprehensive and reliable quantitative basis for subsequent wireless bridge optimization of transmission path selection.

[0063] Furthermore, this embodiment of the invention selects the optimal transmission path for the wireless bridge from the candidate transmission path set based on transmission stability. This selection uses transmission stability as the core screening criterion, locking in the path that best avoids interference and reduces signal attenuation, ensuring the reliability and efficiency of the wireless bridge transmission. For example, in a park's candidate paths, a path with a transmission stability of 0.95 (higher than other paths by 0.1-0.2) that avoids substation interference is selected, ensuring continuous transmission of monitoring data.

[0064] Specifically, the path with the highest transmission stability value can be selected from the set of candidate transmission paths as the optimized transmission path for the wireless bridge.

[0065] By utilizing the optimized transmission path, this embodiment of the invention applies the selected optimized transmission path to the actual signal transmission of the wireless bridge, allowing data to be transmitted along an interference-resistant and low-attenuation path, thereby directly improving the stability and efficiency of signal transmission.

[0066] Specifically, you can first configure the wireless bridge's operating frequency band, transmit power, and other parameters to adapt it to the characteristics of the optimized transmission path; then start signal transmission and monitor it in real time to ensure that the data is transmitted stably along the optimized path, and quickly adjust it based on the path characteristics if fluctuations occur.

[0067] like Figure 2 The diagram shown is a functional block diagram of the wireless bridge signal transmission optimization system based on multi-dimensional interference tracing according to the present invention.

[0068] The wireless bridge signal transmission optimization system 200 based on multi-dimensional interference tracing described in this invention can be installed in an electronic device. Depending on the functions implemented, the wireless bridge signal transmission optimization system based on multi-dimensional interference tracing may include a signal acquisition module 201, a signal quality analysis module 202, an interference tracing module 203, and a signal transmission optimization module 204. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0069] In this embodiment of the invention, the functions of each module / unit are as follows: The signal acquisition module 201 is used to acquire the raw signal and operating environment data of the wireless bridge. The operating environment data includes frequency spectrum data, obstacle distribution data and real-time signal strength profile data. The signal quality analysis module 202 is used to identify the signal interference components of the wireless bridge using the frequency spectrum data, identify the signal blocking area of ​​the wireless bridge based on the obstacle distribution data, analyze the signal attenuation characteristics of the signal blocking area on the wireless bridge, and construct the signal quality vector of the wireless bridge based on the signal attenuation characteristics and the profile data. The interference tracing module 203 is used to construct the interference feature matrix of the wireless bridge using the original signal, the signal interference component and the signal quality vector, and to use the interference feature matrix to trace the interference source of the wireless bridge to obtain the interference factors. The interference factors include the spatial orientation, frequency band distribution and intensity variation law of the interference source. The signal transmission optimization module 204 is used to construct a set of candidate transmission paths for the wireless bridge based on the interference factors, calculate the transmission stability of each path in the set of candidate transmission paths, select an optimized transmission path for the wireless bridge from the set of candidate transmission paths based on the transmission stability, and use the optimized transmission path to perform signal transmission to the wireless bridge.

[0070] In detail, the modules in the wireless bridge signal transmission optimization system 200 based on multi-dimensional interference tracing described in this embodiment of the invention employ the same methods as described above. Figure 1 The method used here is the same as the wireless bridge signal transmission optimization method based on multi-dimensional interference tracing described above, and can produce the same technical effect, so it will not be repeated here.

[0071] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0072] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A wireless bridge signal transmission optimization method based on multi-dimensional interference tracing, characterized in that, The method includes: The raw signal and operating environment data of the wireless bridge are collected. The operating environment data includes frequency spectrum data, obstacle distribution data, and real-time signal strength profile data. Using the frequency spectrum data, the signal interference components of the wireless bridge are identified. Based on the obstacle distribution data, the signal blocking area of ​​the wireless bridge is identified, and the signal attenuation characteristics of the signal blocking area on the wireless bridge are analyzed. Based on the signal attenuation characteristics and the profile data, the signal quality vector of the wireless bridge is constructed. Using the original signal, the signal interference components, and the signal quality vector, an interference feature matrix of the wireless bridge is constructed. Using the interference feature matrix, interference sources of the wireless bridge are traced to obtain interference factors. The interference factors include the spatial location, frequency band distribution, and intensity variation of the interference source. Based on the interference factors, a candidate transmission path set for the wireless bridge is constructed, the transmission stability of each path in the candidate transmission path set is calculated, and based on the transmission stability, an optimized transmission path for the wireless bridge is selected from the candidate transmission path set. The optimized transmission path is then used to perform signal transmission to the wireless bridge.

2. The wireless bridge signal transmission optimization method based on multi-dimensional interference tracing as described in claim 1, characterized in that, Calculating the transmission stability of each path in the candidate transmission path set includes: Identify the path feature parameters of each path in the candidate transmission path set, and perform parameter normalization on the path feature parameters to obtain standard parameters; The intensity variation pattern of interference factors in each path of the candidate transmission path set is queried, and the standard parameters are adaptively weighted according to the intensity variation pattern to obtain a weighted path feature vector; The transmission stability of each path in the candidate transmission path set is calculated using the weighted path feature vector.

3. The wireless bridge signal transmission optimization method based on multi-dimensional interference tracing as described in claim 2, characterized in that, Calculating the transmission stability of each path in the candidate transmission path set includes: Using the weighted path feature vector, the link stability of each path in the candidate transmission path set is calculated; Based on the link stability, the path stability of each path in the candidate transmission path set is calculated; The frequency-distance coupling factor of the interference source corresponding to the wireless bridge is analyzed, and the path stability is weighted and adjusted according to the frequency-distance coupling factor to obtain the transmission stability.

4. The wireless bridge signal transmission optimization method based on multi-dimensional interference tracing as described in claim 1, characterized in that, The analysis of the signal attenuation characteristics of the wireless bridge by the signal blocking area includes: Identify the spatial point cloud data of the signal obstruction area, and use the spatial point cloud data to construct a three-dimensional model of the obstruction in the communication environment corresponding to the wireless bridge; Using the aforementioned occlusion 3D model, the direct propagation path and diffraction path of the transmitted signal corresponding to the wireless bridge are identified; Calculate the free space loss of the direct propagation path and the additional loss of the diffraction path, and use the free space loss and the additional loss to construct a signal attenuation characteristic map of the signal blocking region; The signal attenuation characteristics of the wireless bridge are identified using the signal attenuation characteristic map.

5. The wireless bridge signal transmission optimization method based on multi-dimensional interference tracing as described in claim 1, characterized in that, Using the interference feature matrix, interference sources are traced to the wireless bridge to obtain the interference factors, including: The interference feature matrix is ​​then processed into a grid to obtain a feature grid; Extract the mean signal strength, peak interference power, and signal quality quantization value of each grid cell in the feature grid to obtain the grid feature set; Anomaly detection is performed on the grid feature set to construct an anomaly spatiotemporal distribution map of the wireless bridge based on the anomaly detection results. Based on the aforementioned abnormal spatiotemporal distribution map, the spatial orientation, frequency band distribution, and intensity variation patterns of the interference sources of the wireless bridge are analyzed to identify the interference factors.

6. The wireless bridge signal transmission optimization method based on multi-dimensional interference tracing as described in claim 1, characterized in that, Using the original signal, the signal interference components, and the signal quality vector, an interference feature matrix of the wireless bridge is constructed, including: The characteristic parameters of the original signal are identified, and the characteristic parameters are arranged in a time series to generate a feature sequence of the original signal. The characteristic parameters include instantaneous signal-to-noise ratio, phase jitter value, packet loss rate, and adjacent channel leakage ratio. The signal interference components are subjected to data structuring processing to obtain an interference feature sequence; The original signal feature sequence, the interference feature sequence, and the signal quality vector are aligned in time and space to obtain a three-dimensional aligned dataset. Using the three-dimensional aligned dataset, an interference feature matrix of the wireless bridge is constructed.

7. The wireless bridge signal transmission optimization method based on multi-dimensional interference tracing as described in claim 1, characterized in that, Based on the signal attenuation characteristics and the profile data, the signal quality vector of the wireless bridge is constructed, including: The maximum, minimum, and average signal strength of the real-time signal corresponding to the wireless bridge are identified using the profile data. Based on the maximum and minimum signal strength values, the signal strength fluctuation range of the wireless bridge is calculated. Utilizing the aforementioned signal attenuation characteristics, the signal attenuation coefficient of the wireless bridge is analyzed; Based on the average signal strength, the signal strength fluctuation range, and the signal attenuation coefficient, the signal quality vector of the wireless bridge is constructed.

8. The wireless bridge signal transmission optimization method based on multi-dimensional interference tracing as described in claim 1, characterized in that, Using the frequency spectrum data, identifying the signal interference components of the wireless bridge includes: The frequency spectrum data is subjected to a short-time Fourier transform to obtain a time-frequency spectrum. The narrowband interference component, background noise interference component, and impulse interference component of the wireless bridge are identified using the time-spectrum diagram. The signal interference components are obtained by analyzing the center frequency and bandwidth of the narrowband interference component, the average power spectral density of the background noise interference component, and the occurrence time, frequency and peak energy of the pulse interference component.

9. The wireless bridge signal transmission optimization method based on multi-dimensional interference tracing as described in claim 1, characterized in that, Based on the aforementioned interference factors, a candidate transmission path set for the wireless bridge is constructed, including: Based on the aforementioned interference factors, the interference avoidance area of ​​the wireless bridge is analyzed, and the basic path range of the wireless bridge is determined according to the interference avoidance area. Based on the basic path range and signal attenuation characteristics, obstacles to signal transmission are screened for the wireless bridge to obtain a set of feasible paths; Interference adaptability verification is performed on each path in the feasible path set, and the feasible path set is filtered according to the verification results of the interference adaptability verification to obtain a candidate transmission path set.

10. A wireless bridge signal transmission optimization system based on multi-dimensional interference tracing, characterized in that, The system includes: The signal acquisition module is used to acquire the raw signal and operating environment data of the wireless bridge. The operating environment data includes frequency spectrum data, obstacle distribution data, and real-time signal strength profile data. The signal quality analysis module is used to identify the signal interference components of the wireless bridge using the frequency spectrum data, identify the signal blocking area of ​​the wireless bridge based on the obstacle distribution data, analyze the signal attenuation characteristics of the signal blocking area on the wireless bridge, and construct the signal quality vector of the wireless bridge based on the signal attenuation characteristics and the profile data. The interference tracing module is used to construct the interference feature matrix of the wireless bridge using the original signal, the signal interference components and the signal quality vector, and to use the interference feature matrix to trace the interference source of the wireless bridge to obtain the interference factors. The interference factors include the spatial location, frequency band distribution and intensity variation law of the interference source. The signal transmission optimization module is used to construct a set of candidate transmission paths for the wireless bridge based on the interference factors, calculate the transmission stability of each path in the set of candidate transmission paths, select an optimized transmission path for the wireless bridge from the set of candidate transmission paths based on the transmission stability, and use the optimized transmission path to perform signal transmission to the wireless bridge.

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