Spectrum monitoring equipment cooperative deployment method and system based on dynamic disturbance field response

Abstract

The embodiment of the application provides a spectrum monitoring equipment cooperative deployment method and system based on a dynamic disturbance field response, relates to the technical field of electromagnetic spectrum monitoring, and comprises the following steps: collecting heterogeneous environment situation data of a monitoring area to construct a dynamic disturbance intensity field, calling a plurality of path loss values and a plurality of interference intensity values of a plurality of fixed spectrum monitoring nodes to perform dynamic signal-to-interference-and-noise ratio threshold determination, reversely segmenting a monitoring coverage blind area and mapping a priority atlas of a region to be compensated; triggering an incremental decision engine iteration of spectrum monitoring cooperative deployment compensation in the priority atlas of the region to be compensated, obtaining a blind area fine tuning deployment scheme to reconstruct the compensation of the monitoring coverage blind area. The prior art usually adopts fixed monitoring point deployment to perform static spectrum monitoring, which leads to the problems that coverage deficiency or signal leakage failure is prone to occurring in a dynamic electromagnetic environment scene, and then the monitoring comprehensiveness and the anti-interference capability are affected.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic spectrum monitoring technology, and in particular to a method and system for the collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response. Background Technology

[0002] Existing technologies typically employ fixed monitoring point deployments for static spectrum monitoring, but this deployment method has significant drawbacks in dynamic electromagnetic environment scenarios.

[0003] Specifically, since the location and parameters of the monitoring equipment are fixed after deployment, they cannot be dynamically adjusted according to environmental changes, movement of interference sources, or spectrum usage. As a result, fixed monitoring points are prone to signal coverage blind spots when there are complex terrains, weather changes, or sudden interference sources. Some areas may become completely ineffective due to terrain obstruction or electromagnetic attenuation, resulting in incomplete monitoring coverage.

[0004] Meanwhile, static deployment lacks a dynamic assessment and avoidance mechanism for the risk of signal leakage. Especially in densely populated or sensitive areas, the signal from fixed monitoring points may be accidentally leaked to non-target areas, causing compliance risks such as privacy leaks or excessive electromagnetic interference.

[0005] Furthermore, when faced with rapidly changing interference sources, fixed monitoring points cannot adjust their positions in time to get closer to the interference source or avoid strong interference, resulting in a sharp drop in the signal-to-interference-plus-noise ratio, a drastic reduction in the effective monitoring range, and a serious lack of overall anti-interference capability and monitoring network resilience.

[0006] In conclusion, this static and unadaptive deployment mode of monitoring equipment is no longer able to meet the actual needs of modern electromagnetic spectrum monitoring for dynamic adaptability, full coverage, and high robustness.

[0007] It should be noted that the information disclosed in this background section is intended only to enhance the understanding of the overall background of the present invention, and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a collaborative deployment method and system for spectrum monitoring equipment based on dynamic disturbance field response. This solves the problem that existing technologies typically employ fixed monitoring point deployments for static spectrum monitoring, which can easily lead to insufficient coverage or signal leakage in dynamic electromagnetic environments, thus affecting the comprehensiveness of monitoring and anti-interference capabilities. The invention achieves the technical effect of improving the overall anti-interference capability and deployment flexibility of the monitoring network while ensuring comprehensive monitoring coverage. The specific technical solution is as follows:

[0009] According to a first aspect of the present invention, a method for the coordinated deployment of spectrum monitoring equipment based on dynamic disturbance field response is provided, the method comprising:

[0010] Using the geographical boundary of the monitoring area as the spatial constraint, heterogeneous environmental situation data is collected to construct an online disturbance field and output a dynamic disturbance intensity field. Multiple deployment coordinates of multiple fixed spectrum monitoring nodes are projected onto the dynamic disturbance intensity field, and multiple path loss values ​​and multiple interference intensity values ​​are retrieved. Based on the multiple path loss values ​​and multiple interference intensity values, a dynamic signal-to-interference-plus-noise ratio (SIR) threshold is determined for the multiple fixed spectrum monitoring nodes to obtain multiple elastic coverage radii. The multiple elastic coverage radii are spatially fused according to the multiple deployment coordinates to obtain a monitoring capability elastic cloud map of the monitoring area, which is used to reversely segment monitoring coverage blind spots. By spatially superimposing and mapping the monitoring coverage blind spots onto the dynamic disturbance intensity field, a priority map of the area to be compensated is obtained. An incremental decision-making iteration for collaborative deployment compensation of spectrum monitoring is triggered in the priority map of the area to be compensated to obtain a blind spot fine-tuning deployment scheme. Incremental deployment of spectrum monitoring equipment is used to perform short-term reconfigurable compensation for the monitoring coverage blind spots.

[0011] In one implementation, dynamic signal-to-interference-plus-noise ratio (SINNR) threshold determination is performed on the plurality of fixed spectrum monitoring nodes based on the plurality of path loss values ​​and the plurality of interference intensity values ​​to obtain a plurality of elastic coverage radii, and the following processing is also performed:

[0012] Based on multiple preset transmit power and multiple path loss values ​​of the multiple fixed spectrum monitoring nodes, multiple received signal strengths are calculated; preset background noise and the multiple interference strength values ​​are fused to generate a panoramic interference noise strength; multiple dynamic signal-to-interference-plus-noise ratio (SINNR) thresholds are allocated according to the multiple spatial heterogeneous interference ratios of the multiple interference strength values ​​relative to the panoramic interference noise strength; multi-directional ray scanning is performed with the multiple fixed spectrum monitoring nodes as the center to calculate the grid SINNR, and the minimum effective coverage distance is located by comparing with the multiple dynamic SINNR thresholds to obtain the multiple elastic coverage radii.

[0013] In one implementation, an incremental decision-making iteration for collaborative deployment compensation of spectrum monitoring is triggered by the priority map of the area to be compensated, resulting in a blind zone fine-tuning deployment scheme. The incrementally deployed spectrum monitoring equipment performs short-term reconfigurable compensation for the monitoring coverage blind zone, and the following processing is also performed:

[0014] Based on the perturbation gradient distribution of the priority map of the area to be compensated, multiple initial deployment scheme individuals are generated in the monitoring coverage blind zone. Population initialization is performed to obtain multiple parent deployment scheme individuals. The individual merits of the multiple parent deployment scheme individuals are evaluated based on the perturbation gradient weighted coverage gain to obtain multiple fitness values. According to the ascending order of the multiple fitness values, P superior deployment scheme individuals are selected, and crossover and recombination perturbation based on differential mutation is performed to obtain P experimental individuals. Greedy selection iteration with online feedback of perturbation field is performed on the P experimental individuals until the maximum number of iterations is reached, and the blind zone fine-tuning deployment scheme is output.

[0015] In one implementation, after temporarily deploying spectrum monitoring equipment in the monitoring coverage blind zone according to the blind zone fine-tuning deployment scheme, the equipment deployment anti-interference closed-loop optimization update is performed based on the feedback of measured interference signal characteristics and the robustness verification of the deployment scheme.

[0016] In one implementation, the heterogeneous environmental situation data includes geographic elevation data, real-time meteorological sounding data, distribution of legal spectrum equipment, and measured interference signal characteristics.

[0017] In one implementation, using the geographical boundary of the monitoring area as the spatial constraint range, heterogeneous environmental situation data is collected, a disturbance field is constructed online, a dynamic disturbance intensity field is output, and the following processing is also performed:

[0018] A two-dimensional Cartesian coordinate system is established based on the geographical boundary of the monitoring area. The monitoring area is then rasterized to obtain a geographic raster index matrix. Within this geographic raster index matrix, rasterized propagation loss modeling is performed on the real-time meteorological sounding data and geographic elevation data to generate a static environmental disturbance field matrix. Using the distribution of legal spectrum equipment as the reference field source, the interference field source of the measured interference signal characteristics is reconstructed to calculate the man-made interference intensity matrix. Finally, the dynamic disturbance intensity field is generated by raster-aligning and stitching the static environmental disturbance field matrix and the man-made interference intensity matrix.

[0019] In one implementation, the real-time meteorological radiosonde data and geographic elevation data are modeled for raster propagation loss in the geographic raster index matrix to generate a static environmental disturbance field matrix, and the following processing is also performed:

[0020] The geographic raster index matrix is ​​used to perform terrain shading loss modeling on the geographic elevation data, and the terrain shading loss matrix is ​​output. The geographic raster index matrix is ​​used to perform atmospheric attenuation coefficient calculation on the real-time meteorological sounding data, and the meteorological attenuation matrix is ​​output. The terrain shading loss matrix and the meteorological attenuation matrix are aligned and fused to generate the static environmental disturbance field matrix.

[0021] In one implementation, using the distribution of the legal spectrum devices as a reference field source, interference field source reconstruction of the measured interference signal characteristics is performed to calculate the man-made interference intensity matrix, and the following processing is also performed:

[0022] Based on the distribution of legal spectrum devices, analyze the transmission parameters of the devices to obtain the co-channel interference intensity field matrix of legal devices; use spatial spectrum estimation technology to trace the radiation source of the measured interference signal characteristics and locate the coordinates of illegal interference sources; using the coordinates of the illegal interference sources as the radiation center, fuse the co-channel interference intensity field matrix of legal devices, and calculate the man-made interference intensity matrix in the monitoring area using the free space path loss model.

[0023] According to a second aspect of the present invention, a collaborative deployment system for spectrum monitoring equipment based on dynamic disturbance field response is provided, the system comprising:

[0024] The system comprises the following components: a dynamic disturbance field construction unit, a node coordinate projection unit, a node coordinate projection unit, a node coordinate system, and a node coordinate system. The node coordinate system projects multiple deployment coordinates of multiple fixed-spectrum monitoring nodes onto the dynamic disturbance intensity field and retrieves multiple path loss values ​​and multiple interference intensity values. An elastic radius determination unit performs dynamic signal-to-interference-plus-noise ratio (SIR) threshold determination on the multiple fixed-spectrum monitoring nodes based on the path loss values ​​and interference intensity values ​​to obtain multiple elastic coverage radii. An elastic cloud map generation unit merges the multiple elastic coverage radii with the multiple deployment coordinates to obtain an elastic cloud map of the monitoring capability of the monitoring area, thereby reversibly segmenting monitoring coverage blind spots. A priority map generation unit maps the monitoring coverage blind spot space onto the dynamic disturbance intensity field to obtain a priority map of the area to be compensated. An incremental decision execution unit triggers incremental decision iterations for collaborative deployment compensation of spectrum monitoring in the priority map of the area to be compensated, obtaining a blind spot fine-tuning deployment scheme and incrementally deploying spectrum monitoring equipment to perform short-term reconfigurable compensation of the monitoring coverage blind spot.

[0025] Beneficial effects of the embodiments of the present invention:

[0026] The solution provided in this invention uses the geographical boundary of the monitoring area as the spatial constraint range to collect heterogeneous environmental situation data, including geographical elevation, real-time weather, distribution of legal equipment, and measured interference signals. A dynamic disturbance intensity field is constructed online to accurately quantify the spatial distribution of composite electromagnetic disturbances caused by terrain shading, meteorological attenuation, and human interference. The deployment coordinates of multiple fixed-spectrum monitoring nodes are projected onto this dynamic disturbance intensity field. The path loss value and interference intensity value of each node's grid are retrieved. Then, based on the fusion result of preset background noise and measured interference, a dynamic signal-to-interference-plus-noise ratio (SNR) threshold is determined, and the elastic coverage radius of each node is adaptively calculated to avoid coverage misjudgments caused by static thresholds. Multiple elastic coverage radii are fused based on the spatial distribution of each node's deployment coordinates to generate a monitoring capability elastic cloud map. This map is used to reversely segment and identify monitoring coverage blind spots, achieving a visual assessment of the overall coverage situation. The monitoring coverage blind spots are spatially superimposed and mapped onto the dynamic disturbance intensity field. Based on the interference intensity distribution, a priority map of the area to be compensated is generated, providing a clear spatial direction for blind spot compensation. Triggered by the priority graph, an incremental decision engine for collaborative deployment compensation of spectrum monitoring is initiated. Using perturbation gradient-weighted coverage gain as the fitness function, a blind zone fine-tuning deployment scheme is generated through differential mutation and greedy selection. Mobile spectrum monitoring equipment is incrementally deployed for short-term reconfigurable compensation. The entire process, through closed-loop optimization with online feedback, ensures that the deployment scheme is always synchronized with the real electromagnetic environment, significantly improving the comprehensiveness of the monitoring network's coverage, anti-interference capability, and deployment flexibility in dynamic electromagnetic scenarios. This achieves the technical effect of improving the overall anti-interference capability and deployment flexibility of the monitoring network while ensuring comprehensive monitoring coverage. Of course, implementing any product or method of this invention does not necessarily require achieving all the advantages described above simultaneously. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0028] Figure 1 A schematic diagram of the collaborative deployment method for spectrum monitoring equipment based on dynamic disturbance field response provided by the present invention is shown.

[0029] Figure 2 A schematic diagram of the structure of the spectrum monitoring equipment collaborative deployment system based on dynamic disturbance field response provided by the present invention is shown.

[0030] Figure labeling: 1. Dynamic disturbance field construction unit; 2. Node coordinate projection unit; 3. Elastic radius determination unit; 4. Elastic cloud map generation unit; 5. Priority map generation unit; 6. Incremental decision execution unit. Detailed Implementation

[0031] To facilitate understanding of the present invention, a more complete description of the invention will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein; rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the invention.

[0032] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0033] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0034] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0035] The present invention provides a method and system for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response, which is used to solve the problem that the existing technology usually uses fixed monitoring points for static spectrum monitoring, which easily leads to insufficient coverage or signal leakage faults in dynamic electromagnetic environment scenarios, thereby affecting the comprehensiveness of monitoring and anti-interference capabilities.

[0036] Example 1: See Figure 1 The flowchart of the collaborative deployment method of spectrum monitoring equipment based on dynamic disturbance field response provided in this embodiment of the invention includes:

[0037] Step S100: Using the geographical boundary of the monitoring area as the spatial constraint range, collect heterogeneous environmental situation data, construct the disturbance field online, and output the dynamic disturbance intensity field.

[0038] In one implementation, the heterogeneous environmental situation data includes geographic elevation data, real-time meteorological sounding data, distribution of legal spectrum equipment, and measured interference signal characteristics.

[0039] In one implementation, the geographical boundary of the monitoring area is used as the spatial constraint range. Heterogeneous environmental situation data is collected, the disturbance field is constructed online, and the dynamic disturbance intensity field is output. Step S100 may further include:

[0040] Step S110: Establish a two-dimensional Cartesian coordinate system based on the geographical boundary of the monitoring area, and perform rasterization segmentation on the monitoring area to obtain a geographic raster index matrix.

[0041] Step S120: In the geographic raster index matrix, perform rasterization propagation loss modeling on the real-time meteorological sounding data and geographic elevation data to generate a static environmental disturbance field matrix.

[0042] Step S130: Using the distribution of the legal spectrum devices as the reference field source, reconstruct the interference field source of the measured interference signal characteristics to calculate the man-made interference intensity matrix.

[0043] Step S140: Generate the dynamic disturbance intensity field by aligning and stitching the static environmental disturbance field matrix and the human disturbance intensity matrix together.

[0044] In one implementation, the real-time meteorological sounding data and geographic elevation data are modeled for raster propagation loss in the geographic raster index matrix to generate a static environmental disturbance field matrix. Step S120 may further include:

[0045] Step S121: Use the geographic raster index matrix to perform terrain shading loss modeling on the geographic elevation data and output the terrain shading loss matrix.

[0046] Step S122: Calculate the atmospheric attenuation coefficient of the real-time meteorological sounding data using the geographic raster index matrix, and output the meteorological attenuation matrix.

[0047] Step S123: Align and fuse the terrain shading loss matrix and the meteorological attenuation matrix to generate the static environmental disturbance field matrix.

[0048] In one implementation, using the distribution of the legal spectrum devices as a reference field source, the interference field source is reconstructed based on the measured interference signal characteristics to calculate the man-made interference intensity matrix. Step S130 may further include:

[0049] Step S131: Based on the legal spectrum device distribution analysis device transmission parameters, obtain the legal device co-frequency interference intensity field matrix.

[0050] Step S132: Use spatial spectrum estimation technology to trace the radiation source of the measured interference signal characteristics and locate the coordinates of the illegal interference source.

[0051] Step S133: Using the coordinates of the illegal interference source as the radiation center, fuse the co-frequency interference intensity field matrix of the legitimate device, and calculate the man-made interference intensity matrix in the monitoring area using the free space path loss model.

[0052] Specifically, in civilian communication scenarios such as densely populated urban areas or industrial electromagnetically sensitive areas, the monitoring area is essentially a dynamic electromagnetic interference hotspot distribution area. This embodiment uses the geographical boundaries of the monitoring area, such as urban administrative divisions or industrial zone fences, as spatial constraints to collect heterogeneous environmental situation data, including geographic elevation data, real-time meteorological sounding data, distribution of legal spectrum devices, and measured interference signal characteristics. The geographic elevation data consists of rasterized terrain undulations and building height data described by a digital elevation model (DEM); the real-time meteorological sounding data includes vertical distribution data of atmospheric temperature and humidity profiles, precipitation intensity, and refractive index gradients; the distribution of legal spectrum devices is a vector layer showing the latitude and longitude coordinates, transmission frequency bands, and power parameters of registered wireless devices; and the measured interference signal characteristics are the time-domain waveforms, spectral peak values, and angle of arrival information of illegal radiation sources collected on-site.

[0053] A two-dimensional Cartesian coordinate system is established based on the geographical boundary of the monitoring area. The two-dimensional Cartesian coordinate system uses the key inflection point of the geographical boundary as the reference origin. For example, the southwest corner of the area is selected as the coordinate zero point. The positive X-axis points due east and the positive Y-axis points due north. The latitude and longitude coordinates are converted into Cartesian coordinates through georeferencing technology to ensure the geometric consistency of spatial calculations.

[0054] When the monitoring area is divided into grids, the grid size is dynamically determined according to the terrain complexity. For example, fine grids such as 100m×100m units are used in flat urban areas, and coarse-grained grids such as 500m×500m units are used in mountainous areas. Each grid is assigned a unique row and column index number to form the geographic grid index matrix.

[0055] The geographic raster index matrix not only records the Cartesian coordinates of the center point of each raster, but also stores the set of latitude and longitude coordinates of the vertices of the raster boundary, and constructs a topological table of spatial adjacency relationships between rasters. For example, the raster numbered G23 forms an adjacency chain with G24 to the east and G13 to the north.

[0056] The geographic raster index matrix serves as the basic framework for spatial computing, enabling subsequent operations such as propagation loss modeling and interference intensity mapping to be performed using the raster as the smallest processing unit. For example, the Bund area in Shanghai is divided into 152 rasters. Among them, the center coordinates of the Lujiazui raster are X=54321.7 and Y=31245.3. The boundary vertices include four sets of latitude and longitude pairs from 121.49° to 121.50° east longitude and from 31.23° to 31.24° north latitude. Its west side is adjacent to the Nanjing East Road raster, and its south side is adjacent to the Huangpu River water area raster.

[0057] The rasterized structure of the geographic raster index matrix described in this embodiment effectively supports the dynamic update mechanism of the environmental disturbance field. When a new building causes local terrain changes, only the index attributes of the affected raster need to be reconstructed, without the need for a global recalculation.

[0058] When performing terrain shading loss modeling on geographic elevation data using a geographic raster index matrix, the terrain undulation characteristics of each raster cell in the geographic raster index matrix are first extracted based on the digital elevation model, including key parameters such as slope, aspect, and relative elevation difference.

[0059] Next, based on the line-of-sight principle of electromagnetic wave propagation, the ray tracing algorithm is used to calculate whether the propagation path from the transmitter to the receiver is blocked by terrain obstacles. For example, in a hilly area, when the monitoring point is located on the back slope, the ray tracing will determine the obstruction angle and distance of the path by the ridge in front, and then calculate the diffraction loss based on the blade obstacle diffraction model. Specifically, it includes determining the proportion of Fresnel zone blockage caused by the terrain. If the blockage proportion exceeds the threshold, a precise multi-blade peak diffraction superposition algorithm is used, and finally the terrain shading loss matrix is ​​output.

[0060] The terrain shading loss matrix records the terrain attenuation of each grid cell in decibels. For example, a valley area with mountains on three sides has an average loss of 22 decibels, while a flat riverbank area has a loss of only 3 decibels. The construction process of the terrain shading loss matrix strictly relies on digital elevation data with sub-meter accuracy to ensure the accuracy of attenuation calculation for complex terrain.

[0061] When using a geographic raster index matrix to calculate the atmospheric attenuation coefficient of real-time meteorological sounding data, the vertical distribution profiles of temperature, humidity, pressure, and precipitation intensity over the target area are first obtained through upper-air meteorological sounding. Then, the discrete meteorological sounding points are mapped to the three-dimensional spatial coordinates of each raster unit using Kriging spatial interpolation technology to obtain the meteorological parameter vector of each raster.

[0062] Next, based on the oxygen absorption model in ITU-R Recommendation 676, the oxygen resonance attenuation in a specific frequency band, such as around 60 GHz, is calculated. Simultaneously, the broadband water vapor additional loss is calculated based on the water vapor continuous absorption model, and the liquid water scattering attenuation is calculated using the ITU-R Recommendation 838 rain attenuation model combined with real-time precipitation rate. Finally, the attenuation components of oxygen, water vapor, and liquid water are integrated over the propagation path length to output the meteorological attenuation matrix. The resulting meteorological attenuation matrix dynamically reflects the spatial heterogeneity of electromagnetic wave propagation caused by meteorological conditions such as fog, rain, and humidity.

[0063] After ensuring that the grid cells of the terrain shading loss matrix and the meteorological attenuation matrix are strictly aligned through spatial coordinate mapping of the geographic raster index matrix, an adaptive weight allocation strategy is adopted for matrix fusion.

[0064] Specifically, in areas dominated by the shading effect, such as canyon terrain, terrain loss is given a higher weight; in areas sensitive to meteorological influences, such as coastal areas, meteorological attenuation is given a higher weight. The specific fusion formula is: Total loss = Terrain loss × Terrain weight + Meteorological loss × Meteorological weight + Terrain-meteorological cross-correction term.

[0065] The cross-correction term uses a machine learning model to predict complex interaction effects, such as inversion layers exacerbating valley diffraction or strong winds leading to enhanced rainfall attenuation, ultimately generating a static environmental disturbance field matrix. This static environmental disturbance field matrix provides a comprehensive environmental loss benchmark for each grid, supporting the subsequent construction of a dynamic disturbance intensity field and the calculation of the network's elastic coverage radius.

[0066] In this embodiment, legal spectrum devices refer to various types of devices that have been registered and officially authorized by the radio management agency within the monitoring area and legally transmit radio signals in a specific frequency band, such as civilian mobile communication base stations, radio and television transmission towers, walkie-talkie repeaters, aviation navigation radars, weather radars, and industrial telemetry and remote control equipment.

[0067] The distribution of legal spectrum devices refers to the spatial location database of these devices within the monitoring area. The distribution of legal spectrum devices records the precise geographical coordinates, operating frequency band, transmit power, antenna gain pattern and other engineering parameters of each legal device, which are used to calculate the background co-channel interference caused by the signals of legal devices to the monitoring system.

[0068] Key engineering parameters such as geographic coordinates, operating frequency band, transmit power, and antenna pattern of each legal spectrum device are extracted from the distribution of legal spectrum devices to serve as the basis for subsequent electromagnetic propagation calculations. Then, for each legal device, an electromagnetic propagation model, such as a modified model that considers the influence of terrain shading, is used to calculate the signal strength generated by that legal device at each geographic grid point in the geographic grid index matrix of the monitoring area.

[0069] The signal strength calculation in this embodiment focuses on the signals emitted by these legitimate devices on their operating frequency bands, which will inevitably cause co-channel or adjacent channel interference to the same or adjacent frequency bands used by the monitoring system composed of multiple fixed spectrum monitoring nodes.

[0070] The interference signal intensities of all legitimate devices at each grid point are summed to generate a two-dimensional or three-dimensional co-channel interference intensity field matrix for the legitimate devices. Each element of the legitimate co-channel interference intensity field matrix corresponds to the total intensity of legitimate background interference at a grid point, quantified in decibels, milliwatts, or similar units, providing spatial distribution benchmark data for subsequent differentiation between legitimate and illegitimate interference.

[0071] The measured interference signal characteristics refer to the abnormal radio signal samples actually collected and reported by multiple spectrum monitoring sensors of the multiple fixed spectrum monitoring nodes fixed in the monitoring area. The signal characteristic parameters included include precise center frequency, bandwidth, modulation pattern, arrival time, pulse width, angle of arrival, and time difference when receiving from multiple stations.

[0072] Spatial spectrum estimation is a high-resolution direction-finding technique based on sensor array signal processing. Its core principle is to construct a spatial spectrum function by utilizing the phase difference caused by the path difference when multiple receiving antenna elements (in this example, multiple fixed spectrum monitoring nodes) located at different spatial positions receive signals from the same radiation source. The direction of arrival of the signal is then estimated by searching for the peak value of this spectrum function. Common spatial spectrum estimation algorithms include the MUSIC algorithm (multiple signal classification algorithm) and Capon's minimum variance distortionless response beamforming algorithm.

[0073] Radiation source backtracking refers to using direction finding technology combined with the precise location information of the monitoring station itself, and using geometric methods such as single-station multi-time period direction finding cross-location or multi-station time difference location, to reverse calculate the specific geographical coordinates in space of the unauthorized illegal radiation sources that generated these measured interference signals.

[0074] The final located coordinates of the illegal interference source include latitude and longitude information as well as possible altitude information. These coordinates serve as the core input for subsequent calculations of its interference field distribution, representing the precise location of the unauthorized illegal radiation source in space.

[0075] The coordinates of the illegal interference source located in step S132 are used as the starting point of electromagnetic wave radiation. Next, it is necessary to determine the transmission power and antenna gain parameters of the illegal interference source. The transmission power and antenna gain parameters can be inferred from the signal strength or set according to typical values, and serve as the basic input for subsequent propagation calculations.

[0076] The basic model of electromagnetic wave propagation theory is called, specifically the free space path loss model. The free space path loss model describes the law that electromagnetic wave power decreases with the square of the propagation distance in an ideal, unobstructed, and non-reflective homogeneous medium. The path loss is directly proportional to the square of the distance and inversely proportional to the square of the wavelength.

[0077] Starting from the coordinates of the illegal interference source, the distance to each geographic grid point in the monitoring area is calculated grid by grid. The path loss value is obtained by substituting it into the free space path loss model (formula). Then, combined with the transmission power and antenna gain of the illegal interference source, the intensity of the illegal interference signal received by each grid point is calculated, thereby generating an illegal interference signal intensity distribution matrix that describes the two-dimensional or three-dimensional distribution of the illegal interference source signal coverage space.

[0078] Subsequently, the illegal interference signal intensity distribution matrix is ​​spatially aligned with the legal device co-frequency interference intensity field matrix generated in step S131 to ensure that the grid coordinates of the two matrices correspond strictly. Then, power superposition is performed at each grid point, that is, the legal background interference power and the illegal interference power are linearly added or added after conversion to decibel and milliwatt units, and finally the artificial interference intensity matrix is ​​generated.

[0079] Each element in the man-made interference intensity matrix represents the total intensity of man-made radio interference generated at that grid point by all known legitimate devices and located illegal devices. It integrates background co-channel interference caused by legitimate sources and malicious interference caused by illegal sources, providing key quantitative basis for subsequent assessment of the signal reception quality of the monitoring network, identification of the impact range of interference sources, and optimization of deployment schemes.

[0080] The dynamic disturbance intensity field is essentially a multi-attribute data field organized by grid index. Each grid point stores two independent values ​​simultaneously: a path loss value from the static environmental disturbance field matrix and an interference intensity value from the human-caused interference intensity matrix. In the subsequent step S300, when projecting the deployment coordinates of the fixed spectrum monitoring nodes onto this dynamic disturbance intensity field, the corresponding grid is located based on the spatial position of the node. Then, the path loss value and interference intensity value are extracted from the attribute records of that grid for subsequent calculations.

[0081] This embodiment achieves the technical effect of providing dynamic input for subsequent monitoring network deployment optimization by constructing a dynamic disturbance intensity field, based on the accurate quantification of the degree of electromagnetic disturbance.

[0082] Step S200: Project multiple deployment coordinates of multiple fixed spectrum monitoring nodes onto the dynamic disturbance intensity field, and retrieve multiple path loss values ​​and multiple interference intensity values.

[0083] In this embodiment, the fixed spectrum monitoring node is a fixed spectrum monitoring device installed at a fixed geographical location within the target monitoring area for real-time acquisition of radio signals. Each node has unique latitude and longitude coordinates.

[0084] The projection operation refers to spatially matching the geographic coordinates of these fixed spectrum monitoring nodes with the previously constructed geographic raster index matrix to determine which raster cell each fixed spectrum monitoring node falls into, thereby obtaining the raster index number corresponding to that fixed spectrum monitoring node.

[0085] The dynamic disturbance intensity field, as a multidimensional data field pre-constructed in step S100, stores two key components of the electromagnetic disturbance at each grid point: the comprehensive environmental loss benchmark value in the static environmental disturbance field matrix and the total intensity value of human interference in the human interference intensity matrix.

[0086] Therefore, once the coordinates of a fixed spectrum monitoring node are located to a specific grid, two values ​​can be directly retrieved from the data record of that grid: one is the path loss value, which represents the total static environmental loss at that grid point determined by terrain shading and meteorological attenuation, in decibels; the other is the interference intensity value, which represents the total intensity of man-made radio interference at that grid point generated by all legitimate devices and located illegal devices, in decibels and milliwatts. These values ​​serve as the basic input for subsequent evaluation of the actual received signal quality of the node and calculation of the elastic coverage radius.

[0087] Step S300: Based on the multiple path loss values ​​and multiple interference intensity values, perform dynamic signal-to-interference-plus-noise ratio threshold determination on the multiple fixed spectrum monitoring nodes to obtain multiple elastic coverage radii.

[0088] In one implementation, dynamic signal-to-interference-plus-noise ratio (SINR) threshold determination is performed on the plurality of fixed spectrum monitoring nodes based on the plurality of path loss values ​​and the plurality of interference intensity values ​​to obtain a plurality of elastic coverage radii. Step S300 may further include:

[0089] Step S310: Calculate multiple received signal strengths based on multiple preset transmit powers and multiple path loss values ​​of the multiple fixed spectrum monitoring nodes.

[0090] Step S320: Fuse the preset background noise and the multiple interference intensity values ​​to generate the panoramic interference noise intensity.

[0091] Step S330: Assign multiple dynamic signal-to-interference-plus-noise ratio (SINNR) thresholds based on the multiple spatial heterogeneity interference ratios of the multiple interference intensity values ​​relative to the panoramic interference noise intensity.

[0092] Step S340: Using the multiple fixed spectrum monitoring nodes as the center, perform multi-directional ray scanning to calculate the grid signal-to-interference-plus-noise ratio (SINR), compare it with the multiple dynamic SINR thresholds, perform minimum effective coverage distance positioning, and obtain the multiple elastic coverage radii.

[0093] In this embodiment, the preset transmission power refers to the transmission power value set by the spectrum monitoring equipment installed at each fixed spectrum monitoring node under normal operating conditions. By subtracting the path loss value from the preset transmission power, the received signal strengths of the multiple fixed spectrum monitoring nodes are obtained. The received signal strength is measured in decibels and milliwatts, reflecting the remaining power level of the node's transmitted signal when it reaches itself or other receiving locations after experiencing terrain undulations, building obstructions, and atmospheric attenuation. It is one of the basic inputs for subsequent judgment on whether the signal can be effectively detected.

[0094] The preset background noise refers to the background noise power in the monitoring area caused by natural electromagnetic activities such as thermal noise, cosmic noise, and atmospheric noise under the condition that there are no human-made transmission signals. It is usually preset to a fixed value by the radio management agency through long-term measurement.

[0095] Multiple interference intensity values ​​of the grid where the fixed spectrum monitoring node is located are extracted from the dynamic disturbance intensity field constructed in step S200. Then, the preset background noise power and interference intensity value at the location of each fixed spectrum monitoring node are linearly summed according to the power superposition principle. That is, the background noise power and interference intensity power are added and converted back to decibel and milliwatt units to obtain the panoramic interference noise intensity that represents the total noise and interference mixed power background of the multiple fixed spectrum monitoring nodes. This is used as the denominator when calculating the signal-to-interference-plus-noise ratio. The panoramic interference noise intensity determines the minimum threshold at which the signal can be identified.

[0096] The spatial heterogeneous interference ratios of the multiple interference intensity values ​​relative to the panoramic interference noise intensity are calculated. The spatial heterogeneous interference ratio refers to the weight ratio of the interference intensity value at each fixed spectrum monitoring node location in the panoramic interference noise intensity. This ratio shows significant differences between different nodes because the terrain shading conditions, meteorological attenuation levels, and spatial distribution of legal and illegal interference sources are all different at each node.

[0097] In this implementation, the dynamic signal-to-interference-plus-noise ratio (SINR) threshold is the minimum SINR standard set to ensure that the monitoring network can reliably distinguish the target signal from noise and interference. The dynamic SINR threshold does not use a uniform fixed value, but is adaptively adjusted according to the proportion of spatial heterogeneous interference of each fixed spectrum monitoring node.

[0098] For example, a higher threshold can be set at fixed spectrum monitoring nodes with a high interference ratio to avoid interference signals being misidentified as target signals, while a lower threshold can be set at nodes with a low interference ratio to make full use of their favorable electromagnetic environment and improve detection sensitivity. This allows each node to obtain the receiver sensitivity requirements that best match its actual electromagnetic environment.

[0099] Starting from the geographic coordinates of each fixed spectrum monitoring node, virtual rays are emitted along multiple standard directions such as east, south, west, north, northeast, southeast, northwest, and southwest in the geographic grid index matrix of the monitoring area. Each ray passes through a series of grids radiating outward from the node center in sequence.

[0100] For each grid through which the ray passes, the received signal strength of the node's transmitted signal reaching the grid is calculated based on the free space path loss model and the static environmental disturbance value of the grid. Then, the instantaneous signal-to-interference-plus-noise ratio of the grid is calculated by combining the panoramic interference noise intensity of the same grid.

[0101] The signal-to-interference-plus-noise ratio (SIR) value is compared with the dynamic SIR threshold assigned to the node in step S330. If the SIR of the grid is greater than or equal to the threshold, the grid is considered to be effectively covered by the node; otherwise, it is considered not covered.

[0102] Search grid by grid outward from the node center along each ray direction, and record the first grid position where the signal-to-interference-plus-noise ratio is lower than the threshold. The distance from this position to the node center is the effective coverage boundary in that direction.

[0103] The minimum value among the coverage boundary distances in all directions is taken as the elastic coverage radius of the corresponding fixed spectrum monitoring node. This radius represents the minimum effective coverage range that the fixed spectrum monitoring node can stably and reliably monitor signals under the combined effects of current static environmental disturbances and human interference.

[0104] It should be understood that the elastic coverage radius will be dynamically updated as environmental factors and interference intensity change, providing key spatial constraint parameters for subsequent deployment optimization and network resilience analysis.

[0105] Step S400: Based on the multiple deployment coordinate spaces, fuse the multiple elastic coverage radii to obtain the monitoring capability elastic cloud map of the monitoring area, so as to reversely segment the monitoring coverage blind spots.

[0106] Step S500: By mapping the monitoring coverage blind zone space onto the dynamic disturbance intensity field, a priority map of the area to be compensated is obtained.

[0107] By spatially fusing the previously determined deployment coordinates with their corresponding elastic coverage radii, the actual stable coverage range of each fixed-spectrum monitoring node in the monitoring area is plotted, thereby generating an elastic monitoring capability cloud map. This elastic monitoring capability cloud map clearly marks the effectively covered grid areas. Grids not covered by any nodes can be extracted through reverse identification; these continuous or discontinuous grids constitute the monitoring coverage blind spots.

[0108] The spatial location of the monitoring blind zone is superimposed and mapped to the dynamic disturbance intensity field grid by grid. The existing interference intensity value is extracted on each blind zone grid. The blind zone grid with higher interference intensity means that it needs to be reinforced in a harsh electromagnetic environment. Therefore, it is given a higher compensation priority. Finally, a priority map of the area to be compensated is generated, which provides a clear spatial direction for subsequent deployment adjustments or mobile equipment scheduling.

[0109] Step S600: Incremental decision-making iteration is triggered in the priority map of the area to be compensated to obtain a blind zone fine-tuning deployment scheme, and incremental deployment of spectrum monitoring equipment is used to perform short-term reconfigurable compensation for the monitoring coverage blind zone.

[0110] In one implementation, an incremental decision-making iteration for collaborative deployment compensation of spectrum monitoring is triggered by the priority map of the area to be compensated, resulting in a blind zone fine-tuning deployment scheme. The incrementally deployed spectrum monitoring equipment performs short-term reconfigurable compensation for the monitoring coverage blind zone. Step S600 may further include:

[0111] Step S610: Based on the perturbation gradient distribution of the priority map of the area to be compensated, generate multiple initial deployment scheme individuals in the monitoring coverage blind zone, perform population initialization, and obtain multiple parent deployment scheme individuals.

[0112] Step S620: Evaluate the individual merits of the multiple parent deployment schemes based on perturbation gradient weighted coverage gain to obtain multiple fitness values.

[0113] Step S630: Based on the ascending order of the fitness values, select P individuals with superior deployment schemes, perform crossover and recombination perturbation based on differential mutation, and obtain P experimental individuals.

[0114] Step S640: Perform greedy selection iteration of online feedback of the perturbation field on the P test individuals until the maximum number of iterations is reached, and output the blind zone fine-tuning deployment scheme.

[0115] In one implementation, after temporarily deploying spectrum monitoring equipment in the monitoring coverage blind zone according to the blind zone fine-tuning deployment scheme, the equipment deployment anti-interference closed-loop optimization update is performed based on the feedback of measured interference signal characteristics and the robustness verification of the deployment scheme.

[0116] Specifically, in this embodiment, the disturbance gradient distribution in the priority map of the area to be compensated reflects the degree of change in interference intensity with spatial location within the monitoring coverage blind zone. Areas with higher gradient values ​​indicate a more complex electromagnetic environment and greater difficulty in signal monitoring.

[0117] When generating multiple initial deployment schemes for monitoring coverage blind spots, weighted random sampling is performed based on the perturbation gradient distribution, so that the probability of grids in high gradient areas being selected as equipment deployment coordinates is significantly higher than that in low gradient areas.

[0118] Each initial deployment scheme here consists of a set of Cartesian coordinates of a mobile electromagnetic spectrum monitoring device. These coordinates are not evenly distributed in the blind zone, but are more densely clustered in the region with high perturbation gradient, thus ensuring that the initial population contains more candidate solutions located in the key complex blind zone from the beginning, providing spatially targeted and diverse parent individuals for subsequent evolutionary iterations.

[0119] It should be understood that, in this embodiment, the perturbation gradient weighted coverage gain is the core indicator for measuring the individual performance of each parent deployment scheme. It combines the elastic coverage radius of each mobile electromagnetic spectrum monitoring device in the scheme with the perturbation gradient value of its grid. Specifically, it is calculated by summing the products of the elastic coverage radius of all devices in the individual and the perturbation gradient value of the corresponding grid. The sum obtained is the weighted coverage gain of the individual. The larger the gain, the better the scheme can deploy the equipment in high-value blind areas with complex electromagnetic environments, thereby more effectively making up for the coverage gap. This weighted coverage gain is the fitness value.

[0120] The fitness values ​​are sorted from smallest to largest. Since a larger fitness value indicates a better solution, the P deployment solutions at the bottom are the most fitness-advantaged individuals, and they are selected as the basis for evolutionary operations.

[0121] The differential mutation operation randomly selects three different individuals from the P advantageous deployment schemes, calculates the coordinate difference vector of two of these individuals, multiplies it by a scaling factor, and then adds it to the coordinate vector of the third individual to generate a mutation vector. This mutation vector represents the new position searched in a certain direction in the solution space.

[0122] The crossover and recombination operation mixes the mutated vector with the current dominant individual according to a certain crossover probability. For example, on each coordinate dimension, the value of the mutated vector is inherited or the original individual value is retained according to probability, thereby generating an experimental individual.

[0123] The cross-recombination perturbation process in this embodiment ensures that the newly generated test individuals inherit the superior genes of the dominant individuals, and introduce random perturbations through difference vectors, enabling the exploration of equipment deployment locations in blind spots that have not yet been fully searched.

[0124] For example, the equipment coordinates of the three advantageous deployment schemes are concentrated on the mountaintop, valley, and hillside within the blind zone, respectively. Their difference vectors indicate the movement trend from the valley to the mountaintop. By weighted summation, a new coordinate located on the hillside may be generated, thereby discovering a potential better deployment point.

[0125] Greedy selection iteration is performed on the P test individuals finally obtained through cross-recombination perturbation until the maximum number of iterations is reached, and the blind zone fine-tuning deployment scheme is output.

[0126] Specifically, the online feedback of the disturbance field refers to reprojecting the equipment coordinates of the test individual generated in each iteration onto the dynamic disturbance intensity field, extracting the interference intensity value and path loss value of the grid where it is located, and recalculating the weighted coverage gain of the test individual, i.e. the fitness value, in combination with the elastic coverage radius, so that the evaluation results are always synchronized with the current electromagnetic environment in real time.

[0127] The greedy selection strategy compares the fitness of each experimental individual with its corresponding parent individual, retaining the individual with higher fitness into the next generation population. If the experimental individual has higher fitness, it replaces the parent individual; otherwise, it retains the parent individual.

[0128] This generational optimization process continues until the preset maximum number of iterations is reached. At this point, the individual with the highest fitness in the population becomes the blind zone fine-tuning deployment scheme.

[0129] For example, after 50 iterations, the coordinates of the equipment, which were initially randomly distributed in various blind spots, gradually converged towards the canyon area with high perturbation gradient. The final output solution precisely deployed the equipment at several key passes, achieving effective coverage of the most complex blind spots with the fewest possible equipment.

[0130] According to the blind zone fine-tuning deployment plan, the spectrum monitoring equipment is temporarily deployed in the monitoring coverage blind zone. The temporary deployment here refers to the temporary placement of the mobile electromagnetic spectrum monitoring equipment at the designated grid position in the monitoring coverage blind zone according to the coordinates of the newly added equipment given in the blind zone fine-tuning deployment plan. These equipment are added extra, rather than adjustments to the original fixed monitoring node positions.

[0131] After deployment, these new devices immediately began monitoring, collecting real-time data on the characteristics of actual interference signals in the blind zone, including the time-domain waveform, spectral peak, and angle of arrival of illegal radiation sources, and feeding this measured data back to the computing system.

[0132] The robustness of the current blind zone fine-tuning deployment plan is verified based on feedback data, that is, to assess whether these new equipment can maintain the expected flexible coverage radius in a real electromagnetic environment, and whether the signal leakage risk of the entire blind zone is still within the compliance range.

[0133] If the verification reveals a significant deviation between the measured interference intensity of certain grids within the blind zone and the previously established dynamic disturbance intensity field, resulting in the actual coverage effect of the newly added equipment not meeting expectations, a closed-loop optimization update will be automatically initiated. The fine-tuning optimization process in steps S610 to S640 will be called back, and a new blind zone fine-tuning deployment scheme will be generated using the corrected dynamic disturbance intensity field as input, thereby achieving anti-interference adaptive adjustment of the deployment of temporarily added equipment.

[0134] For example, if a strong interference source is found in the blind zone, causing a sharp drop in the signal-to-interference-plus-noise ratio in the surrounding area, the coordinates and quantity of the next temporary additional equipment will be dynamically adjusted based on this feedback. More additional equipment will be deployed in key locations near the interference source to improve local coverage and ensure that the entire monitoring network maintains the best flexible coverage performance after blind zone compensation.

[0135] This embodiment overcomes the shortcomings of existing technologies in deploying mobile electromagnetic spectrum monitoring equipment, which are characterized by static settings and lack of dynamic adaptability. It achieves the technical effect of improving the overall anti-interference capability and deployment flexibility of the monitoring network while ensuring comprehensive monitoring coverage.

[0136] Example 2: Based on the same inventive concept as the collaborative deployment method of spectrum monitoring equipment based on dynamic disturbance field response in the foregoing examples, this invention provides a collaborative deployment system for spectrum monitoring equipment based on dynamic disturbance field response. See [link to example]. Figure 2 As shown, the system includes:

[0137] The system comprises the following components: a dynamic disturbance field construction unit 1, which collects heterogeneous environmental situation data with the geographical boundary of the monitoring area as the spatial constraint range, constructs the disturbance field online, and outputs a dynamic disturbance intensity field; a node coordinate projection unit 2, which projects multiple deployment coordinates of multiple fixed spectrum monitoring nodes onto the dynamic disturbance intensity field and retrieves multiple path loss values ​​and multiple interference intensity values; an elastic radius determination unit 3, which performs dynamic signal-to-interference-plus-noise ratio threshold determination on the multiple fixed spectrum monitoring nodes based on the multiple path loss values ​​and multiple interference intensity values ​​to obtain multiple elastic coverage radii; an elastic cloud map generation unit 4, which merges the multiple elastic coverage radii according to the multiple deployment coordinates to obtain an elastic cloud map of the monitoring capability of the monitoring area, thereby reversibly segmenting the monitoring coverage blind zone; a priority map generation unit 5, which maps the monitoring coverage blind zone space onto the dynamic disturbance intensity field to obtain a priority map of the area to be compensated; and an incremental decision execution unit 6, which triggers incremental decision iteration for spectrum monitoring collaborative deployment compensation in the priority map of the area to be compensated, obtains a blind zone fine-tuning deployment scheme, and incrementally deploys spectrum monitoring equipment to perform short-term reconfigurable compensation for the monitoring coverage blind zone.

[0138] In one implementation, the elastic radius determination unit 3 is further used for:

[0139] Based on multiple preset transmit power and multiple path loss values ​​of the multiple fixed spectrum monitoring nodes, multiple received signal strengths are calculated; preset background noise and the multiple interference strength values ​​are fused to generate a panoramic interference noise strength; multiple dynamic signal-to-interference-plus-noise ratio (SINNR) thresholds are allocated according to the multiple spatial heterogeneous interference ratios of the multiple interference strength values ​​relative to the panoramic interference noise strength; multi-directional ray scanning is performed with the multiple fixed spectrum monitoring nodes as the center to calculate the grid SINNR, and the minimum effective coverage distance is located by comparing with the multiple dynamic SINNR thresholds to obtain the multiple elastic coverage radii.

[0140] In one implementation, the incremental decision execution unit 6 is further configured to:

[0141] Based on the perturbation gradient distribution of the priority map of the area to be compensated, multiple initial deployment scheme individuals are generated in the monitoring coverage blind zone. Population initialization is performed to obtain multiple parent deployment scheme individuals. The individual merits of the multiple parent deployment scheme individuals are evaluated based on the perturbation gradient weighted coverage gain to obtain multiple fitness values. According to the ascending order of the multiple fitness values, P superior deployment scheme individuals are selected, and crossover and recombination perturbation based on differential mutation is performed to obtain P experimental individuals. Greedy selection iteration with online feedback of perturbation field is performed on the P experimental individuals until the maximum number of iterations is reached, and the blind zone fine-tuning deployment scheme is output.

[0142] In one implementation, the incremental decision execution unit 6 is further configured to:

[0143] After temporarily deploying spectrum monitoring equipment in the monitoring coverage blind zone according to the blind zone fine-tuning deployment scheme, the equipment deployment anti-interference closed-loop optimization and update is carried out based on the feedback of measured interference signal characteristics and the robustness verification of the deployment scheme.

[0144] In one implementation, the dynamic disturbance field construction unit 1 is further used for:

[0145] The heterogeneous environmental situation data includes geographic elevation data, real-time meteorological sounding data, distribution of legal spectrum equipment, and measured interference signal characteristics.

[0146] In one implementation, the dynamic disturbance field construction unit 1 is further used for:

[0147] A two-dimensional Cartesian coordinate system is established based on the geographical boundary of the monitoring area. The monitoring area is then rasterized to obtain a geographic raster index matrix. Within this geographic raster index matrix, rasterized propagation loss modeling is performed on the real-time meteorological sounding data and geographic elevation data to generate a static environmental disturbance field matrix. Using the distribution of legal spectrum equipment as the reference field source, the interference field source of the measured interference signal characteristics is reconstructed to calculate the man-made interference intensity matrix. Finally, the dynamic disturbance intensity field is generated by raster-aligning and stitching the static environmental disturbance field matrix and the man-made interference intensity matrix.

[0148] In one implementation, the dynamic disturbance field construction unit 1 is further used for:

[0149] The geographic raster index matrix is ​​used to perform terrain shading loss modeling on the geographic elevation data, and the terrain shading loss matrix is ​​output. The geographic raster index matrix is ​​used to perform atmospheric attenuation coefficient calculation on the real-time meteorological sounding data, and the meteorological attenuation matrix is ​​output. The terrain shading loss matrix and the meteorological attenuation matrix are aligned and fused to generate the static environmental disturbance field matrix.

[0150] In one implementation, the dynamic disturbance field construction unit 1 is further used for:

[0151] Based on the distribution of legal spectrum devices, analyze the transmission parameters of the devices to obtain the co-channel interference intensity field matrix of legal devices; use spatial spectrum estimation technology to trace the radiation source of the measured interference signal characteristics and locate the coordinates of illegal interference sources; using the coordinates of the illegal interference sources as the radiation center, fuse the co-channel interference intensity field matrix of legal devices, and calculate the man-made interference intensity matrix in the monitoring area using the free space path loss model.

[0152] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0153] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response, characterized in that, include: Using the geographical boundaries of the monitoring area as the spatial constraint range, heterogeneous environmental situation data are collected, disturbance fields are constructed online, and dynamic disturbance intensity fields are output. Multiple deployment coordinates of multiple fixed spectrum monitoring nodes are projected onto the dynamic disturbance intensity field to retrieve multiple path loss values ​​and multiple interference intensity values. Based on the multiple path loss values ​​and multiple interference intensity values, a dynamic signal-to-interference-plus-noise ratio threshold determination is performed on the multiple fixed spectrum monitoring nodes to obtain multiple elastic coverage radii; By fusing the multiple deployment coordinate spaces and the multiple elastic coverage radii, an elastic cloud map of the monitoring capability of the monitoring area is obtained, which is then used to segment monitoring coverage blind spots in reverse. By overlaying and mapping the monitoring coverage blind zone onto the dynamic disturbance intensity field, a priority map of the area to be compensated is obtained; Incremental decision-making iterations are triggered by the priority map of the area to be compensated to conduct collaborative deployment compensation of spectrum monitoring, resulting in a blind zone fine-tuning deployment scheme. Incremental deployment of spectrum monitoring equipment is used to perform short-term reconfigurable compensation for the monitoring coverage blind zone.

2. The method for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response as described in claim 1, characterized in that, Based on the multiple path loss values ​​and multiple interference intensity values, a dynamic signal-to-interference-plus-noise ratio (SINNR) threshold determination is performed on the multiple fixed spectrum monitoring nodes to obtain multiple elastic coverage radii, including: Based on the multiple preset transmit power and multiple path loss values ​​of the multiple fixed spectrum monitoring nodes, multiple received signal strengths are calculated; By fusing preset background noise and the multiple interference intensity values, a panoramic interference noise intensity is generated; Based on the proportion of spatial heterogeneity interference relative to the panoramic interference noise intensity of the multiple interference intensity values, multiple dynamic signal-to-interference-plus-noise ratio thresholds are assigned; Centered on the multiple fixed spectrum monitoring nodes, multi-directional ray scanning is performed to calculate the grid signal-to-interference-plus-noise ratio (SINR). By comparing the SINR with the multiple dynamic SINR thresholds, the minimum effective coverage distance is determined, and the multiple elastic coverage radii are obtained.

3. The method for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response as described in claim 1, characterized in that, Incremental decision-making iterations are triggered in the priority map of the area to be compensated to initiate collaborative deployment compensation for spectrum monitoring, resulting in a blind zone fine-tuning deployment scheme. Incremental deployment of spectrum monitoring equipment is then used to perform short-term reconfigurable compensation for the monitoring coverage blind zone, including: Based on the perturbation gradient distribution of the priority map of the area to be compensated, multiple initial deployment scheme individuals are generated in the monitoring coverage blind zone, and population initialization is performed to obtain multiple parent deployment scheme individuals. The individual performance of the multiple parent deployment schemes is evaluated based on the perturbation gradient weighted coverage gain, resulting in multiple fitness values; Based on the ascending order of the fitness values, P individuals with superior deployment schemes are selected, and crossover and recombination perturbations based on differential mutation are performed to obtain P experimental individuals; Greedy selection iteration is performed on the P experimental individuals for online feedback of the perturbation field until the maximum number of iterations is reached, and the blind zone fine-tuning deployment scheme is output.

4. The method for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response as described in claim 3, characterized in that, After temporarily deploying spectrum monitoring equipment in the monitoring coverage blind zone according to the blind zone fine-tuning deployment scheme, the equipment deployment anti-interference closed-loop optimization and update is carried out based on the feedback of measured interference signal characteristics and the robustness verification of the deployment scheme.

5. The method for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response as described in claim 1, characterized in that, The heterogeneous environmental situation data includes geographic elevation data, real-time meteorological sounding data, distribution of legal spectrum equipment, and measured interference signal characteristics.

6. The method for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response as described in claim 5, characterized in that, Using the geographical boundaries of the monitoring area as spatial constraints, heterogeneous environmental situation data are collected, and an online disturbance field is constructed to output a dynamic disturbance intensity field, including: A two-dimensional Cartesian coordinate system is established based on the geographical boundary of the monitoring area, and the monitoring area is rasterized to obtain a geographic raster index matrix; In the geographic raster index matrix, the real-time meteorological sounding data and geographic elevation data are modeled for raster propagation loss to generate a static environmental disturbance field matrix. Using the distribution of the legal spectrum devices as the reference field source, the interference field source of the measured interference signal characteristics is reconstructed to calculate the man-made interference intensity matrix; The dynamic disturbance intensity field is generated by aligning and stitching the static environmental disturbance field matrix and the human disturbance intensity matrix together.

7. The method for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response as described in claim 6, characterized in that, In the geographic raster index matrix, raster propagation loss modeling is performed on the real-time meteorological sounding data and geographic elevation data to generate a static environmental disturbance field matrix, including: The geographic raster index matrix is ​​used to perform terrain shading loss modeling on the geographic elevation data, and the terrain shading loss matrix is ​​output. The atmospheric attenuation coefficient is calculated using the geographic raster index matrix on the real-time meteorological sounding data, and a meteorological attenuation matrix is ​​output. The terrain shading loss matrix and the meteorological attenuation matrix are aligned and fused to generate the static environmental disturbance field matrix.

8. The method for collaborative deployment of spectrum monitoring equipment based on dynamic disturbance field response as described in claim 6, characterized in that, Using the distribution of the legal spectrum devices as the reference field source, the interference field source is reconstructed based on the measured interference signal characteristics to calculate the man-made interference intensity matrix, including: Based on the distribution of the legal spectrum devices, the transmission parameters of the devices are analyzed to obtain the field matrix of co-channel interference intensity of the legal devices; Spatial spectrum estimation technology is used to trace the radiation source of the measured interference signal and locate the coordinates of the illegal interference source. Using the coordinates of the illegal interference source as the radiation center, the intensity field matrix of the co-frequency interference of the legitimate equipment is fused, and the man-made interference intensity matrix is ​​calculated in the monitoring area using the free space path loss model.

9. A collaborative deployment system for spectrum monitoring equipment based on dynamic disturbance field response, characterized in that, For implementing the method steps of any one of claims 1 to 8, comprising: The dynamic disturbance field construction unit is used to collect heterogeneous environmental situation data with the geographical boundary of the monitoring area as the spatial constraint range, construct the disturbance field online, and output the dynamic disturbance intensity field. The node coordinate projection unit is used to project multiple deployment coordinates of multiple fixed spectrum monitoring nodes onto the dynamic disturbance intensity field, and retrieve multiple path loss values ​​and multiple interference intensity values. The elastic radius determination unit is used to perform dynamic signal-to-interference-plus-noise ratio threshold determination on the multiple fixed spectrum monitoring nodes based on the multiple path loss values ​​and multiple interference intensity values, so as to obtain multiple elastic coverage radii. The elastic cloud map generation unit is used to fuse the multiple elastic coverage radii based on the multiple deployment coordinate spaces to obtain the monitoring capability elastic cloud map of the monitoring area, so as to reversely segment the monitoring coverage blind spots. The priority map generation unit is used to obtain the priority map of the area to be compensated by superimposing and mapping the monitoring coverage blind zone space onto the dynamic disturbance intensity field; The incremental decision execution unit is used to trigger incremental decision iteration for spectrum monitoring collaborative deployment compensation in the priority map of the area to be compensated, to obtain a blind zone fine-tuning deployment scheme, and incrementally deploy spectrum monitoring equipment to perform short-term reconfigurable compensation for the monitoring coverage blind zone.

Images