Unmanned aerial vehicle low-altitude defense signal management and control method and system based on communication coordination

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and particularly to a method and system for controlling low-altitude defense signals of UAVs based on communication collaboration. The method includes: acquiring wireless signals; performing progressive joint identification processing on wireless signals in the target frequency band to distinguish and isolate target signals from legitimate communication signals; obtaining the relative frequency band positional relationship between the target signal and the legitimate communication signal; using this relative frequency band positional relationship as the core constraint, performing dynamic constraint generation processing of control signals to obtain control signals acting on the frequency band where the target signal is located; and performing directional control on the target signal based on these control signals. Throughout the entire process of controlling the target signal, the target signal state is simultaneously subjected to multi-dimensional quantification processing to obtain multi-level control effects; and the generation parameters of the control signals are adjusted iteratively and adaptively according to different control effects. This solution, through a closed-loop control system, can accurately control illegal UAVs and ensure legitimate communication.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method and system for controlling UAV low-altitude defense signals based on communication collaboration. Background Technology

[0002] With the rapid popularization of drone technology and its expanding application scenarios, the public safety hazards caused by illegal drone intrusions are becoming increasingly prominent, making low-altitude defense and control a core need in the field. Currently, signal control has become the mainstream method for low-altitude drone defense due to its advantages such as fast response, no physical damage, and wide control range. Its core is to identify and interfere with the communication signals of illegal drones, cutting off their link with the ground remote controller to achieve control.

[0003] However, existing drone signal control technologies still suffer from many common defects, making it difficult to meet the requirements of precise control under communication coordination and to achieve coordinated advancement of illegal drone control and legitimate communication protection. Existing technologies generally suffer from insufficient signal recognition accuracy, poor adaptability to control signals, limited positioning accuracy, inability to quantify control effects, and a lack of adaptive adjustment capabilities. They also exhibit problems such as ambiguous judgment of key parameters and contradictory technical logic, leading to easy interference with legitimate communications, low control accuracy, high implementation difficulty, and unsustainable effects during the control process. These technologies are ill-suited to the actual application needs in the current low-altitude airspace. Therefore, a new drone low-altitude defense signal control method that balances precise control with legitimate communication protection and possesses adaptive capabilities is urgently needed. Summary of the Invention

[0004] This invention uses a closed-loop management system to accurately control illegal drones and ensure legitimate communications.

[0005] The technical solution proposed in this invention is: a method for controlling low-altitude defense signals of unmanned aerial vehicles based on communication cooperation, the method comprising: It collects wireless signals, performs progressive joint identification processing on the wireless signals in the target frequency band, and distinguishes and isolates the target signals from legitimate communication signals; The relative frequency band position relationship between the target signal and the legitimate communication signal is obtained. Using the relative frequency band position relationship as the core constraint, dynamic constraint generation processing of control signals is performed to obtain the control signals acting on the frequency band where the target signal is located. Based on the control signals, directional control is performed on the target signal. Throughout the entire process of controlling the target signal, the target signal state is simultaneously quantified in multiple dimensions to obtain multi-level control effects. Based on different control effects, the generation parameters of the control signal are adjusted in a differentiated adaptive iterative manner.

[0006] Preferably, the specific processing procedure for the progressive joint identification is as follows: A full-domain scan and data analysis of wireless signals in the target frequency band is performed to extract basic characteristic parameters representing the frequency band usage status. The first round of signal screening is completed according to preset screening rules to select a set of suspected signals that meet the characteristic trends of the target signals. A refined feature extraction operation is performed on the suspected signal set to mine exclusive features that can identify the source attributes of the signal. A feature comparison model between suspected signals and legitimate communication signals is established. The two types of signals are accurately distinguished by calculating the feature matching degree. At the same time, the target signals after distinction are located by frequency band and isolated to form a standardized recognition result dataset.

[0007] Preferably, the specific process for obtaining the relative positional relationship of the frequency bands is as follows: Retrieve the target signal frequency band identifier and the legitimate communication signal frequency band identifier from the recognition result dataset, and construct a dual-signal frequency band spatial distribution model; The frequency band boundaries, frequency band overlap areas, and frequency band spacing of the two types of signals are quantized and analyzed to determine the relative position of the target signal frequency band in the entire frequency band, as well as the adjacency or isolation relationship between the target signal frequency band and the legitimate communication signal frequency band. The obtained position parameters, boundary parameters, and spacing parameters are integrated to generate a standardized dataset of frequency band relative position relationships.

[0008] Preferably, the specific process for generating the dynamic constraints of the control signal is as follows: The frequency band relative position relationship dataset is imported into the control signal generation model. The generation constraints are set with the legal communication signal frequency band as the protection boundary to limit the frequency band coverage and energy distribution area of ​​the control signal. By combining the target signal-specific features in the recognition result dataset, we construct the matching and association rules between the control signal and the target signal, and determine the range of core generation parameters for the control signal. The control signal generation operation is performed according to the constraints and adaptation rules. The frequency band compliance of the generated signal is verified, signal components that exceed the constraints are removed, and a control signal that only applies to the frequency band of the target signal is formed.

[0009] Preferably, the specific execution process of performing targeted control on the target signal based on the control signal is as follows: The control signal obtained by dynamic constraint generation and processing of the control signal is output to the signal control execution link according to the preset execution logic to ensure that the control signal is accurately applied to the frequency band of the target signal. During the execution of targeted control, the frequency band changes and signal strength fluctuations of the target signal are tracked simultaneously, and the output power and range of the control signal are adjusted in real time to maintain the compatibility between the control signal and the target signal. Continuously verify the frequency range of the controlled signals to ensure that they do not intrude into the frequency bands of legitimate communication signals, thereby achieving effective control over target signals while ensuring the normal transmission of legitimate communication signals.

[0010] Preferably, the specific process of the multi-dimensional quantization is as follows: During the process of controlling the signal in a directional manner, the target signal is collected in real time and analyzed in multiple dimensions to extract the core quantitative parameters that characterize the signal's operating status and to build a multi-dimensional status monitoring system. The collected state parameters are processed by time-series organization and standardization preprocessing to eliminate invalid data and interference components during signal acquisition and transmission. Establish a quantitative assessment model for control effectiveness, input preprocessed state parameters into the model for calculation, classify control effectiveness levels according to preset judgment rules, and generate a quantitative assessment report containing parameter data, time series information and level results.

[0011] Preferably, the specific process for achieving the multi-level control effect is as follows: By retrieving the trend of changes in status parameters and the basis for level classification from the quantitative assessment report, an in-depth analysis of the control response status of the target signal is conducted to clarify the signal status characteristics corresponding to the control effectiveness level. Establish a level verification mechanism to review and verify the preliminary level of control effectiveness by combining the execution duration of control signals and parameter input, and eliminate level misjudgments caused by instantaneous interference; The approved control effectiveness levels are associated with and stored with the corresponding status parameter features to form a hierarchical control effectiveness dataset.

[0012] Preferably, the specific process of the differentiated adaptive iterative adjustment is as follows: Retrieve the dataset of hierarchical control effect, construct an adjustment rule system for control signal generation parameters based on different control effect levels, and match differentiated adjustment dimensions and adjustment ranges for different levels; Based on the adjustment rules corresponding to the current control effectiveness level, the core generation parameters of the control signal are extracted, parameter calculation and adjustment operations are performed, and an updated parameter dataset is generated. The updated parameter dataset is validated to ensure that the parameter adjustments comply with the frequency band constraints and the control signal adaptation rules, thus forming optimized parameters that can be directly input into the dynamic constraint generation step of the control signal.

[0013] The present invention also provides a communication-coordinated UAV low-altitude defense signal control system, the system being used to execute the communication-coordinated UAV low-altitude defense signal control method described above.

[0014] The present invention also provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the aforementioned communication-coordinated UAV low-altitude defense signal control method.

[0015] The beneficial effects of this invention are: 1. Through a two-stage progressive identification process, early differentiation and parallel isolation of UAV signals from legitimate communication signals are achieved, avoiding invalid scanning and misidentification issues at the source. On the one hand, a coarse screening is performed based on frequency band occupancy characteristics to quickly eliminate invalid signals whose power, bandwidth, and modulation methods do not conform to the characteristics of UAV signals, significantly reducing the workload of subsequent fine processing and improving the efficiency of full-domain scanning. On the other hand, a fine radio frequency fingerprint extraction is performed on suspected UAV signals to enhance the targeting and accuracy of signal identification, completing the differentiation and isolation from legitimate communication signals in advance, laying the foundation for subsequent interference control, effectively reducing the risk of misinterpreting legitimate communication, and improving the overall reliability of signal identification.

[0016] 2. By dynamically constraining the interference waveform, precise control of interference energy is achieved, forming an integrated control process of precise in-band suppression and strict out-of-band protection, balancing interference effectiveness with legitimate communication protection. On the one hand, the relative positional relationship between the illegal drone frequency band and the legitimate communication frequency band is determined by RF fingerprint recognition results. This is used as a constraint condition to generate the interference waveform, ensuring that the interference energy only acts on the sub-frequency band where the illegal signal is located, fundamentally preventing the interference signal from intruding into the legitimate communication frequency band. On the other hand, the dynamic constraint generation mechanism makes the interference waveform precisely matched with the illegal drone signal, improving the in-band suppression effect and solving the pain points of poor adaptability, easy misinterpretation of legitimate communication, or control failure of traditional interference methods, thus achieving the synergistic advancement of precise control and legitimate communication protection.

[0017] 3. Through closed-loop optimization, precise control of countermeasure effects and dynamic optimization of interference parameters are achieved, ensuring continuous and stable control effectiveness. On the one hand, the feedback monitoring module quantifies the drone signal in multiple dimensions, forming a scientific multi-level countermeasure effect judgment result, accurately identifying control effectiveness, and avoiding misjudgment. On the other hand, interference parameters are adjusted differently according to different judgment levels to construct a closed-loop optimization system that can adapt to the dynamic changes of drone signals. It can optimize the interference effect in real time without manual intervention, ensuring effective control of illegal drones in different states, improving the flexibility and reliability of technology implementation, and adapting to low-altitude defense needs in multiple scenarios. Attached Figure Description

[0018] Figure 1 A flowchart of a communication-coordinated method for controlling low-altitude defense signals of unmanned aerial vehicles (UAVs); Figure 2A flowchart illustrating the progressive joint identification and processing of a communication-coordinated UAV low-altitude defense signal control method. Figure 3 A flowchart for generating dynamic constraints on control signals in a communication-coordinated UAV low-altitude defense signal control method; Figure 4 A flowchart illustrating the targeted control execution process of a communication-coordinated UAV low-altitude defense signal control method. Figure 5 This is a flowchart illustrating the multi-dimensional quantization and adaptive iteration of a communication-coordinated UAV low-altitude defense signal control method. Detailed Implementation

[0019] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0020] It is understood that the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0021] like Figure 1 As shown, a method for controlling low-altitude defense signals of unmanned aerial vehicles (UAVs) based on communication coordination is characterized by the following: It collects wireless signals, performs progressive joint identification processing on the wireless signals in the target frequency band, and distinguishes and isolates the target signals from legitimate communication signals; The relative frequency band position relationship between the target signal and the legitimate communication signal is obtained. Using the relative frequency band position relationship as the core constraint, dynamic constraint generation processing of control signals is performed to obtain the control signals acting on the frequency band where the target signal is located. Based on the control signals, directional control is performed on the target signal. Throughout the entire process of controlling the target signal, the target signal state is simultaneously quantified in multiple dimensions to obtain multi-level control effects. Based on different control effects, the generation parameters of the control signal are adjusted in a differentiated adaptive iterative manner.

[0022] The target frequency band is specifically 400MHz-6GHz, which covers the communication bands of the vast majority of illegal drones and common legal communication bands (such as the 2.4GHz and 5GHz civilian communication bands), ensuring comprehensive monitoring without blind spots. The progressive joint identification processing, dynamic constraint generation and processing of control signals, multi-dimensional quantization processing, and differentiated adaptive iterative adjustment form a complete closed loop. Each step provides input for subsequent steps, and the results of subsequent steps inversely optimize the preceding steps, achieving precise control through communication collaboration. The core solution is to address the shortcomings of existing technologies, such as unconstrained interference, false interference with legitimate communication, and unsustainable control effects.

[0023] Among them, target signals specifically refer to wireless communication signals emitted by illegal drones, while legal communication signals include civilian mobile phone communication signals, radio and television signals, and legal private network communication signals. The distinction and isolation between the two is a prerequisite for achieving precise control and to avoid affecting legal communications during the control process.

[0024] Furthermore, the specific processing steps for progressive joint identification are as follows: A full-domain scan and data analysis of wireless signals in the target frequency band is performed to extract basic characteristic parameters representing the frequency band usage status. The first round of signal screening is completed according to preset screening rules to select a set of suspected signals that meet the characteristic trends of the target signals. A refined feature extraction operation is performed on the suspected signal set to mine exclusive features that can identify the source attributes of the signal. A feature comparison model between suspected signals and legitimate communication signals is established. The two types of signals are accurately distinguished by calculating the feature matching degree. At the same time, the target signals after distinction are located by frequency band and isolated to form a standardized recognition result dataset.

[0025] like Figure 2 As shown, the full-domain scanning adopts a frequency band-by-frequency band continuous scanning mode with a scanning rate of 100MHz / s to ensure that the full coverage scan of the 400MHz-6GHz frequency band is completed in a short time. During the scanning process, three basic characteristic parameters of each frequency band are collected simultaneously: signal power, frequency bandwidth, and signal modulation method, which serve as core parameters characterizing the usage status of the frequency band.

[0026] The preset screening rules are set based on the typical characteristics of the target signal (illegal drone signal), specifically: signal power between -80dBm and -40dBm, frequency bandwidth between 100kHz and 1MHz, and modulation method of FSK (frequency shift keying) or PSK (phase shift keying). Signals that meet all three conditions are included in the suspected signal set, and signals with too weak power, mismatched bandwidth, or abnormal modulation method are eliminated to reduce the workload of subsequent fine processing.

[0027] Refined feature extraction focuses on the radio frequency fingerprint features of UAV signals, specifically including four unique features: carrier frequency offset, signal rise time, signal fall time, and symbol rate. These features are unique and can accurately identify the UAV model and signal source, distinguishing them from the characteristics of legitimate communication signals.

[0028] The feature comparison model adopts a distance-based matching model. The specific logic is as follows: First, a feature template library of legitimate communication signals is established. Then, the four exclusive features of the suspected signal are compared one by one with the features of all legitimate communication signals in the template library. The Euclidean distance between the two is calculated. If the Euclidean distance is greater than a preset threshold (the threshold is set to 0.8 and calibrated based on a large amount of experimental data), it is determined to be the target signal. If the Euclidean distance is less than or equal to the preset threshold, it is determined to be a legitimate communication signal, thus completing the accurate differentiation between the two types of signals.

[0029] The feature template library for legitimate communication signals is constructed using a multi-source acquisition, standardized storage, and dynamic updating approach. It clearly defines the feature sources, storage formats, and update mechanisms to ensure the feature comparison model can be practically implemented. For different types of legitimate communication signals, corresponding acquisition methods are used to obtain specific features (carrier frequency offset, signal rise time, signal fall time, symbol rate), enabling independent acquisition without relying on operator cooperation.

[0030] Among them, civilian mobile phone communication signals (4G / 5G) are received by the signal acquisition terminal to obtain legal mobile phone communication signals from the public network. The demodulation mode is turned on, the frame structure and modulation parameters of the signal are analyzed, and the carrier frequency offset (the difference from the standard carrier frequency), the signal rise time (the time for the signal to rise from 10% of the peak value to 90% of the peak value), the signal fall time (the time for the signal to fall from 90% of the peak value to 10% of the peak value), and the symbol rate (the number of symbols transmitted per unit time) are collected for each frequency band. 100 sets of valid data are collected for each frequency band, and the average value is taken as the standard exclusive feature of this type of signal.

[0031] The broadcast television signal receives the local legal broadcast television signal through a dedicated receiving antenna, pre-records a standard signal segment (10 seconds in length), performs fine analysis on the recorded signal, extracts the above four exclusive features, repeats the sampling 50 times, removes outliers and takes the average value as the standard feature.

[0032] For legitimate private network communication signals in known frequency bands, a targeted acquisition method is used, connecting to the private network signal transmitter (with compliant authorization) to collect its unique characteristics. For private network signals in unknown frequency bands, after capturing the signal through full-domain scanning, a legitimacy verification mechanism is added. Specifically, the unknown frequency band signal undergoes multi-dimensional feature comparison, which is compared with the features of existing target signals (illegal drones) in the template library. If the Euclidean distance is ≤0.8 (consistent with the target signal judgment threshold), it is judged as an illegal signal and is not marked; if the Euclidean distance is >0.8, and the signal power, bandwidth, and modulation method meet the typical characteristics of legitimate private network signals (power ≥-60dBm, bandwidth ≥1MHz, modulation method is QPSK or QAM), it is judged as a legitimate private network signal, its features are analyzed, and a private network identifier is marked, completing feature acquisition and avoiding template library contamination.

[0033] The template library uses a relational database for storage. The database structure is divided into three levels: "signal type table - feature parameter table - calibration data table". The signal type table stores the legal communication signal types (such as 4G, 5G, broadcast television, private network) and their corresponding frequency bands. The feature parameter table stores the four unique feature standard values, deviation ranges and acquisition timestamps for each signal type. The calibration data table stores feature calibration parameters (such as the influence coefficients of ambient temperature and humidity on the features). The feature vector format adopts the standardized format of "signal type code - feature name - standard value - deviation range" to ensure that each comparison operation has a clear carrier, which facilitates quick retrieval and comparison.

[0034] The template library employs a dual update mode to ensure synchronization with dynamic changes in the characteristics and frequency bands of legitimate communication signals. Regular updates involve quarterly re-collection and calibration of all feature parameters in the template library, replacing outdated feature values ​​and updating deviation ranges to ensure feature accuracy. Real-time updates automatically trigger a feature re-collection process when changes to legitimate communication frequency bands or signal characteristics are detected (e.g., temporary frequency band changes in private networks or communication standard upgrades). This process collects new, exclusive features, updates the corresponding entries in the database, generates an update log, and synchronizes it to the feature comparison model, ensuring the comparison logic matches the latest signal features. A manual update entry is also provided, allowing manual input of new legitimate communication signal types and features to adapt to unexpected scenarios.

[0035] Frequency band positioning employs the peak frequency method, which determines the start and end frequencies of the target signal by capturing the frequency range corresponding to the power peak of the target signal, achieving a positioning accuracy of up to 10kHz. Signal isolation utilizes frequency band isolation technology, which physically isolates the target signal frequency band from the legitimate communication signal frequency band by setting an isolation frequency band (width of 50kHz), preventing mutual interference between the two types of signals and ensuring that subsequent control signals only apply to the target signal frequency band.

[0036] The target signal's location coordinates are obtained using angle-of-arrival (AOA) positioning technology combined with direction-finding antenna (DF) amplitude comparison. This process ensures accurate and verifiable location coordinates, supporting antenna alignment for directional control and addressing issues related to the compatibility of DF array spacing and the lack of a power attenuation model. Deploying a multi-element ultra-wideband DF antenna array solves the full-band compatibility problem. This array contains four evenly distributed ultra-wideband DF antenna elements, operating in the 400MHz-6GHz frequency range. It eliminates the need for dynamic element spacing, with a fixed spacing of 0.2 meters, accommodating the full 400MHz-6GHz band. For the 400MHz low-frequency band, algorithmic compensation improves resolution; for the 6GHz high-frequency band, optimized antenna element layout reduces coupling, ensuring accurate capture of target signal amplitude and phase differences, achieving omnidirectional target signal reception. Simultaneously, a frequency band adaptive calibration module is integrated into the antenna array to calibrate signal acquisition deviations in real time across different frequency bands, guaranteeing full-band positioning accuracy.

[0037] After the direction-finding antenna array receives the target signal, it simultaneously collects the amplitude and phase values ​​of the target signal received by the four antenna elements. The amplitude difference of the target signal reaching different antenna elements is calculated by the amplitude comparison method. Combined with the preset antenna array calibration parameters, the azimuth range of the target signal is initially determined (accuracy can reach ±1°). Combining Angle of Arrival (AOA) positioning technology, a correlation model between the target signal's angle of arrival and its spatial location is established based on the spatial coordinates of the antenna array. The initially determined azimuth angle is substituted into the model, and the elevation angle is calculated by combining the power attenuation law of the target signal. The power attenuation adopts a free space path loss model, which is as follows: the path loss value is equal to 20 multiplied by the logarithm of the distance (in kilometers) (base 10), plus 20 multiplied by the logarithm of the frequency (in megahertz) (base 10), plus 32.45, in decibels. The path loss value also satisfies the following conditions: the path loss value is equal to the target signal's transmitted power (estimated through signal feature analysis) plus the transmitting antenna gain (default value is 0.5dB, suitable for most small illegal drones), plus the receiving antenna gain (fixed at 15dB), minus the target signal power received by the antenna array. Considering that the transmission power of illegal drones cannot be directly obtained, a signal feature analysis estimation method is adopted. This method is based on the specific characteristics of the target signal, such as carrier frequency offset and symbol rate, combined with a preset drone signal power database (covering the transmission power range of common small illegal drones, 10mW-100mW, corresponding to power values ​​of -20dBm to -10dBm), and matches the closest transmission power as the estimated value to ensure that the distance calculation is feasible.

[0038] By substituting the estimated transmit power, known antenna gain, and measured receive power into the path loss formula, the propagation distance between the target signal and the antenna array is calculated. Combined with the azimuth angle, the elevation angle of the target signal is calculated (accuracy up to ±0.5°). The calculated azimuth and elevation angles are used as the spatial positioning coordinates of the target signal. Combined with the latitude and longitude coordinates of the direction-finding antenna array itself, the approximate latitude and longitude coordinates of the target signal (illegal drone) can be further calculated, forming complete positioning coordinate data. The positioning coordinate data undergoes validity verification, eliminating abnormal coordinates caused by signal interference (using the 3σ criterion, coordinate values ​​exceeding the average ±3 standard deviations are judged as outliers and replaced with positioning coordinates from adjacent times), ensuring the accuracy of the positioning coordinates. The verified positioning coordinates (azimuth and elevation angles, with optional latitude and longitude) are integrated with the target signal's frequency band information, specific characteristic parameters, and the frequency band boundary information of legitimate communication signals to form a standardized identification result dataset, providing accurate positioning basis for subsequent antenna direction adjustment in the directional control phase.

[0039] The standardized recognition result dataset contains frequency band information of the target signal, specific feature parameters, positioning coordinates, and frequency band boundary information of legitimate communication signals, providing complete data support for the subsequent acquisition of frequency band relative positional relationships.

[0040] Furthermore, the specific process for obtaining the relative positional relationships of the frequency bands is as follows: Retrieve the target signal frequency band identifier and the legitimate communication signal frequency band identifier from the recognition result dataset, and construct a dual-signal frequency band spatial distribution model; The frequency band boundaries, frequency band overlap areas, and frequency band spacing of the two types of signals are quantized and analyzed to determine the relative position of the target signal frequency band in the entire frequency band, as well as the adjacency or isolation relationship between the target signal frequency band and the legitimate communication signal frequency band. The obtained position parameters, boundary parameters, and spacing parameters are integrated to generate a standardized dataset of frequency band relative position relationships.

[0041] The target signal frequency band is identified by its start and end frequencies, while the legal communication signal frequency band is identified by the preset frequency range of various legal communication signals (e.g., 2.4GHz-2.4835GHz for WiFi communication and 87MHz-108MHz for broadcast television). After retrieval, a dual-signal frequency band spatial distribution model is constructed in ascending order of frequency. This model uses frequency as the horizontal axis and signal power as the vertical axis to visually represent the target signal frequency band and the legal communication signal frequency band, facilitating subsequent quantization analysis. Quantization analysis of the frequency band boundaries employs a threshold determination method, using a signal power drop to 1 / 10 of its peak power as the boundary criterion to accurately determine the start and end boundaries of the two signal frequency bands, with boundary errors controlled within 5kHz. Quantization analysis of overlapping frequency band regions is achieved by comparing the frequency ranges of the two signal types. If the target signal frequency band intersects with a legal communication signal frequency band, the frequency range and bandwidth of the intersection region are calculated; otherwise, it is determined to be a non-overlapping region. The quantization analysis of frequency band spacing involves calculating the frequency difference between the termination boundary of the target signal frequency band and the starting boundary of the adjacent legal communication signal frequency band. If the difference is greater than 0, the signal is isolated, and the difference is the isolation interval. If the difference is equal to 0, the signal is adjacent. If the difference is less than 0, the signal is overlapping.

[0042] Location parameters include the starting and ending positions of the target signal frequency band within the entire frequency band (400MHz-6GHz), as well as its center frequency position. Boundary parameters include the starting and ending boundary values ​​for the two types of signal frequency bands; spacing parameters include the isolation spacing value and the bandwidth value of the overlapping area. These parameters are integrated according to the format "target signal identifier - legal communication signal identifier - parameter type - parameter value" to generate a standardized frequency band relative position relationship dataset, ensuring a unified data format that can be directly imported into subsequent control signal generation models.

[0043] Furthermore, the specific process for generating dynamic constraints for control signals is as follows: The frequency band relative position relationship dataset is imported into the control signal generation model. The generation constraints are set with the legal communication signal frequency band as the protection boundary to limit the frequency band coverage and energy distribution area of ​​the control signal. By combining the target signal-specific features in the recognition result dataset, we construct the matching and association rules between the control signal and the target signal, and determine the range of core generation parameters for the control signal. The control signal generation operation is performed according to the constraints and adaptation rules. The frequency band compliance of the generated signal is verified, signal components that exceed the constraints are removed, and a control signal that only applies to the frequency band of the target signal is formed.

[0044] like Figure 3As shown, the control signal generation model is a dynamic generation model based on signal adaptability. Its core logic prioritizes constraints over adaptation; that is, it first satisfies the protection constraints of legitimate communication signals, and then achieves precise adaptation with the target signal. The generation constraints are specifically divided into two categories: first, frequency band constraints, where the frequency band coverage of the control signal must completely fall within the target signal's frequency band, and the distance between it and the legitimate communication signal's frequency band must be no less than 50kHz, strictly prohibiting intrusion into the legitimate communication signal's frequency band; second, energy constraints, where the energy distribution of the control signal within the target signal's frequency band is uniform, and the energy peak value does not exceed -30dBm, avoiding excessive energy causing additional interference to the surrounding environment.

[0045] The matching and association rules between the control signal and the target signal are constructed based on the target signal-specific features in the recognition result dataset. The specific correspondence is as follows: the carrier frequency offset of the target signal determines the carrier frequency of the control signal, and the carrier frequency of the control signal must be consistent with the carrier frequency of the target signal, with a deviation not exceeding 1kHz; the symbol rate of the target signal determines the symbol rate of the control signal, and the symbol rate of the control signal is 1.2 times that of the target signal to ensure effective interference with the communication link of the target signal; the modulation method of the target signal determines the modulation method of the control signal, and the same modulation method (FSK or PSK) as the target signal is adopted to improve the interference effect.

[0046] The core generation parameters include carrier frequency range, symbol rate range, modulation method, and signal power range. The carrier frequency range is consistent with the target signal frequency band, the symbol rate range is 1.1-1.3 times the target signal symbol rate, and the signal power range is -40dBm to -30dBm. The control signal generation operation uses waveform synthesis. Based on the core generation parameters, a signal generator synthesizes a control signal waveform that meets the requirements. Frequency band compliance verification uses spectrum analysis. The generated control signal is imported into a spectrum analyzer to check whether its frequency band coverage and energy distribution area meet the constraints. If there are signal components that exceed the target signal frequency band, intrude into the legitimate communication signal frequency band, or have abnormal energy distribution, they are removed using filtering techniques until the control signal fully meets the constraints and adaptation rules, ultimately forming a control signal that only acts on the target signal frequency band and is precisely adapted to the target signal.

[0047] Furthermore, the specific execution process of performing targeted control on the target signal based on the control signal is as follows: The control signal obtained by dynamic constraint generation and processing of the control signal is output to the signal control execution link according to the preset execution logic to ensure that the control signal is accurately applied to the frequency band of the target signal. During the execution of targeted control, the frequency band changes and signal strength fluctuations of the target signal are tracked simultaneously, and the output power and range of the control signal are adjusted in real time to maintain the compatibility between the control signal and the target signal. Continuously verify the frequency range of the controlled signals to ensure that they do not intrude into the frequency bands of legitimate communication signals, thereby achieving effective control over target signals while ensuring the normal transmission of legitimate communication signals.

[0048] like Figure 4 As shown, the preset execution logic is to first locate, then output, and then track. Specifically, based on the target signal's location coordinates (azimuth and elevation angles) in the recognition result dataset, the antenna direction of the signal control execution stage is adjusted. The antenna steering controller uses a mechanical servo motor, with an azimuth adjustment step of 0.1° and an elevation adjustment step of 0.05°. The controller receives positioning coordinate commands and drives the antenna to precisely steer, accurately aligning the antenna with the target signal's source direction. The directional gain is set to 15dB to ensure the control signal accurately radiates to the target signal's location, preventing leakage into legitimate communication areas. Then, according to the preset output power (initial output power is -35dBm), the control signal is stably output to the target signal's frequency band to prevent signal leakage. The target signal's frequency band changes and signal strength fluctuations are tracked using real-time spectrum monitoring technology at a frequency of 10 times / second, capturing real-time changes in the target signal's start frequency, end frequency, and signal power. When the target signal's frequency band shifts, the control signal's frequency band coverage is adjusted synchronously to ensure the control signal always covers the target signal's frequency band. When the target signal strength increases, the output power of the control signal is appropriately increased (maximum not exceeding -30dBm); when the target signal strength decreases, the output power of the control signal is appropriately decreased (minimum not lower than -40dBm), always maintaining the compatibility between the control signal and the target signal to ensure stable interference effect. Continuous verification of the control signal's frequency band range employs a real-time frequency band boundary detection method, detecting the control signal's frequency band boundary every 500 milliseconds and comparing it with the frequency band boundary of legitimate communication signals. If a tendency for the control signal to intrude into the legitimate communication signal's frequency band is detected, filtering is immediately initiated to adjust the control signal's frequency band range and simultaneously reduce the signal energy in the corresponding area, ensuring that the control signal always operates within the target signal's frequency band.

[0049] The core objective of targeted control is to interfere with the communication link of the target signal, preventing illegal drones from receiving commands normally and enabling them to hover, return to base, or make an emergency landing. At the same time, through continuous verification and adjustment, it ensures that legitimate communication signals are not affected in any way, truly achieving the synergistic goal of "precise control and legitimate protection".

[0050] Furthermore, the specific process of multi-dimensional quantization is as follows: During the process of controlling the signal in a directional manner, the target signal is collected in real time and analyzed in multiple dimensions to extract the core quantitative parameters that characterize the signal's operating status and to build a multi-dimensional status monitoring system. The collected state parameters are processed by time-series organization and standardization preprocessing to eliminate invalid data and interference components during signal acquisition and transmission. Establish a quantitative assessment model for control effectiveness, input preprocessed state parameters into the model for calculation, classify control effectiveness levels according to preset judgment rules, and generate a quantitative assessment report containing parameter data, time series information and level results.

[0051] like Figure 5 As shown, real-time acquisition, multi-dimensional analysis, and directional control of the target signal are carried out simultaneously, with an acquisition frequency of 10 times / second. The acquired signal data includes four core quantitative parameters: target signal power, frequency stability, symbol error rate, and link connectivity. These four parameters characterize the operating status of the target signal from different dimensions, forming a multi-dimensional status monitoring system. Frequency stability is represented by the frequency fluctuation value, i.e., the difference between the actual frequency of the target signal and the standard frequency; the smaller the difference, the better the frequency stability. The symbol error rate does not require obtaining the correct symbols of the target signal as a benchmark. It adopts a method based on signal demodulation and statistical inference. Specifically, the acquired target signal is demodulated (the demodulation method matches the target signal modulation method; non-coherent demodulation is used for FSK signals, and coherent demodulation is used for PSK signals). After demodulation, the symbol sequence of the target signal is obtained. Based on the general symbol rules of UAV communication (such as the commonly used Manchester coding and NRZ coding rules), combined with a multi-mode verification mechanism, the number of symbols that do not conform to the rules is counted as the number of erroneous symbols. To address potential proprietary coding protocols, a symbol sequence consistency check is added. If the symbol sequence at multiple consecutive sampling times shows no obvious pattern and deviates significantly from the general coding pattern, it is determined to be proprietary coding. In this case, the symbol error rate is replaced by the signal demodulation success rate, which is the complement of the ratio of the number of successfully demodulated symbols to the total number of symbols (1 - demodulation success rate). This avoids misjudgment of the error rate due to proprietary coding. The total number of symbols is the total number of symbols obtained after demodulation. The symbol error rate is calculated and updated in real time to ensure data validity.

[0052] The connectivity criterion for link connectivity is that valid data packets containing the target signal are detected for three consecutive sampling times (the data packets must contain a complete frame header, data segment, and checksum, and the checksum must pass verification). Checksum verification employs a multi-algorithm compatible mode, defaulting to CRC-16 polynomial (0x8005) verification, while also supporting parity check and CRC-32 as alternative algorithms. Technicians can choose the appropriate algorithm based on the actual scenario. If all three algorithms fail verification, the checksum is deemed invalid, and the data packet is not included in the valid range. Connectivity probability is calculated using a sliding window statistical method. The sliding window size is set to 10 sampling times (i.e., 1 second). The number of connected sampling times within the window is counted in real time. The connectivity probability is the ratio of the number of connected sampling times within the window to the total number of sampling times in the window, ranging from 0 to 1, where 1 represents complete connectivity (connected for all 10 sampling times) and 0 represents complete disconnection (disconnected for all 10 sampling times). The window is updated in real time to ensure the real-time performance and accuracy of the probability calculation.

[0053] Time-series processing involves sorting the collected parameter data according to the collection timestamp to form a time-series dataset, facilitating subsequent analysis of parameter trends. Standardization preprocessing employs two steps: normalization and outlier removal. Normalization maps all parameter data to the 0-1 interval, eliminating dimensional differences between parameters. Specifically, the normalization method involves subtracting the minimum value from the actual value of each parameter, then dividing by the difference between the maximum and minimum values. The minimum and maximum values ​​are determined using a sliding window extreme value statistical method. The sliding window size is consistent with the connectivity probability statistical window, set to 10 sampling times (1 second), and the extreme values ​​within the window are updated in real-time to prevent global extreme values ​​from being affected by early outliers, ensuring stable normalization results. Outlier removal uses the 3σ criterion: calculating the mean and standard deviation of each parameter, and identifying parameter values ​​exceeding the mean ± 3 times the standard deviation as outliers (invalid or interfering data). These outliers are then replaced with parameter values ​​from adjacent time points to ensure data accuracy.

[0054] The quantitative assessment model for control effectiveness employs a weighted summation model. The specific logic is as follows: different weights are assigned to four core quantization parameters, with symbol error rate weighted at 0.4, link connectivity weighted at 0.3, signal power weighted at 0.2, and frequency stability weighted at 0.1. The normalized values ​​of the four preprocessed parameters are multiplied by their respective weights and summed to obtain the quantified control effectiveness value (range 0-1). The preset judgment rules are: a quantization value ≥ 0.8 indicates Level 1 control effectiveness (complete control); 0.5 ≤ quantization value < 0.8 indicates Level 2 control effectiveness (partial control); and a quantization value < 0.5 indicates Level 3 control effectiveness (no control). The quantitative assessment report is generated in the format of "collection time - parameter data - preprocessed data - quantization value - control effectiveness level," providing accurate data support for obtaining subsequent multi-level control effectiveness and differentiated adaptive iterative adjustments.

[0055] Furthermore, the specific process of achieving the effects of multi-level control is as follows: By retrieving the trend of changes in status parameters and the basis for level classification from the quantitative assessment report, an in-depth analysis of the control response status of the target signal is conducted to clarify the signal status characteristics corresponding to the control effectiveness level. Establish a level verification mechanism to review and verify the preliminary level of control effectiveness by combining the execution duration of control signals and parameter input, and eliminate level misjudgments caused by instantaneous interference; The approved control effectiveness levels are associated with and stored with the corresponding status parameter features to form a hierarchical control effectiveness dataset.

[0056] The analysis of the trend of state parameter changes adopts the time-series trend analysis method. Trend fitting is performed on the core quantitative parameters (power, frequency stability, symbol error rate, and link connectivity status) of 10 consecutive sampling times in the quantitative assessment report. The change law of parameters over time is analyzed to determine the signal state characteristics corresponding to different control effect levels. Specifically, the signal state characteristics corresponding to Level 1 control effect (complete control) are: symbol error rate ≥ 0.8, link connectivity probability ≤ 0.2, continuous decrease in signal power, and frequency fluctuation value ≥ 0.5kHz. The criterion for determining the trend of continuous decrease in signal power is that the signal power value shows a decreasing trend within 5 consecutive sampling times (i.e., 0.5 seconds), and the power difference between two adjacent sampling times is not less than 0.5dBm. A natural fluctuation exclusion mechanism is added, that is, combined with the frequency stability parameter for auxiliary judgment. If the frequency fluctuation value < 0.2kHz (signal stable, no obvious movement or obstruction interference), it is judged as a continuous decrease caused by control; if the frequency fluctuation value ≥ 0.2kHz (signal stable, no obvious movement or obstruction interference), it is judged as a continuous decrease caused by control. If the signal is unstable (possibly due to movement or obstruction), the judgment time needs to be extended to 10 consecutive sampling moments (1 second), and the power difference between adjacent moments must not be less than 0.3 dBm before it can be determined as a continuous decrease caused by control measures. This avoids misjudging natural fluctuations as control effects and reduces misjudgments of the control level. The signal state characteristics corresponding to the level 2 control effect (partial control) are 0.5 ≤ symbol error rate < 0.8, 0.2 < link connectivity probability ≤ 0.5, small signal power fluctuation, and frequency fluctuation between 0.2-0.5 kHz. The signal state characteristics corresponding to the level 3 control effect (no control) are symbol error rate < 0.5, link connectivity probability > 0.5, stable signal power, and frequency fluctuation < 0.2 kHz. The level verification mechanism employs a dual verification logic of duration verification and parameter verification. Duration verification requires that the initially classified control effect level be maintained for at least 3 seconds. If the duration of a certain level is less than 3 seconds, it is judged as a misjudgment of the level caused by transient interference and is rejected. Parameter verification requires retrieving the execution duration of the control signal and the parameter input. If the execution duration of the control signal is less than 5 seconds or the core generated parameters are not set according to the adaptation rules, resulting in a discrepancy between the initially classified level and the actual control effect, it is judged as a misjudgment and is rejected.

[0057] The specific verification process is as follows: First, the preliminary control effect levels are filtered by duration, retaining levels with a duration ≥ 3 seconds; then, the filtered levels are verified by comparing the execution duration of the control signal with the parameter input to confirm the rationality of the level division; finally, the levels that pass the verification are confirmed to ensure the accuracy of the level division. The associated storage involves linking the verified control effect levels (Level 1, Level 2, and Level 3) with the corresponding signal state characteristics (the value range and trend of the four core quantitative parameters) one-to-one. A graded control effect dataset is generated according to the format "Level Number - State Parameter Characteristics - Verification Result." This dataset will serve as the core basis for subsequent differentiated adaptive iterative adjustments, ensuring the adjustment process is targeted.

[0058] Furthermore, the specific process of differentiated adaptive iterative adjustment is as follows: Retrieve the dataset of hierarchical control effect, construct an adjustment rule system for control signal generation parameters based on different control effect levels, and match differentiated adjustment dimensions and adjustment ranges for different levels; Based on the adjustment rules corresponding to the current control effectiveness level, the core generation parameters of the control signal are extracted, parameter calculation and adjustment operations are performed, and an updated parameter dataset is generated. The updated parameter dataset is validated to ensure that the parameter adjustments comply with the frequency band constraints and the control signal adaptation rules, thus forming optimized parameters that can be directly input into the dynamic constraint generation step of the control signal.

[0059] The adjustment rule system for the control signal generation parameters is built based on a hierarchical control effect dataset. Different adjustment dimensions and magnitudes are set for three different control effect levels to ensure that the adjusted parameters can accurately optimize the control effect. At the same time, the target signal symbol rate mentioned in the adjustment rules refers to the current real-time value of the target signal (i.e., the symbol rate after dynamic changes during tracking). The system collects and updates the symbol rate of the target signal in real time. When the target signal is a frequency-hopping UAV and the symbol rate is dynamically adjusted, the current real-time symbol rate is used as the adjustment benchmark to ensure that the control signal and the target signal are always compatible. The specific adjustment rules are as follows: For Level 1 control (complete control), the adjustment dimension is signal power, and the adjustment range is -5dBm (i.e., reducing the output power of the control signal by 5dBm). The adjustment logic is that the target signal is now completely controlled, and reducing the power can reduce energy consumption, while avoiding excessive interference with the surrounding environment, without affecting the control effect. For Level 2 control (partial control), the adjustment dimensions are signal power and symbol rate, and the adjustment range is signal power +3dBm (increasing the output power by 3dBm) and symbol rate +0.1 times the target signal symbol rate (increasing the symbol rate to 1.1 times the target signal). The adjustment logic is that the target signal is not now completely controlled. For control, increasing power can enhance interference intensity, and increasing symbol rate can further disrupt the communication link of the target signal, thus improving the control effect. For level three control effect (uncontrolled), the adjustment dimensions are carrier frequency, signal power, and symbol rate. The adjustment range is: carrier frequency calibrated to the current carrier frequency of the target signal (deviation not exceeding 1kHz), signal power +5dBm (increasing output power by 5dBm), and symbol rate +0.2 times the symbol rate of the target signal (increasing the symbol rate to 1.2 times the target signal). The adjustment logic is that the target signal is not effectively interfered with at this time, and the core parameters need to be comprehensively adjusted to ensure that the control signal and the target signal are accurately matched and quickly improve the interference effect.

[0060] The specific process for parameter calculation and adjustment is as follows: First, retrieve the adjustment rules corresponding to the current control effect level and extract the core generation parameters of the control signal (carrier frequency, signal power, symbol rate); then, calculate the adjusted parameter values ​​based on the adjustment magnitude. For example, if the current signal power is -35dBm, and the control effect is level 2, the adjusted power would be -35dBm + 3dBm = -32dBm; finally, organize all the adjusted core parameters to generate an updated parameter dataset. Validity verification employs a "dual verification" logic. The first layer is frequency band constraint verification, checking whether the adjusted carrier frequency and signal frequency band coverage meet the requirements to ensure no intrusion into legitimate communication signal frequency bands. The second layer is adaptation rule verification, checking whether the adjusted parameters are adapted to the specific characteristics of the target signal to ensure that the control signal can effectively interfere with the target signal. If the verification fails, the specific quantization rules are as follows: If the controlled signal frequency band intrudes into the legitimate communication signal frequency band, fine-tune the carrier frequency of the controlled signal with an adjustment step of 10kHz, while simultaneously reducing the signal power by 1dBm until the frequency band falls entirely within the target signal frequency band; if the carrier frequency offset exceeds 1kHz, calibrate the carrier frequency with a step of 0.5kHz until the deviation is ≤1kHz; if the symbol rate exceeds 1.1-1.3 times the symbol rate of the target signal, adjust with a step of 0.05 times the current symbol rate of the target signal until it falls within the suitable range; if the signal power exceeds the range of -40dBm to -30dBm, adjust with a step of 1dBm until it meets the requirements. After fine-tuning, perform the validity verification again until the parameters meet the requirements, forming optimized parameters that can be directly input into the dynamic constraint generation step of the controlled signal, ensuring the adaptive characteristics of iterative adjustment and avoiding blind trial and error.

[0061] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0062] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0063] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the stated principles, the implementation of the present invention may have any variations or modifications.

Claims

1. A method for controlling low-altitude defense signals of unmanned aerial vehicles (UAVs) based on communication collaboration, characterized in that: The method includes: It collects wireless signals, performs progressive joint identification processing on the wireless signals in the target frequency band, and distinguishes and isolates the target signals from legitimate communication signals; The relative frequency band position relationship between the target signal and the legitimate communication signal is obtained. Using the relative frequency band position relationship as the core constraint, dynamic constraint generation processing of control signals is performed to obtain the control signals acting on the frequency band where the target signal is located. Based on the control signals, directional control is performed on the target signal. Throughout the entire process of controlling the target signal, the target signal state is simultaneously quantified in multiple dimensions to obtain multi-level control effects. Based on different control effects, the generation parameters of the control signal are adjusted in a differentiated adaptive iterative manner.

2. The method for controlling low-altitude defense signals of unmanned aerial vehicles based on communication cooperation according to claim 1, characterized in that, The specific processing steps for the progressive joint identification are as follows: A full-domain scan and data analysis of wireless signals in the target frequency band is performed to extract basic characteristic parameters representing the frequency band usage status. The first round of signal screening is completed according to preset screening rules to select a set of suspected signals that meet the characteristic trends of the target signals. A refined feature extraction operation is performed on the suspected signal set to mine exclusive features that can identify the source attributes of the signal. A feature comparison model between suspected signals and legitimate communication signals is established. The two types of signals are accurately distinguished by calculating the feature matching degree. At the same time, the target signals after distinction are located by frequency band and isolated to form a standardized recognition result dataset.

3. The method for controlling low-altitude defense signals of unmanned aerial vehicles based on communication cooperation according to claim 2, characterized in that, The specific process for obtaining the relative positional relationship of the frequency bands is as follows: Retrieve the target signal frequency band identifier and the legitimate communication signal frequency band identifier from the recognition result dataset, and construct a dual-signal frequency band spatial distribution model; The frequency band boundaries, frequency band overlap areas, and frequency band spacing of the two types of signals are quantized and analyzed to determine the relative position of the target signal frequency band in the entire frequency band, as well as the adjacency or isolation relationship between the target signal frequency band and the legitimate communication signal frequency band. The obtained position parameters, boundary parameters, and spacing parameters are integrated to generate a standardized dataset of frequency band relative position relationships.

4. The method for controlling low-altitude defense signals of unmanned aerial vehicles based on communication cooperation according to claim 3, characterized in that, The specific process for generating the dynamic constraints of the control signals is as follows: The frequency band relative position relationship dataset is imported into the control signal generation model. The generation constraints are set with the legal communication signal frequency band as the protection boundary to limit the frequency band coverage and energy distribution area of ​​the control signal. By combining the target signal-specific features in the recognition result dataset, we construct the matching and association rules between the control signal and the target signal, and determine the range of core generation parameters for the control signal. The control signal generation operation is performed according to the constraints and adaptation rules. The frequency band compliance of the generated signal is verified, signal components that exceed the constraints are removed, and a control signal that only applies to the frequency band of the target signal is formed.

5. The method for controlling low-altitude defense signals of unmanned aerial vehicles based on communication cooperation according to claim 4, characterized in that, The specific execution process of performing targeted control on the target signal based on the control signal is as follows: The control signal obtained by dynamic constraint generation and processing of the control signal is output to the signal control execution link according to the preset execution logic to ensure that the control signal is accurately applied to the frequency band of the target signal. During the execution of targeted control, the frequency band changes and signal strength fluctuations of the target signal are tracked simultaneously, and the output power and range of the control signal are adjusted in real time to maintain the compatibility between the control signal and the target signal. Continuously verify the frequency range of the controlled signals to ensure that they do not intrude into the frequency bands of legitimate communication signals, thereby achieving effective control over target signals while ensuring the normal transmission of legitimate communication signals.

6. The method for controlling low-altitude defense signals of unmanned aerial vehicles based on communication cooperation according to claim 5, characterized in that, The specific process of the multi-dimensional quantization is as follows: During the process of controlling the signal in a directional manner, the target signal is collected in real time and analyzed in multiple dimensions to extract the core quantitative parameters that characterize the signal's operating status and to build a multi-dimensional status monitoring system. The collected state parameters are processed by time-series organization and standardization preprocessing to eliminate invalid data and interference components during signal acquisition and transmission. Establish a quantitative assessment model for control effectiveness, input preprocessed state parameters into the model for calculation, classify control effectiveness levels according to preset judgment rules, and generate a quantitative assessment report containing parameter data, time series information and level results.

7. The method for controlling low-altitude defense signals of unmanned aerial vehicles based on communication cooperation according to claim 6, characterized in that, The specific process for achieving the multi-level control effect is as follows: By retrieving the trend of changes in status parameters and the basis for level classification from the quantitative assessment report, an in-depth analysis of the control response status of the target signal is conducted to clarify the signal status characteristics corresponding to the control effectiveness level. Establish a level verification mechanism to review and verify the preliminary level of control effectiveness by combining the execution duration of control signals and parameter input, and eliminate level misjudgments caused by instantaneous interference; The approved control effectiveness levels are associated with and stored with the corresponding status parameter features to form a hierarchical control effectiveness dataset.

8. The method for controlling low-altitude defense signals of unmanned aerial vehicles based on communication cooperation according to claim 7, characterized in that, The specific process of the differentiated adaptive iterative adjustment is as follows: Retrieve the dataset of hierarchical control effect, construct an adjustment rule system for control signal generation parameters based on different control effect levels, and match differentiated adjustment dimensions and adjustment ranges for different levels; Based on the adjustment rules corresponding to the current control effectiveness level, the core generation parameters of the control signal are extracted, parameter calculation and adjustment operations are performed, and an updated parameter dataset is generated. The updated parameter dataset is validated to ensure that the parameter adjustments comply with the frequency band constraints and the control signal adaptation rules, thus forming optimized parameters that can be directly input into the dynamic constraint generation step of the control signal.

9. A communication-coordinated low-altitude defense signal control system for unmanned aerial vehicles (UAVs), characterized in that: The system is used to execute the communication-coordinated low-altitude defense signal control method for unmanned aerial vehicles as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the communication-cooperative UAV low-altitude defense signal control method according to any one of claims 1-8.

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