A method, device and equipment for evaluating mutual interference risk of multi-type communication equipment

Abstract

The application relates to the field of communication technology, in particular to a mutual interference risk assessment method, device and equipment of multi-type communication equipment. The method comprises the following steps: acquiring signal characteristic parameters of different communication equipment in a cooperative work area, constructing a unified signal model and generating an initial signal; constructing a water acoustic channel simulation model based on water area dynamic environment parameters; inputting the initial signal into the simulation model, simulating propagation distortion and superimposing a receiving end field strength, and generating a composite signal; and based on the comparison result of the composite signal and the initial signal, mutual interference analysis and risk quantification are carried out. The application solves the compatibility problem of heterogeneous equipment cooperative evaluation by constructing a unified signal model, overcomes the defect of large distortion of the traditional static model by constructing a water acoustic channel simulation model combined with dynamic environment parameters. The composite signal is generated by simulating signal propagation distortion and field strength superposition, the underwater physical mutual interference process is truly restored, the quantitative index is calculated based on the comparison result, and the transformation from qualitative analysis to quantitative evaluation is realized.

Description

Technical Field

[0001] This application relates to the field of communication technology, specifically to a method, apparatus, and device for assessing mutual interference risks among multiple types of communication devices. Background Technology

[0002] As underwater operations become increasingly complex, communication, navigation, and detection (CMM) equipment often needs to work collaboratively within the same underwater space. Current technologies for assessing acoustic interference between devices primarily employ static analysis methods based on free-space propagation models, or perform frequency domain compatibility simulations only for a single type of equipment. In practical engineering applications, passive avoidance methods such as hardware filtering and time-division multiplexing are typically relied upon to reduce interference. These methods lack proactive assessment mechanisms for signal interference during multi-device collaborative operation, making them ill-suited for operational scenarios with densely deployed, multi-source, heterogeneous equipment.

[0003] However, underwater acoustic channels exhibit significant spatiotemporal variations, making it difficult for existing assessment methods to reflect the deep modulation effects of dynamic environmental parameters on signal propagation. Traditional solutions often rely on static empirical formulas, resulting in low channel model fidelity and an inability to accurately simulate signal distortion. These shortcomings lead to significant discrepancies between interference assessment results and actual operating conditions in complex underwater collaborative scenarios, hindering the accurate identification of potential mutual interference risks. When severe interference occurs between devices, operators lack quantified risk levels and specific risk source locations, making it difficult to implement timely and effective measures such as frequency changes and power reductions. This can easily result in communication interruptions, navigation deviations, or false alarms, thereby affecting the reliability and safety of underwater operations. Therefore, a mutual interference risk assessment method that can address these shortcomings is urgently needed. Summary of the Invention

[0004] The problem is that the mutual interference assessment methods for underwater communication, navigation and exploration equipment in related technologies cannot reflect dynamic environmental effects due to the use of static channel models, and lack quantitative assessment and early warning mechanisms for the coordinated mutual interference of heterogeneous equipment, resulting in low assessment accuracy and inability to effectively guide interference avoidance.

[0005] In a first aspect, embodiments of this application provide a method for assessing the mutual interference risk of multiple types of communication devices, the mutual interference risk assessment method comprising: Obtain signal characteristic parameters of different types of communication devices within the collaborative operation area, construct a unified signal model for all communication devices based on the signal characteristic parameters, and generate initial signals; A simulation model of the underwater acoustic channel is constructed based on the dynamic environmental parameters of the collaborative operation water area; The initial signal is input into the underwater acoustic channel simulation model to simulate the propagation distortion of the initial signal in the channel and the superposition of the field strength at the receiving end, thereby generating the composite signal at the receiving end. The composite signal is compared with the initial signal, and mutual interference analysis and risk quantification are performed based on the comparison results.

[0006] In conjunction with the first aspect, in one implementation, the step of constructing a unified signal model for all communication devices and generating an initial signal based on signal characteristic parameters includes: using a unified mathematical expression to characterize the signal characteristic parameters of different functional devices in the time and frequency domains, and normalizing signals of different systems into an initial signal containing amplitude, phase, and frequency information.

[0007] In conjunction with the first aspect, in one implementation, the construction of the underwater acoustic channel simulation model based on dynamic environmental parameters of the collaborative operation water area includes: A sound velocity profile model, a medium sound absorption model, and a dynamic boundary condition model are constructed based on dynamic environmental parameters. The sound velocity profile model, the medium sound absorption model, and the dynamic boundary condition model are integrated into the propagation operator of the underwater acoustic channel simulation model.

[0008] In conjunction with the first aspect, in one embodiment, the step of inputting the initial signal into the underwater acoustic channel simulation model to simulate the propagation distortion of the initial signal in the channel and the superposition of the field strength at the receiving end includes: Calculate the multipath delay of signal propagation using a sound velocity profile model; Calculate the frequency-dependent attenuation of signal propagation using a medium sound absorption model; Calculate Doppler scaling of signal propagation using a dynamic boundary condition model; The multi-source signals, after undergoing multipath delay, frequency-dependent attenuation, and Doppler scaling, are superimposed in the time domain at the receiving end.

[0009] In conjunction with the first aspect, in one embodiment, after generating the composite signal at the receiving end, the method further includes: Multidimensional decoupling of composite signal features is performed on the composite signal to generate a composite signal feature parameter matrix; where... The composite signal characteristic parameter matrix includes at least the composite frequency distribution characteristics, instantaneous amplitude variation characteristics, global integrated signal-to-noise ratio, and signal integrated distortion.

[0010] In conjunction with the first aspect, in one implementation, the multidimensional composite signal feature decoupling of the composite signal includes: Perform a full-band fast Fourier transform on the composite signal and output the energy density spectrum distribution of the composite signal in the time-frequency domain as the composite frequency distribution characteristic; Extract the time-domain envelope curve of the composite signal and calculate the ratio of instantaneous peak power to average power as the instantaneous amplitude change characteristic; The overall signal-to-noise ratio is obtained by calculating the power ratio between the desired device signal component and the sum of the other device interference components and background noise in the composite signal. The overall signal distortion is calculated by solving the cross-correlation coefficient between the composite signal and the initial signal.

[0011] In conjunction with the first aspect, in one implementation, the multidimensional composite signal feature decoupling of the composite signal includes: Using the initial signal as a reference, differential mapping and coherent demodulation are performed with the composite signal feature parameter matrix. The type of mutual interference is identified by analyzing the deviation of the signal features.

[0012] In conjunction with the first aspect, in one implementation, the step of using the initial signal as a reference and performing differential mapping and coherent demodulation with the composite signal feature parameter matrix to identify the type of mutual interference by analyzing the deviation of signal features includes: If the frequency domain energy density spectrum in the feature parameter matrix of the composite signal overlaps with that of the initial signal, it is identified as co-frequency interference. If the ratio of the instantaneous peak power to the average power of the time-domain envelope curve in the feature parameter matrix of the composite signal exceeds a preset threshold, it is identified as amplitude suppression interference. If the total harmonic distortion and phase deviation variance of the waveform in the composite signal characteristic parameter matrix exceed the preset range, it is identified as phase distortion interference.

[0013] Secondly, embodiments of this application provide a mutual interference risk assessment device for multiple types of communication devices, the mutual interference risk assessment device comprising: The modeling module is used to acquire the signal characteristic parameters of different types of communication devices within the collaborative operation area, construct a unified signal model for all communication devices based on the signal characteristic parameters, and generate an initial signal. The channel simulation module is used to construct an underwater acoustic channel simulation model based on the dynamic environmental parameters of the collaborative operation water area. The channel simulation module is also used to input the initial signal into the underwater acoustic channel simulation model to simulate the propagation distortion of the initial signal in the channel and the superposition of the field strength at the receiving end to generate the composite signal at the receiving end. The evaluation module is used to compare the composite signal with the initial signal and perform mutual interference analysis and risk quantification based on the comparison results.

[0014] Thirdly, embodiments of this application provide a mutual interference risk assessment device, characterized in that the mutual interference risk assessment device includes a processor, a memory, and a mutual interference risk assessment program stored in the memory and executable by the processor, wherein when the mutual interference risk assessment program is executed by the processor, it implements the steps of the mutual interference risk assessment method as described in any one of the above.

[0015] The beneficial effects of the technical solutions provided in this application include: This application addresses the compatibility issue of heterogeneous device collaborative evaluation by constructing a unified signal model and overcomes the large distortion of traditional static models by building an underwater acoustic channel simulation model incorporating dynamic environmental parameters. By simulating signal propagation distortion and field strength superposition to generate composite signals, the underwater physical mutual interference process is realistically reproduced. Finally, quantitative indicators are calculated based on comparison results, realizing the transformation from qualitative analysis to quantitative assessment and significantly improving the accuracy and guidance of mutual interference risk assessment. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an embodiment of the mutual interference risk assessment method of this application; Figure 2 This is a schematic diagram illustrating the signal model construction in the mutual interference risk assessment method of this application; Figure 3 This is a schematic diagram of the hardware structure of the mutual interference risk assessment device involved in the embodiments of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0018] The problem is that the mutual interference assessment methods for underwater communication, navigation and exploration equipment in related technologies cannot reflect dynamic environmental effects due to the use of static channel models, and lack quantitative assessment and early warning mechanisms for the coordinated mutual interference of heterogeneous equipment, resulting in low assessment accuracy and inability to effectively guide interference avoidance.

[0019] In a first aspect, embodiments of this application provide a method for assessing the mutual interference risk of multiple types of communication devices, the mutual interference risk assessment method comprising: Step S1: Obtain the signal characteristic parameters of different types of communication devices within the collaborative operation area, construct a unified signal model for all communication devices based on the signal characteristic parameters, and generate an initial signal.

[0020] Step S1 above includes: Step S1a: Clarify the types and quantities of communication, navigation, and detection equipment participating in the collaborative work, including the specific models and technical parameters of communication equipment, navigation equipment, and detection equipment, and extract the core signal characteristic parameters for each type of equipment.

[0021] Specifically, the core signal characteristic parameters include: Communication equipment: signal frequency range (e.g., 1kHz-100kHz), modulation method (e.g., FSK, PSK), signal amplitude, transmission rate. Navigation equipment: carrier frequency, phase stability, signal bandwidth, positioning signal period. Detection equipment: detection signal frequency, pulse width, amplitude variation pattern, detection period.

[0022] Step S1b: Based on the above signal characteristic parameters, construct a unified signal model, use a unified mathematical expression to characterize the signal characteristic parameters of different functional devices in the time and frequency domains, and normalize the signals of different systems into initial signals containing amplitude, phase and frequency information.

[0023] It is worth noting that this application effectively eliminates the differences in signal representation between heterogeneous devices by using a unified mathematical expression to represent and normalize the signals of different functional devices in the time and frequency domains. This not only achieves the standardization of multi-source signals at the mathematical level, ensuring the consistency of subsequent simulation calculations, but also preserves core physical characteristics such as amplitude, phase, and frequency, avoiding the loss of key mutual interference information due to model simplification, and providing a concrete implementation path for the construction of a unified signal model.

[0024] In one specific embodiment, the unified signal model includes: a communication device signal model, a navigation device signal model, and a detection device signal model; wherein, Communication equipment signal model:

[0025] in, The signal amplitude, For the center frequency, For the initial phase, It is a modulated signal.

[0026] Navigation device signal model:

[0027] in, The signal amplitude, For carrier frequency, The phase varies with time.

[0028] Detection equipment signal model:

[0029] in, The amplitude varies over time. The pulse width. To detect the signal frequency.

[0030] Step S2: Construct an underwater acoustic channel simulation model based on the dynamic environmental parameters of the collaborative operation water area.

[0031] In one optional embodiment, the underwater acoustic channel simulation model of this application is constructed based on ray acoustics theory and a dynamic cascaded delay network structure. Furthermore, external dynamic environmental parameters are incorporated into the simulation model through the following cascaded physical mapping.

[0032] In conjunction with the above optional embodiments, step S2 includes: Step S2a: Construct a sound velocity profile model, a medium sound absorption model, and a dynamic boundary condition model based on dynamic environmental parameters.

[0033] Step A: Geometric topology and dynamic boundary operator configuration.

[0034] Specifically, in the simulated three-dimensional Cartesian coordinate system, launch nodes are set according to the physical deployment locations of each communication and guidance device in the collaborative operation. TX i (Corresponding to the i-th type of equipment) and the dynamic coordinate vector of the receiving node RX at the receiving end; the sea surface boundary is abstracted as a resonant grid operator that fluctuates with the wind and waves, and its reflection coefficient is set as a time-varying function; the seabed boundary is fitted as a rough surface with undulating terrain based on multibeam bathymetry data, and the corresponding acoustic impedance boundary conditions are given according to the type of seabed sediment (such as sandy or muddy), and its base reflection coefficient is set.

[0035] Step B, Construction of spatiotemporal heterogeneous sound velocity profile: Calculate the multipath delay of signal propagation using the sound velocity profile model.

[0036] Specifically, the system acquires a set of dynamic environmental parameters of the collaborative operation area in real time, including the vertical gradient distribution of seawater temperature, salinity, and still water depth. It then dynamically applies the Mackenzie empirical formula for sound velocity to construct a real-time sound velocity profile for the current area. Finally, it uses the equation of process to iteratively optimize the sound propagation path, dynamically calculating the propagation velocity from each transmitting node. TX i The M main characteristic acoustic ray paths to the receiving node RX (including the direct path, the sea surface reflection path, the seabed reflection path, and the multi-order refraction path) are used to accurately obtain the number of multipaths and the geometric length of each path.

[0037] Step C, Propagation Attenuation Operator Configuration: Calculate the frequency-dependent attenuation of signal propagation using the medium absorption model.

[0038] Specifically, the model internally cascades a propagation loss operator consisting of geometric spread loss and medium sound absorption loss:

[0039] Where r is the physical propagation distance of a certain characteristic sound ray; Represents the geometric spread loss of spherical waves; The dynamic sound absorption coefficient of seawater, calculated using the Ainslie-McColm empirical formula, is determined by the input real-time seawater temperature. ,salinity and still water depth Dynamic drive, used to adjust the signal in real time at different operating frequency bands The rate of physical energy decay.

[0040] Step D, Time-varying Doppler effect injection: Calculate the Doppler scaling of signal propagation using a dynamic boundary condition model.

[0041] Specifically, a time-varying Doppler delay operator is cascaded along each characteristic acoustic ray path to obtain the dynamic empirical flow velocity of the ocean environment. and the velocity of random perturbations at nodes caused by sea waves As a boundary excitation, the instantaneous Doppler scaling factor is calculated. Physical simulation of the time-varying characteristics of the channel is achieved by dynamically interpolating and resampling the time axis.

[0042] Step S2b: Superimpose the time-domain field strength of the multi-source signals after multipath delay, frequency-dependent attenuation and Doppler scaling at the receiving node.

[0043] It should be noted that the sound velocity profile model, the medium absorption model, and the dynamic boundary condition model are integrated into the propagation operator to form an underwater acoustic channel simulation model. This application accurately simulates the unique distortion mechanism of the underwater channel by calculating multipath delay, frequency-dependent attenuation, and Doppler scaling using each sub-model. Based on this, time-domain field strength superposition reflects the physical energy accumulation effect of multi-source signals at the receiver, conforming to the wave superposition principle. This allows the simulation results to reflect signal waveform-level distortion, not just power-level interference, thus capturing complex mutual interference phenomena that cannot be detected by static models.

[0044] Step S3: Input the initial signal into the underwater acoustic channel simulation model to simulate the propagation distortion of the initial signal in the channel and the superposition of the field strength at the receiving end to generate the composite signal at the receiving end. Specifically, the initial signals of the mathematical models generated by various devices in step S1 are used as inputs, i.e., the initial signals of the communication device, navigation device, and detection device are used as boundary excitation sources and input into the configured underwater acoustic channel simulation model to quantitatively calculate the physical distortion of each signal during spatial propagation. The process includes: Step S3a, Delay and Phase Transform Calculation: Based on the geometric lengths of each feature path calculated above. and Doppler scaling factor Dynamically generate the time-varying delay function for the j-th propagation path. :

[0045] The initial signal of any type of device When traversing the j-th path, the received waveform is rewritten as follows: This directly reflects the time delay spread caused by the multipath effect, as well as the phase accumulation disturbance and frequency shift caused by water flow and single-unit motion in the time domain.

[0046] Step S3b, Superposition of Time-Domain Field Strength of Multi-Source Heterogeneous Signals: Following the actual collaborative operation sequence of each communication and detection device, after the initial signals undergo spatial propagation attenuation and time delay variations along their corresponding paths, the instantaneous sound pressure field strength is seamlessly superimposed at the receiving node in the underwater acoustic target space (such as the location of a specific passive receiver or hydrophone). The resulting dynamically evolving composite signal... The mathematical expression is:

[0047] Where n is the total number of collaborative devices in the current space; Let be the real-time amplitude attenuation coefficient of the i-th device on the j-th multipath; This is to represent the background noise of the ocean accompanied by the dynamic fluctuations of wind and waves on the sea surface. Through this physical field strength superposition mechanism, the true overlapping form of signals from heterogeneous devices in the physical carrier is fully presented.

[0048] Step S3c: Decouple the composite signal features in multiple dimensions to generate a composite signal feature parameter matrix; wherein the composite signal feature parameter matrix includes at least composite frequency distribution features, instantaneous amplitude change features, global signal-to-noise ratio, and signal distortion.

[0049] Understandably, this application breaks with the conventional limitation of "static comparison only for parameters of a single device or a single frequency band" and insists on using physical time-domain composite signals generated by overlapping multiple devices. It serves as the sole reference source for subsequent crosstalk analysis. Its technical advantage lies in the fact that crosstalk under multi-source collaboration is not essentially a simple superposition of discrete parameters, but rather a signal distortion caused by the superposition of nonlinear physical fields. Only by analyzing the composite signal can subsequent steps, through receiver matched filtering, fully extract phenomena such as the envelope suppression of small-amplitude signals by large-amplitude signals and the constellation phase deflection caused by multipath crosstalk.

[0050] Specifically, step S3c includes: Step A: Analyze the characteristics of the composite frequency distribution: For composite signals A full-band Fast Fourier Transform is performed to output the energy density spectrum distribution of the composite signal in the time-frequency domain. The composite frequency distribution characteristics are used to accurately capture the frequency band overlap boundaries between heterogeneous signals.

[0051] Step B: Analyze the instantaneous amplitude change characteristics: Extract the time-domain envelope curve of the composite signal, calculate the ratio of instantaneous peak power to average power, and use the instantaneous amplitude change characteristics to quantify the degree of dynamic amplitude suppression of weak signal devices by high-power devices.

[0052] Step C: Calculate the overall signal-to-noise ratio: Calculate the power ratio between the desired device signal component and the sum of the interference components of other devices and background noise in the composite signal.

[0053] Step D: Calculate the overall signal distortion: By solving the cross-correlation coefficient between the composite signal and each original initial signal in step S1, the total harmonic distortion and phase deviation variance of the waveform are calculated. The overall signal distortion is used to characterize the risk of degradation in modulation and demodulation accuracy of navigation and communication signals due to mutual interference.

[0054] It is worth noting that this application constructs a multi-dimensional evaluation index system by decoupling the multi-dimensional features of the composite signal and generating a parameter matrix that includes frequency distribution, amplitude variation, signal-to-noise ratio, and distortion. This multi-perspective feature extraction method avoids the one-sidedness of single-index evaluation, separates the complex composite signal into analyzable independent feature components, and comprehensively reflects the impact of mutual interference on signal quality in different dimensions such as frequency domain, time domain, and demodulation accuracy, providing a data foundation for refined risk assessment.

[0055] Step S4: Compare the composite signal with the initial signal, and perform mutual interference analysis and risk quantification based on the comparison results.

[0056] In an optional implementation, the initial signal is used as a reference and differential mapping and coherent demodulation are performed with the composite signal feature parameter matrix. By analyzing the deviation of the signal features, three core mutual interference types are identified.

[0057] Understandably, this application establishes a reference benchmark for evaluation by using the initial signal as a reference control group and performing differential mapping and coherent demodulation with the composite signal feature parameter matrix, enabling the interference effect to be accurately "differentiated". This method highlights the signal characteristic changes caused by mutual interference, effectively suppresses the influence of background noise, and concretizes the abstract "mutual interference" into an analyzable "feature deviation", laying a logical foundation for subsequent accurate identification of mutual interference types.

[0058] In conjunction with the above optional implementation methods, the specific analysis and extraction process includes: Step S4a, quantitative decoupling analysis of frequency band reuse interference: If the frequency domain energy density spectrum in the characteristic parameter matrix of the composite signal overlaps with the initial signal, it is identified as co-frequency interference.

[0059] Analysis mechanism: Extract the nominal operating frequency band boundaries of the i-th and k-th types of devices in step S1; simultaneously, perform power spectral density (PSD) estimation on the composite signal to obtain the global power spectral function. .

[0060] Computational logic: A filter bank is set to dynamically extract the overlapping frequency domain windows of signals from each pair of devices in the composite spectral space. The interference overlap degree in overlapping frequency bands is solved using an integral operator. :

[0061] Judgment criterion: If the calculated interference overlap is... If the bandwidth coupling threshold is exceeded (e.g., 80%), the system will automatically determine that there is frequency band reuse interference between the i-th type of device and the k-th type of device, and latch the boundary of the overlapping frequency band.

[0062] Step S4b, quantitative decoupling analysis of amplitude suppression interference: If the ratio of instantaneous peak power to average power of the time-domain envelope curve in the composite signal characteristic parameter matrix exceeds a preset threshold, it is identified as amplitude suppression interference.

[0063] Analysis mechanism: The time-domain composite signal is input into a dedicated matched filter or coherent demodulator configured for the i-th type of device. By utilizing the cross-correlation characteristics of the signal, the real-time desired useful signal waveform of the i-th type of device after channel attenuation is extracted.

[0064] Calculation logic: Extract the instantaneous time-domain envelope of the component waveform and calculate its actual received amplitude after impairment. ; compare it with the initial nominal transmission amplitude of the device in step S1. and the pure path loss calculated from the channel model in the previous step. Perform differential comparison to solve for the gain drop caused by the influx of signals from other high-power devices. :

[0065] Judgment criterion: If the gain drop ratio If the amplitude exceeds the preset suppression tolerance threshold (e.g., 30%), it indicates that the weak signal device is being masked by the nonlinear energy of a high-power strong signal, and thus amplitude suppression interference is determined to exist.

[0066] Step S4c, quantitative decoupling analysis of phase distortion interference: If the total harmonic distortion and phase deviation variance of the waveform in the composite signal characteristic parameter matrix exceed the preset range, it is identified as phase distortion interference.

[0067] Analysis mechanism: For devices that are extremely sensitive to phase, such as navigation and high-order communication, the extracted real-time desired component waveform is sent to a phase detector or IQ quadrature demodulation module to extract its instantaneous demodulated phase sequence that evolves over time.

[0068] Calculation logic: The instantaneous demodulated phase sequence is dynamically time-warped and aligned with the ideal initial phase mapping function corresponding to step S1. After eliminating the absolute propagation delay, the phase deviation variance (or root mean square error) in the entire time domain is calculated. :

[0069] Judgment criterion: If the phase deviation variance If the phase tolerance boundary is exceeded (such as the variance threshold corresponding to ±10°), it indicates that the multipath delay overlap and heterogeneous signal crosstalk have destroyed the reference of coherent demodulation at the receiver, and the system determines that there is phase distortion interference.

[0070] It is understood that the embodiments of this application provide specific rules for determining the type of mutual interference. By classifying frequency domain overlap, amplitude ratio exceeding the threshold, and distortion variance exceeding the range as co-channel interference, amplitude suppression interference, and phase distortion interference, respectively, automated diagnosis of interference sources is achieved. This mapping relationship, which associates different physical characteristic deviations with specific interference types, not only clarifies the nature of mutual interference but also identifies different solutions for different interference types. This allows the evaluation results to directly guide subsequent avoidance measures, significantly improving the practicality of the solution.

[0071] Step S4d, Risk Quantification: Construct a mutual interference risk quantification index system, including four dimensions: interference intensity, impact range, duration, and degree of equipment performance degradation. Specifically, this includes: Interference Intensity I: Calculated based on the parameter differences between the composite signal and the initial signal. ,in This represents the actual parameter difference value. This represents the maximum permissible parameter difference.

[0072] Scope of impact R: The proportion of the number of devices affected by interference to the total number of collaborative devices.

[0073] Duration T: Records the duration of the mutual interference phenomenon (unit: s); Equipment performance degradation degree D: Calculated based on the percentage decrease in key performance indicators of the equipment (such as the transmission rate of communication equipment and the positioning accuracy of navigation equipment).

[0074] Furthermore, by using weighted operators to fuse the decoupled interference intensity, impact range, duration, and equipment performance degradation, the final comprehensive mutual interference risk value is calculated. :

[0075] Wherein, the weight coefficient vector The establishment of this system is strongly correlated with the dynamic operational task priority of underwater heterogeneous equipment collaborative operation, as well as the physical tolerance limit of each device to interference: Specifically, based on the physical dependence of communication, navigation and detection functions on the current mission stage (such as long-range navigation of submersibles, multi-node collaborative search, and close rendezvous and docking), a pairwise comparison judgment matrix is ​​constructed; by solving the maximum eigenvalue of the matrix and its corresponding eigenvector, and after consistency verification, the weight vector under the current working condition is adaptively calculated.

[0076] In some alternative embodiments, during the "close rendezvous and docking" phase, the navigation and positioning accuracy directly determines safety, and the weighting coefficient of the degree of equipment performance degradation (specifically navigation error) will be increased by adaptively adjusting the judgment matrix; while during the "marine environment collaborative search" phase, the weighting of the detection interference intensity will be increased to ensure that the risk assessment results perfectly match the actual underwater physical operation scenario.

[0077] Step S4e: Risk warning and risk point identification.

[0078] Specifically, step S4e includes: Step A: Set risk warning thresholds: Based on the security requirements of the actual application scenario, classify the risk levels and set corresponding thresholds.

[0079] In one optional embodiment, the low-risk level corresponds to a risk warning threshold: The risk warning threshold corresponding to the medium-risk level is: High-risk levels correspond to risk warning thresholds: .

[0080] Step B, Risk Point Identification: Based on the mutual interference analysis results, identify the specific risk points corresponding to each risk level, including: the combination of equipment that causes mutual interference (e.g., "communication equipment A + detection equipment B"), the frequency band range in which mutual interference occurs, and the key causes of mutual interference (e.g., frequency band reuse, excessively high signal amplitude).

[0081] Step C, Early Warning Information Generation: Based on the comprehensive mutual interference risk value and risk points, generate early warning information, including risk level, risk quantification value, risk point details and response suggestions (such as adjusting the operating frequency band of a certain device or reducing the signal amplitude).

[0082] Step D, Output of Early Warning Information: Output early warning information through a visual interface or via SMS, voice, etc., to remind staff to take timely control measures.

[0083] Furthermore, this application provides a first specific embodiment, the scenario of which is: a collaborative scenario of marine exploration communication, navigation and detection equipment. Application scenario: In a certain marine exploration operation, communication equipment X (model: TC-200, operating frequency band 10kHz-20kHz, modulation method FSK, signal amplitude 5V), navigation equipment Y (model: ND-300, carrier frequency 25kHz-30kHz, phase stability ±0.5°), and detection equipment Z (model: DE-400, detection signal frequency 15kHz-22kHz, pulse width 10ms) work together, and it is necessary to assess the mutual interference risk among the three.

[0084] It includes the following steps: Step 1: Signal Model Construction Communication device X signal model: ,in , It is an FSK modulated signal; Navigation device Y-signal model: ,in , The range of variation is ±0.5°; Z-signal model of the detection device: ,in , The time-varying amplitude is 1V-3V; Step 2, Underwater Acoustic Channel Simulation: Following the physical channel construction mechanism described in Step 1, this example uses a cascade of specific physical formulas to transform the input temperature, salinity, depth, multipath, and flow velocity parameters into a channel transfer function. The specific integration and solution steps are as follows: Step A: Incorporation calculation of spatial propagation attenuation characteristics: The set environmental parameters—seawater temperature of 25℃, salinity of 35, and still water depth of 500m—were imported into the Ainslie-McColm empirical formula for medium sound absorption. The dynamic sound absorption coefficient of communication device X (center frequency 15kHz) was calculated to be 1.58Db / km. Simultaneously, the corresponding sound absorption benchmarks for the nominal frequencies of navigation device Y and detection device Z were also calculated. Combining this with the spherical wave geometric spread loss operator, the dynamic absorption coefficient of the current water depth as a function of physical distance was directly constructed. A dynamically evolving full-band spatial propagation loss operator is used to instantaneously control the energy attenuation of various heterogeneous signal sources.

[0085] Step B: Embedding of temporal cascaded networks for spatial multipath effect parameters: This example sets three main characteristic acoustic ray propagation paths. The physical geometric lengths of their direct path, sea surface reflection path, and seabed reflection path are optimized iteratively by a ray acoustic tracing engine and mapped to the corresponding basic time delay spread. The set time delay difference array is then used to... and the array of branch amplitude attenuation coefficients It is directly injected into the multipath cascaded delay network operator.

[0086] Specifically, this manifests as follows: any initial signal, when passing through this channel network, is split into three waveform components at the receiving spatial node, each with independent energy fading and time delay differences, i.e.:

[0087] This explicit temporal convolution structure fully integrates the inter-symbol interference and signal envelope distortion characteristics caused by spatial multipath into the simulation model.

[0088] Step C, Doppler modulation injection of dynamic time-varying characteristics: To characterize the channel parameter variation caused by ocean currents, this example introduces a time-varying perturbation function, causing the channel's global attenuation coefficient to be superimposed with a time-varying fluctuation term within the simulation period, with a maximum variation range boundary constraint of ±0.005dB / m. Simultaneously, the empirical dynamic flow velocity of the port or sea area is set to 1.2m / s as the boundary excitation input to the time-varying Doppler filter bank, and continuous resampling is performed on the time axis. This transforms the multipath delay function into a function dynamically perturbed by the time-varying flow velocity, achieving a physical-level simulation injection of the macroscopic time-varying dynamic characteristics of the underwater acoustic channel.

[0089] Step 3: Signal Propagation and Superposition: Input the initial signals from the three types of equipment into the underwater acoustic channel model to simulate the composite signal. (Unit: V) Analysis shows that the frequency overlap between the communication device X and the detection device Z in the composite signal reaches 85%. Step 4: Interference Analysis and Risk Quantification Mutual interference identification: It is determined that there is frequency band reuse interference (communication X and detection Z), but there is no significant amplitude suppression interference or phase distortion interference; Index Calculation: Interference Intensity (Frequency overlap exceeds threshold), range of influence (Two devices were interfered with), duration (Continuous interference throughout the simulation period), degree of equipment performance degradation (The transmission rate of communication device X decreased by 40%). Weight settings: , , , Comprehensive mutual interference risk value ; Step 5: Risk Warning and Risk Point Identification Risk level: Medium risk ; Risk point: Communication device X and detection device Z share the same frequency band (overlapping frequency band 15kHz-20kHz), which is the core cause of mutual interference; Warning message: "Medium risk warning! Communication device X and detection device Z have frequency band reuse interference, with a comprehensive risk value of 0.65. It is recommended to adjust the operating frequency of communication device X to 15kHz-25kHz to avoid frequency band overlap."

[0090] In summary, this invention has significant technical advantages, including wide applicability, high accuracy in assessment, precise risk quantification, timely and effective early warning, and strong practicality. Specifically, by constructing a unified signal model covering communication, detection, and navigation equipment, this method overcomes the limitations of existing technologies that only target single devices, comprehensively capturing the mutual interference relationships between multiple devices. It constructs a physical-level underwater acoustic channel model incorporating all physical elements, including multipath propagation, medium absorption, Doppler shift, nonlinear acoustic harmonic distortion, sea surface wave boundary scattering, and sound ray focusing defocusing effects. Furthermore, it utilizes a time-domain field strength superposition operator to accurately reconstruct the physical distortion and field overlap patterns of heterogeneous signals in complex dynamic media, eliminating blind spots in interference analysis under complex noise backgrounds and significantly improving the accuracy and reliability of the assessment. By establishing a multi-dimensional risk quantification index system to calculate specific risk values, it achieves quantitative assessment and scientific management of mutual interference risks. By classifying risks based on risk thresholds and generating targeted early warning suggestions, it achieves a shift from passive judgment to proactive early warning, effectively avoiding the risk of equipment performance degradation. Moreover, this method requires no hardware modification, can be implemented solely through software algorithms, is low-cost, and adaptable to various scenarios such as marine exploration, possessing strong practicality and promotional value.

[0091] Secondly, embodiments of this application also provide a device for assessing the mutual interference risk of multiple types of communication devices, comprising: The modeling module is used to acquire the signal characteristic parameters of different types of communication devices in the collaborative operation area, construct a unified signal model for all communication devices based on the signal characteristic parameters, and generate an initial signal. The channel simulation module is used to construct an underwater acoustic channel simulation model based on the dynamic environmental parameters of the collaborative operation water area. The channel simulation module is also used to input the initial signal into the underwater acoustic channel simulation model to simulate the propagation distortion of the initial signal in the channel and the superposition of the field strength at the receiving end to generate the composite signal at the receiving end. The evaluation module is used to calculate the mutual interference quantification index based on the comparison results between the composite signal and the initial signal.

[0092] The functions of each module in the aforementioned mutual interference risk assessment device correspond to the steps in the aforementioned mutual interference risk assessment method embodiment, and their functions and implementation processes will not be described in detail here.

[0093] Thirdly, embodiments of this application provide a mutual interference risk assessment device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0094] Reference Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of the mutual interference risk assessment device involved in the embodiments of this application. In the embodiments of this application, the mutual interference risk assessment device may include a processor, a memory, a communication interface, and a communication bus.

[0095] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0096] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the mutual interference risk assessment equipment, as well as interfaces used for interconnecting the mutual interference risk assessment equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0097] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0098] The processor can be a general-purpose processor, which can call the mutual interference risk assessment program stored in the memory and execute the mutual interference risk assessment method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the mutual interference risk assessment program is called can refer to the various embodiments of the mutual interference risk assessment method of this application, and will not be repeated here.

[0099] Those skilled in the art will understand that Figure 3 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0100] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0101] The present application has a computer-readable storage medium storing a mutual interference risk assessment program, wherein when the mutual interference risk assessment program is executed by a processor, it implements the steps of the mutual interference risk assessment method as described above.

[0102] The method implemented when the mutual interference risk assessment procedure is executed can be referred to in various embodiments of the mutual interference risk assessment method of this application, and will not be repeated here.

[0103] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0104] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0105] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0106] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0107] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0108] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0109] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for assessing the mutual interference risk of multiple types of communication devices, characterized in that, The mutual interference risk assessment method includes: Obtain signal characteristic parameters of different types of communication devices within the collaborative operation area, construct a unified signal model for all communication devices based on the signal characteristic parameters, and generate initial signals; A simulation model of the underwater acoustic channel is constructed based on the dynamic environmental parameters of the collaborative operation water area; The initial signal is input into the underwater acoustic channel simulation model to simulate the propagation distortion of the initial signal in the channel and the superposition of the field strength at the receiving end, thereby generating the composite signal at the receiving end. The composite signal is compared with the initial signal, and mutual interference analysis and risk quantification are performed based on the comparison results.

2. The mutual interference risk assessment method as described in claim 1, characterized in that, The step of constructing a unified signal model for all communication devices based on signal characteristic parameters and generating an initial signal includes: using a unified mathematical expression to characterize the signal characteristic parameters of different functional devices in the time and frequency domains, and normalizing signals of different systems into an initial signal containing amplitude, phase, and frequency information.

3. The mutual interference risk assessment method as described in claim 1, characterized in that, The underwater acoustic channel simulation model constructed based on dynamic environmental parameters of the collaborative operation water area includes: A sound velocity profile model, a medium sound absorption model, and a dynamic boundary condition model are constructed based on dynamic environmental parameters. The sound velocity profile model, the medium sound absorption model, and the dynamic boundary condition model are integrated into the propagation operator of the underwater acoustic channel simulation model.

4. The mutual interference risk assessment method as described in claim 3, characterized in that, The step of inputting the initial signal into the underwater acoustic channel simulation model to simulate the propagation distortion of the initial signal in the channel and the superposition of the field strength at the receiving end includes: Calculate the multipath delay of signal propagation using a sound velocity profile model; Calculate the frequency-dependent attenuation of signal propagation using a medium sound absorption model; Calculate Doppler scaling of signal propagation using a dynamic boundary condition model; The multi-source signals, after undergoing multipath delay, frequency-dependent attenuation, and Doppler scaling, are superimposed in the time domain at the receiving end.

5. The mutual interference risk assessment method as described in claim 1, characterized in that, After generating the composite signal at the receiving end, the method further includes: Multidimensional decoupling of composite signal features is performed on the composite signal to generate a composite signal feature parameter matrix; where... The composite signal characteristic parameter matrix includes at least the composite frequency distribution characteristics, instantaneous amplitude variation characteristics, global integrated signal-to-noise ratio, and signal integrated distortion.

6. The mutual interference risk assessment method as described in claim 5, characterized in that, The decoupling of multidimensional composite signal features from composite signals includes: The initial signal is used as a reference standard to perform differential mapping and coherent demodulation with the feature parameter matrix of the composite signal, and the mutual interference type is identified by analyzing the deviation of the signal features.

7. The mutual interference risk assessment method as described in claim 6, characterized in that, The differential mapping and coherent demodulation using the initial signal as a reference and the feature parameter matrix of the composite signal includes: Perform a full-band fast Fourier transform on the composite signal and output the energy density spectrum distribution of the composite signal in the time-frequency domain as the composite frequency distribution characteristic; Extract the time-domain envelope curve of the composite signal and calculate the ratio of instantaneous peak power to average power as the instantaneous amplitude change characteristic; The overall signal-to-noise ratio is obtained by calculating the power ratio between the desired device signal component and the sum of the other device interference components and background noise in the composite signal. The overall signal distortion is calculated by solving the cross-correlation coefficient between the composite signal and the initial signal.

8. The mutual interference risk assessment method as described in claim 7, characterized in that, The method of identifying the type of mutual interference by analyzing the deviation of signal characteristics includes: If the frequency domain energy density spectrum in the characteristic parameter matrix of the composite signal overlaps with that of the initial signal, it is identified as co-frequency interference. If the ratio of the instantaneous peak power to the average power of the time-domain envelope curve in the feature parameter matrix of the composite signal exceeds a preset threshold, it is identified as amplitude suppression interference. If the total harmonic distortion and phase deviation variance of the waveform in the composite signal characteristic parameter matrix exceed the preset range, it is identified as phase distortion interference.

9. A device for assessing the mutual interference risk of multiple types of communication equipment, characterized in that, The mutual interference risk assessment device includes: The modeling module is used to acquire the signal characteristic parameters of different types of communication devices in the collaborative operation area, construct a unified signal model for all communication devices based on the signal characteristic parameters, and generate an initial signal. The channel simulation module is used to construct an underwater acoustic channel simulation model based on the dynamic environmental parameters of the collaborative operation water area. The channel simulation module is also used to input the initial signal into the underwater acoustic channel simulation model to simulate the propagation distortion of the initial signal in the channel and the superposition of the field strength at the receiving end to generate the composite signal at the receiving end. The evaluation module is used to perform mutual interference analysis and risk quantification based on the comparison results of the composite signal and the initial signal.

10. A mutual interference risk assessment device, characterized in that, The mutual interference risk assessment device includes a processor, a memory, and a mutual interference risk assessment program stored in the memory and executable by the processor, wherein when the mutual interference risk assessment program is executed by the processor, it implements the steps of the mutual interference risk assessment method as described in any one of claims 1 to 8.

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