A task scheduling monitoring method and system based on a signal monitoring machine

Abstract

The present application relates to the technical field of task scheduling processing, in particular to a task scheduling monitoring method and system based on a signal monitoring machine, comprising the following steps: capturing a mixed sound pressure signal generated by underwater base station and vehicle communication through a submerged signal monitoring machine, separating the direct wave and reflected wave components; calculating the current sound pressure channel quality score according to the frequency dispersion index of the direct wave component and the time delay dispersion degree of the reflected wave component; setting an adaptive threshold, triggering a multi-level decision response based on the deviation degree of the channel quality score and the adaptive threshold, and generating a channel degradation alarm at the same time. The present application optimizes the task resource allocation strategy while ensuring the reliability of communication, and is suitable for various complex operation scenarios such as underwater unmanned platform, emergency communication and ocean monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of task scheduling processing, in particular to a task scheduling monitoring method and system based on a signal monitoring machine. BACKGROUND

[0002] With the wide application of underwater communication networks and autonomous operating platforms (such as unmanned underwater vehicles and submerged buoy systems), the real-time and robustness of underwater task scheduling are facing new challenges. Due to the complexity of the marine environment, the underwater acoustic channel is often affected by factors such as temperature-salinity structure, sea current shear, and seabed reflection, showing significant frequency dispersion characteristics, multipath interference, and channel instability, which easily leads to communication quality fluctuations. Traditional task scheduling strategies often rely on static channel indicators or preset thresholds, lacking dynamic perception ability of the actual acoustic propagation state, resulting in frequent task interruptions or serious resource waste in complex sea conditions.

[0003] Existing researches mostly focus on modulation optimization at the transmitting end or fault tolerance enhancement at the receiving end, while the integrated design of monitoring type channel evaluation and task regulation is still relatively lacking. Especially in the passive monitoring scene, how to efficiently extract channel quality information from non-cooperative communication signals and construct an adaptive task response mechanism is a key problem that needs to be solved for current marine communication and task systems. SUMMARY

[0004] The present application provides a task scheduling monitoring method and system based on a signal monitoring machine, which can analyze the underwater acoustic channel state in real time based on the signal monitoring machine and drive the monitoring method of task scheduling adjustment, has accurate identification ability for frequency dispersion distortion and multipath interference, and can dynamically match different levels of correction strategies, thereby improving the reliability of task execution and the overall adaptability of the system without increasing the communication burden.

[0005] A task scheduling monitoring method based on a signal monitoring machine, comprising the following steps:

[0006] S1, sound pressure signal acquisition: capturing the mixed sound pressure signal generated by the communication between the underwater base station and the vehicle through the submerged signal monitoring machine, separating the direct wave and reflected wave components;

[0007] S2, channel quality evaluation: calculating the current sound pressure channel quality score according to the frequency dispersion index of the direct wave component and the time delay spread of the reflected wave component;

[0008] S3, dynamic monitoring trigger: setting an adaptive threshold, triggering a multi-level decision response based on the deviation of the channel quality score from the adaptive threshold, and generating a channel degradation alarm.

[0009] Optionally, the S1 comprises: synchronously collecting original mixed sound pressure signals in four directions through the four-element hydrophone array arranged circumferentially around the subsurface casing, generating a sound field spatial sampling matrix, and performing time delay difference compensation calculation on the sound field spatial sampling matrix to identify the direct wave main path direction with the strongest energy and the three main reflected wave secondary path directions.

[0010] Optionally, the S1 further comprises reflected wave reconstruction: using a time reversal mirror algorithm based on the three main reflected wave secondary path directions to reversely project a detection pulse to a reflection interface, and reconstructing a reflected wave propagation path according to echo phase distortion characteristics.

[0011] Optionally, the separating direct wave and reflected wave components specifically comprises: taking the signal of the direct wave main path direction as a direct wave component, and taking the reconstructed reflected wave propagation path signal as a reflected wave component.

[0012] Optionally, the S2 comprises double-path degradation coefficient calculation, specifically comprising:

[0013] extracting the number of phase mutation points of the direct wave in a preset frequency band, denoted as N, and measuring the average energy attenuation slope between adjacent mutation points;

[0014] identifying the number of reflection clusters with long lag time in the reflected wave, denoted as M, and calculating the mean value of Doppler spread variance of each reflection cluster;

[0015] calculating the dispersion degradation coefficient a and the time delay degradation coefficient β based on the average energy attenuation slope and the mean value of Doppler spread variance.

[0016] Optionally, the S2 further comprises sound speed gradient correction, specifically comprising: obtaining sound speed profile data measured by a subsurface temperature and salinity sensor in real time, calculating the relative deviation rate of the sound speed at the current depth layer and the standard sound speed, and generating a correction factor δ based on the relative deviation rate.

[0017] Optionally, the sound pressure channel quality score is calculated as: Q=100×δ / (1+α+β), wherein Q is the sound pressure channel quality score, and 100 is an ideal channel reference value.

[0018] Optionally, the multi-level decision response comprises:

[0019] mild response: extending the non-urgent task scheduling interval to 3 times the reference value;

[0020] moderate response: freezing the non-urgent task scheduling thread, and activating the acoustic self-correction basic instruction;

[0021] severe response: executing non-urgent task forced hibernation, activating the acoustic self-correction enhanced instruction, and triggering the subsurface buoyant preplan.

[0022] Optionally, the channel degradation alarm comprises an alarm level, a degradation cause, and a correction measure.

[0023] A task scheduling monitoring system based on a signal monitor, for implementing the above-mentioned task scheduling monitoring method based on a signal monitor, comprising the following modules:

[0024] A monitoring and separation unit: for collecting mixed sound pressure signals through a submerged load signal monitor and separating direct wave and reflected wave components;

[0025] A channel evaluation unit: for calculating a channel quality score based on the direct wave dispersion index and the reflected wave time delay spread;

[0026] A monitoring response unit, for setting an adaptive score threshold, and triggering a multi-stage scheduling response and generating a degradation alarm according to the score deviation degree.

[0027] The beneficial effects of the present application are:

[0028] The present application realizes multi-channel acquisition and component separation of mixed underwater acoustic signals through a submerged load monitor, constructs a dual-path channel degradation model combining the characteristics of direct wave and reflected wave, introduces a sound speed profile correction mechanism to form an adaptive scoring system, and uses the scoring results to drive task scheduling responses and activate hierarchical acoustic correction instructions, ultimately forming a closed-loop mechanism of monitoring, evaluation, scheduling, and correction synergy. The present application optimizes the task resource allocation strategy while ensuring communication reliability, and is suitable for various complex operation scenarios such as underwater unmanned platforms, emergency communication, and ocean monitoring.

[0029] The present application proposes a dual-path degradation coefficient modeling method based on the direct wave dispersion characteristics and the reflected wave time delay characteristics, constructs channel score indicators by extracting the number of phase discontinuity points, energy attenuation slope, reflected cluster number, and Doppler spread variance, and introduces a sound speed profile correction factor to make the scoring results highly consistent with the actual underwater acoustic propagation environment. Compared with the traditional evaluation mechanism based only on signal-to-noise ratio, this method improves the adaptability and interpretability of channel evaluation in complex water areas.

[0030] The present application designs a three-level task scheduling response mechanism according to the deviation degree of the channel quality score and the adaptive threshold, and links it with the priority adjustment of non-urgent tasks, thread freezing, task hibernation, and submerged buoy floating plan. Through dynamic scoring to guide scheduling control logic, adaptive task convergence and resource protection in low-quality communication environment are realized, and the priority scheduling of emergency tasks is ensured not to be disturbed. This mechanism is particularly suitable for underwater networking scenarios affected by severe environmental fluctuations, and has stronger system stability.

[0031] The present application proposes a hierarchical sound school correction instruction system for different levels of channel degradation: moderate response activates the basic correction strategy (such as frequency band switching and error correction redundancy enhancement), and severe response further enables enhanced sound school correction instructions, including symbol rate pre-compensation based on Doppler spread and orthogonal frequency division pilot injection mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only a part of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.

[0033] Figure 1 The monitoring method of the embodiment of the present application is shown in the figure;

[0034] Figure 2 The four-element hydrophone array of the embodiment of the present application is shown in the figure;

[0035] Figure 3 The function module of the monitoring system of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0036] The present application will be described in detail below with reference to the drawings and specific embodiments. For some known technologies, other alternative ways can also be used by those skilled in the art to implement them; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the present application.

[0037] As shown in the figure, a task scheduling monitoring method based on a signal monitoring machine includes the following steps: Figures 1-2

[0038] S1, sound pressure signal acquisition: through the underwater signal monitoring machine, the mixed sound pressure signal generated by the underwater base station and the vehicle communication is captured, and the direct wave and the reflected wave components are separated; the mixed sound pressure signal is the mixture of all sound wave signals generated by the underwater base station and the vehicle communication, including direct wave, reflected wave, multipath interference, etc., which is the total input perceived by the monitoring system.

[0039] S11, multi-dimensional signal acquisition: through the four-element hydrophone array arranged circumferentially on the buoy hull, marked as A, B, C, D, the original sound pressure signals in four directions are synchronously collected to generate a sound field space sampling matrix. As shown in the figure, the array adopts a diamond layout structure. The array spacing is set to be half the wavelength of underwater sound wave (i.e. 7.5 cm @ 10 kHz), so as to enhance the resolution accuracy of the wave direction of arrival. The sound field space sampling matrix is represented as: Figure 2

[0040] The present application proposes a hierarchical sound school correction instruction system for different levels of channel degradation: moderate response activates the basic correction strategy (such as frequency band switching and error correction redundancy enhancement), and severe response further enables enhanced sound school correction instructions, including symbol rate pre-compensation based on Doppler spread and orthogonal frequency division pilot injection mechanism.​ ;

[0041] wherein, represents a hydrophone acquired in time domain;

[0042] S12, Direction of Arrival analysis: Time delay difference compensation calculation is performed on the sound field spatial sampling matrix, to identify the strongest direct wave main path direction and three main reflected wave secondary path directions.

[0043] S121, Cross-correlation time delay estimation: For any pair of hydrophones , the cross-correlation function is calculated:

[0044] ;

[0045] The maximum value corresponding to the time delay is extracted: ;

[0046] wherein, represents a hydrophone acquired in time , the sound pressure signal, represents a hydrophone acquired in time , the sound pressure signal, represents a time delay variable, used to calculate the time offset between two signals, represents a hydrophone and the cross-correlation function between them, used to measure the similarity of two signals at time delay ; represents and the optimal cross-correlation time delay estimation value (i.e. the time delay that reaches the maximum correlation);

[0047] Calculate for six pairs of combinations (AB, AC, AD, BC, BD, CD), and construct the measured time delay difference matrix.

[0048] S122, Spatial beam scanning and time delay difference compensation superposition: In the preset scanning angle set , for each direction , calculate the theoretical time delay difference of the direction:

[0049] ; wherein, represents a hydrophone distance, represents the effective sound speed, which is obtained through the following temperature-salinity depth correction model: ;

[0050] wherein, Represents the reference speed of sound. For real-time salinity, temperature, and depth sensor data, This is an empirical correction factor. This is the baseline value.

[0051] Compensation for changes in sound velocity ensures that time delay estimation remains highly accurate across different deep channels.

[0052] S123, Spatial Power Spectrum Construction: For each direction Align the four-channel signals according to theoretical time delay compensation: ;

[0053] And calculate the superposition energy: ;

[0054] in, Indicates the first A hydrophone in the direction of wave arrival The time delay value that should be compensated. This indicates that all channel signals are determined according to the angle of arrival. The composite signal formed by superposition after compensation (signal after directional focusing). Indicates direction angle The corresponding spatial energy power spectrum value reflects the signal energy intensity in that direction;

[0055] Obtain directional power spectrum The peak point is the dominant direction of signal energy.

[0056] S124, Extract the set of directions with angle of arrival:

[0057] Main Peak Direction Defined as the direction of the direct wave;

[0058] Three secondary peak directions Defined as the main reflection path direction.

[0059] S13, Reflected Wave Reconstruction: Using the three reflection path directions identified in S12, the following time-reflection mirror processing is performed to achieve reverse modeling of the reflection path and interface recognition. The execution steps include:

[0060] Transmit a linear frequency modulated detection pulse toward the target direction;

[0061] Capture echo signals from the sea surface, seabed, or other obstacle interfaces;

[0062] Extract the phase distortion hotspot region from the echo and invert the topological features of the reflection interface.

[0063] Specifically as follows:

[0064] S131, Directional linear frequency modulated pulse emission: along the direction of the secondary peak. ) transmit a frequency-modulated probe signal: ; wherein, is the start frequency, is the frequency modulation rate, is the pulse width, denotes the number of the reflection path, denotes the angle of arrival of the th reflection path.

[0065] S132, echo acquisition and reflection phase analysis: receive the echo reflected by the seabed, sea surface or obstacle , do short-time Fourier transform on the echo, extract the phase spectrum: , compare the phase distortion features with the preset feature table, classify the reflection interface:

[0066] Table 1: Preset feature table

[0067]

[0068] denotes the phase spectrum of the echo signal corresponding to the th reflection path at the frequency , denotes the Fourier transform of the reflection path echo signal , denotes the phase angle of the complex number, that is, the angle part of the complex number.

[0069] S14, signal component extraction: after completing the path direction recognition and reflection reconstruction, perform component extraction of direct wave and reflected wave, and separate the sound field signal into two categories based on the following standards:

[0070] Direct wave component extraction standard:

[0071] The direction is (the main path direction angle);

[0072] The signal satisfies: Doppler shift Hz and pulse width distortion rate ;

[0073] This component is defined as .

[0074] Reflection wave component extraction and labeling:

[0075] The reconstructed signal of each direction is taken as a reflection wave;

[0076] ​​​Additional labels such as: "bottom once reflection", "metallic debris twice reflection", etc.

[0077] The set of reflected wave components is denoted as: ; The echo signal reconstructed by the th reflection path is denoted as The interface type corresponding to the th reflection path is denoted as

[0078] In underwater communication systems, the direct wave is usually emitted by a target with relatively low speed, such as a fixed base station or a low-speed vehicle; for underwater acoustic signals with a center frequency of 5-10 kHz, the Doppler shift is generally not more than ±5 Hz when the relative speed is within ±1-2 m / s; the reflected wave and the scattered wave are more susceptible to water flow, obstacles or motion boundary disturbances, and often have a larger frequency shift, so setting a threshold of ±5 Hz can effectively distinguish between stable direct waves and distorted reflection path signals.

[0079] In the case of short propagation path and uniform medium (such as direct path), the pulse shape of the acoustic signal is well maintained, and the width distortion is very small. Experiments show that the pulse width of the normal direct wave during transmission is usually less than 5-8%, while the reflected wave or the diffracted wave is easily distorted in shape after experiencing multiple boundary reflections, refractions or medium changes, and setting an 8% threshold can effectively filter out these severely damaged non-direct wave signals.

[0080] S2, channel quality assessment: according to the frequency dispersion index of the direct wave component and the time delay dispersion degree of the reflected wave component, the current sound pressure channel quality score is calculated.

[0081] S21, two-path degradation coefficient calculation:

[0082] ① Direct wave degradation index extraction: analyze the direct wave signal in the frequency band [3kHz, 8kHz]; extract the number of phase mutation points, denoted as , and the determination criteria are: the phase change amplitude is greater than 45°; calculate the average energy attenuation slope (in the logarithmic domain) between any adjacent mutation points, denoted as ;

[0083] denotes the phase distortion density, reflecting the frequency dispersion, denotes the energy attenuation rate, representing the signal dissipation intensity.

[0084] The number of phase mutation points is extracted as follows:

[0085] 1. Time-frequency conversion: the direct wave signal Short-time Fourier transform (STFT) or continuous wavelet transform (CWT) is performed to extract the complex spectrum signal of the target frequency band (3-8 kHz) :

[0086] ; where represents the phase spectrum of the frequency component at time , represents the complex spectrum of the direct wave signal at frequency and time , represents the amplitude spectrum (modulus) of ;

[0087] 2. Phase jump detection: select a center frequency kHz, such as 5 kHz; calculate the phase difference between adjacent time frames: ; if , it is considered that a phase mutation occurs here, and the total number is accumulated as :

[0088] ; where is an indicator function, represents the selected center frequency, represents the time interval between adjacent time frames, represents the phase change amount of adjacent time frames, represents the phase mutation determination threshold, .

[0089] The average energy decay slope is calculated as follows:

[0090] 1. Obtain the sequence of mutation point positions: record the time index of all phase mutation points as sequence , and the corresponding amplitude spectrum is ; represents the amplitude spectrum value at time ,

[0091] 2. Log amplitude difference: make log difference of all adjacent mutation point amplitudes, and calculate the slope:

[0092] ; represents the log amplitude decay slope between the i-th and i+1-th mutation points;

[0093] Take the absolute value mean as : ; The unit is "log amplitude per second", and the larger it is, the more intense the energy decay is, and the more unstable the channel is.

[0094] Reflection wave degradation index extraction: count the number of reflection clusters whose lag exceeds 50 ms, denoted as , analyze the spectrum of each reflection cluster, calculate the variance of its Doppler spread, denoted as , take the mean value of all clusters;

[0095] represents the intensity of multipath interference, measures the number of complex reflection structures, represents the degree of Doppler distortion, reflects the instability of the frequency shift of the reflected signal.

[0096] Degradation coefficient calculation:

[0097] Frequency dispersion degradation coefficient: ;

[0098] Time delay degradation coefficient: ;

[0099] The frequency dispersion degradation coefficient is used to calculate the degree of degradation of the direct wave, mainly reflecting the frequency-dependent distortion phenomenon (i.e. frequency dispersion) in the channel, usually caused by factors such as water temperature stratification and turbulence. represents the number of phase mutation points detected by the direct wave in the specified frequency band (3-8 kHz), the more the number, the worse the phase stability, the more serious the signal dispersion; represents the average energy decay rate between these mutation points, the larger the absolute value, the faster the signal energy decreases, indicating that the signal is more likely to dissipate during transmission;

[0100] Take the natural logarithm of the product of the two:

[0101] First, amplify the degree of dispersion degradation under the joint action of the two;

[0102] Second, through logarithmic compression, prevent the value from being amplified too much in the case of sudden distortion;

[0103] 0.6 is a weight factor, used to adjust the calculation result to a reasonable score proportion.

[0104] The larger the value, the worse the stability of the direct wave, the more serious the frequency dispersion, and the lower the communication reliability.

[0105] The time delay degradation coefficient is used to evaluate the degree of multipath interference caused by reflected waves, especially interference wave clusters with significant time delay and frequency spread characteristics. represents the number of reflection clusters whose lag exceeds 50 ms compared with the direct wave, the more the number, the more complex the multipath, the stronger the interference to the main signal; use ​Instead of linear form, it is to control the growth rate at high value, so that the score is more in line with the physical law (i.e. bounded increase); The mean value of Doppler spread variance of all reflection clusters, i.e. the instability of reflected wave frequency drift; use Extract its dominant dimension, reflect channel volatility; Weight factor 0.3 is a regulatory item, used to control the contribution of frequency spread to total interference; The whole is multiplied by the normalization factor 0.4, so that In the total score, a reasonable proportion. The larger, the more serious the lag exists in the channel, the more unstable the frequency of the reflected wave is, and the lower the communication quality is.

[0106] S22, sound speed gradient correction: real-time acquisition of water layer sound speed profile from the temperature-salinity-depth (CTD) sensor carried by the buoy And compared with the standard sound speed Calculate the relative deviation rate:

[0107] ;

[0108] According to this, the sound speed correction factor is generated: ; Wherein, The sound speed deviation rate reflects the acoustic difference of the water body in the sea area, The sound speed profile correction factor is used to adjust the score baseline value to adapt to the actual sound speed environment.

[0109] S23, quality score generation: combine the degradation degree and sound speed correction to generate the sound pressure channel quality score:

[0110] ; Wherein, The channel quality score, the theoretical range is about [15, 100], 100 is the ideal baseline score, the denominator form ensures that the lower limit of the score is not 0, The dynamic correction baseline enhances environmental adaptability.

[0111] The sound pressure channel quality score formula is a scoring model that considers the sound speed environment and the influence of double-path degradation:

[0112] The above molecule: ideal score × environmental correction factor, 100 indicates the full score under the ideal channel, which is the theoretical baseline of the communication system quality, and δ is the sound speed profile correction factor, which is derived from the temperature-salinity-depth change measured by the buoy, and can reflect the deviation degree of the current water body sound speed environment from the standard sound speed. The design form of δ is exponential decay, when the sound speed deviation is larger, δ tends to 0.94-0.95, and when it is closer to the standard value, it tends to 1;

[0113] The deviation of sound velocity will directly affect the propagation delay, path refraction and frequency stability, so the score benchmark value is reasonably corrected to make the whole model dynamically adapt to different water characteristics.

[0114] The denominator above: , represents the degree of dispersion degradation of the direct wave, represents the time delay interference degree of the reflected wave, 1 is the base term, which ensures that the denominator is not zero, and also ensures that the score will not exceed 100 even in the best environment (α=0, β=0).

[0115] Using structure, it is equivalent to considering the channel quality as "ideal state" weakened by several loss factors (α and β), the larger the value, the more serious the degradation, and the lower the score.

[0116] S3, dynamic monitoring trigger: set adaptive threshold, trigger multi-level decision response based on the deviation of channel quality score from adaptive threshold, and generate channel degradation alarm.

[0117] S1 proposes to "capture the mixed sound pressure signal generated by the communication between the underwater base station and the vehicle", which is the information perception entrance of the whole monitoring method. Based on signal monitoring, "freeze non-emergency task scheduling and activate acoustic self-correcting instructions", this method has the ability to dynamically control task scheduling when the communication channel deteriorates, thereby realizing the monitoring and adaptive adjustment of task scheduling.

[0118] S31, three-level response decision: based on the comparison between the channel quality score and the adaptive score threshold , the following three-level scheduling response strategy is executed:

[0119] Response rules:

[0120] Mild response (score close to but not lower than threshold):

[0121] ;

[0122] Moderate response (score deviates from threshold by a large margin):

[0123] , ;

[0124] Severe response (score far below threshold):

[0125] ;

[0126] Among them, the channel quality score obtained by S2, represents the adaptive threshold. ; wherein, represents the average score in the past period, represents the sound speed profile correction factor calculated by the temperature-salinity-depth sensor, represents the set safety adjustment coefficient, taking 5-10 points.

[0127] S32, acoustic self-correcting enhancement instruction set:

[0128] S321, basic instruction set (applicable to moderate response):

[0129] Carrier switching: switch the communication frequency to the anti-multipath frequency band ;

[0130] Encoding reinforcement: increase the forward error correction coding redundancy to 40%;

[0131] When the channel quality score is poor but does not reach the serious degradation level, the system will start the basic correction instruction, mainly including two measures:

[0132] Carrier switching to anti-multipath frequency band (12-15 kHz): Multipath interference is mainly caused by the superposition of reflected signals on the underwater reflection path. At higher frequencies, the energy of the reflected wave decays faster and the interference effect is smaller. Therefore, switching the communication frequency to the 12-15 kHz range can significantly weaken the phase overlap effect of the multi-path signal.

[0133] Increase the error correction coding redundancy to 40%: By increasing the redundancy bits, the receiving end can more effectively recover the damaged data. This reinforced forward error correction mechanism can tolerate more bit errors, enhancing the communication reliability under light and moderate degradation conditions.

[0134] S322, enhanced instruction set (applicable to severe response):

[0135] Doppler pre-compensation modulation: During underwater transmission, affected by ocean currents and platform motion, the signal will produce frequency drift (Doppler effect), causing inter-symbol interference. This strategy dynamically reduces the symbol transmission rate according to the extracted reflected wave frequency shift spread width Vr. Specifically, according to the mean value of the reflected wave Doppler spread variance , dynamically adjust the symbol rate:

[0136] ; wherein, is the symbol rate adjustment amount;

[0137] Injection channel sounding pilot: insert a pilot subframe every 5ms, use orthogonal frequency division scheme, the pilot uses Zadoff-Chu sequence; the subcarrier allocation is odd index number, which ensures the frequency domain orthogonality between pilot signal and data signal, these pilot signals can be used to estimate the change characteristics of underwater channel (such as gain, phase, time delay) in real time, so as to dynamically adjust the receiver parameters, so as to improve the channel estimation accuracy.

[0138] S33, constructing a structured channel degradation alarm vector: alarm vector , , ;

[0139] Among them:

[0140] Alarm level (according to response level setting):

[0141] ;

[0142] Degradation cause, according to And Relative dominance:

[0143] ;

[0144] The current activated acoustic correction measure code, for example:

[0145] B01 basic instruction;

[0146] E11 enhanced instruction + pilot enable.

[0147] As shown in Figure 3 The task scheduling monitoring system based on signal monitor is used to realize the above monitoring method, which includes the following modules:

[0148] Monitoring and separating unit: used for collecting mixed sound pressure signals by underwater signal monitor and separating direct wave and reflected wave components;

[0149] Channel evaluation unit: used for calculating channel quality score based on direct wave dispersion index and reflected wave time delay spread;

[0150] Monitoring response unit, for setting adaptive score threshold, and triggering multi-level scheduling response and generating degradation alarm according to score deviation degree.

[0151] The present application encompasses any alternatives, modifications, equivalent methods and solutions made to the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0152] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can also be made, which should be considered as the protection scope of the present application.

Claims

1. A task scheduling and monitoring method based on a signal listener, characterized in that, Includes the following steps: S1, captures the mixed acoustic pressure signal generated by the communication between the underwater base station and the vehicle through the submersible signal listening device, and separates the direct wave and reflected wave components; S2, calculate the current acoustic pressure channel quality score based on the dispersion index of the direct wave component and the time delay spread of the reflected wave component; specifically including: The number of phase abrupt change points of the direct wave in the preset frequency band is denoted as . Measure the average energy decay slope between adjacent mutation points ; Number of phase transition points The extraction is as follows: Time-frequency conversion: for direct wave signals Perform a short-time Fourier transform to extract the complex spectrum signal of the preset frequency band. : ;in This indicates that the frequency component is in time. Phase spectrum on Indicates the frequency of the direct wave signal. and time The complex spectrum below, express The amplitude spectrum; Phase jump detection: Select a center frequency and calculate the phase difference between adjacent time frames. ;like If this occurs, it is considered that a phase transition has taken place, and the cumulative total is taken as... : ; in, For indicator functions, Indicates the selected center frequency. Indicates the time interval between adjacent time frames. This represents the amount of phase change between adjacent time frames. This indicates the threshold for determining a phase abrupt change. ; Average energy decay slope The calculation is as follows: Obtain the sequence of mutation point locations: Record the time indices of all phase mutation points as a sequence. The corresponding amplitude spectrum is ; Indicates time Amplitude spectrum value at; Logarithmic amplitude differencing: Take the logarithmic difference of the amplitudes at all adjacent abrupt change points and calculate the slope. ; This represents the slope of the logarithmic magnitude decay between the i-th and (i+1)-th mutation points; Take the absolute mean as : The larger the value, the more severe the energy attenuation and the less stable the channel. The number of reflection clusters in the reflected wave with a lag time exceeding 50 ms is denoted as . Calculate the mean variance of Doppler broadening for each reflection cluster. ; Indicates the intensity of multipath interference and measures the number of complex reflective structures. It indicates the degree of Doppler distortion and reflects the frequency shift instability of the reflected signal; The dispersion degradation coefficient α and the time delay degradation coefficient β are calculated based on the average energy attenuation slope and the mean of the Doppler broadening variance. The specific calculation is as follows: Dispersion degradation coefficient: ; Delay degradation factor: ; 0.6 is a weighting factor used to adjust the calculation results to a reasonable scoring ratio, and 0.4 is a normalization factor; It also includes sound velocity gradient correction, specifically involving acquiring real-time sound velocity profile data measured by the mooring temperature, salinity, and depth sensor, calculating the relative deviation rate between the sound velocity at the current depth layer and the standard sound velocity, and generating a correction factor based on the relative deviation rate. , is represented as , ;in, The sound velocity deviation rate reflects the acoustic differences in the sea area, and is obtained in real time from the temperature, salinity, and depth sensors carried by the underwater mooring. and the standard speed of sound Compare and calculate the sound speed deviation rate: ; S3 sets an adaptive threshold, triggers a multi-level decision response based on the deviation between the channel quality score and the adaptive threshold, and generates a channel degradation alarm.

2. The task scheduling and monitoring method based on a signal listening machine according to claim 1, characterized in that, In S1, a four-element hydrophone array arranged circumferentially on the buoy shell synchronously acquires raw mixed sound pressure signals from four directions, generates a sound field spatial sampling matrix, performs time delay difference compensation calculation on the sound field spatial sampling matrix, and identifies the main path direction of the direct wave with the strongest energy and the secondary path directions of the three main reflected waves.

3. The task scheduling and monitoring method based on a signal listening machine according to claim 2, characterized in that, S1 further includes reflected wave reconstruction: based on the three main secondary path directions of reflected waves, a time-reflection mirror algorithm is used to project a probe pulse in the opposite direction to the reflection interface, and the propagation path of the reflected wave is reconstructed according to the phase distortion characteristics of the echo.

4. The task scheduling and monitoring method based on a signal listening machine according to claim 3, characterized in that, The separation of direct wave and reflected wave components specifically includes: taking the signal in the main path direction of the direct wave as the direct wave component, and taking the reconstructed reflected wave propagation path signal as the reflected wave component.

5. The task scheduling and monitoring method based on a signal listening machine according to claim 1, characterized in that, The sound pressure channel quality score is calculated as follows: Q = 100 × δ / (1 + α + β), where Q is the sound pressure channel quality score and 100 is the ideal channel reference value.

6. The task scheduling and monitoring method based on a signal listening machine according to claim 1, characterized in that, The multi-level decision response includes: Mild response: Extend the non-urgent task scheduling interval to 3 times the baseline value; Moderate response: Freeze non-urgent task scheduling threads and activate basic acoustic self-calibration instructions; Heavy response: Force hibernation for non-urgent tasks, activate acoustic self-calibration enhancement command and trigger the buoy surfacing contingency plan.

7. The task scheduling and monitoring method based on a signal listening machine according to claim 6, characterized in that, The channel degradation alarm includes the alarm level, the cause of degradation, and the correction measures.

8. A task scheduling and monitoring system based on a signal listener, used to implement the task scheduling and monitoring method based on a signal listener as described in any one of claims 1-7, characterized in that, Includes the following modules: Monitoring and separation unit: used to acquire mixed sound pressure signals and separate direct wave and reflected wave components through a submersible signal monitoring device; Channel evaluation unit: used to calculate channel quality score based on direct wave dispersion index and reflected wave delay spread; The monitoring response unit is used to set adaptive scoring thresholds and trigger multi-level scheduling responses and generate degradation alarms based on the degree of score deviation.

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