2761 results about "Signal interference" patented technology

The invention discloses a topographic surveying and mapping system and method of an unmanned aerial vehicle, and belongs to the technical field of topographic surveying and mapping. The system comprises the following modules: a data acquisition module which is responsible for acquiring high-precision terrain and environmental parameter data of a target area by using an unmanned aerial vehicle platform; the data fusion and correction module is responsible for fusing multi-source data and meteorological information and eliminating errors among sensors; the multi-dimensional feature analysis and recognition module is used for performing feature extraction and classification on data of each sensor through multi-dimensional feature analysis, and recognizing and separating an environment interference signal and a real building deformation signal; the interference elimination and deformation extraction module is used for carrying out pattern recognition and quantitative analysis on periodic non-deformation interference and accurately extracting real deformation characteristics of a building from complex dynamic noise; the real-time analysis module is used for realizing real-time data processing and feedback and ensuring that high-precision building change information is quickly provided; and the visualization module provides multi-dimensional dynamic visualization display including a building change heat map, an interference distribution map and a time sequence curve.

The invention discloses a hazardous chemical substance safety production risk monitoring and dynamic early warning system, particularly relates to the technical field of industrial safety monitoring, and is used for solving the problem of monitoring failure caused by signal interference or offset in a mixed leakage scene of various hazardous chemical substances in the prior art. Multi-source monitoring data such as gas concentration and temperature are acquired in real time through a data acquisition module, and a conflict identification module detects reverse fluctuation characteristics of sensor data; the leakage verification module is used for reversely positioning a leakage source and verifying path shielding interference based on production pipe network topology and real-time flow data; after the physical shielding is eliminated, the physicochemical analysis module analyzes the signal counteracting characteristic of the hazardous chemical substance combination and verifies the matching between the diffusion path and the sensor layout; the dynamic compensation module dynamically corrects the monitoring data according to a physicochemical reaction mechanism; the risk assessment module generates a mixed leakage risk grade and triggers a graded emergency response instruction; accurate identification and dynamic early warning of mixed leakage risks are realized.

The invention discloses an electronic lead seal automatic detection method applied to logistics tracking, and relates to the technical field of Internet of Things safety monitoring. The problems of packaging data loss, tampering risk increase and real-time monitoring failure caused by wireless communication signal interference, transmission delay and state updating lag of an existing electronic lead seal are solved. The method comprises the following steps: fusing multi-physical field sensing data through a space-time correlation sampling algorithm, and constructing an anti-interference characteristic matrix; evaluating communication quality based on the dynamic probability network model, and triggering a multi-path fragmentation concurrent transmission strategy to avoid signal attenuation; a hybrid reasoning model is deployed at an edge end to screen key event data, and the transmission efficiency is optimized in combination with differential coding and an IEEE 1588 clock synchronization mechanism; the cloud end adopts a space-time diagram fusion analysis model to carry out cross-modal abnormal association scoring, corrects misjudgment and updates an edge model; according to the invention, the communication reliability, the real-time transmission efficiency and the anomaly detection precision of the lead sealing state data in a complex environment are obviously improved.

The invention relates to the technical field of carrier communication, in particular to a multimode power line carrier communication method and a carrier communication module, and the method comprises the steps: determining a frequency coincidence degree through analyzing the coincidence degree of a frequency corresponding to a positive number amplitude value on a spectrogram of each modal component in a carrier signal and a signal frequency range of the carrier signal, and determining a difference index by analyzing the difference between each characteristic modal component and all modal components of the noise reference signal in frequency, analyzing the centralized distribution condition of the carrier signal in energy, and determining a step length adjustment value in combination with the signal interference degree. And carrying out noise reduction processing on the carrier signal in the power line carrier communication process at the current moment. The invention aims to eliminate noise interference in carrier signals and improve the reliability of power line carrier communication.

The invention relates to a signal interference optimization method and device. The method comprises the following steps: monitoring network parameters in a mobile communication network, and evaluating the interference level of each target frequency band in the mobile communication network according to the network parameters; generating a frequency allocation strategy according to the interference level, the frequency allocation strategy being used for dynamically matching the communication signal to the optimal frequency band; in addition, according to the interference level and the network parameters, a power adjustment strategy is generated, and the power adjustment strategy is used for dynamically adjusting the transmitting power of each base station in the mobile communication network so as to reduce signal interference. According to the application, the collateral dredging parameters are monitored in real time, and the optimal frequency band is dynamically matched for the communication signal according to the network parameters, so that the frequency band with serious interference is avoided, and the communication quality is ensured; meanwhile, according to the power adjustment strategy, the transmitting power of each base station can be dynamically adjusted based on a feedback mechanism, the signal interference is reduced while the communication requirement is met, the communication quality is further improved, and the increasing communication requirement of the user is met.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention relates to the technical field of road engineering, in particular to a construction positioning method and system for simulation equipment in a steel bridge deck pavement twinning scene, and the method comprises the steps: obtaining the positioning information and construction data of a construction site in real time, and constructing a construction site data set; performing primary processing on the construction site data set through a positioning algorithm to obtain a positioning position and construction dynamic characteristics of equipment; based on a GIS technology and a BIM architecture, constructing a three-dimensional twinborn scene model of the steel bridge deck, and numbering the three-dimensional model according to bridge deck pile numbers and pile-by-pile coordinate information; and matching the processed equipment positioning position data with the three-dimensional twin scene, and displaying the real-time position and the movement track of the equipment through a model. According to the invention, the problem of inaccurate positioning data caused by signal interference in the steel bridge deck pavement construction process is effectively solved, the accurate monitoring of the equipment position and the construction track is realized, and the comprehensive monitoring capability of the construction progress and quality is improved.

The invention discloses a radio frequency signal interference intelligent suppression management system and method based on image recognition. The system comprises a multi-source sensing module, a fusion analysis module, a dynamic suppression module and an execution feedback module. The multi-source sensing module forms a radio frequency original signal set containing frequency spectrum features and time domain waveforms; the fusion analysis module receives the radio frequency original signal set and extracts an interference source feature vector in the radio frequency original signal set through a pre-trained convolutional neural network to generate a fusion decision signal; the dynamic suppression module receives the fusion decision signal and activates a corresponding algorithm in a preset suppression strategy library according to the interference type identifier; and the execution feedback module receives the real-time control signal and drives the tunable filter, the phased-array antenna and the power amplifier to execute interference suppression operation. According to the intelligent suppression management system and method for the radio-frequency signal interference based on image recognition, the problems of fuzzy positioning of the radio-frequency interference source, single suppression strategy and lack of environmental perception capability can be solved.

The invention discloses an online multi-channel rapid gas chromatographic analysis system and method, particularly relates to the technical field of gas chromatographic analysis, and is used for solving the problems of baseline noise rise and trace component detection precision reduction caused by physical coupling and signal interference when multiple detectors work in parallel in the prior art. Interference signal features of adjacent detection channels are extracted, a chaotic or linear compensation signal is dynamically generated in combination with harmonic primary and secondary sorting and recursive quantization factor analysis, and common-mode interference of a target channel is suppressed. Electromagnetic isolation parameters of a shielding structure are adjusted in real time based on the baseline signal after noise suppression, and accurate matching of impedance and interference spectrum is achieved; further separating time domain overlapped signals and analyzing asymmetric distortion characteristics, and eliminating system errors through normalization calibration; dynamic interference suppression, electromagnetic shielding optimization and signal distortion traceability are combined, the anti-interference capability and analysis precision of multi-channel detection are remarkably improved, and the method is suitable for high-throughput rapid detection scenes of complex samples.

The invention relates to the technical field of wireless communication network optimization, in particular to a wireless network neighbor cell interference optimization method and system based on self-supervised learning. Federal learning and global model updating; performing deep reinforcement learning spectrum allocation decision; advanced wireless communication technology application; the block chain guarantees execution of the smart contract; closed-loop feedback optimization driven by user experience; the method has the beneficial effects that the interference mode between the cells is automatically identified and predicted through the self-supervised learning model, and the spectrum allocation strategy is dynamically optimized in combination with deep reinforcement learning, so that the signal interference between the cells is effectively reduced, and the overall stability of the network is improved. Especially in a high-density user scene, the system can respond to interference changes in real time, and the problem of performance reduction caused by adjacent cell interference is reduced.

The invention relates to a blood oxygen and heart rate monitoring system and method based on PPG signals and motion data, and relates to the technical field of medical health and intelligent wearable equipment. The system comprises a PPG signal acquisition module, a motion sensor module, a signal preprocessing module, a dynamic signal segmentation and quality evaluation module, an oxyhemoglobin saturation calculation module, a heart rate calculation module, a motion state recognition module, a self-adaptive Kalman filtering module and an output display module. The method and the device have the effects of realizing a function of identifying the motion state by the wearable equipment, improving the signal interference suppression capability and widening the application scene.

The invention discloses an intelligent traceable hazardous waste management system based on RFID, particularly relates to the technical field of hazardous waste intelligent supervision, and is used for solving the problems of unreliable data acquisition, misreading and misreading and state updating delay caused by signal interference of an existing RFID system in a high-density hazardous waste storage scene. The method comprises the following steps: dynamically dividing warehouse sub-regions, distributing a unique grouping identification code, and combining a directional antenna array partition scanning technology to avoid signal crosstalk; by analyzing the multi-path phase fluctuation rate and the polarization direction offset, a main interference frequency band is dynamically identified by using a covariance decomposition algorithm, and the communication frequency is switched, so that the tag identification rate in a complex electromagnetic environment is improved; scheduling a target sub-region scanning process based on a warehouse-in and warehouse-out instruction, synchronously verifying a label state and an actual position, generating an operation link space-time atlas, actively tracing cross-region time logic contradictions and triggering full-disk scanning; and full-process accurate tracing of the hazardous waste container and complete data management and control in a high-density scene are realized.

The invention relates to the technical field of anti-interference, in particular to a communication signal interference elimination method and system, and the method comprises the following steps: obtaining communication signal time domain data, calculating a signal energy change rate, a signal abrupt change rate and a phase drift rate, marking and distinguishing a signal region according to a calculation result, and obtaining a communication signal non-stationary interval identification result. According to the method, the time domain data of the communication signal is subjected to fine-grained calculation, and the signal energy change rate, the signal abrupt change rate and the phase drift rate are extracted, so that accurate signal region distinguishing is realized, the regression modeling parameter range is adjusted, the influence of instantaneous data of an abrupt change region is reduced, and the stability of signal reconstruction is improved; according to the method, signal-to-noise ratio calculation and interference power fluctuation amplitude evaluation are combined, channel state parameters are dynamically adjusted, interference intensity evaluation is more accurate, the filtering step length, the weight coefficient and the gain coefficient are optimized based on error information, signal processing adapts to different types of interference, and integrity and stability of communication signals are enhanced.

The invention belongs to the technical field of medical image processing, and particularly relates to an automatic positioning and typing method for cervical posterior longitudinal ligament ossification, which is characterized in that ossification focus spatial distribution characteristics of CT images and spinal cord morphological parameters of MRI images are synchronously analyzed based on CT and MRI images, a vertebral body-ossification-spinal cord spatial relationship is explicitly modeled through anatomical structure topological constraint, and a cervical vertebra-ossification-spinal cord spatial relationship is obtained. Diffuse calcification artifacts, heterogeneity signal interference and multi-modal data registration deviation are overcome, accurate positioning and typing diagnosis of the ossification are achieved, multi-dimensional feature fusion analysis is achieved, the limitation of traditional single iconography index diagnosis is broken through, space continuity and clinical parameters are integrated through a hybrid classifier, and the accuracy of diagnosis is improved. The accuracy and robustness of typing judgment are improved, a structured diagnosis report is automatically generated in the whole process, and the clinical decision-making efficiency and the standardization level are remarkably improved; the principle is scientific and reliable, and the ossification continuity index, the spinal canal invasion rate and the spinal cord compression grading parameters are automatically calculated according to the typing standard.

The present disclosure relates to methods, apparatuses, and systems that support reconfigurable intelligent surface (RIS) management in wireless systems. For instance, aspects of the disclosure enable operational aspects of an RIS to be controlled to reduce signal interference caused by operation of the RIS. Aspects of the disclosure also enable RIS control to be allocated between different base stations, such as to enable base stations to access an RIS for providing signal communication for UEs served by the base stations.

This disclosure is directed to shared antenna tuning. An electronic device may receive a global navigation satellite system (GNSS) tune request to tune a shared antenna to a GNSS signal frequency. The electronic device may then tune the antenna to the GNSS signal frequency and enable a GNSS receiver. The electronic device may also receive a cellular tune request to tune the antenna to a cellular frequency. The electronic device may tune the antenna to a cellular frequency and may deactivate the GNSS receiver or blank the GNSS receiver. In some embodiments, the electronic device may also communicate with a Low Earth Orbit (LEO) satellite. During LEO satellite communication, the electronic device may transmit a signal to blank a GNSS L1 receiver to avoid signal interference with the LEO satellite communication, and activate a GNSS L5 receiver to receive GNSS signals.

The invention discloses a redundant communication implementation device and method of a battery management system. The device comprises a master control unit, a master control unit and a slave control unit, the slave control unit is used for monitoring the battery module and sending monitoring data to the master control unit; the main control unit and the general control unit are connected to form a redundant communication ring network; the master control unit is used for responding to a control instruction or a query instruction of the master control unit, collecting monitoring data of the slave control units and feeding back the monitoring data to the master control unit; the main control unit is used for issuing a battery management control instruction or a query instruction to the main control unit and receiving feedback data of the main control unit; each main control unit comprises a main control board and a Bypass relay, the main control board is connected with the Bypass relay, and the Bypass relay is used for bypassing the main control board when the main control board fails and loses power. According to the device and the method, communication interruption caused by line signal interference can be avoided, and system communication faults caused by damage of the main control board can be prevented.

The invention provides a signal shielding method based on deep reinforcement learning, and belongs to the technical field of artificial intelligence and communication crossing. The method comprises the steps that S1, a large number of historical wireless communication signals are acquired to construct a data set to train a signal interference strategy generation model; s2, deploying the signal interference strategy generation model to a signal shielding device; s3, verifying and analyzing the signal shielding instruction to obtain a shielding parameter, collecting a real-time wireless communication signal, and inputting the real-time wireless communication signal and the shielding parameter into a deployed signal interference strategy generation model to obtain a real-time signal interference strategy; and S4, the signal shielding device modulates the real-time interference signal based on the real-time signal interference strategy, and the power amplifier performs power amplification on the real-time interference signal and then transmits the real-time interference signal to the outside through the radio frequency antenna so as to perform signal shielding. The method has the advantages that the accuracy, the real-time performance and the expansibility of signal shielding are greatly improved, and the power consumption of signal shielding is reduced.

The invention belongs to the field of biomedical engineering, and particularly relates to a puncture operation tissue recognition system based on bioelectrical impedance, which comprises a dual-electrode impedance needle used for collecting electric signal data in real time; the feedback display equipment is internally integrated with a trained random forest model and is used for processing the electric signal data according to the trained random forest model to obtain a classification result; the multi-frequency bioelectrical impedance analyzer is used for displaying the real-time acquired waveform and displaying the classification result of the random forest model at the same time; the dual-electrode impedance needle comprises a needle tube structure and an electrode structure located at the head of the needle tube structure. According to the invention, electric signals of different tissues are effectively obtained, and measurement errors and signal interference are reduced.

The invention discloses a GNSS real-time deception jamming detection method and system based on deep learning, and belongs to the field of satellite navigation signal processing, and the method comprises the steps: collecting Doppler frequency shift signals of a GNSS receiver in real time, and obtaining an initial training data set; an LSTM prediction model with an incremental learning mechanism is constructed, the LSTM prediction model comprises a double-layer LSTM network and a full connection layer, a gating mechanism is introduced into the double-layer LSTM network, and the initial training data set is adopted to add Gaussian white noise to the prediction model for model training; performing sliding window processing on the initial training data set, and extracting a multi-dimensional feature vector; inputting the trained prediction model for prediction to obtain a model prediction value; calculating a residual error between a model predicted value and a real value, calculating a standard deviation of the residual error after eliminating a preheating period, and dynamically setting a threshold value based on the standard deviation of the residual error; and when the absolute value of the residual exceeds a threshold value and the sampling point processed by the sliding window is located in a preset interference time window, judging that a signal interference event occurs.

A method and apparatus for mitigation of GNSS-signal interference using an adaptive notch filter (ANF) operates based on signals received from one or more satellites of a Global Navigation Satellite System (GNSS) such as GPS, GLONASS, etc. In one embodiment, an apparatus comprises a notch filter having a tunable zero frequency of a transfer function receives an input signal and generates an output signal. A bandpass filter coupled to the output of the notch filter receives the output signal. An adaptive block is coupled to the bandpass filter and adjusts the notch filter parameters in order to achieve the minimization of a specific cost function.

The invention relates to the technical field of power system communication, and discloses a fault diagnosis method and system for a substation communication network, and the method comprises the steps: obtaining multi-band signal data, and carrying out the fast Fourier transform to obtain spectrum distribution data; according to the spectrum distribution data, performing frequency band division to obtain an initial power ratio; performing adaptive filtering according to the initial power ratio and the multi-band signal data to obtain a fundamental wave amplitude fluctuation characteristic; performing wavelet decomposition according to the fundamental wave amplitude fluctuation characteristics to obtain abnormal signal time-frequency energy distribution; performing K-means clustering division according to the abnormal signal time-frequency energy distribution, and matching a harmonic fault database to obtain a fault type identifier; and according to the fault type identifier and the abnormal signal time-frequency energy distribution, ring network topology propagation time delay analysis is carried out, a fault position coordinate is positioned, and a fault diagnosis report is generated. According to the method, the network fault can be accurately diagnosed for multi-band complex signal interference.

The invention relates to an unmanned aerial vehicle detection countering method, which comprises the following steps of: deploying a multi-dimensional sensing network to carry out full-band instantaneous scanning, synchronously acquiring noise characteristics of radio signals, material characteristics of a terahertz wave band and Doppler effect characteristics, mapping to a virtual twinborn body, reconstructing physical parameters, a communication protocol and a behavior mode of a target unmanned aerial vehicle, performing depth comparison by combining a disguise sample library generated by the antagonism generation network, and generating a holographic target file containing a three-dimensional motion vector, an energy feature and a potential threat intention; a target quantum encryption communication link is interfered through a quantum entanglement signal, a controllable plasma cloud cluster is generated, navigation and image transmission signals of the controllable plasma cloud cluster are selectively attenuated, the controllability of the target is judged, and an uncontrollable target is repelled through coded sound waves; and sending a control instruction containing a biological heuristic obstacle avoidance algorithm in stages, and guiding the control instruction to an intelligent recovery cabin. According to the invention, accurate detection, intelligent identification, safe countering and reliable takeover of the unmanned aerial vehicle are realized, and the airspace safety guarantee capability is improved.

The invention relates to the field of waterproof sockets, and discloses a multi-scene adaptive intelligent waterproof socket remote regulation and control system and method, and the method comprises the steps: obtaining multi-source sensing data of a socket deployment environment, and constructing a multi-scene adaptive input data set in combination with a parameter fusion strategy and a signal interference compensation mechanism; environment feature recognition is carried out on the multi-scene adaptive input data set, and typical application modes in different scenes are extracted by using a socket state tag extraction algorithm; based on the state mapping relation model, a remote regulation and control strategy optimization algorithm is adopted to dynamically analyze the operation state of the socket, and the matching degree of the current operation mode and the environment scene is identified; an intelligent judgment mechanism is introduced, pre-execution verification is carried out on the multi-scene regulation and control suggestion set, and potential control conflicts are recognized in combination with historical operation data and real-time control instructions; and performing instruction issuing management on the remote control behavior of the socket based on the optimized execution strategy. The invention has the advantage of improving the universality of the intelligent waterproof socket.

The invention relates to the technical field of lithium ion battery testing, and discloses a lithium ion battery life loss evaluation method and device, a medium and equipment, and the method comprises the following steps: S1, collecting multi-modal dynamic data of a lithium ion battery in a charge-discharge cycle process; s2, performing time-frequency domain conjoint analysis on the multi-modal dynamic data, and extracting a battery aging sensitive feature set; s3, constructing a multi-scale coupling model of battery life loss; by constructing a multi-modal data fusion mechanism and a dynamic feature extraction system, during lithium ion battery life loss evaluation, change trends of electrochemical impedance spectroscopy and heat distribution key parameters are captured in real time based on time-frequency domain conjoint analysis, sensor signal distortion and drift problems can be identified, the extraction precision of aging sensitive features is improved, and the accuracy of lithium ion battery life loss evaluation is improved. The problem of characteristic errors caused by signal interference in traditional evaluation is solved, and the accuracy and reliability of life loss evaluation are ensured.

The invention relates to the technical field of pipeline leakage detection, in particular to an intelligent pipeline leakage monitoring system based on a positioning fitting technology, which comprises a sensor configuration and signal preprocessing module, a signal interference processing module, a real-time monitoring and abnormity identification module and a positioning fitting and leakage evaluation module, by deploying the sensor network, determining and optimizing the positions of the sensors and preprocessing sensor signals, the monitoring efficiency of the sensors can be improved; the reliability and integrity of data transmission are guaranteed through anti-interference processing of sensor signals; possible leakage points are preliminarily identified by monitoring and analyzing the state of the pipeline, and data support is provided for subsequent data analysis. The accurate position of the suspected leakage point is obtained through the positioning fitting technology, leakage evaluation analysis is carried out, measures can be taken, and a basis is provided for subsequent decision making. The method is used for solving the technical problem that the current pipeline leakage monitoring efficiency is not high.

The invention relates to the technical field of artificial intelligence, in particular to an agent integration optimization method and system based on multi-modal interaction, and the method comprises the following steps: obtaining a time point statistical frequency difference value of a multi-modal event, comparing a trend with offset, screening out a non-linkage event, extracting a semantic comparison context, positioning a modal in-situ, and obtaining a multi-modal event time point statistical frequency difference value; and calculating the priority of a response offset sorting channel, judging interlaced and densely set interference marks, screening fast and undisturbed channel rebinding content, and obtaining an integrated optimization scheme. According to the method, abnormal modal recognition is achieved through modal frequency fluctuation and response offset trend conjoint analysis, semantic fragments are combined with context continuity and task density constraints to improve screening precision, channel response delay difference sorting is clear in priority relation, and a signal interference area is positioned through amplitude mutation and time intersection nodes. And channel path elimination and content remapping guarantee response smoothness, accurate matching between expression intention and channel response is realized, and modal collaboration consistency and task execution stability are enhanced.

The invention discloses a self-adaptive suppression method and system for multi-band signal interference of a mobile terminal, and the method comprises the steps: collecting a multi-band radio-frequency signal power spectrum, extracting an interference feature vector, inputting the interference feature vector into a neural network prediction model, outputting future interference intensity probability distribution, generating a prediction frequency spectrum according to the probability distribution, and combining the current channel state information, pre-compensation processing is carried out through a double-level equalizer; the balancing strategy is dynamically adjusted according to the resource state of the mobile terminal, specifically, when the CPU load exceeds a first preset value or the electric quantity is lower than a second preset value, a second-level equalizer in the double-level equalizer is forbidden, and the steps S20-S40 are executed again till the CPU load and the electric quantity meet preset conditions; and a sliding window verification mechanism is adopted, DTW distances of the predicted frequency spectrum and the actually measured frequency spectrum are compared, and the frequency band weight coefficient of the feature extraction matrix is dynamically updated. According to the method, the signal interference suppression capability is improved through the synergistic effect of time-frequency feature enhancement, hybrid model prediction, dynamic resource scheduling and closed-loop optimization.

The invention relates to the technical field of analysis of physical and chemical properties of soil, in particular to a dry farming soil fertility rapid detection and fertility improvement method, which comprises the following steps: sorting a colloidal phase and a dissolved phase of a humic acid complex based on density difference and dielectric property difference through a gradient centrifugation and dielectrophoresis enriched physical field synergistic separation technology; a two-channel detection time sequence is optimized by adopting a genetic algorithm, a non-overlapping time sequence signal flow is generated by taking a signal interference suppression ratio as a fitness function, and time domain cross interference is suppressed. A programmable resistance-capacitance compensation model is constructed, parallel capacitor array parameters are dynamically adjusted to correct electrochemical phase angle offset, and a three-dimensional fertility distribution curved surface and a residual map are generated in combination with a Kriging interpolation model. On the basis of residual spectrum spatial distribution characteristics, a cross-linked slow-release system is deployed to improve the organic matter binding capacity of a humic acid deficiency area, a mesoporous zeolite-based passivator is applied to regulate and control an ion migration path, a closed-loop feedback mechanism is formed through dynamic phase response monitoring, and accurate detection and directional remediation of soil fertility are achieved.

The invention relates to the technical field of intelligent monitoring, in particular to an underwater robot monitoring device and method based on muon imaging and navigation positioning, and the device comprises a transmission and control subsystem which is used for driving an underwater robot to complete a target action task in an underwater pipeline; the muon imaging positioning subsystem is used for acquiring high-energy imaging information and a three-dimensional coordinate position of the underwater robot so as to assist the underwater robot in completing a target action task; the underwater scanning, monitoring and early warning subsystem is used for evaluating a water sample of the underwater pipeline to generate a water quality evaluation report and acquiring surrounding terrain and geological feature information of the underwater pipeline to generate an underwater three-dimensional image; and the multi-source data fusion transmission base station is used for fusing the water quality evaluation report, the high-energy imaging information and the underwater three-dimensional image to generate a state evaluation report. Therefore, the problems of insufficient penetrating power, low resolution, signal interference and the like of the existing underwater monitoring technology and the like under the conditions of deep water, high turbidity or complex terrains are solved.