948 results about "Anti jamming" patented technology

The invention provides a point cloud data processing method and device of a laser radar, equipment and a medium, and the method comprises the steps: carrying out the adaptive local neighborhood construction of each query point cloud, and determining a local neighborhood point set of the query point cloud; determining a plurality of geometric features of each query point cloud based on the local neighborhood point set; performing normalization processing and dynamic weight optimization on the plurality of geometric features of each query point cloud, constructing a comprehensive feature of each query point cloud, dividing the plurality of original point cloud data based on the plurality of comprehensive features, and determining a plurality of point cloud categories; and carrying out noise removal processing on the original point cloud data in each point cloud category, determining updated point cloud data, repeatedly carrying out local neighborhood point set determination and point cloud division processing on the updated point cloud data until a convergence condition is met, and outputting the denoised point cloud data. Effectiveness and anti-interference capability of feature fusion are improved, and intelligent identification and denoising of the point cloud and accurate retention of the effective point cloud are realized.

The invention relates to the related technical field of dust detection, and provides a mining induced charge and light scattering fusion dust detection device and method.The dust detection device comprises a workbench, a sampling assembly and a detection assembly, and the sampling assembly comprises a sampling air pump, a conical airflow homogenizer and a closed sampling cavity; the detection assembly comprises an annular sliding rail, a light scattering detection module and an electrostatic induction detection module; the dust detection method comprises the following steps: S1, device initialization and zero calibration; s2, sampling preparation of dust-containing airflow; s3, dual-module synchronous signal acquisition; s4, data processing and temperature and humidity compensation; s5, carrying out anti-interference protection assembly; and S6, data fusion and result output. S7, device cleaning and standby; through cooperative work of the double detection modules, dust detection signals in different concentration intervals are accurately captured, wide-range coverage from low concentration to high concentration is achieved, and synchronous monitoring of dust concentration and particle size characteristics is achieved.

The invention provides a well drilling anti-interference communication system based on a VCSEL (Vertical Cavity Surface Emitting Laser), the VCSEL and a manufacturing method thereof, and the manufacturing method comprises the following steps: calculating the light field intensity distribution of a high-order transverse mode through an intrinsic mode expansion method and a Laguerre-Gaussian model based on the epitaxial structure parameters of a laser; determining a target area which begins to perform downward annular etching on the surface of the p-DBR layer, wherein the target area coincides with a light field intensity peak area of the high-order transverse mode; performing annular etching in the target area to form an annular groove, and etching at least seven pairs of p-DBR layers inwards in the groove; the communication system comprises a signal transmitting end arranged on the derrick side, and the signal transmitting end is provided with the vertical cavity surface emitting laser; the p-DBR region of the vertical cavity surface emitting laser is provided with an annular etching structure; the graded-index fluorinated plastic optical fiber is used as a transmission medium and is connected with the derrick side and the logging room side; and the optical signal receiving end is arranged at the logging room side and is used for receiving and converting the optical signal into an electric signal.

The invention relates to the technical field of audio forgery detection, in particular to an audio forgery detection method and system based on lightweight federated adversarial training, and the method comprises the steps: a client extracts audio features through a lightweight network, and updates a model through local adversarial training; the server aggregates the client parameters to generate a global model and issues the global model; and the client performs forgery detection by using the global model. According to the method, a federal learning framework is adopted, original audio is not out of the local, only compressed model parameters are uploaded, high-performance collaborative learning under privacy protection is realized, a data island is broken, and the generalization ability is improved; time domain, frequency domain and frequency spectrum residual multi-dimensional features are fused with cross-modal attention, and adversarial disturbance training is introduced locally, so that feature resolution and anti-interference robustness are enhanced; through combination of pruning, quantification and distillation lightweight processing, the communication and calculation overhead is remarkably reduced, and mobile and IoT edge equipment can be efficiently deployed.

The invention relates to the technical field of equipment state monitoring, in particular to an emergency cut-off valve bolt looseness detection method and system based on multi-modal fusion. The method specifically comprises the following steps of multi-source data synchronous acquisition, accelerometer installation and data processing, environmental adaptability preprocessing, bolt area optical flow feature extraction, Kalman filtering multi-source fusion, bolt looseness detection and loose bolt positioning. According to the emergency cut-off valve bolt looseness detection method based on multi-modal fusion, online, automatic and real-time accurate monitoring and early warning are achieved, the reliability and the anti-jamming capability of the system are remarkably improved, a high-precision and quantifiable objective diagnosis result is provided, good economical efficiency and large-scale deployment feasibility are achieved, and the method is suitable for popularization and application. The industrial pain point that a traditional contact type sensor scheme is difficult to apply to an industrial site with a large number of bolts in a large scale due to too high cost and technical complexity is effectively solved.

The application discloses an online error calibration method for a hemispherical resonator gyro based on frequency superposition, which solves the problem that the traditional error calibration method for a hemispherical resonator gyro cannot realize online calibration of gain error, bias angle error and phase error in the same working mode. The application utilizes high-frequency cosine and sine reference signals to demodulate two-channel detection signals and extract high-frequency components in the detection signals. Subsequently, an associated model is established by using the high-frequency cosine and sine given control signals and the demodulated signals to realize online identification of the gyro error, and the identification result is applied to online calibration of the channel gain error, bias angle error and phase error, thereby enhancing the anti-interference ability of the hemispherical resonator gyro under variable temperature conditions. The application is mainly used for simultaneous online calibration of the channel gain error, bias angle error and phase error.

The invention discloses a cleaning end point judgment method and system for a semiconductor process chamber, and the method comprises the following steps: obtaining optical parameters of characteristic gas generated in a cleaning process, carrying out the data processing of the optical parameters, and obtaining a normalized absorptivity A; acquiring an electrical parameter V of characteristic gas generated in the cleaning process; performing data processing on the optical parameter and the electrical parameter V to obtain an optical end point index, an optical weight, an electrical end point index and an electrical weight; obtaining a decision index S; setting a first threshold value of the normalized absorption rate A and a second threshold value of the decision index S; when the decision index S is continuously lower than the second threshold value, it is judged that cleaning is completed. The cleaning end point judgment system comprises a semiconductor cavity, a tail gas emission pipeline and a detection device. The detection device is arranged on the tail gas emission pipeline to detect the concentration of the characteristic gas in real time. According to the invention, the anti-interference capability in the detection process can be improved.

The invention provides a complex mine radar adaptive anti-interference detection method based on reinforcement learning. Relates to the technical field of mine safety monitoring and intelligent sensing. Multi-domain features are extracted based on the echo signals; constructing a reinforcement learning state space design composite reward function; an incomplete information game adversarial reinforcement learning algorithm is introduced to carry out iterative training on the radar strategy network; selecting and executing the action of the initial anti-interference strategy according to the current environment state in the actual detection process; on-line fine tuning and updating are carried out on parameters of the radar strategy network based on the real-time feedback data; performing performance evaluation; and if the preset performance threshold is not met, adjusting the weight of the composite reward function and the radar strategy network parameters, and performing iterative optimization until an optimal anti-interference strategy is obtained. According to the invention, multi-source interference adaptive suppression and stable detection are realized, the target detection precision is improved, the false alarm rate is reduced, and the environmental adaptability and long-term robustness of the system in unmanned tunneling, intelligent transportation and safety monitoring scenes are enhanced.

The invention relates to the technical field of electric power facility safety monitoring, in particular to a substation equipment foundation settlement observation system and method, and the system comprises an infrared monitoring array and a data processing terminal; the method comprises the following steps: acquiring and correcting real-time three-dimensional point cloud data of the basic surface of the equipment; obtaining an effective settling volume; environment parameters or equipment vibration signals are monitored in real time, the collection frequency of the infrared monitoring array is automatically adjusted according to the monitoring result, and an early warning signal is sent out when the effective settling volume exceeds a preset threshold value; through software and hardware cooperation, the problems that in the prior art, a monitoring blind area is large, the anti-interference capacity is weak, and a linkage mechanism is lacked are solved, an intelligent settlement observation solution is provided for a substation equipment foundation, and safe and stable operation of a power grid is effectively guaranteed.

The invention relates to the technical field of data lines, and particularly discloses a data line system and a data line for intelligently switching data transmission and charging, and the system comprises a dual-core synchronization management module which is used for enabling dual cores to enter an initial detection state through encryption synchronization signaling when equipment is connected; the asynchronous signal processing module is used for generating state judgment parameters through multi-scale sliding window processing and time-frequency domain feature fusion; the consensus decision arbitration module outputs a backtracking trigger signal through dynamic projection transformation and self-adaptive threshold comparison; the state backtracking recovery module is used for generating a retry instruction with a random delay period based on the historical backtracking success rate and the signal quality evaluation; the safety mode control module is used for identifying dominant features by analyzing historical failure records and switching to a safety operation mode; through distributed dual-core cooperative processing and an intelligent fault recovery mechanism, the technical problems of confusion of protocol stacks and deadlock of a state machine in a complex electromagnetic environment are solved, and the anti-interference capability and reliability of the system are improved.

The invention discloses a signal control system for a mining locomotive, and aims to solve the problems of weak interference resistance and low safety of a traditional system. The system comprises a signal acquisition module, a dynamic frequency coding processing module, an intelligent decision-making module, an execution module, a communication module and a power supply module. The signal acquisition module acquires the position, speed and environmental parameters of the locomotive through an infrared position, speed and temperature and humidity sensor; the dynamic frequency coding module monitors the signal-to-noise ratio of a sub-band every 10 ms, when the signal-to-noise ratio of the sub-band is lower than 15 dB, the sub-band is automatically switched to a high-signal-to-noise-ratio sub-band larger than or equal to 20 dB, an encryption mapping table is generated for synchronous updating, and the equipment deals with sudden interference through an emergency band. The intelligent decision-making module generates a control instruction, and speed adjustment and parking are achieved through the execution module. An explosion-proof cable and a low-frequency through-the-earth technology are combined for communication, and a power supply is of an explosion-proof type and has multiple protections. The system improves the anti-interference performance and safety of signal transmission, adapts to the mine environment, and supports the intelligent operation of mine transportation.

The invention discloses a GNSS anti-interference closed-loop phase self-correction method based on deep learning, and relates to the technical field of satellite navigation and artificial intelligence, and the method comprises the steps: obtaining an anti-interference array output signal and weight information through an array antenna and space-time adaptive processing; extracting a carrier-to-noise ratio, a weight phase feature and historical positioning quality, and constructing a deep learning feature vector; reasoning an array element-level phase correction reference quantity by using a multi-layer perceptron model, and realizing accurate phase compensation of an array output signal by combining phase error layered modeling, quick-change disturbance prediction and weight sensitivity estimation; an anti-interference perception observation covariance dynamic scaling mechanism is introduced into RTK positioning calculation; on-line learning and model updating are triggered through a positioning quality evaluation result, so that the system has an adaptive evolution capability. According to the method, the GNSS carrier phase quality and the positioning precision under the strong interference condition can be remarkably improved, deep fusion of anti-interference processing and high-precision positioning is realized, and the method has important engineering application value.

The invention relates to the technical field of array signal processing, in particular to a space-frequency power inversion anti-interference method fusing SMI-LDL decomposition. The problems that an existing space-time anti-interference technology is high in calculation complexity, and real-time processing is difficult to achieve in limited resources of a navigation receiver are mainly solved. The method comprises the following steps: firstly, converting a radio frequency signal received by an array into a digital intermediate frequency signal and carrying out sliding window partitioning processing; windowing the data block and performing FFT (Fast Fourier Transform) to obtain a frequency domain array; then calculating an auto-covariance matrix R of other array elements except the reference array element and a cross-covariance matrix r of the reference array element and other array elements; lDL decomposition is carried out on the auto-covariance matrix to obtain a lower triangular matrix L and a diagonal matrix D, and inversion is carried out on the lower triangular matrix L and the diagonal matrix D respectively, so that huge operand caused by direct inversion of the covariance matrix is avoided; and finally, calculating an optimal weighting vector according to a power inversion criterion, and performing spatial filtering on the frequency domain data.

The invention belongs to the technical field of optical device control, and discloses a fast reflector sliding mode control method and related equipment, and the method comprises the steps: building a closed-loop error model of a fast reflector; the closed-loop error model comprises a lumped disturbance item jointly formed by external disturbance and system parameter measurement deviation; setting a fixed time RBF disturbance observer; the fixed-time RBF disturbance observer is used for estimating a lumped disturbance item so as to output a lumped disturbance estimated value, and the fixed-time RBF disturbance observer is configured with a weight updating law which enables an estimation error to converge within fixed time irrelevant to an initial state; constructing a sliding mode controller based on a lumped disturbance estimated value output by the fixed-time RBF disturbance observer and a closed-loop error model; a sliding mode controller is used for controlling the fast reflecting mirror; therefore, the control precision and the anti-interference capability are improved, and the problems that the controller depends on the disturbance upper bound and the disturbance estimation convergence time depends on the initial state are solved.

The invention discloses a Beidou user machine high-precision time service and positioning method based on a low-orbit high-stability reference, and relates to the field of vehicle-mounted electronic communication. The method comprises the following steps: firstly, receiving a low-orbit satellite time service signal through an SBOX, generating a high-stability time service reference signal through anti-interference enhancement and delay compensation, and transmitting the high-stability time service reference signal to a Beidou user machine; the Beidou user machine calibrates drifting and fluctuation of an original time service signal of the Beidou user machine according to a low-orbit high-stability time service reference, Beidou high-precision and low-orbit high-stability time service output is achieved through double-source fusion, and finally high-precision positioning calculation is completed and packaged into CAN data; and finally, the information is transmitted to a vehicle-mounted information unit through a vehicle-mounted CAN bus, so that navigation and position reporting are realized. According to the invention, the high stability of the low earth orbit satellite and the high precision of the Beidou are complemented and cooperated, the time service precision and continuity are considered through Kalman filtering fusion and extended Kalman positioning solution, the problems of insufficient stability and poor equipment collaboration of a traditional Beidou user machine are solved, and the high stability and precision requirements of military and commercial use are met.

The invention discloses a hoisting digital twin system based on Beidou sensing and intelligent fusion and a management method, and relates to the technical field of intelligent construction, the system comprises a Beidou intelligent positioning device used for collecting Beidou positioning data and IMU attitude data of a hoisting member in real time; the layered multi-error fusion suppression unit is used for receiving the data preprocessed by the Beidou intelligent positioning equipment, and sequentially performing multi-path error decomposition, data fusion and dynamic error compensation to output corrected Beidou positioning and IMU attitude data; and the modularized hoisting digital twinborn collaborative management and control platform is used for accessing and fusing the corrected Beidou positioning and IMU attitude data, environment data and video data, and driving a BIM model to perform synchronous display. The method solves the problems of low positioning precision and poor anti-interference capability of traditional hoisting, realizes safe management and control of the process and improvement of the operation efficiency, and is suitable for hoisting operation of large bridge prefabricated parts in complex environments such as sea crossing and mountainous areas.

The invention relates to the technical field of electronic exploder control, in particular to an anti-electromagnetic interference dynamic threshold control system and method for an electronic exploder, and aims to solve the problem that the electronic exploder is easily interfered in a complex electromagnetic environment to cause false triggering or missing detection. According to the system and the method, surrounding electromagnetic signals are acquired, time domain, frequency domain, waveform distortion and interference source features are extracted and fused to generate clustering feature vectors, and the signals are divided into pure interference, pure detonation control and superposed signals by using an improved K-means algorithm; and based on the clustering result and the interference intensity, adjusting an identification threshold in real time through a staged dynamic threshold adjustment formula, introducing a multi-dimensional reference factor of historical interference data after the interference is weakened, and stably recovering the threshold to a normal level by adopting a staged adaptive recovery mechanism. According to the system and the method, the anti-interference capability of the electronic detonator in a complex electromagnetic environment can be remarkably improved, and the safety and the accuracy of detonation operation are ensured.

The invention discloses a defect detection method and system based on insulator multi-modal image fusion, and relates to the technical field of electrical equipment defect detection, multi-modal image fusion is performed based on an attention mechanism improved RFN-Nest image fusion model, fusion of infrared image temperature anomaly features and visual image structure detail features is enhanced, and the defect detection accuracy is improved. The information entropy, mutual information and other indexes of the generated fusion image are remarkably superior to those of an original model and a traditional fusion method, high-quality data support is provided for a detection task, then defect detection is carried out based on an attention mechanism improved YOLOv8 target detection model, the defect feature discrimination capability and the anti-interference capability are improved, and the detection efficiency is improved. The problems of missing report and false report of insulator defects in a complex scene are effectively solved, the performance is remarkably improved compared with a traditional independent link design scheme, and the method can be directly applied to an actual electric power inspection scene.

The single-phase bidirectional single-stage isolation matrix converter comprises a pre-stage circuit and a post-stage circuit, and the pre-stage circuit comprises an LC filter on the power grid side, a pre-stage single-phase matrix conversion circuit and an LLC resonance circuit with a high-frequency transformer; the post-stage circuit comprises a post-stage conversion circuit and an output filter capacitor; the front-stage single-phase matrix conversion circuit comprises four bidirectional switch assemblies of a full-bridge structure. Each bidirectional switch assembly comprises two unidirectional controllable MOS tubes which are reversely connected in series, one end of each bidirectional switch assembly is connected to an L line or an N line of a power grid, and the other end of each bidirectional switch assembly is connected to the LLC resonance circuit. The rear-stage conversion circuit comprises a bidirectional MOS (Metal Oxide Semiconductor) tube bridge circuit and three high-frequency transformer secondary windings which are in triangular connection or star connection; and two end points of each secondary winding are respectively connected to the upper MOS tube and the lower MOS tube of the corresponding bridge arm and then are connected to output positive and negative ends. The converter has the advantages of high power density, low current harmonic, high power factor and the like, and is small in size, strong in anti-interference capability, wide in buck-boost range, and safe and reliable in commutation.

The invention discloses an elevator steel belt damage detection and quantitative analysis system based on eddy current and magnetic flux leakage dual-mode detection, and aims to solve the problems of single-mode information loss and serious industrial field strong noise interference in the existing steel belt detection. The system synchronously integrates an eddy current sensor, a magnetic flux leakage sensor and an encoder through a multi-probe array adapter; and multi-dimensional damage physical information in the steel strip is obtained. An improved wavelet packet transform-empirical mode decomposition (WPT-EMD) collaborative noise reduction algorithm is adopted, and in combination with a sub-band energy entropy and a self-attention mechanism, non-stationary mechanical noise is effectively filtered out. A multi-rule feature extraction engine is used for extracting smooth residual errors, derivative mutation and other features, a support vector machine (DE-SVM) model introducing physical priori knowledge weights is constructed, and the small sample recognition problem is solved in combination with a virtual sample generation technology. The method can realize high-precision positioning and quantitative evaluation of steel strip damage under complex working conditions, and has the characteristics of strong anti-interference capability, high identification accuracy and good generalization performance.

The invention relates to the technical field of automatic identification and data acquisition, discloses a control method and system for positioning a scanning gun based on an RFID electronic chip, and aims to solve the problems of remnant detection failure and misjudgment caused by shielding and deformation of existing video monitoring. A unique identification RFID chip is arranged on the surface of an object, a multi-protocol scanning gun terminal is combined to capture label signals in real time, a space coordinate mapping module is used for achieving three-dimensional positioning, a track generation engine restores a moving path, and a retention judgment unit recognizes abnormal retention according to a time threshold value and a behavior mode and triggers an alarm linkage mechanism. According to the method, an active identity binding and trajectory tracking system is constructed, so that the dependence on a visual environment is eliminated, the remnant identification precision, the anti-interference capability and the response speed in a security scene are remarkably improved, and all-weather stable operation and large-scale convenient deployment are supported.

The invention discloses a high-precision dynamic linear displacement metering device based on laser scattering suppression, and relates to the related technical field of displacement monitoring, and the device comprises a host, rails, a stabilizing mechanism, supporting legs, a measuring mechanism, a data processing mechanism, a data processing mechanism, a data processing mechanism, a data processing mechanism, a data processing mechanism and a data processing mechanism, the host is slidably installed above the rails and is used for horizontally moving the host; the stabilizing mechanism is installed between the rails and is used for keeping the host in a horizontal state; the stabilizing mechanism is connected between the track and the main machine, the two adjusting mechanisms are installed between the stabilizing mechanism and the main machine and used for adjusting the horizontal angle and the inclination angle of the main machine, and the track, the stabilizing mechanism, the supporting feet and the adjusting mechanisms are arranged to cooperate with each other. The invention relates to a multi-dimensional angle adjusting device, in particular to a multi-dimensional angle adjusting device, which solves the problem that existing equipment cannot realize horizontal movement, autonomous leveling and multi-dimensional angle adjustment in industrial field application. The problems that an existing dynamic linear displacement metering device is insufficient in environmental adaptability and weak in anti-interference capacity, and the measurement stability is limited by external conditions are solved.

The invention discloses a radar anti-interference method based on frequency domain modeling and image collaboration, and the method comprises the steps: S10, carrying out the preprocessing of an original echo, and finally packaging a preprocessing result into a three-order tensor form; s20, performing FNO interference feature extraction and identification, and outputting interference type probability distribution and an interference-to-signal ratio estimation value through a de-wharf; s30, selectively processing the image, and reconstructing the histogram into an SAR image in an equalization manner; s40, evaluating the credibility of the image, and outputting an imaging credibility score between 0 and 1 through a regression head; s50, generating a credibility-interference combined criterion for judging the reliability of the radar guidance state; s60, a loss function is designed, back propagation is carried out through a total loss function, the model gradient is updated, and model parameters are optimized; and S70, dynamic anti-interference strategy matching: retrieving candidate anti-interference actions from a preset strategy library based on the interference types, performing grading and sorting according to complexity, and when the joint risk score is lower than a set threshold value, activating the optimal high-complexity anti-interference process of the corresponding interference type.

The invention discloses a high-precision beam alignment method for an Internet of Vehicles ground-non-ground converged communication system and a vehicle-mounted communication terminal, and belongs to the technical field of cellular and satellite converged communication. A two-stage control strategy of'coarse tracking + fine tracking 'is adopted. The method comprises the following steps: firstly, based on GNSS / INS fusion positioning and satellite orbit prediction (SGP4 model), quickly calculating and finishing initial coarse alignment of a wave beam; and then, constructing a local beam scanning window, collecting received signal intensities (RSSI) in multiple directions as observed values, introducing an extended Kalman filter (EKF) algorithm, and establishing a state space model for recursive estimation, thereby realizing high-precision and dynamic closed-loop optimization and tracking of a beam pointing angle. According to the invention, the speed and the precision of beam alignment are obviously improved, the anti-interference capability and the link stability of the system in a dynamic environment are enhanced, and the continuous communication service in the sky-ground fusion Internet of Vehicles is effectively guaranteed.

The invention relates to the technical field of gas detection, and discloses an underground coal mine all-optical cooperative multi-parameter gas detection anti-interference noise suppression system and method.The method comprises the steps that original optical characteristic signals are collected in an underground coal mine area to be detected, and an interference characteristic parameter set and an environment compensation vector are constructed; calculating a real-time estimation noise variance and a real-time credibility weight of each detection channel based on the interference characteristic parameters; performing inversion on the original optical characteristic signal to obtain an original concentration, performing regression compensation by using an environment compensation vector, performing weighted fusion on the compensated concentration based on a real-time credibility weight, and outputting a final fusion concentration; and finally outputting a composite decision in combination with the interference characteristic parameters. According to the method, the quantitative mapping of the interference parameter and the noise variance is established, so that active sensing and adaptive weighted fusion of the health state of the detection channel are realized, the problem of measurement distortion caused by sensor performance degradation is effectively solved, and the robustness and accuracy of the system are remarkably improved.

The invention discloses a high-temperature nitriding atmosphere concentration closed-loop control device and a high-temperature nitriding atmosphere concentration closed-loop control process. The device comprises a nitriding atmosphere supply module, a flow regulation module, a reaction device, a concentration detection module, a cooling device, a tail gas treatment device, a controller and an auxiliary detection module. A concentration detection module is arranged at the tail end of a reaction device to collect nitrogen concentration signals in real time, pressure and temperature signals collected by an auxiliary detection module are combined, and after the signals are processed by a controller with a built-in PID adjustment algorithm, the gas inlet flow of a flow adjustment module is fed back and adjusted, so that a flow-nitrogen concentration double-closed-loop control system is formed. The problems that an existing open-loop control device is low in nitrogen concentration control precision, poor in stability and weak in anti-interference capacity are solved, it can be ensured that the nitrogen concentration on the surface of the material is stabilized within an effective nitridation interval, and the device is suitable for various nitridation process scenes such as material nitridation and semiconductor device preparation.

The invention discloses a low-altitude defense scene low-slow small target identification method, a terminal, a medium and a product. According to the method, firstly, a video frame is processed through a multi-scale feature adaptive target detection network, the network integrates a global self-attention mechanism to capture a remote dependency relationship, shallow details and deep semantic information are fused by adopting a dynamic weighting multi-scale feature fusion structure, and a target bounding box and a category are output in combination with an optimized loss function. And then, a time sequence level multi-target identity keeping and trajectory generating module is used, stable association of cross-frame target identities is realized through fusion of Kalman filtering motion prediction and appearance feature matching, and a target life cycle is managed in cooperation with a trajectory maintenance mechanism. According to the method, the problems of low-speed small target feature weakening, complex background interference, unstable multi-target tracking and the like are effectively solved, the recognition precision, the anti-interference capability and the tracking continuity are remarkably improved, and meanwhile, the low-altitude defense real-time requirement is met.

The invention discloses an anti-interference method suitable for a satellite navigation signal, and belongs to the field of satellite navigation signal anti-interference, and the method comprises the steps: collecting multi-channel satellite navigation signal data through a receiver array, processing an initial signal through a self-adaptive filtering algorithm to preliminarily reduce noise interference, and obtaining a preliminarily purified signal; extracting a spatial correlation matrix according to the preliminary purification signal, decomposing the matrix by adopting a principal component analysis algorithm to identify dominant spatial features, and determining an interference direction vector; obtaining the spectrum distribution of the direction suppression signal, further separating the spectrum overlapping part by adopting a principal component analysis algorithm, and judging the residual interference residue; through fusion of the direction suppression signal and the residual interference residue, an adaptive filtering algorithm is adopted to iteratively optimize the weight so as to enhance the navigation signal strength, and an optimized enhanced signal is obtained; and calculating a positioning parameter according to the optimized enhanced signal, judging whether the parameter stability meets a preset threshold value, and obtaining a final stable signal.

The invention provides a power plant equipment sound abnormity identification and health prediction method based on granular computing and an LSTM network, and the method comprises the steps: employing an array pickup and a high-dynamic-range microphone array in a complex and high-noise background environment of a power plant, and combining an anti-interference filtering and beam forming algorithm, thereby achieving the sound abnormity identification and health prediction of the power plant equipment. According to the method, directional, multi-channel and non-contact sound acquisition is carried out on key parts of equipment, acquired sound signals are preprocessed, converted into time domain, frequency domain and time-frequency domain representations and coded into multi-dimensional numerical vectors, and compared with a rule-based expert system, the method has higher generalization ability and real-time response ability; a large language model is introduced, so that the output is closer to an engineering language and is suitable for operation and maintenance personnel to understand and execute; a self-defined knowledge base or safety semantic filtering is supported, and closed-loop deployment in an industrial field is facilitated; the system can be in butt joint with an intelligent inspection system, and full-link linkage of voice recognition, health assessment and suggestion generation is achieved.

The invention relates to the technical field of Internet of Things physical layer security, in particular to an Internet of Things digital twinborn antagonism defense method and system oriented to an electromagnetic spectrum. Comprising the following steps: constructing an electromagnetic spectrum digital twin of a target Internet of Things system to simulate a physical signal environment in a high-fidelity manner; in the twinborn body, an intelligent optimization algorithm is utilized to generate an antagonistic signal sample aiming at the communication model; carrying out adversarial training on a communication model by using the samples to enhance the robustness of the communication model, wherein a meta-learning framework can be adopted in the training process to improve the generalization adaptive capacity; establishing a dynamic synchronous closed loop between the digital twinborn body and a real physical environment, and realizing model calibration and real threat sample back feeding; when an attack is detected in a real environment, multiple defense strategies are simulated and evaluated in the twin, and an optimal strategy is selected and issued to physical equipment for execution. According to the invention, digital twinning, antagonistic machine learning and real-time closed-loop control are combined, and the anti-interference capability of the Internet of Things system is actively refined in advance at low cost.