1262 results about "Signal parameter" patented technology

The invention provides a precise interference method and system for pilot signals of a remote controller of an unmanned aerial vehicle, and the method comprises the steps: receiving and processing an image transmission signal and a remote control signal of the unmanned aerial vehicle, and analyzing a starting time, a transmission period and different types of image transmission protocol classification results of the image transmission signal; extracting the time period of the remote control signal and the occurrence time of a pilot symbol in the remote control signal; generating optimized interference signal parameter configuration by utilizing a reinforcement learning algorithm, analyzing historical data by utilizing a long short-term memory network to obtain a historical analysis result, and predicting an optimal interference opportunity; monitoring a wireless communication environment around the unmanned aerial vehicle by using a real-time spectrum sensing technology so as to identify a potential conflict frequency band, and selecting a target interference frequency band by using a frequency agility technology; determining a precise interference method based on the historical analysis result, the optimal interference opportunity and the target interference frequency band; according to the invention, the interference precision and effect on the pilot signal of the remote controller of the unmanned aerial vehicle are improved.

The invention provides a control method for optimizing operation of high-power short-pulse microwave equipment, and aims to improve the accuracy of microwave interference signals, the energy utilization efficiency and the system adaptability. The method comprises the following steps: firstly, acquiring position information, motion trail data, electromagnetic scattering characteristics and environmental electromagnetic parameters of a target object; and then, according to the target position prediction data, the electromagnetic scattering characteristics and the environmental electromagnetic parameters, generating a microwave pulse waveform control instruction. And controlling the phased-array antenna array to generate a directional microwave beam based on the control instruction and the target position prediction data. And collecting real-time response data of the target, dynamically updating the target motion trend prediction model according to the real-time response data, and recalculating microwave pulse signal parameters. And finally, according to the updated signal parameters and the target position prediction data, adjusting the transmitting power, the working time sequence and the beam direction of the phased-array antenna array so as to maintain the optimal interference effect, reduce unnecessary energy consumption and improve the stability and adaptability of the system.

The invention relates to the technical field of signal parameter measurement, and provides a parameter measurement method for a frequency modulation monitoring antenna, which realizes self-adaptive monitoring and accurate measurement of a complex electromagnetic environment through multi-dimensional data acquisition, intelligent analysis and dynamic optimization, and deeply integrates environment perception, signal processing and parameter optimization. A complete technical chain is formed, so that the parameter measurement method can have self-learning and self-adjusting capabilities, can cope with environmental changes and interference challenges in real time, remarkably improves the monitoring efficiency and the measurement precision, and provides a systematic solution for the application of a frequency modulation monitoring antenna in a complex scene.

The invention provides a signal optimization transmission system and method based on HPLC and HRF dual-mode communication, a signal acquisition and processing module of the signal optimization transmission system based on HPLC and HRF dual-mode communication is used for acquiring signals of two communication modes of HPLC and HRF in real time, preprocessing the signals and extracting key signal parameters; the dual-mode switching module is used for dynamically switching an HPLC mode and an HRF mode based on channel quality; the access module is used for constructing a longitudinal backbone network and a transverse Mesh network, forming a hybrid topology, optimizing a routing path and coordinating resource allocation and conflict avoidance of HPLC and HRF; the intelligent decision module is used for predicting future interference intensity according to the historical interference data and adjusting channel parameters; the central coordinator module is used for network initialization and resource allocation; according to the system and the method, the HPLC mode and the HRF mode are automatically switched according to the channel quality, the longitudinal HPLC backbone network is combined with the transverse HRF Mesh network to form a four-dimensional communication network, and the robustness in a complex scene is remarkably improved.

Disclosed in the present invention is a non-autoregressive Transformer-based modeling method for a 4-level pulse amplitude modulation high-speed transmitter. The method involves establishing a deep learning model having an encoder-decoder architecture to predict the behavior of a 4-level pulse amplitude modulation transmitter. An encoder processes unordered non-sequential inputs, including an input signal parameter and link parameters, to generate a context vector and then transmit same to a decoder. The decoder uses both the context vector generated by the encoder and a transmitter output signal sequence to generate a categorical probability distribution for each point in the sequence one by one. The model is trained using a random masking strategy, and inference is performed by means of non-autoregressive decoding and filtering, so that the model can perform parallel prediction on an output sequence, and perform a filtering process to predict an output signal. Compared to traditional simulation methods, the present invention achieves a significant acceleration effect, particularly when processing multi-link systems.

The invention discloses a terahertz-based curved surface thermal barrier coating nondestructive thickness measurement system and method, and the method comprises the steps: firstly carrying out the coordinate calibration and reverse reconstruction of a sample piece and a mechanical arm through an optical scanning and trinocular vision measurement system, and building a virtual space; then matching the virtual space with the actual space, extracting a normal vector of the curved surface of the sample piece, performing path planning on the mechanical arms to generate a control script, and controlling the double mechanical arms to carry a terahertz measuring head to scan the sample piece in a manner of being vertical to the surface of the sample piece to obtain a terahertz signal; and finally, constructing a terahertz signal analytical model, optimizing terahertz simulation signal parameters through a self-adaptive teaching and learning optimization algorithm, and fitting a detection signal until an optimal solution is obtained, namely the thickness of the thermal barrier coating. According to the invention, through cooperation of the two mechanical arms and path planning, rapid detection of a complex curved surface structure is adapted; by establishing an analytical model and combining with an optimization algorithm, the detection precision is improved, and high-precision nondestructive detection suitable for a complex curved surface structure is realized.

The invention provides a multi-target automatic tracking method and system based on an unmanned intelligent turntable. According to the method, electromagnetic echoes and environment depth data are synchronously collected through a detector, and an observation data set is constructed; separating thermal radiation, surface texture and motion features, establishing a four-dimensional feature space containing space-time coordinates, and identifying a shielding area; dynamically adjusting signal parameters to enhance the characteristics of the shielding area and feeding back an optimization record; calculating an optimal scanning path based on the parameter optimization value, and driving the sensor array to perform multi-angle scanning to generate a weighted data set; in combination with historical trajectory data, a trajectory prediction model is optimized in parallel through distributed calculation, turntable parameter synchronization and decision making are coordinated, and finally efficient reconstruction of a multi-target continuous trajectory in a shielded environment is realized. According to the technical scheme provided by the invention, through multi-dimensional feature fusion and dynamic parameter optimization, reconstruction of a multi-target trajectory in a shielding environment is realized, and the environmental adaptability and tracking efficiency of an unmanned system are improved.

The invention discloses a method for improving transformer area crosstalk of HPLC (High Performance Liquid Chromatography) and micropower wireless dual-mode communication, which comprises the following steps of: S1, electrifying an acquisition terminal equipment node, and aligning signal acquisition time windows of two communication modes; s2, combining the obtained dual-mode signal parameters with a preset transformer area topology database to generate feature vectors of multi-dimensional features; s3, training to generate a random forest model, inputting feature vectors of multi-dimensional features, dynamically calculating the affiliation weight value of the transformer area, and carrying out nonlinear weighted calculation on the feature vectors; and S4, on the basis of weight comparison and transformer area boundary node processing, correcting measurement errors by combining the transformer area attribution weight value with a preset impedance shielding compensation algorithm, generating a unique transformer area identifier, locking a networking relationship, and executing transformer area attribution judgment. According to the method, the wide coverage advantage of HPLC is reserved, meanwhile, the space attenuation characteristic of wireless communication is used for achieving accurate recognition of the transformer area boundary, and the installation and acquisition stability of the use and acquisition terminal equipment can be improved.

The invention relates to the technical field of display screen driving, in particular to a low-power-consumption display screen driving method and system based on the OLED technology, and the method comprises the steps: S1, carrying out the image feature detection, collecting the inter-frame brightness change rate, the sub-pixel gray level distribution and the regional temperature gradient, and generating a dynamic driving signal parameter; s2, voltage compensation adjustment: dividing a power consumption area according to a brightness change rate, calculating a brightness compensation coefficient and correcting the voltage by combining gray level distribution and a temperature gradient, and realizing sub-pixel voltage dynamic adjustment; and S3, refresh frequency control: adjusting a local refresh rate according to the compensation coefficient and the correction voltage, executing a frequency reduction strategy on a high-power-consumption area, triggering ghosting suppression on a low-power-consumption area, and applying a temperature threshold to constrain a refresh upper limit. According to the invention, energy-saving driving, ghost suppression and brightness balance of the OLED display screen are realized, and the display performance and the system intelligence are effectively improved.

Apparatuses, systems, and techniques that utilize a neural network to infer signal parameters to direct and transmit wireless signals. In at least one embodiment, one or more neural networks are trained, using reinforcement learning techniques, to infer a beam direction to be used by a first device to transmit a signal based, at least in part, on characteristics of another signal being transmitted by a second device.

The invention relates to the technical field of pipeline detection, and discloses a pipeline weld defect analysis method and device based on magnetic flux leakage detection.The method comprises the steps that a composite excitation signal is generated and optimized through a multi-frequency excitation source, a magnetic flux leakage attenuation coefficient and scattering characteristics are calculated in combination with pipeline parameters, and if the attenuation coefficient exceeds a preset attenuation threshold value, the pipeline weld defect is detected; if yes, adjusting the excitation signal for optimization, extracting high-frequency transient and low-frequency stable features of the optimized magnetic flux leakage signal, and generating a preliminary feature vector of the weld defect; mapping the preliminary feature vector to a three-dimensional defect space position, generating a space distribution diagram containing the defect position, optimizing the preliminary feature vector to make a fuzzy region clear, and obtaining the depth and direction of the defect; and according to the depth and direction of the defect, analyzing the spatial distribution characteristics of the preliminary feature vectors, determining the type of the defect, dynamically adjusting excitation signal parameters for optimization, recalculating the attenuation coefficient of the signal, extracting the optimized scattering features, and obtaining a high-precision weld defect positioning result.

Apparatuses, systems, and techniques that utilize a neural network to jointly infer signal parameters to direct and transmit wireless signals. In at least one embodiment, one or more neural networks are trained, using reinforcement learning techniques, to infer hybrid beamforming parameters used by one or more devices to transmit wireless signals.

The invention discloses an offshore wind turbine blade online monitoring method based on voiceprint recognition, which belongs to the field of wind turbine blade acoustic signal analysis, and comprises the following steps: acquiring wind turbine voiceprint data, weather condition data and wind turbine generating capacity data, and constructing a signal processing model; the collected voiceprint signals are subjected to denoising, amplification and feature extraction through a signal amplifier, a filter and a feature extraction unit, a deep learning model is constructed, voiceprint signal parameters, weather data and wind turbine generating capacity are imported into the deep learning model to evaluate future wind turbine blade cracks, a crack evaluation model is constructed, and the wind turbine blade cracks are evaluated. And importing the crack sound source position, the crack depth, the crack length and the estimated damage score into a crack evaluation model to evaluate the crack degree of the fan blade under the future weather condition, grading the fan cracks according to the crack damage score, and giving an alarm. The early warning of the future crack condition of the fan blade is realized by combining the voiceprint signal of the offshore fan blade, the weather condition and the generating capacity.

Novel tools and techniques are provided for implementing detection and estimation of direct and reflected navigation satellite (e.g., global navigation satellite system (“GNSS”), etc.) signal parameters in a multipath environment. In various embodiments, logic of semiconductor package that is disposed on a user device concurrently receives a plurality of signals from a satellite(s), each signal travelling along a different path between each satellite(s) and the user device within a multipath environment. The logic identifies two or more signal peaks that fall within a tracking aperture based on analysis of the received signals, and determines peak parameter estimates for each signal peak based on measurements of signal parameters from at least one signal peak. The logic provides the determined peak parameter estimates for each signal peak to a position engine (“PE”) of the user device to calculate a navigation solution (e.g., position, velocity, and / or time, etc.) for the user device.

The invention discloses a squeeze casting forming quality detection device and system, and belongs to the field of quality detection.According to the shape and size of a squeeze casting, ultrasonic emission signal parameters are adjusted, and self-adaptive preprocessing is conducted on received ultrasonic echo signals; according to the method, data fusion and feature extraction are carried out on ultrasonic echo signals received by multiple channels, abnormal grid units are screened, the abnormal types of the abnormal grid units are identified through a deep learning model, then abnormal areas are divided, and the molding quality score of the extrusion casting part is obtained through comprehensive evaluation on the abnormal areas. Carrying out qualification judgment on the extrusion casting part; detection data is stored, managed and maintained, the detection data is classified, filed and indexed by constructing a product database, a squeeze casting quality tracing path is generated, and data backup and data recovery are regularly performed on the product database; the objective of the invention is to solve the problems of accurate and efficient quality detection and effective data management of extrusion castings.

The invention relates to the technical field of semiconductor testing, and provides a semiconductor testing method and device.The method comprises the steps that electric signal parameters, temperature data and optical image information of a semiconductor device are collected and processed, and a standardized electric signal sequence, a continuous temperature distribution curve and device surface topography features are obtained; analyzing the standardized electric signal sequence and the continuous temperature distribution curve to generate a dynamic working condition mapping matrix; and performing mode matching on the dynamic working condition mapping matrix by using a preset fault mode library, identifying potential defect types and position coordinates, inputting the device surface topography features and the potential defect types and position coordinates into a composite scoring model, generating a comprehensive quality score, and then performing grading to obtain a semiconductor test result. By generating the comprehensive quality score and then grading, the semiconductor test result is obtained, so that the accuracy of defect identification is improved, and the problems that the multi-dimensional correlation analysis is lacked and the dynamic working condition of the device is difficult to comprehensively evaluate in the semiconductor test are solved.

The invention relates to the field of data synchronization, in particular to a dynamic synchronization method and system for communication data of a combat simulation platform, and the method comprises the steps: obtaining communication data streams of a radar node and an electromagnetic interference node, generating a unified semantic tag, and detecting that the continuous packet loss times of the communication data streams exceed a preset packet loss time threshold value; predicting through Kalman filtering to obtain a predicted strike unit coordinate of the current packet loss, and generating a compensation data packet; before the compensation data packet is injected, the displacement deviation between the predicted strike unit coordinate and the follow-up actual received strike unit coordinate is compared, and if the displacement deviation exceeds a preset displacement fault-tolerant threshold value, a synchronous rollback mechanism is triggered; counting link loads through a sliding time window to obtain network real-time loads, and allocating bandwidth resources according to dynamic priority weights; the problem that real-time synchronization of radar detection data and electromagnetic interference signal parameters is abnormal in the interception cooperation process of a striking unit in a high-dynamic combat simulation scene is solved, and the data synchronization precision in simulation is effectively improved.

The invention discloses a wireless radio frequency data monitoring and distribution platform system and method based on data analysis, and belongs to the technical field of wireless communication. Wireless radio frequency signal parameters are acquired, data processing is performed on the signal parameters, a signal evaluation model is constructed, and the signal parameters are imported into the signal evaluation model to evaluate the signal-to-noise ratio. Constructing a channel occupation and multipath time delay extension evaluation model, importing the channel occupation time and the power and time delay of the multipath channel into the channel occupation and multipath time delay extension evaluation model to evaluate the signal condition, and constructing an equipment interference model; the method comprises the following steps: importing signal parameters and position distances of field working equipment into an equipment interference model to evaluate the interference degree of the working equipment on a wireless radio frequency transmission signal, constructing an optimal frequency band scoring model, and importing a signal-to-noise ratio, a channel occupancy rate, multipath time delay extension and equipment interference conditions into the optimal frequency band scoring model to perform frequency band selection. And the reliability and the real-time performance of wireless communication in an industrial scene are improved.

The invention discloses an eSIM service cost intelligent optimization method based on different operator information sources, and particularly relates to the technical field of operator network management. The method comprises the following steps: collecting eSIM tariff rules of a plurality of operators and real-time charging data of eSIM terminals, extracting tariff terms and conditional expense trigger mechanisms hidden in charging behaviors of different operators by using an association rule mining algorithm, and constructing a multi-level cost structure model; on the basis of a network registration behavior log of the eSIM terminal, predicting the probability that each operator signal source has an abnormal registration event, and evaluating the risk that each operator signal source triggers a hidden cost trap in combination with a multi-level cost structure model; and according to the real-time network signal parameters of the eSIM terminal, a signal source recommendation strategy is generated in combination with the risk assessment result, and the eSIM terminal is driven to execute a signal source switching operation, so that real-time adjustment and cost optimization control of communication network selection are realized, and the service stability of the eSIM in a cross-network use scene is improved.

The invention provides an unmanned aerial vehicle navigation decoy method and system based on multimode satellite signal counterfeiting. The method comprises the following steps: firstly, acquiring original signal parameters, covering signal time delay, carrier phase and signal intensity distribution, of multimode satellite signals in an unmanned aerial vehicle navigation link in a target area in real time; secondly, extracting spatio-temporal characteristics through signal time delay and carrier phase, and generating time domain synchronous forged navigation parameters in combination with signal strength dynamic change; dynamically adjusting the signal time delay correction and the carrier phase offset in the forged navigation parameters; meanwhile, dynamic compensation is carried out on the navigation message based on the three-dimensional coordinate increment, and a multimode forged navigation signal covering the receiving end of the unmanned aerial vehicle is generated; and finally, superposing the forged navigation signal and the original signal parameter to form a deceptive navigation track for inducing the unmanned aerial vehicle to deviate from the original route. According to the technical scheme provided by the invention, the multi-mode compatibility and concealment of navigation decoy of the unmanned aerial vehicle can be improved.

Disclosed are a beam management method and apparatus, applied to a network device, comprising: receiving first feedback information reported by a user equipment, the first feedback information comprising measured values of signal parameters of a first beam set in N historical time windows, the signal parameters comprise physical layer reference signal receiving power L1-RSRP, reference signal receiving quality RSRQ and signal to interference plus noise ratio SINR, and N is an integer greater than or equal to 1; performing beam prediction based on the first feedback information to obtain a beam prediction result, the beam prediction result comprising a second beam set and / or second beam information, the second beam set being a subset of a third beam set, and the third beam set being a set of candidate beams used by the network device for transmitting data; the second beam information comprises a predicted value of a signal parameter of each beam in the second beam set. By implementing the embodiments of the present application, the accuracy of beam prediction can be improved, and the signaling overhead and time delay of beam prediction can be reduced.

The invention relates to a lower limb diagnosis training system, and belongs to the field of medical diagnosis and rehabilitation training. The system comprises a detection part used for collecting data information related to the lower limbs of a patient; and the control unit is used for analyzing and processing the data information so as to calculate and obtain a diagnosis result. The control unit can calculate a neural function index based on the electroneurographic signal parameters, calculate a joint activity index based on the joint activity, calculate a muscle strength index based on the muscle strength evaluation value, and further calculate an initial lower limb function comprehensive score; the initial lower limb function comprehensive score is used as an initial diagnosis result to output a control signal of a numerical range of a combination of traction forces in multiple directions, so that electromyographic signal parameters and blood flow parameters acquired by a detection part in real time in the training process of a patient can be used by a control unit for correcting the initial lower limb function comprehensive score; and obtaining a dynamic lower limb function comprehensive score for adjusting the specific numerical value of each traction force distribution.

The invention belongs to the technical field of calibration simulation, and particularly discloses a cable partial discharge calibration simulation system based on a programmable control technology, and the system comprises a signal parameter setting module which can set standard signal parameters through a signal generator, and covers the frequency, the amplitude, the pulse width and the repetition frequency. The signal coupling transmission module enables the signal generator to generate a signal and couple the signal to a cable. The environment parameter acquisition module is responsible for acquiring environment temperature, humidity, magnetic field intensity and electric field intensity of the signal generator and the cable at each monitoring point. The environmental parameter correction module monitors signal parameters of each monitoring point of the cable in real time according to the collected environmental data and analyzes temperature, humidity, magnetic field and electric field correction indexes. And the signal parameter correction module corrects and analyzes the cable signal parameters accordingly. And the deviation degree abnormity judgment module analyzes the deviation degree index based on the standard and the corrected signal parameter, and judges whether the deviation degree index is abnormal or not for calibration.

The invention provides a signal exception processing method and system, a server and a storage medium, and relates to the field of servers, the method is applied to the signal exception processing system, the signal exception processing system comprises a substrate management controller and a hardware extension module, and the hardware extension module is used for performing function extension on a central processing unit and a universal input and output interface. Wherein the substrate management controller does not immediately respond to the signal sent by the hardware extension module, but can firstly take the signal as a to-be-detected signal, and detect the signal according to a preset signal parameter to determine whether the signal form is abnormal or not. And if the to-be-detected signal is determined to be abnormal, in order to distinguish whether the to-be-detected signal is a normal signal, the substrate management controller can also actively acquire a register value corresponding to the signal from the hardware extension module, and determines whether to respond to the to-be-detected signal according to whether the logic meaning of the to-be-detected signal is consistent with the logic meaning of the register value. In this way, signal abnormity can be actively detected, and error response can be avoided.

The invention relates to the technical field of submarine cable fault positioning, in particular to a submarine cable fault positioning system based on underwater beacons. The method comprises the steps that an underwater acoustic beacon array unit monitors and collects submarine cable fault sound wave signals in real time based on underwater acoustic beacons; the fault signal processing analysis unit extracts submarine cable fault sound wave signal parameters based on a non-point source space sound source waveform analysis model, and generates multi-parameter fault sound source feature vectors; the sound source positioning analysis algorithm unit performs three-dimensional inversion positioning based on the multi-parameter fault sound source feature vector and the arrival time difference of the submarine cable fault sound wave signal to the underwater acoustic beacon, and obtains the spatial position information of the submarine cable fault point; and the data communication alarm management unit sends the spatial position information of the submarine cable fault point to a shore-based operation and maintenance system. The submarine cable fault positioning method is used for realizing a submarine cable fault positioning technology for quickly and accurately positioning and identifying spatial extension characteristics in a complex submarine environment.

The invention relates to the technical field of Bluetooth control, and discloses a signal receiving sensitivity optimization method and system for a low-power-consumption dual-mode Bluetooth chip, and the method comprises the steps: monitoring a signal receiving environment of the Bluetooth chip, obtaining the characteristics of the signal environment, switching the Bluetooth chip to a precise mode or a low-consumption mode according to the characteristics of the signal environment, and achieving the optimization of the signal receiving sensitivity of the low-power-consumption dual-mode Bluetooth chip. In a precise mode or a low consumption mode, according to a preset signal parameter adjustment scheme, radio frequency communication and demodulator parameters are optimized in real time to ensure that the signal receiving sensitivity meets the standard, the signal effect of the Bluetooth chip is synchronously monitored, the signal parameter scheme is adjusted and optimized according to the monitoring result, and the radio frequency communication and demodulator parameters are continuously adjusted. By intelligently switching the working modes and dynamically adjusting the parameters, the receiving sensitivity of the Bluetooth chip in different signal environments is optimized, the Bluetooth device is ensured to keep stable communication in various environments through real-time synchronous monitoring and adjustment, and the problem that the Bluetooth chip is difficult to balance energy consumption and communication quality in the prior art is solved.

The invention provides a coal rock fracture damage mode classification method and system. The coal rock fracture damage mode classification method comprises the steps that collected acoustic emission signals are preprocessed, and damage categories are marked for various signal parameter data; inputting various signal parameter data and annotation data and training an integral coal rock fracture damage mode classification model, wherein the model comprises an adaptive diversity wavelet kernel module, a CWT-based feature extraction layer, a BiLSTM feature fusion layer and a classification layer; the adaptive diversity wavelet kernel module is used for extracting time domain features, frequency domain features and CWT features of various signal parameter data, adaptively adjusting wavelet kernel parameters, generating a diversified wavelet kernel set, inputting the diversified wavelet kernel set into a CWT-based context feature extraction layer, including global branches and local branches of parallel calculation, and respectively extracting global features and local features; carrying out feature fusion on the global features and the local features through a BiLSTM feature fusion layer; and inputting the fusion features into a classification layer, and classifying the coal rock fracture damage modes. According to the method, the coal rock fracture damage modes can be classified.

The invention discloses a multi-network communication method and device applied to unmanned driving, and belongs to the technical field of unmanned driving, and the method comprises the steps: obtaining 5G network signal parameter data from a 5G multi-network link channel, and filtering out the 5G multi-network link channel reaching the standard; the NPU neural network is used for conducting quantization processing on the standard 5G multi-network link channel network signal parameters, a comprehensive score of the standard 5G multi-network link channel network signal parameters is obtained, and the sequence of the current optimal link channel and other backup link channels is further obtained; adjusting the optimal link channel in real time according to a comparison result of a preset condition and the real-time 5G network signal parameter data associated with the current optimal link channel; and keeping communication with the cloud server based on the optimal link channel adjusted in real time. The multi-network communication module is composed of at least three independent 5G + C-V2X full-network communication modules and an auxiliary USIM card slot, the network can be optimized in real time along with vehicle operation, and it is ensured that the vehicle always uses the optimal 5G network.

The invention discloses a secondary modulation interference signal generation method and device, a storage medium and equipment, and belongs to the technical field of radar countermeasure. The method comprises the following steps: a jammer processes a target SAR signal to obtain a digital SAR signal and calculates a signal parameter; generating a distance-direction phase modulation signal, a distance-direction sine function modulation signal, a distance-direction frequency shift modulation signal, an azimuth-direction phase modulation signal, an azimuth-direction sine function modulation signal and an azimuth-direction frequency shift modulation signal according to the signal parameters by combining a required interference type; according to the generated signals and the digital SAR signals, range direction modulation and azimuth direction modulation are completed, and finally digital interference signals are obtained; and converting the digital interference signal into an analog interference signal and forwarding the analog interference signal to the target SAR system, and generating different types of interference effects through imaging processing. According to the invention, nine different types of two-dimensional interference effects can be flexibly generated, and the central position of the interference result can be accurately controlled.

The invention relates to the technical field of 5G communication, in particular to a 5G network signal low-altitude coverage test method and system, and the method comprises the steps: obtaining regional geographic information and base station layout information; based on geographic information and base station layout, constructing a three-dimensional digital map, dividing a dynamic test grid, and determining an initial flight path; according to a multi-source sensor of the unmanned aerial vehicle, in combination with the initial flight path, collecting 5G signal parameters and environmental data of the grid points through a preset frequency; a wavelet transform filtering algorithm is adopted for calibration, abnormal values are removed, and feature vectors are generated; according to the feature vectors, a coverage evaluation model is constructed in combination with a three-dimensional digital map, and a low-altitude coverage blind area and an interference type are identified; and if a low-altitude coverage blind area or abnormal interference is detected, dynamically adjusting the flight path of the unmanned aerial vehicle through an A-star algorithm, testing a target area, and generating a coverage optimization scheme based on a test result. Therefore, the problems of low test efficiency, lagging data processing and analysis and the like in the prior art are solved.