207 results about "Signal perception" patented technology

The invention discloses a base station facility abnormity intelligent monitoring method and system, and the method comprises the steps: employing a multi-scale wavelet fusion pulse neural network to construct a dynamic signal perception topology according to the real-time data of a base station physical layer, and generating a space-time aligned base station full-dimension feature tensor; inputting the full-dimensional feature tensor of the base station into a fault mode confrontation distillation module, reconstructing a normal working condition manifold of the base station based on a physical constraint variational auto-encoder, and outputting an abnormal feature vector with a fault fingerprint identifier; fault propagation graph modeling is carried out on the abnormal feature vectors, and a base station fault root cause topological graph is generated in combination with back propagation credibility verification; and inputting the base station fault root cause topological graph into a toughness self-healing strategy generator, and finally outputting a base station facility self-healing strategy set meeting real-time constraint. By utilizing the embodiment of the invention, the accuracy and the real-time performance of abnormity monitoring can be improved, and the operation reliability and the communication service quality of the base station are improved.

The invention provides a directional energy multi-target interference system based on an ultra-wideband phased array technology, and the system integrates signal perception, target recognition, interference signal generation and phased array control, so as to achieve the precise interference of an unmanned aerial vehicle cluster. The signal sensing module scans an airspace target through the ultra-wideband phased-array antenna array and obtains spectrum characteristics of a target signal. The target identification module analyzes signal features, identifies an individual target and predicts a motion track and a communication protocol of the individual target. And the phased array control module calculates and adjusts antenna beam forming parameters, and dynamically controls the radiation direction, phase and transmitting power of the antenna. The interference signal generation module generates a matched interference waveform based on the target communication protocol and the trajectory information. The ultra-wideband phased-array antenna array adopts a digital beam forming technology. According to the invention, efficient and accurate directional energy interference can be realized under multiple frequency bands, the confrontation capability of the unmanned aerial vehicle cluster is improved, and the method is suitable for defense tasks in electronic warfare and complex electromagnetic environments.

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention relates to the technical field of wireless communication, in particular to a wireless communication anti-interference enhancement system based on multi-mode signal fusion. Comprising an intelligent signal transceiving module, a signal sensing and preprocessing module, an interference feature coding and classification module, an interference suppression decision module, an interference suppression module, a signal fusion module, an interference suppression performance evaluation module, an interference feature library and an interference suppression strategy library, the interference feature coding and classification module adopts a three-layer progressive architecture of multi-dimensional manifold feature extraction, feature transformation and coding inspired by a group theory, and deep learning classification based on chaotic dynamics, extracts interference features from three dimensions of time domain, frequency domain and nonlinearity, and realizes accurate interference identification; the signal fusion module adopts a multi-level fusion strategy, and dynamically adjusts the fusion weight according to the signal quality; the interference suppression module selects an optimal suppression strategy according to the interference type, and executes frequency domain cancellation, space domain cancellation and time domain cancellation; and performance evaluation feedback forms a closed-loop optimization mechanism.

The invention discloses a dynamic compensation intelligent formwork real-time regulation and control method and system, and relates to the technical field of building intelligent control. According to the method, pulse signal sensing and cooperative compensation between nodes are realized by constructing a distributed neural unit network, and a three-level compensation mechanism is triggered when displacement or stress is detected to exceed a threshold value. A generative adversarial network is innovatively adopted to construct a virtual scene library to simulate extreme working conditions, a cross-scene migratable strategy library is generated through feature alignment, and intelligent strategy matching of a new construction site environment is realized. A full-structure rigidity field is dynamically generated based on an elastic mechanical model, a supporting arm rigidity switching path is optimized in combination with an ant colony algorithm, and directional dissipation of wind vibration energy is achieved through triangular rigid network construction. The system adopts a biological heuristic self-organizing algorithm to realize redundant network reconstruction when nodes fail, a supporting arm adopts a shape memory alloy and carbon fiber composite structure, and a micro strain sensor is integrated to realize directional release of corrosion stress. According to the method, the adaptive capacity of the formwork system under the complex working condition is effectively improved, and the structural failure rate is reduced through multi-modal compensation strategy fusion.

A while-drilling multi-element signal sensing and early warning method for coal rock dynamic disasters comprises the steps that in the early stage of coal seam recovery, bedding gas extraction drilling is utilized, while-drilling signals generated in the drilling period of each drill rod are collected in real time through a multi-parameter sensing system integrated with a drilling machine, and while-drilling data are obtained based on the while-drilling signals; constructing a mechanical parameter inversion model, and calculating the compressive strength of the coal rock mass; a gas parameter inversion model is constructed, and gas pressure and coal rock mass permeability are obtained through decoupling; according to the method, coal rock mass compressive strength, gas pressure and coal rock mass permeability are used as core indexes, a spatial interpolation method is adopted to realize continuous representation of disaster risk distribution of a whole area, and a comprehensive risk index is established accordingly; and according to the value range of the comprehensive risk index, further quantifying the risk level of the coal and rock mass dynamic disaster based on the comprehensive risk index. According to the method, real-time sensing, accurate inversion and active prevention and control of coal rock dynamic disasters can be realized, and the problems of low detection accuracy, poor real-time performance, insufficient overall performance and the like in the traditional technology can be effectively solved.

The invention relates to the technical field of radars, and particularly discloses a self-adaptive radar system and algorithm optimization method suitable for a complex multipath environment, and the system comprises a signal sensing module, a multipath analysis module, an antenna control module, a data processing module, a target detection module, an intelligent decision module, a system monitoring module, a communication transmission module, and a man-machine interaction module. According to the adaptive radar system suitable for the complex multipath environment and the algorithm optimization method, the multipath anti-interference capability of the radar system is remarkably improved, interference of multipath signals on the radar system is effectively reduced through innovative system architecture design, adaptive antenna design and an advanced data processing algorithm, and the system performance is improved. In addition, due to the self-adaptability and the real-time processing capability of the system, the system can quickly adapt to different multi-path environments, and stable operation in various complex scenes is ensured.

The invention discloses wearable equipment with body temperature measurement and body temperature early warning functions. The wearable equipment comprises a signal sensing and collecting module, a metabolism dynamic estimation module, a thermal dynamic state deduction module, an autonomic nerve stress analysis module, a multi-mode risk assessment module, a graded early warning and intervention execution module and a closed-loop optimization and self-adaptive calibration module. Compared with the prior art, the method has the following advantages and effects that through fusion of multi-modal physiological, environmental and behavior data and based on dynamic metabolism estimation, thermal dynamics deduction and autonomic nerve stress analysis, accurate and foresight evaluation and personalized grading early warning of the heatstroke risk of the user are achieved, and the early warning accuracy and intervention effectiveness are remarkably improved.

The invention discloses an intelligent hydraulic system of a roller press and a control method. The method comprises the steps that roll gap and pressure signals are collected in real time, and the average roll gap, the roll gap difference and the change rate are calculated; calculating an optimal system total pressure setting reference value in real time through a preset pressure-roll gap-material characteristic coupling model based on the target peak pressure, the current average roll gap and the material characteristic parameters; judging the working condition according to the roll gap difference and the change rate: under the stable working condition, taking the reference value as a double-side pressure set value; under the abnormal working condition, differential adjustment is carried out on double-side pressure set values on the basis of the abnormal working condition; and finally, the pressure set value is output to the hydraulic executing mechanism, and the state of the energy accumulator set is synchronously adjusted. The system correspondingly comprises a signal sensing unit, a hydraulic execution unit, an adjustable rigidity energy storage unit and an intelligent control unit. Constant-effect, stable and self-adaptive operation of the roller press under variable working conditions is achieved, and the grinding efficiency, the equipment stability and the intelligent level are remarkably improved.

The invention relates to the field of artificial intelligence, and discloses a brain-like computing-driven visual information instant analysis method and system, which comprises the following steps of: firstly obtaining real-time perception data and a neural pulse signal, analyzing a signal perception curve to construct a brain-like computing network, then identifying visual information characteristics through the network, analyzing a preset model computing architecture, and obtaining a brain-like computing network; optimizing a neuron mapping relation to obtain an optimized mapping network, analyzing a path based on the analysis information, determining a time sequence distribution state of target data, calculating an analysis efficiency value, determining an analysis optimization direction, generating an instant analysis strategy, calculating an analysis attenuation rate, determining an analysis state according to the attenuation rate, analyzing an analysis dimension, and finally constructing an optimization analysis scheme. According to the invention, the intelligent analysis efficiency of the visual information can be improved.

The invention provides a non-circular signal sensing method based on a weighted coherence matrix, which belongs to the technical field of wireless communication, and comprises the following steps: acquiring a complex value receiving signal, and calculating a real value augmented sample covariance matrix; based on the real value augmented sample covariance matrix, constructing a real value sample coherence matrix, and calculating a corresponding weight coefficient; constructing test statistics based on the weighted sum of squares of the real value sample coherence matrix and the weight coefficient; and calculating a sensing threshold based on the false alarm probability, comparing the sensing threshold with the test statistics, and determining a spectrum sensing result. According to the method, the second-order statistical property of the non-circular signal is described by using the real-value augmented sample covariance matrix, and weighted square fusion is performed on the real-value sample coherence matrix in combination with the weight coefficient driven by the sample data, so that better sensing performance can be obtained.

The invention discloses a multi-mode emotion resonance system with an emotion feedback closed loop, and belongs to the field of artificial intelligence and human-computer interaction. The system comprises eight modules including a multi-modal emotion acquisition module, a modal alignment and fusion module, a high-dimensional emotion modeling module, an individualized context construction module, a resonance generation and adjustment module, a behavior prediction triggering module, an interactive feedback deposition module and a strategy closed-loop evolution module. Micro expressions and acoustic and physiological features are sensed through multi-source signals, a subjectively-adjusted emotion vector field and a dynamic context map are constructed, a resonance reaction path is generated through heterogeneous map mapping and multi-layer reasoning, closed-loop linkage of sensing, reasoning, resonance, response and regeneration is finally achieved, and continuous emotional sharing and strategy self-adaptive optimization among individuals are supported. The method has the beneficial effects that closed-loop adjustment of cross-modal emotion recognition and individualized emotional response is realized, and the emotion understanding depth and response adaptability in human-computer interaction are remarkably improved.

The invention discloses a radar signal intelligent sorting method based on a GraphGated graph convolutional neural network, relates to the radar signal intelligent sorting method based on the GraphGated graph convolutional neural network, and belongs to the field of information and communication engineering. The objective of the invention is to solve the problem of difficulty in signal sorting of unknown signal sensing in a high-pulse-density environment, complex inter-pulse signal modulation and transient conditions of a current passive radar, an electronic reconnaissance radar, a cognitive radar, frequency spectrum situation sensing and the like. The method comprises the following steps of: 1, assuming that V pulse description words are received within time T; 2, constructing an adjacent matrix A based on the V preprocessed pulse description words, and constructing a radar pulse description word graph marked with node category attributes based on the adjacent matrix A; 3, obtaining a trained graph convolution model; 4, acquiring B to-be-tested pulse description words, and outputting feature vectors of the B pulse description words based on the trained model; and obtaining a clustering result through a clustering algorithm.

The invention relates to the technical field of military simulation and training, discloses a training system for analog signal combat and a training method thereof, and aims to solve the problems of insufficient confrontation authenticity, strategy solidification, cognitive depth deficiency and weak multi-domain cooperative capability of a traditional training system. According to the method, a dynamic confrontation deduction architecture with linkage of a physical domain, an electromagnetic domain and a cognitive domain is constructed, a distributed radio frequency array, a programmable attenuator and a multipath reflection simulator are deployed to realize high-fidelity signal propagation simulation, and full-band signal perception and feature extraction are completed in combination with a broadband digital receiver cluster and a central data fusion platform. An intelligent confrontation agent cluster driven by deep reinforcement learning and a cross-domain collaborative decision center are introduced; according to the technical scheme, the authenticity of the training environment, the intelligent adaptability of confrontation, the objectivity of evaluation and the sustainable evolution ability of the system are remarkably improved, and the signal game, dynamic response and cooperative command ability of trainees are effectively hammered.

The invention discloses a passenger car partition air conditioner self-adaptive adjustment method based on in-car multi-source signal perception, relates to the technical field of passenger car air conditioner adjustment, and aims to solve the problem of poor passenger car partition air conditioner adjustment efficiency. According to the method, multi-source environment data in a carriage is collected, unified scale processing is carried out in combination with nonlinear compression, the data is mapped to a three-dimensional space to construct a microenvironment topology tensor dynamically updated along with time, and environment states of different areas are accurately expressed; constructing a space graph structure based on state similarity and disturbance sensitivity weight, executing clustering division and correcting boundaries, and forming thermal environment partitions with adjustment values; a fuzzy adjustment participation multi-channel control structure is constructed for each partition, a control factor is dynamically generated according to an error signal and a disturbance trend, an adjustment result is output and mapped into an air conditioner equipment control parameter, partition cooperative adjustment of air supply, air exchange and purification is achieved, and the space response capacity and comfort matching level of an air conditioner control system are improved.

The invention discloses a variable array type piping detection radar device, a piping monitoring device and an identification method. Comprising a radar support which is adjustable in a longitudinal-transverse three-level mode, 16 radar units are distributed in a 4 * 4 matrix mode, an edge calculation module is integrated to achieve local noise reduction and preprocessing of signals, the device is carried on a self-propelled platform, and a multi-mode monitoring system is constructed by combining a visible infrared double-spectrum probe of an acoustic-optical-electric integrated mast, a voiceprint recognition module and a Beidou positioning system. According to the monitoring method, day and night monitoring weight is dynamically adjusted, bidirectional LSTM is combined with an attention mechanism to extract features, noise robustness is optimized through adversarial training, and piping risk factor fusion calculation is achieved. According to the invention, a closed-loop monitoring system with environment self-adaption, signal multi-source perception and intelligent decision is constructed, and high-efficiency and high-precision monitoring and early warning of piping are realized; and comprehensive breakthrough is realized on core indexes such as positioning precision, identification accuracy and environmental adaptability.

The invention discloses a sensing, storage and calculation integrated three-dimensional integrated chip, a preparation method of the sensing, storage and calculation integrated three-dimensional integrated chip and a calculation method of the sensing, storage and calculation integrated three-dimensional integrated chip. The sensing memory unit comprises a stacked resistive random access memory device group, a sensing device and a gating device; the stacked resistive random access memory device group comprises at least two resistive random access memory devices which are stacked in the thickness direction; the sensing device is used for receiving an external signal and converting the external signal into an input signal, and the output end of the sensing device is coupled with the top electrode of the stacked resistive random access memory device group; the gating device is configured to receive a control signal and switch on and off according to the control signal, and the gating device is coupled with the top electrode of the stacked resistive random access memory device group; wherein the stacked resistive random access memory device group is arranged between the sensing device and the gating device along the width direction, and the at least two resistive random access memory devices share one top electrode, so that the at least two resistive random access memory devices synchronously receive an input signal or are synchronously switched on and off.

The invention discloses an image recognition phototransistor with sensing, storage and calculation integration. The phototransistor shows the performance advantage of integration of sensing, storage and calculation: the photoactivation ion layer (PbI2) initiates a controllable ion migration effect under illumination, WSe2 channel conductivity is dynamically modulated through the HfO2 dielectric layer, and optical signal sensing and nonvolatile conductivity state storage are synchronously realized; on the basis of the simulated conductance gradient regulated and controlled by the grid voltage, the convolution operation of a photo-generated signal can be directly completed on the device level, and the whole process integration of light input, conductance calculation and result output in a single device is realized. By utilizing the performance of integrating the array structure and the device sensing, storage and calculation, the data output after sharpening calculation of the 3 * 3 device array is used for learning and training, and the target identification accuracy rate of 99.3% can be realized in the efficient learning process only needing four training periods, which is obviously superior to that of a device which is not sharpened.

The invention discloses an intelligent sound signal sensing method and system based on time-frequency feature fusion, and relates to the technical field of intelligent sound signal processing, and the method comprises the steps: obtaining an original audio signal; performing short-time Fourier transform on the original audio signal to obtain a plurality of frequency spectrum features; inputting the plurality of frequency spectrum features into a plurality of neural networks for processing, and extracting frequency domain depth features; processing the original audio signal by using a one-dimensional convolutional neural network, and extracting time domain depth features; splicing and fusing the frequency domain depth feature and the time domain depth feature in a channel dimension to obtain a time-frequency fusion feature; and inputting the time-frequency fusion feature into a full connection layer classifier to obtain a final audio scene recognition result. According to the invention, by designing a double-branch network architecture of time domain and frequency domain parallel processing, time domain waveform information and frequency domain amplitude and phase information are effectively fused, and in an end-to-end training scene, the accuracy of audio recognition is remarkably improved.

The invention provides a fast charger based on deep learning and a fast charging multi-protocol adaptive method thereof, and the fast charger comprises a signal sensing module which is used for carrying out the multi-mode sensing of an original signal of an access device, and obtaining a sensing vector; the perception decision module is used for inputting the perception vector into a lightweight deep learning model and outputting a perception decision, and the perception decision comprises protocol probability distribution, recommended power gears and safety suggestions; and the protocol matching module is used for carrying out self-adaptive matching of the fast charging protocol according to the perception decision. According to the fast charger based on deep learning and the fast charging multi-protocol adaptive method thereof, the lightweight deep learning model is adopted to adaptively output the protocol probability distribution, the method does not depend on a preset rule base and a sequence attempt mechanism, the protocol matching efficiency is higher, and in addition, the fast charging efficiency is improved. Decision factors such as recommended power gears and safety suggestions are introduced to form constraint conditions, the charging speed and the battery safety are balanced, and the suitability is better.

The invention belongs to the related technical field of sensors, and provides a multi-parameter integrated acquisition optical fiber sensor and a monitoring system thereof in order to solve the problem that high-precision monitoring of a static signal and a dynamic signal is difficult to consider by an existing sensor at the same time, a sensing cavity is sequentially divided into a static signal sensing area and a dynamic signal sensing area in the axial direction; the static signal sensing area adopts a multi-core fiber, fiber gratings are respectively engraved on the multi-core fiber, and the wavelength demodulation of the static signal is realized through the central wavelength drift distance of the fiber gratings; the dynamic signal sensing area is provided with a dynamic signal sensing unit, the dynamic signal sensing unit comprises F-P air microcavities formed between the dynamic signal sensing unit and the end faces of all fiber cores of the multi-core optical fiber, and intensity demodulation of dynamic signals is achieved through cavity length changes of the F-P air microcavities. According to the invention, a wavelength modulation and intensity modulation parallel composite demodulation mechanism is adopted, and decoupling acquisition of static strain, temperature and dynamic vibration is realized in a single sensor.

The invention relates to a measurement and control signal intelligent sensing method based on a deep neural network, and belongs to the technical field of radio signal processing and artificial intelligence crossing. According to the method, a time-frequency graph data set of broadband measurement and control signal simulation and actual measurement data is firstly constructed, and then a target detection model is trained based on the mixed simulation and actual measurement time-frequency graph data set. In the reasoning stage, based on a bounding box obtained by a target detection model, the confidence coefficient of sub-signals and time-frequency parameters such as the signal starting moment, the duration, the bandwidth and the center frequency in original record data can be obtained, and intelligent sensing of broadband measurement and control signals is achieved. According to the method, the time-frequency sub-signal bounding boxes which are not overlapped with each other are randomly generated, and the bandwidth, the center frequency and the time domain position of the sub-signals are reversely controlled, so that the generation of the time-frequency graph target labeling information is more accurate and efficient.

The invention provides a bearing load real-time monitoring and hydraulic pressure self-adaptive adjusting system and method, and the system comprises a signal sensing unit which is used for collecting a bearing vibration signal, a hydraulic system pressure signal and a main shaft rotating speed signal; and the signal processing and calculating unit is connected with the signal sensing unit and is used for processing the collected signals, estimating the bearing load based on a pre-trained soft measurement model and generating a hydraulic control instruction. The dependence of traditional bearing load monitoring on an expensive force sensor is broken through, and by integrating operation signals such as vibration, hydraulic pressure and rotating speed which are easy to obtain, the bearing load monitoring precision is improved. According to the method, the incidence relation between signals and loads is established through off-line modeling, non-intrusive load real-time estimation is achieved, meanwhile, feed-forward and feedback composite control is adopted, hydraulic pressure is adjusted in a self-adaptive mode according to the estimated loads, traditional empirical fixed setting is replaced, monitoring, adjusting, early warning and maintenance prompting functions are integrated, and real-time monitoring is achieved. The technical problems that a traditional scheme is high in cost, blind in adjustment and dispersed in function are effectively solved.

The embodiment of the invention provides a parameter prediction method based on an electromagnetic wave signal, electronic equipment and a storage medium, and relates to the technical field of electromagnetic wave sensing, and the method comprises the steps: obtaining a signal sampling result in a target grid region, the signal sampling result comprises electromagnetic wave signals from a plurality of detection points sparsely distributed in the target gridding area; according to the signal sampling result, a target generation model is used for generating a plurality of candidate parameter matrixes, the candidate parameter matrixes represent distribution of target parameters in the target gridding area, and the target parameters are parameters sensed by the electromagnetic wave signals; and in the plurality of candidate parameter matrixes, selecting a candidate parameter matrix which is most matched with the reference distribution result to obtain a distribution result of target parameter prediction in the target gridding area, the reference distribution result being determined according to the signal sampling result. According to the embodiment of the invention, the accuracy of obtaining fine-grained perception from sparse sampling can be improved.

The invention relates to the technical field of health data processing, in particular to a handlebar-based health data real-time analysis and prompt system, which comprises a physiological signal sensing module, a trend feature extraction module, a state level identification module, a prompt scheme selection module and a multimode channel prompt module. According to the method, a multi-source signal sequence is constructed for trend characteristic analysis through synchronous acquisition and time window alignment of four types of signals including pulse beat cycle, grip rate, blood oxygen decreasing time period and contact area frequency, the health state level is judged according to a trend cross-coincidence relation, and a corresponding prompt scheme is matched; through cooperation of the signal change trend, real-time perception and grade classification of the physiological abnormal state of the rider are realized, the pertinence and effectiveness of prompt response are improved, the precision and stability of a continuous prompt intervention mechanism are enhanced, and the problems of potential health risk identification delay and lack of personalized regulation and control of prompt are effectively solved.

The invention provides an acousto-optic emergency communication device and equipment suitable for a weak communication scene, and the device and the equipment provide an acousto-optic communication full-modular hardware design, and have the characteristics of acousto-optic signal processing integrated design, acoustic communication, sensing and computing power separation design, high variability of a sensing module and the like. The hardware design of the invention effectively reduces the size required by the module and the computing power requirement on a single chip, and has the capabilities of rapid customization and multi-scene adaptation. The sound sensing module adopts a low-energy-consumption event driving sensing algorithm and an optical link quality prediction algorithm adaptive to the variable sensing module. The driving sensing algorithm is combined with a sensing signal through a sound signal sensing algorithm, flexible dormancy and rapid assessment of a sudden environment can be achieved, the method can be used in a low-computing-power platform, and energy consumption is effectively reduced. The prediction algorithm combines sound perception information and environment information, uses fuzzy logic to realize various input compatibility of various scenes, abstracts various environment information and sensor data, and has multi-scene applicability and high accuracy.

The invention provides a deformation sensing method and a communication device. The method comprises the steps that a first node sends a sensing signal, a second node receives the sensing signal, and at least one sub-signal of the sensing signal reaching the second node through at least one transmission path is obtained. And the second node sends first information for indicating a first phase to the sensing node, the first phase being a phase of a first sub-signal in the at least one sub-signal, or the first phase being a phase difference between the first sub-signal and a second sub-signal in the at least one sub-signal. Wherein the first sub-signal is a sub-signal that the sensing signal reaches the second node after being reflected by a sensed point on the sensed body, and the second sub-signal is a sub-signal that the sensing signal reaches the second node after passing through a reference path in the at least one transmission path. And the sensing node determines whether the sensed body deforms according to the first phase and the reference phase. The deformation measurement cost is reduced, and the timeliness of obtaining the deformation of the sensed body is improved.

A perception configuration method and related device, in the method, a first device determines configuration information, the configuration information is used for configuring a perception block, the perception block comprises a first resource and a second resource, the first resource is used for transmitting a perception signal, the perception signal is sent through a sending beam, and the second resource is used for monitoring a reflection signal of the perception signal, the reflected signal is received through a receiving beam; the first device sends configuration information to the second device; and the second equipment sends the sensing signal on the first resource through the sending beam, and monitors the reflection signal on the second resource through the receiving beam. Therefore, the method can realize configuration for the self-sensing scene.

The invention relates to an intelligent monitoring system and method for deformation of a long tunnel, and the system comprises a multi-dimensional optical fiber sensing network, a data collection and demodulation unit, a data processing and intelligent analysis center, and a safety early warning module, and the multi-dimensional optical fiber sensing network comprises a distributed linear optical fiber sensing system, an optical fiber grating sensing element, and an optical fiber inertia measurement unit. The distributed linear optical fiber sensing system is used for tunnel longitudinal strain distribution measurement and vibration and acoustic signal sensing; the fiber bragg grating sensing element is used for monitoring circumferential strain and longitudinal strain of a key point and longitudinal differential settlement of a tunnel; the optical fiber inertial measurement unit measures overall axis deflection and torsional deformation; the data acquisition and demodulation unit is used for acquiring and demodulating the sensing signal; the data processing and intelligent analysis center is used for deformation category identification, trend prediction and graded early warning; and the safety early warning module sends early warning information to operation management personnel. According to the invention, full-section, high-precision and real-time online monitoring and intelligent early warning of the long tunnel can be realized.

The invention discloses an alien species intrusion detection system and method, and belongs to the technical field of biological control. The system comprises a multi-dimensional signal sensing module, a characteristic spectrum construction module, an intelligent identification and comparison module, a dynamic tracking and positioning module, an abnormity early warning research and judgment module and a cooperative linkage response module, an air-ground-water three-dimensional monitoring network is constructed, and non-contact accurate detection is realized through cooperation of multiple sensors. Specific insects can be accurately trapped, tree insects can be automatically collected and recognized, the insect sound wave monitor can remotely and early warn insect group activities, a three-level progressive feature processing framework is adopted, multi-source data are analyzed in a unified mode, high-precision recognition is achieved, multi-mode insect feature vectors are extracted, the large-scale insect screening requirement is met, and the method is suitable for large-scale insect screening. By means of a sensor cooperation and geographic information fusion technology, a multi-dimensional target motion trail model is constructed, insect trails are updated in real time, mapped to a geographic coordinate system and superposed with an ecological layer, and dynamic information and a spatial decision basis are provided.