378 results about "Signal optimization" patented technology

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.

The invention relates to the technical field of medical equipment and intelligent learning, in particular to a critical patient vital sign dynamic monitoring and intelligent early warning system which comprises a multi-modal data acquisition module, a medical equipment linkage control module and an intelligent early warning decision module. A dual-channel Kalman filtering state updating model, a biological impedance monitoring model and an anti-interference and signal optimization model are established in the multi-modal data acquisition module, and multi-dimensional physiological monitoring information of the critical patient is obtained through the models; a physiological information standardization model and a safety control mechanism are arranged in the medical equipment linkage control module, and then multi-dimensional physiological monitoring information is cooperatively controlled to output multi-modal vital sign data; and the intelligent early warning decision module receives and analyzes the multi-modal vital sign data to obtain power spectral density, and predicts the disease development condition of the critical patient in combination with the depth prediction model and the multi-modal vital sign data. All the modules work cooperatively to achieve dynamic monitoring and intelligent early warning of vital signs of critical patients.

The invention belongs to the technical field of artificial intelligence and multi-modal large model reasoning enhancement, and discloses a multi-modal retrieval enhancement generation method based on gradual group relative strategy optimization. A step-by-step reasoning track construction mechanism is introduced, an original problem is disassembled into a plurality of sub-problems, and in each step, a new retrieval query is autonomously generated in combination with reasoning history and current information requirements, and a most appropriate knowledge source is selected for evidence retrieval; and in the reasoning process, each step of decision and answer obtains a fine-grained reward signal. According to the method, a group relative strategy optimization method is adopted, the query quality of each reasoning step, the knowledge base routing accuracy, the answer content format compliance and the final answer accuracy are used as step-by-step rewards for joint modeling, and model parameters are optimized through global and local multiple feedback signals. The method is remarkably superior to the existing similar technology in tasks such as multi-class multi-modal open domain question answering and complex reasoning, and has excellent answer accuracy, retrieval efficiency and multi-modal adaptive capacity.

The invention relates to the technical field of photovoltaic power station management, and discloses a photovoltaic power station Internet of Things intelligent management and cloud edge collaborative optimization system. The system comprises an operation data acquisition module which is used for acquiring power station data in multiple dimensions, quantitatively identifying an abnormal operation state and generating a management early warning signal; the equipment state evaluation module is used for extracting equipment characteristics and dynamically evaluating the self-maintenance capability to obtain a state evaluation reference value; the edge processing module is used for monitoring parameters such as data traffic and predicting and analyzing edge processing efficiency to obtain an edge calculation evaluation value; the cloud edge collaboration module is used for jointly analyzing collaborative optimization efficiency and generating a regulation and control signal; the optimization parameter generation module is used for generating equipment maintenance and computing power distribution parameters; the system further comprises an authority control module and a dynamic adjustment module. According to the system, intelligent management and cloud edge collaborative optimization of the photovoltaic power station are realized, the operation efficiency and reliability of the power station are improved, and the safety and dynamic adjustment capability are enhanced.

The invention relates to the technical field of RFID tag signal optimization, and discloses an RFID tag signal optimization method and system based on deep learning. According to the method, feature extraction is carried out on a backscattering signal, core features of a multipath channel are quantized from a frequency domain and a time domain respectively, numerical representation of a channel state is further obtained, then obtained data information is used as input of a deep learning network, training and forward calculation are carried out, original emission parameters are adjusted, and a multi-path channel is obtained. Therefore, the conversion from blind extensive control to precise control of the transmitting end is realized, so that the problem of mismatching of parameter control and channel state caused by information splitting of the transmitting end and the receiving end in the prior art is solved; according to the signal optimization method for realizing joint adjustment of transmitting and receiving parameters through an end-to-end deep learning architecture, the optimization effect of an RFID tag signal in a complex environment is remarkably improved.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention discloses a roadbed pavement test and data acquisition system under the action of moving load, which comprises the following steps of: acquiring high-precision pavement unevenness data, decomposing features through a space-time convolution variational self-encoding network and inhibiting noise; based on the normal stiffness and the morphological gradient, the density of the sensor is adjusted by adopting dynamic grid division, and multi-modal data is fused by utilizing Bayesian weight. And adaptively extracting signal details by applying multi-scale transformation, and designing a filter to enhance the damage signal and optimize the structure entropy. And constructing a multi-field coupling grid control equation to realize dynamic topological optimization reconstruction. And establishing a damage evolution state space, and training the prediction model through a multi-stage reinforcement learning framework in combination with experience playback. And constructing a digital twin mapping model, integrating Bayesian optimization and reinforcement learning, and establishing a virtual-real feedback channel to realize dynamic decision optimization. According to the invention, innovative improvement on a traditional test method and a data acquisition system is provided, and meanwhile, complex nonlinear dynamic behaviors can be accurately simulated and analyzed.

The present invention provides a method for optimization of active and passive beamforming and signal reception configuration of a dual IRSs-assisted ISAC system. By jointly optimizing active beamforming at a base station, the reception of a sensing signal by the base station, and passive beamforming at an IRS, the achievable rate of communication users is maximized while ensuring that the signal-to-noise ratio of the sensing signal meets the minimum requirement. In the present invention, to solve the complex nonconvex problem generated, first, fractional programming is used to decouple an optimization problem, then, a successive convex approximation algorithm and an alternating direction method of multipliers are used to transform an intractable nonconvex problem into multiple tractable subproblems, and finally, an alternative optimization method is used to efficiently solve for a high-quality sub-optimal solution. Simulation results show that the provided solution has good convergence and effectiveness, and the solution can effectively improve the performance of IRS-assisted ISAC systems.

The invention discloses a traffic control and guidance system and method based on an ant colony algorithm, belongs to the technical field of road traffic control, and solves the problems that a neural network model in an existing method mainly focuses on respective route conditions of a plurality of bifurcation routes of a bifurcation on a one-way driving road, and the information fusion degree between traffic elements is low. The method comprises the following steps: identifying dynamic characteristic information of vehicles in a road network, pre-constructing a road network optimization model based on an ant colony algorithm combined with deep learning, and analyzing a pheromone spread function cluster solution; according to the invention, the road network optimization model based on the ant colony algorithm and the deep learning is pre-constructed, the collaborative optimization of traffic signal control and vehicle induction is realized, the traffic signal optimization control strategy and the vehicle optimization path strategy are output, so that the signal timing and the induction path can be matched with each other, and the control accuracy is improved. Therefore, the operation efficiency of the traffic system is improved and the accuracy and effectiveness of the guidance strategy are ensured.

The invention discloses an AI-based urban traffic flow prediction and dynamic signal optimization method and system, and relates to the technical field of traffic management, and the method comprises the steps: obtaining real-time traffic data, historical flow data and external environment data of a target region, obtaining multi-source data, and constructing a space-time matrix corresponding to the target region according to the multi-source data; learning the space-time matrix by using a preset space-time fusion model, and outputting a prediction result of the traffic flow in a future preset time period through the space-time fusion model obtained by learning; wherein the space-time fusion model is a combined architecture of a graph convolutional network and a Transform network; and dynamically adjusting traffic signal control parameters in the target area through a multi-agent learning algorithm based on a prediction result. According to the method, closed-loop regulation and control of'data perception-prediction modeling-autonomous decision 'are formed, so that the limitation of spatial feature modeling staticization and time correlation analysis fragmentation of a traditional method is broken through, and the problem of regional imbalance caused by single-point optimization is effectively solved.

The invention provides a smart city construction system based on big data, and relates to the technical field of smart cities, and the system comprises a traffic data collection module, a transmission and preprocessing module, a flow prediction and analysis module, an intelligent signal optimization module, a parking lot scheduling and induction module, a decision support module and a user service module. The traffic flow prediction and analysis module predicts a traffic state in a set time length in the future through a space-time diagram convolutional network in combination with a seasonal autoregression model; and the intelligent traffic signal optimization module is used for dynamically adjusting signal lamp timing based on a deep reinforcement learning algorithm and realizing intersection linkage through a regional cooperative control algorithm. According to the invention, the space-time diagram convolutional network, the deep reinforcement learning intelligent algorithm, the block chain and the distributed computing technology are fused to construct a multi-source data-driven traffic whole-flow intelligent management system, so that the whole-chain intelligence from real-time sensing and dynamic optimization to cross-platform service is realized; and the collaboration, the prediction accuracy and the service ecological openness of the urban traffic system are obviously improved.

The invention relates to the technical field of communication signal optimization, in particular to a distribution automation communication signal optimization method and system for an underground environment. The method comprises the following steps: carrying out a multi-band data transmission test based on a perturbation frequency test packet, and collecting a multi-band power distribution communication signal; dynamic space-time attenuation analysis is carried out, and a multi-band electromagnetic space-time attenuation matrix is constructed; performing node-by-node response delay calculation based on the multi-band power distribution communication signal to generate a full-band delay sensitivity curve; safe and effective frequency band evaluation is carried out based on the multi-frequency-band power distribution communication signals, high-delay frequency band elimination processing is carried out based on a full-frequency-band delay sensitivity curve, and a frequency band hopping optimization pool is constructed; and performing real-time environment interference attenuation analysis on the frequency band hopping optimization pool according to the multi-frequency-band electromagnetic space-time attenuation matrix, and constructing communication frequency band hopping driving logic. According to the invention, through dynamic intelligent communication frequency band hopping, the communication stability and quality of power distribution automation are improved.

The invention discloses a memory signal integrity enhancing system based on multi-dimensional parameter self-adaption, and belongs to the technical field of signal transmission. The system comprises a multi-dimensional signal acquisition module, a multi-dimensional parameter analysis module, a self-adaptive adjustment module, a memory signal optimization module and a feedback monitoring module, the multi-dimensional signal acquisition module is combined with multi-dimensional parameter analysis, signals of a data bus, an address bus and a control bus are synchronously acquired, a dynamic adjustment strategy is generated according to a model, and the self-adaptive adjustment module and the memory signal optimization module are optimized according to the dynamic adjustment strategy. Different optimization means are adopted for different parameter types, a customized optimization system with one parameter and one scheme is formed, it is ensured that the optimized target signal is optimal in amplitude stability, time sequence accuracy, anti-noise capacity and impedance matching degree, the feedback monitoring module is combined with the multi-dimensional parameter analysis module, and the multi-dimensional parameter analysis module is combined with the feedback monitoring module. And through full-dimension index monitoring and dynamic calibration, the continuous adaptability of the system to the running state of the memory and the long-term stability of signal integrity are guaranteed.

The technical scheme of the invention relates to the technical field of control and adjustment, in particular to a crawler-type articulated vehicle braking system applied to a low-temperature environment. Through multi-module collaborative optimization and a closed-loop control mechanism, the problems of unbalanced dynamic distribution of braking force, insufficient redundancy fault tolerance and signal distortion under the low-temperature working condition are solved. The data acquisition module is used for acquiring a hinging angle, an inertial measurement unit attitude, a braking temperature and a vehicle state signal in real time; the optimization control module performs multi-objective optimization on the dynamic weight coefficient, the redundancy switching threshold value and the fuzzy rule parameter based on a genetic algorithm to generate a weight distribution scheme of a hinge angle compensation mode and an inertial measurement unit dominant mode; the adjusting module generates a brake pressure reference value through temperature self-adaptive analysis and synchronizes hydraulic control units of a front vehicle and a rear vehicle, and all the modules remarkably improve the dynamic stability and the low-temperature working condition fault-tolerant capability of a brake system through full-link cooperation of environment perception, parameter iteration and redundancy execution.

A method for the joint active and passive beamforming and received signal optimization in an ISAC system assisted by dual IRSs is provided. The method jointly optimizes active beamforming at Base Station (BS), reception of sensing signals at the Base Station (BS), and passive beamforming at IRSs, so as to maximize communication sum-rate of users while ensuring that SNR of sensing signals meets a minimum requirement. To address the complex non-convex optimization problem, the method first applies fractional programming to decouple problem, then adopts successive convex approximation algorithm and alternating direction method of multipliers to transform intractable non-convex problem into multiple tractable subproblems, and finally employs an alternating optimization method to efficiently acquire the high-quality suboptimal solutions. The simulation results demonstrate that disclosed scheme exhibits satisfactory convergence and effectiveness, and can significantly improve the performance of IRS-assisted ISAC systems.

The invention relates to the technical field of beacon light control, in particular to a beacon light anti-interference control method and system based on environmental perception and dynamic modulation. The method comprises the following steps: extracting an ambient light reference based on adaptive weight sliding filtering; in combination with target detection and trajectory prediction, interference pre-judgment features are obtained; on the basis of an evidence theory, analyzing comprehensive interference evaluation characteristics through the ambient light reference, the interference pre-judgment characteristics and the electromagnetic interference characteristics; fusing meteorological environment perception and influence features and self health state features, and evaluating equipment operation risks; and based on modulation signal optimization characteristics, the comprehensive interference evaluation characteristics and the equipment operation risk, fuzzy control and rule reasoning are utilized to realize dynamic output of anti-interference control parameters. In order to solve the problem that the signal recognition rate is reduced due to the fact that a beacon light is easily affected by multiple factors in a complex ocean navigation channel environment, data are obtained through multi-dimensional environment perception, and dynamic modulation control is achieved after feature extraction and fusion.

The invention discloses a water pump flow and lift online detection device and method using an acoustic emission technology, and relates to the technical field of water pump performance detection. The water pump flow and lift online detection device using the acoustic emission technology comprises an acoustic emission sensor acquisition module, a transient event processing module and a water pump flow and lift detection module. Whether acoustic emission signal optimization is carried out or not is judged according to the quality parameters of the emission signals, the transient events are marked at the same time, then the transient events are classified when the transient events are detected, the transient events are fed back and prompted to the preset personnel, and if the transient events are not detected, the preset personnel are prompted to the preset personnel. If yes, mapping is conducted through acoustic features extracted from acoustic emission signals of the non-transient events to obtain the water pump flow and lift, finally the water pump flow and lift are used for reflecting evaluation of the mapping accuracy of the water pump flow and lift, whether mapping optimization is conducted or not is judged, and the online detection accuracy of the water pump flow and lift is improved. The problem that in the prior art, water pump flow and lift online detection accuracy is low is solved.

The invention relates to the technical field of the Internet of Things, in particular to a sewage detection and analysis system based on the Internet of Things, which comprises a data acquisition unit, a data transmission unit, a data processing and analysis unit and a monitoring and management unit. According to the invention, the self-adaptive bionic sensor array integrating self-cleaning and fault self-healing functions and the mobile patrol robot with autonomous navigation, obstacle avoidance and refined retest capabilities are combined with signal optimization and equipment management of the front-end processing and self-maintenance module; continuous and accurate acquisition and stable operation of multi-dimensional parameters in a complex sewage environment are realized, and a high-quality data source is provided for subsequent data processing, so that the coverage range, data quality and pollution abatement decision efficiency of sewage monitoring are remarkably improved, and fine management and rapid emergency response of the complex sewage environment are effectively supported.

The invention discloses a customer service telephone traffic platform real-time scheduling data optimization method based on edge computing, and relates to the technical field of communication. The customer service telephone traffic platform real-time scheduling data optimization method based on edge computing comprises the following steps: S1, collecting resource data and state monitoring data in an edge computing environment for preprocessing, and constructing a telephone traffic scheduling database after storing the resource data and the state monitoring data; s2, carrying out stability analysis through resource load, response time and preheating progress; s3, fusing task execution, data synchronization, business adaptation and node stability, and evaluating the preheating state of the cold seat; s4, generating a traffic adaptation index through the resource load, the preheating state and the task queue data; and S5, monitoring the resource load, the task queue and the response time of the node in real time to form a load feedback signal, optimizing a task scheduling strategy and generating a load report. The problems that resource scheduling of a customer service telephone traffic platform is insufficient and the preheating state of a cold seat is indefinite under the conditions of high load and high concurrency are solved.

The invention discloses a multi-frequency signal optimization method for intelligent mobile equipment, and the method comprises the steps: collecting equipment attitude and electromagnetic environment parameters through a multi-source sensor array, constructing a three-dimensional electromagnetic propagation model, and initializing the parameters; through dynamic antenna tuning, intelligent base station frequency band selection, MIMO spatial stream dynamic allocation and reinforcement learning driven resource scheduling, antenna performance optimization, base station frequency band optimization and reasonable resource allocation are realized. According to the invention, the problems of fixed antenna tuning, single base station switching and the like of the existing mobile equipment in a multi-band scene are solved, the communication performance can be comprehensively improved, network resources are efficiently utilized, the equipment energy efficiency is optimized, the complex environment adaptability is enhanced, the user experience is improved, and the implementation cost is low.

The invention discloses a portable satellite communication station signal optimization method and system, relates to the field of wireless communication, and is used for synchronously realizing dynamic adaptive management of multiple scattering paths. The method comprises the following steps: generating an earth surface image through multi-modal detection data, and carrying out layered calibration on scattering intensity; identifying a high-reflection sub-region according to the surface image and the layered calibration information thereof, and performing unified management on high-reflection and weak echo paths; based on the gain, interference and angle information of the multiple scattering paths, performing sub-array division on the antenna array according to priorities; phase stepping correction is carried out on the antenna units of the sub-arrays, and emission excitation is combined in adjacent path coverage areas; through a dynamic power scheduling and beam forming algorithm, the direction gain of each sub-array is optimized and sidelobe leakage is controlled. According to the invention, synchronous management of high reflection and weak echo paths can be realized, and the dynamic adaptability to scattering scenes is improved.

The invention discloses a cloud wireless access network millimeter wave signal optimization method based on machine learning, which adopts a double-ring OEO to replace a traditional radio frequency signal source, improves phase noise and improves signal stability. Meanwhile, the gain behavior of the millimeter wave signal is modeled by using a BiLSTM network, and high-precision power prediction is realized by combining the DML chirp effect and the influence of optical fiber dispersion. Besides, the BiLSTM model is optimized through the ACO algorithm, the optimization weight can be flexibly adjusted according to different communication scene requirements, and millimeter wave signals meeting the requirements are generated.

The invention relates to the technical field of medical signal processing, in particular to a BCG signal processing method combining TimeGAN and a Butterworth filter, which comprises the following steps: carrying out preliminary noise reduction on an input BCG signal by adopting a Butterworth band-pass filter to obtain a filtered signal; performing phase compensation on the filtered signal by adopting a bidirectional filtering mode to obtain a signal without phase distortion; the trained TimeGAN model is adopted to carry out signal optimization on the signal without phase distortion, and an optimized signal is obtained; the TimeGAN model comprises a convolutional neural network which is used for inputting data in the TimeGAN model to carry out feature extraction so as to obtain corresponding local features; the long short-term memory network is used for carrying out time sequence modeling on the local features to obtain corresponding time sequence features; the multi-head attention mechanism is used for calculating weighted features based on the time sequence features; and the generator is used for generating an optimized signal according to the weighted characteristics.

The invention provides a wide-range unmanned aerial vehicle sensing system and method based on a wireless relay technology, and relates to the technical field of unmanned aerial vehicle sensing. The system comprises a radio detection device and a wireless relay device deployed on the periphery of the radio detection device, the wireless relay device comprises a receiving antenna, a signal optimization unit and a broadband directional antenna, the receiving antenna is used for capturing radio signals transmitted by the unmanned aerial vehicle, the signal optimization unit is used for amplifying and processing the received signals, and the broadband directional antenna is used for receiving the radio signals transmitted by the unmanned aerial vehicle. The broadband directional antenna is used for re-transmitting a signal to the direction where the radio detection equipment is located; the radio detection device is used for passively receiving and decoding radio signals and capturing information of the unmanned aerial vehicle in real time, and the method comprises the steps of signal receiving and optimizing, signal redirection and transmission, signal decoding and analysis, information feedback and processing and the like. According to the invention, the unmanned aerial vehicle detection requirements of flakey areas such as airports with larger protection requirement ranges can be met, and low cost, low power consumption and high efficiency are ensured.

The invention discloses a traffic signal optimization method based on multi-agent deep reinforcement learning, and belongs to the technical field of traffic signal control, and the method comprises the following steps: carrying out the tracking and track simulation of a vehicle according to the vehicle operation data, and constructing an urban traffic simulation model and a reinforcement intelligent learning body corresponding to the traffic signal lamp of each intersection; constructing a context enhanced state space, performing normalization processing on feature parameters in the context state space, and performing combination to obtain a real-time traffic environment state vector; a congestion index self-adaptive reward is obtained through calculation; according to a heuristic reward shaping method, defining a flow matching degree index and an indication signal period position reward, and combining a congestion index adaptive reward to obtain a traffic signal optimization reward; and according to the traffic signal optimization reward, a multi-agent double-depth Q network is adopted to train and strengthen an intelligent learning body to control traffic signal phase switching. According to the invention, the problem of insufficient traffic signal control flexibility and efficiency in a complex scene is solved.

The invention relates to the technical field of wireless communication, in particular to a wireless repeater multi-band signal strength prediction system based on artificial intelligence, which comprises a multi-band signal acquisition and spatial decomposition module, a spatial-temporal feature extraction module, a channel modeling module and a multi-band signal strength prediction module. According to the method, environmental parameters such as multi-band original signals and air temperature of the wireless repeater are obtained in real time, a detailed feature matrix is generated through spatial signal processing, and a rich and accurate data source is provided for accurate prediction; an LSTM time sequence model is constructed to extract time features, a graph convolution model is used to extract spatial features, the time features and the spatial features are fused, an attention weight vector is optimized, and prediction precision is improved; and multi-band channel modeling is carried out, a path loss compensation model is constructed to correct a prediction result, and powerful support is provided for wireless repeater signal optimization management.

The invention provides a multi-scene-oriented intelligent rail signal timing optimization method and device, and relates to the technical field of intelligent public transportation, and the method comprises the steps: building a basic signal model according to the total number of current arriving vehicles and a signal constraint condition, and carrying out the calculation of each lane operation scene, and obtaining the optimal period duration; calculating the priority buffer time according to the parameters of the lane operation scene and the geometric parameters of the intelligent rail, and constructing the unified period duration through the priority buffer time and the optimal period duration; inputting the unified period duration into the basic signal optimization model, and respectively solving each lane operation scene to generate a signal timing scheme; optimizing signal timing in the remaining time of the current period based on the signal timing scheme to obtain a transition scheme; and performing signal timing switching on the signal timing scheme of the current scene according to the transition scheme to obtain a signal timing result. According to the invention, the problem that the signal timing scheme cannot be smoothly switched in a complex traffic scene is solved.

The invention relates to a multi-channel air pressure monitoring method, an adaptive monitoring circuit and computer equipment, and the method comprises the steps: synchronously collecting an original air pressure signal set in a target region through a multi-channel air pressure sensor, and carrying out the signal optimization processing, thereby obtaining an air pressure signal set; performing data analysis on a signal cluster divided by the air pressure signal set according to a channel grouping relationship to obtain an air pressure monitoring value of each channel, and calculating a differential pressure value between the channels in the cluster; and inputting the air pressure monitoring value and the differential pressure value into a two-dimensional data anomaly monitoring model of a dual-threshold system established based on historical data and environment auxiliary parameters, and outputting an anomaly judgment result and an anomaly weight ratio, so as to solve the problems of asynchronous monitoring data and high anomaly misjudgment rate in the traditional single-channel monitoring method, and improve the accuracy of the monitoring result. And the environmental stability of the target area is ensured.

The invention provides a heparin sodium detection method and system based on deep learning, which are suitable for the technical field of data processing, and the method comprises the following steps: carrying out feature extraction on heparin sodium detection spectrum signal information and heparin sodium detection electrochemical signal information; obtaining heparin sodium detection spectral signal feature information and heparin sodium detection electrochemical signal feature information; according to the heparin sodium detection spectral signal feature information, the heparin sodium detection electrochemical signal feature information, the heparin sodium detection signal feature optimization iteration times and the heparin sodium detection signal feature optimization amplitude information, generating heparin sodium detection signal optimization feature information; and performing classification processing on the heparin sodium detection signal optimization feature information to obtain heparin sodium detection information. The method is used for fully optimizing the heparin sodium detection signal through a deep learning method so as to improve the accuracy, robustness and reliability of heparin sodium detection.

The invention discloses a network signal intelligent optimization and switching method, system and device based on Android and a medium, belongs to the technical field of network signal optimization, and aims to solve the technical problem of how to realize more intelligent and automatic network signal optimization and switching according to various factors such as specific network conditions, user requirements and network quality. The technical scheme adopted by the invention is as follows: signal quality monitoring: monitoring the signal quality of each network connected with equipment in real time, and monitoring the signal quality of the current network through a hardware interface of Android equipment; user behavior analysis: analyzing user behavior characteristics and network requirements by collecting behavior data of the user so as to speculate the requirements of the user on the network; intelligent optimization: dynamically judging and optimizing network connection by utilizing a machine learning or rule engine technology according to the signal monitoring data and a user behavior analysis result, and selecting the most suitable network; and network switching: according to an optimization result, automatically selecting the most suitable network for switching.