1294 results about "Multi parameter" patented technology

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.

The invention discloses a liquid cooling charging module health state prediction system, and relates to the technical field of electrochemical detection, and the system comprises a multi-parameter collection unit which collects the pressure and flow data of a cooling liquid, and is provided with an independent collection node at each branch of a double-gun charging system, and provides basic data for monitoring; the dynamic calibration unit communicates with the acquisition unit, compensates data drift based on a temperature-vibration interference model of machine learning training, executes baseline calibration by using an idle period to update a reference value, and guarantees data accuracy; the leakage detection unit is used for calculating a data change rate after compensation, outputting an early warning according to a preset condition and positioning a leakage branch; and the health state evaluation unit fuses the early warning signal and the historical operation data to generate a prediction result. The problems that tiny leakage detection is difficult and a sensor is prone to interference can be solved, manual inspection is reduced, the maintenance cost is reduced, and the requirement of a high-power charging scene is met.

The invention discloses a 16-channel high-precision synchronous data acquisition system based on a multi-parameter model, which belongs to the technical field of data acquisition and comprises a multi-parameter model driving module, a self-adaptive channel scheduling module, a closed-loop calibration compensation module and a collaborative synchronous optimization module. The multi-parameter model driving module establishes a multi-dimensional optimization model fusing the sampling rate, the channel load and the error trend to output optimization parameters; the adaptive channel scheduling module performs real-time quality evaluation and dynamic resource allocation according to the optimization parameters to generate a scheduling instruction; the closed-loop calibration compensation module monitors a synchronization error in real time and calculates a compensation parameter; the collaborative synchronous optimization module realizes inter-channel parameter sharing and collaborative control to generate a synchronous control signal, and feeds back an optimization result to the multi-parameter model driving module to form a closed loop, and through deep coupling and closed loop feedback of the four modules, high-precision synchronization of multi-channel data acquisition is realized, and the synchronization precision reaches + / -35ns.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.

The invention discloses an extrusion process multi-parameter dynamic cooperative control method and system based on deep learning, and belongs to the technical field of high polymer material processing control, the extrusion process multi-parameter dynamic cooperative control method and system based on deep learning comprises the following specific steps: 1, data acquisition and preprocessing, the method comprises the following steps: acquiring multi-source time sequence data in an extrusion production process in real time through a sensor system, and preprocessing the data to form a standardized data set; the multi-source time sequence data comprises equipment operation parameters, process physical parameters and environment parameters. According to the method, the control system is endowed with powerful nonlinear time sequence prediction and multi-parameter collaborative decision-making capabilities through deep fusion of deep learning and reinforcement learning technologies, the quality trend can be accurately predicted, a collaborative control instruction can be given prospectively, and the problems of parameter coupling and control lag are effectively solved.

The invention discloses a switch equipment performance aging intelligent diagnosis method and device based on multi-parameter fusion, relates to the technical field of power equipment abnormal defect diagnosis, and solves the problem of low precision of aging judgment of switch equipment depending on a single temperature and humidity threshold value or a single gas parameter in the prior art. The method comprises the following steps: acquiring internal and external humiture and internal gas characteristic parameters of equipment; constructing multi-channel standardized monitoring data through time synchronization, filtering and de-noising and exception elimination processing; calculating an absolute humidity difference inside and outside the cabinet, constructing a humidity expectation model, and obtaining a standardized residual error; performing offset detection and trend smoothing processing on the standardized residual error to obtain a cumulant and a smoothing trend quantity, and fusing the cumulant and the smoothing trend quantity to calculate an offset index; outputting a moisture seepage risk score, and triggering early warning when the moisture seepage risk score and the characteristic gas concentration exceed a threshold value. According to the invention, real-time high-precision evaluation of the aging state of the equipment is realized, early hidden dangers are effectively identified, and the safety and reliability of a power system are improved.

The invention relates to the technical field of atmospheric environment prediction, and discloses an atmospheric environment multi-parameter intelligent prediction method based on causal VAE and CNN-LSTM. The method comprises the steps of collecting multi-parameter historical data of an atmospheric environment to form an atmospheric parameter sequence; sequence causal features are extracted, a causal graph structure is constructed, a causal VAE model is trained according to the causal graph structure, and potential causal characterization is generated. And extracting spatial features represented by potential causality through a CNN module, capturing time-dependent features of the spatial features through an LSTM module, fusing the two types of features to obtain a fused feature sequence, and outputting a multi-parameter prediction result through a prediction network. And dynamically adjusting hidden variable distribution parameters of the causal VAE according to the prediction error, updating a causal graph structure according to the adjusted parameters, and re-predicting a subsequent atmospheric parameter sequence by using the updated causal graph. The method can improve prediction precision and generalization ability, and enhance model interpretability and dynamic adaptability.

The invention relates to the technical field of building engineering construction monitoring, and particularly discloses a mass concrete water storage and maintenance process multi-parameter fusion early warning system which comprises a multi-source heterogeneous sensing network, an edge computing gateway, a cloud fusion analysis platform and an early warning execution terminal. The sensing network collects multi-dimensional parameters such as temperature, humidity and stress of concrete and the environment; the cloud platform evaluates the maintenance state and generates a graded early warning instruction based on a multi-physics field coupling model and a fusion early warning algorithm; and the early warning terminal drives the field device to execute differential intervention. According to the invention, comprehensive perception, accurate evaluation and active regulation and control of the states of multiple physical fields in the maintenance process are realized, and the early warning reliability and the maintenance quality are improved.

The invention relates to the technical field of environment regulation and control, and discloses a multi-parameter environment closed-loop regulation and control method and system based on an FPGA, and the method comprises the steps: unifying monitoring data to a same space coordinate system through the FPGA according to the monitoring data collected by a sensor network disposed in an environment, analyzing the coupling relation between different parameter data, and obtaining fusion data; based on the fusion data, analyzing the environment crop state through a preset crop state recognition model, simulating the influence condition of different parameter adjustment processes on the crop state, and generating a first control instruction set; analyzing the degree of deviation between the environmental parameters in the fused data and a preset target value, calculating the priority of each control instruction in the first control instruction set, and merging and optimizing the control instructions; the environmental parameters are corrected; according to the application, the regulation and control efficiency can be improved, the environmental disturbance and the crop demand change can be quickly responded through FPGA parallel processing, and the environmental regulation and control efficiency and the regulation and control effect are improved.

The invention discloses a complex medium controllable source electromagnetic multi-parameter three-dimensional inversion method and system, and the method comprises the steps: carrying out the three-dimensional space discretization of an exploration region, and carrying out the multi-time iteration inversion of a discrete exploration region through initializing a multi-parameter geologic model based on a multi-parameter inversion target function, and obtaining a multi-parameter three-dimensional inversion result; wherein the multi-parameter inversion objective function comprises a data fitting constraint, a model constraint and a cross gradient constraint, and the multi-parameter model comprises resistivity, magnetic conductivity, a charging rate, a frequency correlation coefficient and a time constant; and a hexahedral mesh discrete calculation region is adopted in inversion, and a finite element linear equation set is obtained through a vector finite element method, so that multi-parameter three-dimensional forward modeling calculation is realized. According to the method, the induced polarization effect and the magnetic conductivity influence are considered at the same time, multi-parameter three-dimensional inversion is achieved, the application precision and reliability of the controllable source electromagnetic method under the complex geological condition are remarkably improved, and rich geophysical basis is provided.

The invention discloses a multi-parameter distortion compensation method for a fractured rock mass seepage similar model test, and relates to the technical field of rock mass engineering physical model tests. The method comprises the following steps of: firstly, determining two constraint conditions that the fluid property is invariable and the gravitational acceleration is invariable, correspondingly selecting a basic physical quantity and extracting a core criterion; then analyzing three types of distortion sources of geometric dimensions, dielectric materials and boundary conditions, and establishing a distortion function according to result parameters of seepage flow velocity, seepage flow and seepage pressure; secondly, parameters difficult to control are selected as distortion items, parameters easy to regulate and control are selected as compensation items, the distortion items are substituted into a distortion function to obtain a distortion ratio, the optimal distortion ratio of the compensation items is solved based on a compensation balance formula, and the parameters are adjusted; and finally verifying the interval range of the actually measured distortion ratio. According to the method, the multi-source distortion coupling influence is accurately quantified, the compensation pertinence and precision are improved, operation is easy and convenient, universality is high, and reliable support is provided for underground engineering seepage research.

The invention relates to the technical field of medical care, and discloses an intelligent oral sputum suction system based on multi-parameter feedback and a self-adaptive pressure adjusting method.The method comprises the steps that in a pipeline cleaning state, a standard load lookup table of working parameters of a negative pressure pump and pipeline negative pressure is established; in the sputum suction operation process, a first measurement value of the negative pressure of the pipeline and a second measurement value of the working parameters of the negative pressure pump are obtained in real time; according to the first measurement value, determining a corresponding negative pressure pump working parameter from the standard load lookup table as a standard working parameter, and when the second measurement value is higher than the standard working parameter, determining a compensation amount for correcting the first measurement value based on a difference value between the second measurement value and the standard working parameter, compensating the first measurement value according to the compensation amount to obtain a calibration negative pressure value; and if the calibrated negative pressure value exceeds the preset safety negative pressure upper limit, the working parameters of the negative pressure pump are adjusted to reduce the actual negative pressure. The problem of damage caused by inaccurate reading of the sensor in the prior art is effectively solved.

The invention discloses an aquifer energy storage dynamic pressure regulation and control system and method based on multi-parameter feedback, and relates to the field of pressure regulation and control. Data normalization is ensured through a standardized parameter type set, and high-quality input is provided for a subsequent model; a pressure mapping model trained based on historical data can accurately predict pressure response under an injection-production strategy, and deviation caused by experience dependence is avoided; through partition clustering and mean value calculation, the aquifer with high heterogeneity is converted into a regulation and control unit with uniform characteristics, and the pertinence of an injection-production strategy is remarkably improved; through dynamic adjustment of a pressure interval, the energy storage efficiency reduction caused by an overpressure risk or underpressure is effectively prevented; an introduced physical intervention mechanism breaks through the limitation of pure data optimization, and the hydrogeological condition is directly improved when model adjustment is invalid; the three purposes of stabilizing the pressure of the aquifer, improving the energy storage efficiency and preventing and controlling geological risks are achieved, and scientific support is provided for long-term sustainable energy storage operation.

The invention provides a berthing ship multi-parameter real-time monitoring method and system based on a laser radar, and relates to the technical field of intelligent sensing and automatic monitoring. The method comprises the following steps: acquiring three-dimensional point cloud data; performing dynamic environment filtering processing on the acquired point cloud data; ship point cloud features in the filtered point cloud data are extracted; generating a ship three-dimensional detection frame based on the ship point cloud features; based on a ship three-dimensional detection frame, multi-parameter monitoring of the whole process from long-distance identification to ultimate berthing is achieved by dynamically adjusting monitoring strategies and algorithm parameters. According to the invention, real-time accurate monitoring of key parameters such as ship speed, distance, included angle and the like is realized, the problems of monitoring blind areas, signal interference and the like in the prior art are effectively solved, and the safety and automation level of berthing operation are remarkably improved.

The embodiment of the invention provides a method and a system for processing ocean multi-parameter wide-width imaging data of a Beidou interference altimeter, and relates to the technical field of GNSS-R. The method comprises the following steps: constructing a set of complete and strict data processing chain; through precise calculation of a GNSS-R direct signal and reflection signal delay inequality interference geometric model and correction of actual measurement delay inequality errors, systematic errors in the process of converting original signals into geophysical parameters are remarkably reduced, and the precision and reliability of parameters obtained through final inversion are improved. And on the basis of a self-adaptive data collection strategy of parameter space-time transformation characteristics and a spatial interpolation reconstruction process, discrete strip data is intelligently'woven 'into a continuously-covered wide-swath imaging type data product, so that the data format better meets the analysis requirements in the fields of oceanography, climatics and the like, and the intuition and usability of the product are improved.

The invention discloses a multi-target road transportation path planning method and system based on multiple parameters A * and NSGA-II, and belongs to the technical field of road transportation path optimization. The method comprises the following steps: constructing a large-scale road network directed graph structure; establishing a satellite orbit model and a risk distribution model, calculating a dynamic risk value of a roadside, and forming dynamic road network data; a three-stage initialization strategy is adopted to generate an initial population, transportation time and reconnaissance risks are taken as double targets, evolutionary optimization is carried out through an NSGA-II algorithm, and a Pareto optimal path set is output. The system comprises a corresponding road network module, a risk module and a planning module. According to the method, the technical problems of multi-target conflict, low initial population quality and satellite risk avoidance of a traditional method are solved, and the timeliness, the concealment and the algorithm efficiency of path planning are remarkably improved.

The invention discloses a thermal power plant limited space operation safety and environment multi-parameter monitoring system, and relates to the technical field of operation safety monitoring. A structure parameter model is constructed based on a space volume structure, a gas exchange path, a risk type and an operation position, and multi-parameter sensing nodes are configured; extracting abnormal fluctuation characteristics of the environmental parameters through edge computing nodes; a gas flow model and a heat distribution model are combined to carry out space-time inversion, a risk source is positioned, a space risk heat map is dynamically generated, the position and action state of an operator can be obtained in real time, the exposure probability is calculated, and automatic identification and graded early warning of a high-risk area are realized; the system has high-precision, low-delay and full-coverage monitoring capability, and the intrinsic safety of limited space operation of the thermal power plant is improved.

The invention relates to the technical field of medical instrument detection, discloses a medical whole basket instrument multi-parameter detection system and method, and aims to solve the problem that in an existing CCD image detection technology, a high-brightness reflection area interferes with a detection result due to tiny bloodstain or superposition on the surface of an instrument. The system comprises an imaging module, a light source control module, a data processing module and a carrying platform. According to the method, reflection is reduced through combination of multispectral imaging and an adjustable light source, a reflection area is accurately identified and removed by using an algorithm of combining gradient amplitude and gradient direction entropy, and finally geometric parameters and cleanliness indexes of the instrument are synchronously calculated.

The invention relates to the technical field of fan monitoring, in particular to a BMC (Baseboard Management Controller)-based fan state monitoring method and system. Demand level and energy efficiency modeling; matching coupling judgment is abnormal; carrying out abnormal mechanism cross positioning; performing combined construction of trend portraits; and outputting classification abnormity prompts. According to the invention, on the basis of a collaborative acquisition and hierarchical coupling analysis mechanism of BMC multi-source operation parameters, parameters such as a complete machine load rate, power supply input power, a fan rotating speed, a PWM duty ratio, a motor driving current, an air inlet and exhaust speed and a key heating device temperature are brought into a unified energy efficiency response relation set for modeling and analysis; the sensitivity, the accuracy and the interpretability of abnormal recognition are greatly improved, and the problems that due to the fact that only a single temperature parameter and fault triggering type control are relied on, fan performance degradation is recognized in time, the precision is low, and the response speed is low are effectively solved.

The invention discloses a polymerization process control method and system based on multi-parameter fusion, and belongs to the technical field of automatic control, and the method comprises the steps: obtaining polymerization process parameters and key execution equipment mechanical parameters in a polymerization reaction process, forming a real-time parameter set, and generating a dynamic state mark representing a current comprehensive working condition mode through fusion analysis; determining a cooperative control strategy for realizing an expected effect target by combining a resource mapping table defining control priorities and interaction relationships of the parameters in different working condition modes according to the dynamic state marks; and executing the cooperative control strategy, and generating and issuing a cooperative adjustment instruction containing the synchronous adjustment amount and the adjustment time sequence. According to the method, dynamic state marks are generated by fusing polymerization process parameters and key equipment mechanical parameters, and a cooperative control strategy is determined in combination with a resource mapping table, so that multi-variable cooperative, accurate and self-adaptive control of the polymerization process can be realized, and the stability and efficiency of system operation are improved.

A portable golf putting green putter multi-parameter intelligent training system is characterized by comprising a support (1) used for installing an electromagnetic induction type sensing device (2) and an electric control element and enabling the whole training system to be convenient to carry and move; the sensing devices (2) are installed on the two supporting legs of the support, and at least two sensing devices (2) are installed on each supporting leg; and the electric control element (3) is used for receiving the signal intensity and the time difference obtained by the sensing device (2), transmitting the signal intensity and the time difference to a computer system (4) in real time in a wired or wireless manner, calculating and analyzing the signal intensity and the time difference, displaying the speed and the angle of the push rod in real time, and comparing the speed and the angle with preset standard values, so that a coach or a practicer can improve the speed and the angle. The utility model has the advantages of simple structure, convenience in carrying, low cost and wide application range.

The invention discloses a concrete permeability multi-parameter comprehensive detection system and an evaluation method. The system comprises a data acquisition module, a data processing module, a comprehensive evaluation module and a man-machine interaction module, and synchronous acquisition and calibration of multiple parameters such as electric flux, a chloride ion diffusion coefficient, water absorption and an air permeability coefficient are realized through a unified synchronous trigger mechanism and an environment compensation algorithm; carrying out intelligent processing on the data by utilizing an improved data cleaning, multi-level and multi-modal feature extraction and fusion strategy; and finally, through dynamic fusion of fuzzy comprehensive evaluation, grey correlation analysis and a machine learning classification model, outputting a quantitative grade evaluation result of the concrete permeability. The method can comprehensively and accurately evaluate the permeability of the concrete, remarkably improves the detection precision and efficiency, adapts to a complex environment and a long-term aging process, and provides a scientific basis for durability evaluation and maintenance of a concrete structure.

The invention provides a multi-parameter cooperative control method and system for a chemical product production workshop, and relates to the technical field of intelligent control, and the method comprises the steps: constructing a multi-parameter sparse coupling topology according to the dynamic mutual information amount between parameters; performing topology path sensitivity evaluation, identifying a key coupling chain which has the greatest influence on the reaction stability, and tracking the collaborative change trend of parameters on the chain in real time; establishing a parameter prediction model, and executing the adjustment priority automatic rearrangement of the intra-chain parameters based on a constraint migration mechanism; constructing a self-evolution cooperative control strategy generator, and generating a cooperative regulation and control instruction of the reaction process through online evolution; and the materials are delivered to corresponding heating equipment, cooling equipment, pressure discharging devices and feeding equipment. The technical problems that in the prior art, adjustment is generally conducted on a single parameter, deep analysis on interaction among multiple parameters is lacked, the system cannot be kept stable when the multiple parameters fluctuate, and then equipment failures or safety accidents are caused are solved.

The invention discloses a water supply pipe network dynamic pressure control method and system based on multi-parameter fusion, and relates to the technical field of water supply pipe network control, and the method comprises the steps: carrying out the standardization processing and topological optimization of pipe network geographic information system data, and constructing an urban pipe network topological graph; identifying a pressure sensitive node and a distance sensitive node based on a hydraulic calculation result of the mechanism calculation model; performing association coding on historical monitoring data of the key nodes and a topological connection relationship, and training a machine learning model fusing pipe network topological constraints; collecting real-time monitoring data, inputting the real-time monitoring data into the mechanism calculation model and the machine learning model, and respectively obtaining offline adjustment parameters and online adjustment parameters; and determining a final adjusting instruction through consistency verification to control water supply pump set equipment. According to the method, the calculation precision of the mechanism model and the response speed of the machine learning model are fused, organic unification of accuracy and real-time performance is achieved, and the control stability and reliability under the abnormal working condition are improved.

The invention discloses a dual-polarization radar multi-parameter fusion rainfall estimation system and method, and relates to the technical field of meteorological radars, and the system comprises a data collection module which is used for starting a dual-polarization radar to carry out long-term observation when an observation scene with uniformly distributed light rain weather appears, recording radar polarization parameters at different elevation angles, and carrying out the data collection module; transmitting to the deviation analysis module; the deviation analysis module is used for receiving the radar polarization parameters and calculating ZDR statistical characteristics according to the reference uniform scene; according to the invention, by constructing the antenna isolation elevation angle dependence model, dynamic reverse correction is carried out on ZDR and rhohv measurement values under different elevation angles, the influence of dynamic degradation of the antenna cross polarization isolation along with the scanning elevation angle on the measurement values is effectively eliminated, and real-time sensing and compensation of the isolation degradation along with the dynamic change of the elevation angle are realized; the radar can keep high measurement precision in the whole scanning space, and the conditions of precipitation phase state misjudgment and missing report are reduced, so that the accuracy of precipitation estimation is improved.

The invention relates to the technical field of multi-parameter physiological signal monitoring, in particular to an intelligent laryngeal mask and respiration monitoring system which comprises a laryngeal mask body and a multi-parameter optical fiber measuring system. The optical fiber sensing unit is embedded into the laryngeal mask main body and comprises an acoustic sensing part and an environment sensing part with a functional coating; the signal processing unit demodulates and couples an original signal, combines physical parameters of the coating and inputs the original signal into a multi-parameter coupling correction model, and decouples independent and accurate multiple parameters; and the target thickness of the coating is determined through acoustic correction error minimization and comprehensive calculation that the sensitivity of each environmental parameter reaches the standard. The system can generate an abnormal ventilation alarm and feed back to control the breathing machine. Multi-parameter synchronous monitoring can be achieved, coating interference is eliminated, and breathing management safety and practicability are improved. In the actual application process, the abnormal breathing function caused by abnormal breathing machine state or abnormal laryngeal mask position of a user can be found in time, the purpose of early discovery and early intervention can be achieved, and malignant events are avoided.

The invention relates to the technical field of polycrystalline silicon preparation, and discloses a polycrystalline silicon reduction furnace intelligent regulation and control system based on multi-parameter prediction, which is based on full-dimensional data such as current, raw material purity, silicon rod temperature and the like acquired by a multi-parameter sensing module, and realizes the intelligent regulation and control of a polycrystalline silicon reduction furnace through a burst interference quantification sub-module in a bidirectional linkage fusion prediction module. The deviation entropy quantification interference intensity of the multi-parameter change rate is calculated, migration learning emergency correction terms are synchronously called to dynamically compensate prediction errors, meanwhile, the one-way dependence defect is broken through, and a dynamic physical boundary is provided for a data driving prediction constraint module through physics, electrode contact resistance and in-furnace heat balance parameters; the NMPC module takes a bidirectional fusion high-precision predicted value as a target, solves a multi-target optimization control sequence and issues and executes the multi-target optimization control sequence, and the closed-loop iterative optimization module synchronously updates physical and data model parameters through incremental learning on the basis of an actual regulation and control effect, so that the problems of current regulation lag and formula parameter stiffness are solved, and long-term stable operation of the reduction furnace is supported.

The invention relates to a low-voltage distribution line comprehensive intelligent protection method based on multi-parameter linkage, and the method comprises the following steps: collecting power distribution abnormal data, calculating a trigger interval, screening segments in a period, generating a sequential segment group, analyzing a serial number difference value to form a continuous path, recognizing rhythm abnormality, and eliminating interference flow. And a time fault is checked to construct a closed chain table, a rhythm stable closed loop structure is screened, and an intelligent protection result is output. According to the method, continuous fragments are constructed based on trigger rhythms and event numbers in abnormal data processing, path continuity is judged through difference values, effective flow directions are recognized, disturbance fragments are eliminated in combination with rhythm comparison, stable closed paths are constructed, and sequences with consistent rhythms and continuous numbers are screened to serve as a judgment basis. Accurate description of an abnormal propagation path and effective tracking of a response rhythm are realized, and identification sensitivity and protection strategy adaptability of a distribution line under multi-source interference are enhanced.

The invention discloses a multi-parameter shallow gas detection method which comprises the following steps: S1, self-adaptive sensing network layout and multi-parameter synchronous acquisition: monitoring nodes synchronously acquire underground electrical parameters, soil gas composition and concentration, soil mechanics and pore pressure parameters and an environmental noise background value; accessing a hydro-meteorological data stream of the target area in real time; s2, multi-source interference signal identification and data purification: obtaining a purified multi-parameter data sequence reflecting real geological anomalies; s3, water-gas-force coupling dynamic modeling and migration simulation: outputting a shallow gas migration risk thermodynamic diagram; s4, intelligent diagnosis based on transfer learning and causal discovery: performing time sequence analysis on the purified multi-parameter data sequence, and identifying a gas reservoir core area, a dominant migration channel and dominant control factors influencing gas aggregation and migration; and S5, dynamic risk assessment and graded early warning: quantitatively calculating the risk probability.

The invention discloses a river management-oriented multi-parameter water quality intelligent prediction method and a river management-oriented multi-parameter water quality intelligent prediction system, relates to the technical field of environment monitoring and artificial intelligence, and particularly discloses a water quality intelligent prediction system and a water quality intelligent prediction method. According to the system, original water quality monitoring data is cleaned and repaired through the missing value filling module, so that the data integrity is ensured; performing deep feature extraction and fusion on the multi-parameter water quality data by using a convolutional neural network (CNN) to generate high-dimensional feature mapping; then, a bidirectional long-short-term memory network (Bi-LSTM) model is adopted to carry out forward and backward bidirectional learning on the extracted time sequence features, and a complex dynamic rule and a long-term dependency relationship of water quality parameter changes are accurately captured; and finally, an optimized loss function module is introduced to minimize a training error, so that the prediction accuracy is further improved. According to the method, the problems of insufficient precision, high model calculation complexity and weak multi-parameter collaborative prediction capability caused by data missing in the existing river water quality prediction technology are solved.