727 results about "Intelligent sensor" patented technology

The invention relates to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering operation risk monitoring management system based on multi-sensor fusion. The system comprises a data acquisition module, a data fusion module, an early warning and instruction generation module, and an instruction distribution and verification and correction module. The data acquisition module acquires structural deformation, water flow pressure and environment temperature and humidity data in real time through multiple types of intelligent sensors; the data fusion module inputs the collected data into a dynamic weighted fusion model, and generates a comprehensive monitoring index in combination with a sensor confidence coefficient weight and a historical data deviation rate; the early warning and instruction generation module triggers multi-stage early warning and generates an equipment regulation and control instruction set according to the comprehensive monitoring index and a preset safety threshold value; the instruction distribution module distributes the instruction to an execution terminal through a distributed message queue; and the verification and correction module checks terminal feedback signals by using a back propagation verification mechanism, and corrects the confidence coefficient weight of the model.

The invention discloses a cable fault accurate positioning system based on an intelligent sensor system, and relates to the technical field of power fault positioning, and the cable fault accurate positioning system comprises a cloud monitoring center which is in communication connection with the following modules: a signal acquisition and processing module which is used for deploying the intelligent sensor system along a cable, operation environment data and traveling wave signals in the cable operation process are obtained, and the signals are preprocessed and stored in a cloud data warehouse. According to the invention, the front band-pass filter and the self-adaptive trap filter are combined to filter out high-frequency harmonic waves and specific-frequency interference signals, and the differential sampling technology is adopted to counteract common-mode interference, so that the signal-to-noise ratio of the signals is remarkably improved, a system can accurately capture and process weak traveling wave signals caused by a high-resistance grounding fault in a complex environment, and the fault detection accuracy is improved. Therefore, the fault positioning precision is greatly improved, the positioning error caused by signal interference is reduced, and the determination of the fault position is more reliable and accurate.

The invention provides an intelligent fault diagnosis method and system for a photovoltaic system. The method comprises the following steps: collecting multi-source heterogeneous operation parameter data of the photovoltaic system based on an intelligent sensor network and carrying out standardized preprocessing; local feature extraction is carried out on edge nodes, a frequency moment is calculated, data summary aggregation is carried out through a frequency moment optimization algorithm, and a fusion key feature vector set is constructed by using a multi-modal data fusion model; performing anomaly detection based on the normal behavior mode baseline, and generating an abnormal state alarm signal and a preliminary anomaly type indication; constructing a weighted graph model based on a system topological structure, constructing a fault association sub-graph by using an approximate spanning tree algorithm, accurately positioning a fault unit and diagnosing a fault type; and generating and executing a self-healing operation scheme according to the fault diagnosis conclusion. Through the distributed multi-modal data fusion and approximate spanning tree fault positioning technology, rapid and accurate diagnosis and autonomous recovery of the photovoltaic system fault are realized, and the system reliability and the operation efficiency are improved.

The invention relates to the technical field of cubic hydraulic press monitoring, in particular to a cubic hydraulic press health monitoring method and system based on an intelligent sensor, and the system comprises a health monitoring terminal, a data sensing module, a data processing module, a health diagnosis module, a fault management module, a diagnosis positioning module, a cooperative influence module and a rear-end execution module. According to the method, the time domain, the frequency domain and the time domain-frequency domain combined features are extracted, meanwhile, the state change slope is quantified, early warning of the equipment degradation trend is achieved, a fault type-abnormal feature-weight mapping table is constructed, a fault core component and the specific position are accurately positioned, and the fault core component is accurately determined by matching with digital twinborn visual marks. And meanwhile, the internal relation between components is quantified by constructing a component incidence matrix, the health influence coefficient of the cooperative component is calculated in combination with a fault grade coefficient, key components affected by faults are recognized in advance, a cooperative damage list is formed, and operation and maintenance personnel are assisted to intervene in advance.

The invention relates to the technical field of spinning quality detection, and discloses a jute spinning product quality detection method and system based on an intelligent sensor. The method comprises the following steps: acquiring real-time process parameters of a jute spinning production line, and synchronously capturing yarn surface temperature field distribution in combination with an infrared thermal imager; performing space-time alignment processing on the yarn tension fluctuation data and the temperature field distribution to generate a fused dynamic process characteristic matrix; according to the tension gradient change rate and the temperature field abnormal region coordinates in the dynamic process characteristic matrix, dividing potential occurrence sections of yarn quality defects; a microstructure image of the yarn in the potential generation section is extracted, a fiber arrangement angle deviation value is measured through a polarized light interferometer, and fiber molecular bond vibration frequency data is obtained in combination with a Raman spectrometer; and inputting the fiber arrangement angle deviation value and the molecular bond vibration frequency data into a cascade quality analysis model, and outputting a quantitative index set of the internal structure defects of the yarns.

The invention relates to the technical field of intelligent sensors, in particular to a workshop harmful gas abnormity monitoring method and system based on multi-modal data, and the method comprises the following steps: obtaining the multi-period concentration change of a node, recognizing the reverse direction, forming a bounce fragment, combining node behaviors according to the correlation of time and space, and constructing a diffusion path; and in combination with node toxicity residence and trend change and gas and time information at the convergence position, a workshop multi-modal toxicity anomaly monitoring result is obtained. According to the method, gas fluctuation characteristics are captured by recognizing the concentration change direction and the reversal amplitude, a behavior combination is constructed by combining time and space information, the linkage recognition capability between nodes is enhanced, a path sequence is constructed by depending on the trend direction and the node sequence to present a diffusion track, and extension and mutation nodes are extracted by superposing the residence state and the change trend. The dynamic monitoring range of toxic gas in multiple scenes is expanded, and correlation analysis of behavior traceability, path tracking and abnormal gathering areas is supported.

The invention provides a water quality abnormity early warning system and method based on multi-source data fusion, and relates to the technical field of abnormity early warning, and the method comprises the steps: recognizing a sensitive region in which the water quality is gradually changed and abnormal in a target water area through the time-space correlation characteristics of each piece of water quality multi-source data; performing sensitivity analysis on the response intensity of the water quality gradual change abnormity in the sensitive area to obtain a water quality sensitivity index of the sensitive area; determining an early warning response period when the water quality at the monitoring point is gradually changed and abnormal; performing confidence correction on a water quality abnormal fluctuation early warning threshold value at the monitoring point through the water quality sensitivity index and the early warning response period to obtain an abnormal early warning confidence coefficient of the water quality at the monitoring point; and when the abnormity early warning confidence exceeds a preset confidence threshold, sending a water quality gradual change abnormity verification instruction to an intelligent sensor at the monitoring point, and generating risk alarm information of water quality gradual change abnormity based on feedback verification data. According to the invention, risk early warning can be carried out under the condition that the water quality has gradual abnormal fluctuation.

The invention relates to the technical field of fire safety, in particular to a fire simulation early warning system based on intelligent fire protection multi-source data fusion, which comprises a sensor health degree dynamic evaluation module, an intelligent sensor health degree dynamic evaluation module and a fire simulation early warning module, establishing a two-dimensional feature library of the state of the sensor and environmental interference; and based on the feature library data, a fuzzy comprehensive evaluation method is adopted. By filtering unreliable data of the sensor, distinguishing multi-source contradictory data, dynamically adjusting weights according to fire behavior stages, iteratively updating data credibility and optimizing parameters in a closed-loop mode, the problems of false alarm and missing alarm in the prior art are effectively solved, and the accuracy and practicability of the intelligent fire-fighting early warning system are improved.

The invention provides an electrocardiograph electromagnetic compatibility test data diagnosis method based on an intelligent sensor, and relates to the technical field of electrocardiograph electromagnetic compatibility test. The method includes generating structured test data. And calculating a theoretical interference reference threshold. And generating an initial interference judgment atlas arranged according to the interference time sequence. And constructing a dynamic compensation coefficient matrix, and injecting the dynamic compensation coefficient matrix into the initial interference determination map to obtain a dynamic interference determination map. And calculating the characteristic similarity between the real-time test data in each interference time sequence window and the dynamic interference judgment map, and diagnosing the electromagnetic compatibility state of the electrocardiograph. According to the invention, through multi-dimensional data acquisition and interference identification, through a dynamic reference threshold and a compensation mechanism, environment and equipment state changes are adapted, the diagnosis stability of a complex scene is improved, through real-time data comparison and traceability, the equipment state is rapidly judged, the interference source is positioned, the debugging time is shortened, and finally the measurement accuracy of the electrocardiograph is guaranteed.

The invention relates to the technical field of environmental monitoring of artificial intelligence, and particularly discloses a dangerous waste storage environment real-time monitoring and risk early warning system based on digital twinning, which collects multi-modal environmental data in real time through an intelligent sensor network deployed in a dangerous waste storage facility, and constructs a digital twinning model synchronized with a physical entity; a multi-modal data fusion and physical embedding technology is adopted, and virtual risk parameters of an area which is not actually measured are calculated based on an environment evolution rule; an abnormal association mode between the measured data and the virtual risk parameters is analyzed and identified through the dynamic association network, and risk level judgment and early warning signal triggering are achieved; a sensor monitoring strategy is dynamically adjusted according to an early warning result to form closed-loop management and control; according to the invention, the sensor limitation of the traditional monitoring system is broken through, the advanced accurate early warning of the hidden risk is realized, and the monitoring efficiency is obviously improved through adaptive resource configuration.

The invention provides a metering data automatic calibration method and system, and the method comprises the steps: obtaining an actual measurement output value of a target intelligent sensor for a measured physical quantity in a normal operation process of metering equipment, accessing a standard reference signal outputted by an external standard device subjected to the traceability of the quantity value, reading a pre-stored standard reference value and a standard threshold value, and carrying out the calibration of the measured physical quantity. According to the actually measured output value, the standard reference value and the standard reference signal, obtaining a standardized output value through a fitting mode; when the standardized output value exceeds a standard threshold value, switching to a calibration mode, providing forward and reverse standard input quantities by an external standard device, acquiring a response signal, and calculating a nonlinear compensation coefficient and a hysteresis correction coefficient; and updating the output correction parameter according to the compensation coefficient. By adopting the scheme of the invention, the cost constraint of different error attention in measurement data calibration can be realized, and the calibration robustness is improved.

The invention discloses a belt conveyor intelligent transportation method and system based on an intelligent sensor and electronic equipment, and the method comprises the steps: collecting key parameters from a belt conveyor through a multi-source heterogeneous intelligent sensor, including a state parameter of the belt conveyor, a state parameter of a belt and an environment change parameter; performing synchronous calibration, noise suppression and feature extraction on the multi-source heterogeneous data, and performing multi-source heterogeneous data fusion; constructing a belt conveyor operation multi-dimensional state vector based on the fused data; and inputting the state vector into a prediction model to obtain a future operation state prediction result. According to the method, the problems of'inaccurate prediction, insufficient advance and high missing report rate 'caused by'strong data isomerism, key feature weight deficiency, rare fault samples, insufficient model stability' and the like are solved.

The invention provides a cable fault detection method based on an intelligent sensor, and relates to the technical field of power cable monitoring and fault diagnosis, and the method comprises the steps: enabling each sensor node to independently collect and analyze local data, and immediately carrying out the local preliminary diagnosis and triggering a local reflection action when a transient fault feature is detected; an adjacent node receiving the consensus request packet serves as a consensus verifier, independently verifies the consensus request based on data collected by the consensus verifier in the same time window, votes a verification result and feeds back the verification result to the consensus initiator; and according to the received voting result, if a sponsored ticket exceeds a preset threshold value, the consensus initiator confirms that fault diagnosis is established, and reports a final consensus confirmation result to a cable fault monitoring center. According to the invention, extremely low power consumption, ultra-low delay, efficient communication and high reliability of cable fault monitoring are realized, and real edge intelligence is realized.

The invention discloses an AI-based rail transit dynamic safety early warning system, which comprises a sensing layer for deploying an intelligent sensor network with dynamically adjustable spacing, integrating a steel rail health monitoring suite to carry out microdefect detection on a steel rail, capturing vibration deformation characteristics under high-speed operation by a dynamic monitoring array, acquiring external environment data by an environment sensing module, and sending the external environment data to an early warning layer; preprocessing the collected data by utilizing an edge computing node; according to the cognitive layer, a line adaptive layer performs adaptive fusion on multi-line features through three-stage training of'basic training-meta training-fine tuning 'in combination with a dynamic weight generation mechanism, meanwhile, a causal cognitive module constructs a three-stage variable causal graph, a hybrid inference engine combines a CLIPS symbol inference engine, a Bayesian probability network and a D-S evidence theory, and the line adaptive layer performs multi-line feature fusion on the basis of the CLIPS symbol inference engine. Logic deduction and uncertainty quantification of fault attribution are realized; according to the decision-making layer, a dynamic threshold generator adjusts an early warning boundary in real time based on an LSTM prediction model, and an emergency plan engine recommends a maintenance strategy in combination with a digital twinborn simulation result.

The invention provides a sensor data recovery method and system based on mask perception space-time modeling, and belongs to the technical field of Internet of Things and intelligent sensing data processing. The method comprises the following steps: modeling a missing position into a trainable potential vector representation through an adaptive missing representation module, and avoiding noise introduced by traditional zero filling or mean filling; a missing perception space-time decoding module is adopted, and a dynamic weight distribution mechanism of mask constraint is combined in the decoding process, so that information is effectively prevented from being excessively smooth; designing a space-time dual-channel feature aggregation unit, and capturing spatial dependence and time sequence dependence between the sensors at the same time; and finally realizing accurate completion of a large-scale space-time sensor matrix through an output recovery unit. According to the method, the accuracy and robustness of sensor data restoration can be remarkably improved, the reliability of subsequent monitoring, prediction and anomaly detection is enhanced, and the method has wide engineering application value.

The invention relates to the technical field of power equipment on-line monitoring and fault diagnosis, and discloses a converter transformer bushing end screen voltage abnormity detection device and early warning method.The method comprises the steps that voltage data are collected through a wireless intelligent sensor, soft synchronization and self-adaptive windowing are achieved through a commutation notch feature locking mechanism, and a preprocessing signal is output; topological correlation clustering is executed to construct a virtual health reference, a dynamic residual sequence is extracted through time domain difference, and system side and equipment side features are decoupled; constructing a variational mode decomposition model, and calculating a characteristic entropy value of the intrinsic mode function; and comparing the entropy value with a threshold value, triggering an active impedance disturbance verification mechanism, controlling a sensor to access a load and comparing an actual voltage drop rate with a theoretical voltage drop rate, and determining an insulation degradation or poor contact fault. High-precision synchronization can be achieved without a hardware clock, and the problems that weak fault features are difficult to extract and virtual connection and insulation faults are difficult to distinguish in a high-impedance loop are effectively solved.

The invention relates to the technical field of intelligent sensors, and particularly discloses a smoke sensor monitoring sensing method and system. The system comprises a multi-source environment sensing module, a feature extraction and fusion module, a dynamic threshold decision module and an execution control module, a comprehensive environment state vector is constructed by fusing multi-dimensional environment data, and an alarm threshold is dynamically adjusted based on a deep reinforcement learning model, so that accurate fire identification and hierarchical control are realized, and the system is suitable for popularization and application. And the monitoring reliability, the environmental adaptability and the agriculture and forestry equipment operation efficiency are effectively improved.

The invention belongs to the technical field of hypersonic flight vehicle control, and particularly relates to a hypersonic flight vehicle protection control method considering non-starting of an air inlet channel. The method specifically comprises the steps of establishing a quasi-one-dimensional nominal mechanism model to describe flow field changes of an isolation section and a combustion chamber; adopting a physical information neural network to construct a reduced-order model as a real-time agent of the quasi-one-dimensional model; designing a dual extended Kalman filtering algorithm to realize joint online estimation of model parameters and blind area states, and introducing shock wave position pseudo measurement to form multi-loop feedback; a dual-channel fusion early warning system of a model channel and an intelligent sensor channel is constructed, and robust early warning is realized through adaptive weight; and designing a continuous adaptive model prediction controller, and dynamically adjusting a control strategy according to risk indexes and parameter uncertainty to realize active protection control.

The invention relates to the technical field of micromolecule mineral water monitoring, and discloses a micromolecule mineral water quality monitoring system based on an intelligent sensor. The system comprises a multi-channel spectrum sensor array, an ion selective field effect transistor group, a microfluidic preprocessing module, a dynamic compensation database, a self-adaptive analysis engine, a cloud verification interface and a feedback execution mechanism. The micro-fluidic pretreatment module is used for eliminating bubbles of an original water sample, screening particles and outputting stable liquid flow; the spectrum sensor array captures full-band absorption spectrum data to generate a characteristic matrix, and the transistor group detects concentration fluctuation of calcium ions, magnesium ions and silicate; the adaptive analysis engine performs multi-modal fusion and abnormal marking on the time sequence alignment data stream; the cloud interface performs cross validation on the local result and the regional water quality center reference library to generate a credibility report; and the feedback execution mechanism adjusts the microfluidic flow velocity and the sensor integration time according to a report to realize efficient water quality monitoring.

The invention discloses a liquid cooling case overload monitoring method based on an intelligent sensor. The method comprises the following steps: deploying a sensor network at a key node of a cooling flow channel of a liquid cooling case; all the sensors are connected with an edge computing gateway; the edge computing gateway synchronously collects data of the sensor and generates a time sequence data set; dynamically calculating the overall heat dissipation power P (t), the system flow resistance coefficient K (t) and a parameter eta (t) representing the cooling efficiency based on the time sequence data set; comparing the calculated value with a reference value, and performing overload risk assessment by applying a preset judgment logic; when the evaluation result exceeds a preset risk threshold value, triggering overload early warning, and generating a control instruction; and according to historical operation data of the system, carrying out periodic self-adaptive calibration on the reference value. By deploying the sensor network at the key node of the liquid cooling case and analyzing the collected data through the edge computing gateway, the response speed and the control precision of the system to the dynamic change of the heat source power are improved.

The invention discloses a pump head airtightness detection system based on an intelligent sensor, and belongs to the technical field of airtightness detection. The system specifically comprises the following steps that S1, a historical data acquisition module acquires pump head air tightness detection multi-source historical sample data and transmits the pump head air tightness detection multi-source historical sample data to a data preprocessing module; and S2, the data preprocessing module preprocesses the obtained multi-source historical sample data, and performs physical noise reduction and calibration on the multi-source historical sample data. Three types of multi-source data of acoustics, pressure and thermotics are collected, normal parts, leakage parts, vibration, temperature change and other interference working conditions are covered, all-directional description of the air tightness state of the pump head is achieved, limitation of a single data dimension is avoided, and a special optimization method is adopted for different types of data. The environmental noise is effectively filtered; the pressure data is subjected to curved surface fitting through Kriging interpolation, and abnormal values introduced by fluctuation of the sensor are eliminated.

The invention relates to the technical field of blood transfusion pipeline detection, and discloses a blood transfusion pipeline jitter detection method based on an intelligent sensor. According to the method, an initial control instruction generation layer receives an operation instruction, and a natural language analysis unit identifies a pipeline number and a preset jitter threshold parameter; the multi-mode signal detection layer collects pressure waveform signals and three-dimensional motion track signals of a blood transfusion pipeline in real time, and establishes a feature matching database to store reference signal feature combinations corresponding to different pipeline models. The dynamic mapping layer combines the real-time detection signal features with the reference signal features to perform similarity matching; the double-domain interpolation calculation layer generates an anti-jitter control parameter according to a matching result; the anti-phase jitter elimination execution layer outputs a phase compensation instruction according to the anti-jitter control parameters; the parameter optimization layer updates a feature matching database according to the historical jitter residual volume data; and the abnormal decision-making layer judges an unrestrained jitter state and triggers an alarm protocol, and also implements a full-period updating mechanism to refresh a reference signal feature combination.

The invention discloses a self-adaptive scheduling industrial internet data acquisition control system and strategy, and relates to the technical field of industrial internet data acquisition and self-adaptive scheduling control, and the system comprises a data sensing layer, a deployment multi-type intelligent sensor group, an edge gateway and a hybrid communication module. The sensor supports multi-protocol adaptive access and dynamically switches sampling frequency, the edge gateway preprocesses original data, and the communication module guarantees stable transmission through wired and wireless hybrid networking; the self-adaptive scheduling layer comprises a task grading unit, a load monitoring unit and a dynamic scheduling unit; the edge calculation layer is used for processing tasks with high real-time performance; the cloud collaboration layer stores data and mines, analyzes and constructs an equipment model; the security protection layer realizes identity verification, data encryption and intrusion detection; and the system management layer manages the full life cycle of the equipment and divides user permissions. According to the system, the problem of traditional industrial internet data acquisition scheduling pain points is solved, and the overall operation efficiency and safety of the system are effectively improved.

The invention relates to the technical field of automobile electronic intelligent fault diagnosis, and discloses a multi-modal data fusion automobile air conditioner controller abnormal state monitoring method, which comprises the following steps: deploying five types of intelligent sensor nodes at key positions of an automobile air conditioner system to realize hardware-level time synchronization and adaptive sampling; a fast ICA algorithm is combined with sliding window processing to realize signal separation, a self-adaptive wavelet basis selection mechanism is designed to carry out time-frequency analysis, and principal component analysis is used to carry out feature dimension reduction; constructing a lightweight attention fusion network, dynamically adjusting different modal feature weights, and mining feature relevance through a cross-modal interaction module; a hierarchical progressive anomaly detection architecture is designed, a statistical method and a neural network detection result are fused, a time consistency constraint mechanism is established, and multi-time scale anomaly prediction is realized; and fault type identification and severity evaluation are carried out in combination with an expert knowledge base, and a multi-priority protection strategy and an emergency plan are formulated.

The invention discloses a battery box sealing performance detection mechanism with a pressure real-time monitoring function, and relates to the technical field of intelligent sensor detection. Comprising a case, a downward pressing device, a sliding bearing platform, a support and a balance inflation plate, an upper pressing cavity is communicated with an air path of an inner cavity of a battery box, air pressure self-balance of the upper side and the lower side of a cover plate of the battery box is achieved, an equivalent pressure field is formed, the cover plate bulging or collapsing phenomenon caused by traditional single-side pressurization is effectively eliminated, and detection errors caused by structural deformation are avoided. Through a linkage structure of the plugging ring, the sliding rod and the piezoelectric body, the leakage amount is converted into an electric signal and transmitted to a control system, and real-time judgment of the leakage grade is achieved. When leakage exceeds a threshold value, the system automatically triggers sealing compensation, the sealing ring is driven and pressed downwards through the compensation cavity and the pressing ring mechanism, the sealing piece is attached to the cover plate, a gap between the inflation head and the detection hole is jointly blocked by the sealing ring and the sealing piece, and measurement and control interruption caused by false leakage is prevented.

The embodiment of the invention discloses a BIM-based construction environment monitoring method and device, equipment and a medium. The method comprises the following steps: constructing a BIM environment monitoring model, wherein the BIM environment monitoring model comprises a plurality of virtual monitoring point positions; acquiring real-time monitoring data uploaded by the plurality of intelligent sensor devices; performing space matching on the real-time monitoring data and the virtual monitoring point positions according to the equipment identifiers and the sensor coordinates in the real-time monitoring data and the equipment identifiers and the monitoring point position coordinates of the virtual monitoring point positions, and determining target virtual monitoring point positions corresponding to the real-time monitoring data; for each virtual monitoring point position, risk early warning processing is carried out on the corresponding sensor value through the risk monitoring rule of the target virtual monitoring point position, and risk early warning information of the virtual monitoring point position is obtained; and displaying the BIM environment monitoring model carrying the risk early warning information through a BIM visual platform. According to the embodiment of the invention, visual early warning and monitoring of construction environment risks can be realized.

The invention discloses a fan inspection route planning method and system for an unmanned aerial vehicle carrying an intelligent sensor, and relates to the technical field of inspection planning, and the method comprises the steps: carrying out the grid processing of fan blades, and calculating the prior defect probability of each blade grid; a detection balance decision is made by using the prior defect probability and the shutdown cost; if the detection balance decision is a startup detection decision, obtaining a blade time sequence movement track of the fan blade, and configuring a time sequence target detection position in combination with a prior defect probability; the method comprises the following steps: acquiring structural data of fan blades, performing blade rotation wind field disturbance sensing, and establishing disturbance sensing factors; and executing following mode and static suspension mode decisions by using the disturbance perception factor and the time sequence target detection position, configuring an inspection route, and executing inspection management. The technical problems that in the prior art, route planning is not intelligent, the detection efficiency is low, and the influence of wind field disturbance is large in the fan blade inspection process are solved, and the technical effect of improving the inspection precision and stability is achieved.

The invention relates to the technical field of environment monitoring and intelligent sensors, and particularly discloses a soil pollutant detection and analysis system based on an intelligent sensor. The system comprises a sensor array module, a data preprocessing and fusion module, an ecological toxicity dynamic evaluation module and a decision output module. According to the invention, the biological response sensing unit capable of directly reflecting the ecological effect is integrated, and the ecological toxicity dynamic evaluation model fusing the concentration, the biological response and the environmental factors is innovatively constructed, so that direct and dynamic quantification from the pollutant content to the ecological risk is realized. According to the method, the problem that an existing assessment method is insufficient in biological correlation is solved, the risk assessment result is closer to the real ecological environment effect, and a reliable basis is provided for precise treatment.

The invention relates to the technical field of automatic control, and discloses a magnetic recloser adaptive control method and system for circuit design. The method comprises the steps of collecting multi-dimensional electrical parameters of a distribution line through an intelligent sensor array, constructing a mixed fault feature vector by utilizing a time-frequency domain fusion algorithm, separating instantaneous and permanent fault components by adopting three-stage fault recognition, obtaining fault severity level evaluation based on dynamic weight distribution, and performing fault severity level evaluation based on dynamic weight distribution. And generating a self-adaptive control instruction through grading reclosing strategy matching to execute the opening and closing operation of the magnetic recloser. According to the invention, the technical problem that the magnetic recloser cannot carry out accurate fault recognition and adaptive hierarchical control in a multi-transient fault and permanent fault mixed scene is solved.

The invention discloses an energy consumption optimization method for integrating building structure parameters. The method comprises the following steps: acquiring geometric morphology, enclosure structure, equipment configuration, personnel activity and external environment data of a building in real time by using an intelligent sensor and a building information model; based on the collected data, calculating thermal load and energy consumption distribution of the building, extracting energy efficiency performance indexes, and establishing an energy efficiency baseline model; the thermal performance of the building envelope structure is dynamically adjusted by utilizing a self-adaptive envelope structure technology so as to adapt to external climate change and internal thermal load requirements; based on the optimized enclosure structure characteristics, an energy recovery system is started, and waste heat and air flow energy in the building are converted into available energy; in combination with energy efficiency data fed back in real time, the intelligent scheduling system dynamically adjusts the operation strategy of the building equipment, optimizes the energy efficiency, reduces unnecessary energy consumption and improves the comfort level; the building energy efficiency is evaluated regularly, and it is ensured that the building continuously maintains the optimal energy efficiency under different seasons, environment changes and use requirements.