299 results about "Response sensitivity" patented technology

The invention relates to the technical field of safety monitoring, in particular to a building bearing capacity detection system based on the Internet of Things, which comprises a strain load module, an offset gradient module, a rhythm risk module, a threshold judgment module and a map reconstruction module. According to the method, the maximum strain excursion and direction reversal characteristics of adjacent time windows are analyzed and captured through the dynamic frequency excursion gradient, the response sensitivity to microcosmic bearing capacity fluctuation is improved, a strain jump rhythm map is constructed, sudden change time point time difference, amplitude difference and direction continuity parameters are extracted, and an abnormal load section is accurately positioned. The method comprises the following steps: identifying sudden strain jump events, performing cross-level rhythm map multi-dimensional comparison by applying a dynamic time warping algorithm, screening synchronous mutation nodes to generate a risk set, performing density clustering sorting based on direction consistency parameters, reconstructing a security level topological map, and improving visual spatial relevance and dynamic evolution characteristics of a structural instability path.

The invention relates to the technical field of multifunctional detection, in particular to a multifunctional metering detection system which comprises a time alignment module, an analog-to-digital conversion module, a data coupling module, a vector construction module and an event packaging module. According to the invention, the time alignment is carried out on the measurement data in multiple channels, the synchronization consistency of heterogeneous data is improved, the dynamic analysis of amplitude fluctuation characteristics is combined, the real-time adjustment of the analog-to-digital conversion resolution is realized, the response sensitivity of the system to unsteady signals is enhanced, and the reliability of the system is improved. A composite parameter sequence is constructed by using a cross-channel amplitude and trend relationship, the coupling expression capability among multiple parameters is optimized, the recognition precision of a multi-node linkage trend is improved through state vector modeling and direction difference judgment, an abnormal event path is reconstructed by adopting a time sequence, and a process record is packaged. And the detection data is converted into structured event chain data from original sampling, so that the organizability and traceability of the anomaly detection result are enhanced.

The invention relates to the technical field of state monitoring, and discloses a tower inclination state monitoring method and system. The method comprises the following steps: performing multi-field digital coupling modeling on an electric power tower to obtain a digital twinborn model and calculating an electrical load-structure response sensitivity matrix; collecting a multi-modal sensing characteristic data set including tower inclination angle time sequence data, insulator chain offset, tower body micro-vibration spectrum and foundation settlement gradient; performing time-delay correlation analysis on the electrical event and the structure response to obtain an electrical-structure mapping matrix; tensor decomposition processing is carried out through a multi-mode inclination feature fusion network, and tower inclination mode fingerprints are obtained; and carrying out abnormal separation processing on the tower foundation area, the connection area and the upper structure area, and outputting structural inclination and non-structural inclination judgment results. According to the invention, the accurate mapping relation between the electrical load and the structure response is realized, the false alarm rate is effectively reduced, and the accuracy and timeliness of early warning are improved.

The invention relates to the technical field of LED dimming control, in particular to an LED ceiling lamp control system based on dynamic light source adjustment, which comprises a brightness sensing module, a dimming decision module, a light source regulation and control module, an anomaly detection module and a brightness optimization module. According to the invention, the operation load of the lamp is dynamically measured and calculated by combining ambient light and thermal radiation data, the collaborative perception of illumination intensity and thermal energy distribution is realized, the response sensitivity to weak brightness change is improved by comparing the brightness demand with the real-time output deviation and accurately identifying the dimming trigger condition, and the response sensitivity to weak brightness change is improved by comparing the initial brightness with the feedback difference value. The brightness of a plurality of lamps can be adjusted coordinately, the spatial light environment is kept stable and consistent, overload and temperature rise abnormity can be checked and abnormal lamps can be identified by combining current fluctuation and temperature abrupt change analysis, an intervention record and reaction duration comparison mechanism is introduced into a regulation and control instruction, the response efficiency is improved, and the efficiency is improved. And brightness priority adjustment and overall illumination balance control of the key area are realized.

The invention relates to the technical field of virtual power plant scheduling, and discloses a distributed collaborative optimization scheduling method for a virtual power plant. The method includes collecting an operating state data set of a target virtual power plant. And performing distributed collaborative model construction processing on the operation state data set to generate collaborative scheduling features covering power distribution balance degree, constraint matching closeness and interactive response sensitivity. And calling a pre-trained optimization scheduling model to carry out multi-target collaborative optimization processing on the collaborative scheduling features to obtain an optimization scheduling result and a key collaborative region identifier. And based on the association relationship between the load demand fluctuation sequence and the equipment adjustment capability, performing operation environment compensation correction processing on the optimization scheduling result, and generating a corrected result. And generating a virtual power plant scheduling strategy set including a power transfer path adjustment scheme and an energy storage equipment configuration processing scheme according to the key cooperative region identifier. According to the method, distributed energy resources are effectively integrated through multi-dimensional collaborative optimization and dynamic correction.

The invention discloses a gravity type submerging net cage parameter design optimization method, and relates to the field of mariculture equipment structure optimization, and the method comprises the steps: collecting the multi-period marine environment data of a target sea area, and constructing an environment load sequence with a time-varying characteristic; establishing a three-dimensional coupling finite element model, performing disturbance analysis on a plurality of structure control parameters, and identifying a key design parameter set based on a response sensitivity function; and constructing a multi-objective optimization model including floating and sinking time control, maximum stress minimization and attitude offset constraint, performing global optimization by adopting an adaptive genetic algorithm, obtaining an optimal parameter solution, substituting the optimal parameter solution into the simulation model for dynamic response verification, and if a response index is converged to a target interval, outputting the optimal parameter solution as final design configuration. According to the method, systematic optimization of structural parameters of the submerging and surfacing net cage can be achieved, the stability and safety of the submerging and surfacing net cage under complex sea conditions are improved, and the method has high engineering applicability.

The invention relates to the technical field of image state recognition, in particular to an unhooking and rehooking AI accurate recognition grabbing system and method based on pattern recognition, in the system, node construction is conducted through contour changes, boundary difference values and gray level dynamic states of a hook assembly in an image sequence frame, edge displacement accumulation analysis is combined, meanwhile, through a graph neural network, an image sequence frame is obtained, and the image sequence frame is obtained. Cosine values and coordinate difference values between nodes are subjected to combined comparison, and a path hopping sequence is constructed, so that the response sensitivity to state abrupt change is enhanced, a key path of morphological evolution can still be stably extracted under the condition of complex background interference or local shielding, the anti-interference performance and fault tolerance of space path identification are effectively improved, and the space path identification accuracy is improved. Statistical modeling is further carried out on state rate sudden change points through a hidden Markov model, paragraph merging and invalid fragment removing operation are carried out on abnormal point segments by matching a standard state mode, a state label sequence is constructed, and accurate division of high-confidence and multi-segment continuous states is achieved.

The invention relates to the technical field of optimization control, in particular to an automatic bit feeding optimization control system which comprises a bit pressure frequency extraction module, an amplification trend recognition module, a linkage signal monitoring module, a slippage reaction judgment module and a bit feeding speed adjustment module. According to the method, a frequency set is constructed through extreme value extraction, time span and amplitude difference are combined, a bit pressure fluctuation sequence is formed, resolution of dynamic characteristics is enhanced, trend sections are screened through frequency change cumulants, quantitative recognition of fluctuation trends is achieved, well bottom tracks and torque fluctuation signals are introduced, and a period direction consistency comparison mechanism is established. The signal coupling judgment precision is improved, slip judgment logic is constructed according to the amplitude growth rate and the direction consistency, risk grade classification is completed through a strain energy density index, and multi-dimensional signal linkage recognition, trend quantitative grading and energy index evaluation are achieved; and the risk identification accuracy, the response sensitivity and the control instruction adaptability under the complex drilling working condition are remarkably improved.

The invention relates to the technical field of electric digital data processing, in particular to a data processing method for quality monitoring of a power distribution equipment production line, which comprises the following steps of: calculating a time decay dynamic fluctuation index based on acquired continuous power distribution equipment sensor data flow, and recording data points and timestamps of which variable quantities exceed a threshold value; and obtaining a time decay sampling point sequence. According to the dynamic sensing and time decay modeling mechanism of the continuous power distribution equipment sensor data flow, the time decay dynamic fluctuation index is constructed to capture numerical value abrupt change in a short period, key change points where quality abnormity possibly exists can be positioned in time, timestamps can be recorded, and the response sensitivity to data behaviors before and after event triggering is improved. Dense original data segments are actively intercepted after anomaly recognition, multi-stage discrete wavelet decomposition of time domain signals is carried out, smooth trend and multi-scale mutation features are extracted in a refined mode, and the depicting capacity of key process detail changes of the circuit breaker is enhanced.

The invention relates to the technical field of vibration test, and discloses a micro-seismic environment simulation vibration test board and a test method thereof, which realize high-fidelity simulation of multiple types of micro-seismic scenes through a multi-source composite signal generation and waveform reconstruction technology. The dynamic adjustment capability of the control strategy is improved by using disturbance compensation and an adaptive control mechanism, real-time analysis and early warning feedback are performed on the response state in the test process based on the vibration characteristic evaluation result, and an auxiliary decision basis is provided for the vibration control strategy. Besides, a hierarchical closed-loop regulation and control mechanism is logically formed based on local output regulation among multiple axes and a global control strategy, and closed-loop linkage of the control strategy, execution response, state evaluation and control correction is realized, so that the overall regulation and control precision, response sensitivity and test precision are remarkably improved; accurate simulation and stable control of a three-dimensional vibration field are realized.

The invention relates to the related technical field of high-voltage switch cabinets, in particular to a high-voltage switch cabinet health state assessment method and system, and the method comprises the steps: obtaining an equipment operation parameter sequence of a high-voltage switch cabinet, combining an operation mechanism, determining a first response data segment and a second response data segment, and constructing a state assessment matrix, and identifying an abnormal operation period and a degradation trend cluster, and performing health risk early warning on the high-voltage switch cabinet by using the health state evaluation model. The technical problems that the sensing precision of key events such as switching-on bounce and partial discharge is insufficient, the generalization ability of health state evaluation under complex working conditions is insufficient, and the accuracy of high-voltage switch cabinet health evaluation is limited are solved, and cross correlation and variation correlation of a signal simulation verification chain are taken as an enhancement mechanism, so that the accuracy of high-voltage switch cabinet health evaluation is improved. The method has the technical effects that the response sensitivity of the model to key events is improved, the health risk of the high-voltage switch cabinet is accurately evaluated in combination with an abnormal period and a degraded cluster, and the accuracy of health evaluation of the high-voltage switch cabinet is improved.

The invention relates to the technical field of construction control, in particular to an underground super-long thick and large concrete structure construction control method and system.The method comprises the following steps that through vertical displacement and strain data, trend extraction nodes are analyzed, difference sections are divided, load recognition influence areas are positioned, and structural sections are sequenced according to fluctuation increments; and adjusting cooling, supporting and loading parameters to form an intervention arrangement, and performing year-on-year adjustment based on construction data to generate feedback to complete regulation and control. According to the method, load-deformation dynamic mapping is established through time sequence association of displacement and strain data, a difference section identification mechanism is constructed based on time difference comparison, natural deformation and load surge response are distinguished, fluctuation characteristics of a load surge section are extracted to form response sensitivity ranking, and cooling parameters and a loading sequence are dynamically adjusted in combination with increment comparison. Graded regulation and control are realized, space-time dimensions of construction records and monitoring data are coupled, the crack positioning precision is improved, the predictability of deformation control is enhanced, and the regulation and control redundancy cost is reduced.

The invention provides a combustible gas leakage protection system, which is characterized in that a control module of the combustible gas leakage protection system judges whether the concentration of combustible gas is lower than a first concentration threshold value or not when receiving concentration information detected by a concentration detection module, and generates a first control instruction when the concentration of the combustible gas is not lower than the first concentration threshold value. The control module further generates a second control instruction when the combustible gas concentration is not lower than a second concentration threshold value and the flame detection module detects that no open fire exists, and generates a third control instruction when the combustible gas concentration is not lower than the second concentration threshold value and the open fire exists. And the protection module executes different protection actions according to different control instructions of the control module. According to the application, the response scheme is divided into three grades, and corresponding protection strategies are implemented according to the detection signals of different grades, so that the response sensitivity and accuracy when the combustible gas leaks can be remarkably improved.

The invention relates to the technical field of water level data processing, in particular to a water level data acquisition and processing system based on the Internet of Things, which comprises a trend identification module, a sampling frequency modulation module, a data encapsulation module, an exception elimination module and an instruction linkage module. According to the method, trend recognition is achieved by analyzing the continuous water level change trend, the sampling frequency is dynamically adjusted, so that the sampling interval is matched with the actual water level fluctuation state, the response sensitivity of water level data and the timeliness of data acquisition are improved, and a uniform packaging format is constructed by adopting a timestamp index mechanism and a bidirectional mapping structure; the integrity of a water level data structure and the convenience of index management are enhanced, sudden change breakpoints are recognized through the water level amplitude difference, data are stored in a segmented mode, the data stability and the emergency situation recognition capacity are improved, and the self-adaptive capacity of water level information processing, the maintaining level of data continuity and the coping efficiency of sudden abnormity are overall improved.

The invention provides an intelligent optical cable performance prediction method and system for a data center, and relates to the technical field of data center performance analys.The intelligent optical cable performance prediction method comprises the steps that firstly, an optical cable microstructure disturbance quantization model is constructed, and an optical cable is divided into a plurality of virtual quantization units in the longitudinal direction; each unit packages a real-time stress-strain state parameter and a response sensitivity coefficient of the basic structure unit, inputting a Brillouin scattering spectrum frequency shift data flow collected in real time into the model for disturbance source analysis, generating a physical excitation decomposition sequence, driving a virtual quantization unit to perform state evolution iterative operation according to the physical excitation decomposition sequence, and obtaining a Brillouin scattering spectrum frequency shift data flow model; a stress-strain state evolution track set is obtained, a time-space correlation network of optical cable link disturbance propagation is constructed according to the stress-strain state evolution track set, network flow characteristic analysis is conducted on the time-space correlation network, an early warning node set is extracted, the future evolution trend of the early warning node set is calculated, and serialized optical cable performance abnormity early warning information is generated. The optical cable performance abnormity can be predicted in advance, and stable operation of the data center optical cable is guaranteed.

The invention provides a bridge fracture monitoring method and system based on distributed optical fibers, and relates to the technical field of bridge monitoring, and the method comprises the steps: firstly constructing a stress response association mapping table of the distributed optical fibers and a bridge structure, and recording the information of a layout position, a sensing range parameter, a response sensitivity curve and the like; then, dynamically adjusting the acquisition frequency based on the stress response association mapping table, generating a strain and environment association data set with an acquisition frequency identifier, performing environment interference stripping processing on the strain and environment association data set, and forming a space strain deviation feature set after environment correction; the spatial strain deviation feature set and a time dimension strain data change trend are subjected to correlation modeling, a response sensitivity curve is combined to generate a space-time strain evolution feature with a sensitivity weight, an abnormal evolution section is marked through a multi-fiber cooperative verification mechanism, and a bridge fracture monitoring result containing a verification result is generated. Therefore, the bridge fracture condition can be accurately and comprehensively monitored.

The invention belongs to the technical field of data processing, and particularly relates to a wind disc fault monitoring method and system, and the method comprises the steps: collecting wind disc operation data and environment parameters in real time, constructing an environment response sensitivity tensor, calculating and eliminating environment-induced supply and return water temperature difference deviation, and obtaining a de-environmental operation feature vector. And in combination with the static hydraulic topology and the dynamic behavior difference, generating a dynamic weight between the wind discs, constructing a self-evolution covariance matrix, and realizing dynamic optimization calculation of the Mahalanobis distance. And fault type discrimination is carried out based on the spatial topological relation of the abnormal wind disc, monomer blockage and system-level blockage are accurately distinguished, and intelligent early warning is realized. The method can effectively inhibit the environment disturbance false alarm, and improves the fault positioning accuracy.

The invention relates to the technical field, in particular to an unmanned inspection method and system for a switch cabinet, and the method comprises the following steps: building an incidence relation between signal nodes through a graph neural network, improving the time sequence consistency of multi-source signals, enabling instrument reading and temperature and humidity signals to form a hierarchical structure under a unified time frame, and improving the accuracy of inspection. The logic association of state layering is enhanced, continuous sampling sequence features are extracted through a long-short-term memory network, the response sensitivity and time prediction capability to abnormal signal abrupt change are improved, amplitude matching of temperature difference and current signals is achieved under the same time index, early abnormal nodes are accurately recognized, and a dynamic positioning sequence is established. Through joint tradeoff of distance and energy consumption, path lengthens and energy peak values are reduced, task throughput and equipment availability are improved, timing sequence capture and trend judgment capabilities are enhanced in an anomaly recognition layer, and accuracy and continuity of inspection decision and reliability of safety assessment are integrally improved.

The invention relates to the technical field of gas component analysis, in particular to an air quality detection method based on multifunctional detection, which comprises the following steps: generating an optical characteristic time sequence table through a polarized light ratio, judging pollutant types, extracting a concentration inflection point and a sudden change rate, merging and identifying emergencies and concentration jump, and outputting an air quality detection result. According to the method, the ratio of the reflected light intensity in the primary scattering direction to the reflected light intensity in the secondary scattering direction is recorded, an aerosol optical characteristic time sequence table is constructed, and optical differences are extracted to achieve fine classification of pollutants; by combining scattering angle priority judgment, pollution attribute recognition precision is improved, concentration inflection points are positioned through time sequence difference, sudden change behaviors are recognized through rate analysis, response sensitivity to dynamic pollution changes is enhanced, pollution structure classification and rate statistics are combined, comprehensive interpretation capacity of pollutants under time sequence and attribute two dimensions is enhanced, and the method has the advantages of being high in accuracy and high in reliability. And high-timeliness and high-resolution air quality monitoring can be realized.

The invention relates to the technical field of production safety and intelligent monitoring, in particular to a cosmetic production line safety monitoring method and system based on data analysis, and the method comprises the steps: obtaining real-time operation parameters through a multi-source data collection module, and generating a production environment dynamic mapping graph; extracting a fluctuation trend and abnormal distribution characteristics, and generating a region data adjustment instruction set; adjusting a signal acquisition strategy, and constructing a regional signal alignment state table; identifying the signal intensity variation, and forming a regional signal adjustment mark graph; and finally outputting a monitoring area state evaluation value list. According to the method, the signal fluctuation expression precision and the dynamic regulation and control capability can be remarkably improved, the adaptability and the response sensitivity of safety monitoring in a complex production environment are enhanced, and high-precision local compensation and equipment state quantitative judgment are realized.

The invention relates to the technical field of robot teaching, and discloses a robot infant teaching system and device based on somatosensory interaction. According to the method, a somatosensory data acquisition module acquires skeleton point motion time sequence data and an interactive behavior video stream of a target child through a depth camera and an inertial sensor; the multi-modal feature fusion module is used for extracting dynamic track features from the skeleton motion data, analyzing behavior semantic features of the video stream, and generating a fusion interaction feature set through cross-modal association fusion; the interaction sensitivity calculation module is used for calling a pre-training dynamic weighting network model to carry out attention distribution on the fusion features and generating a teaching interaction sensitivity coefficient set containing different teaching link response sensitivity distributions; the abnormal behavior tracing module is used for generating a teaching behavior abnormal tracing result for indicating an abnormal source link and a deviation type; and the interaction strategy generation module generates a self-adaptive teaching strategy according to the traceability result and sends the self-adaptive teaching strategy to the robot motion control unit.

The invention belongs to the technical field of electronic information, and provides a method for optimizing the stability of a photoelectric information conversion assembly in a severe environment, which comprises the step of optimizing the stability of the photoelectric information conversion assembly in the severe environment. The method comprises the following steps of: synchronously acquiring environment parameters such as temperature, humidity and illumination and equipment current data in a high-temperature and high-humidity environment to construct a multi-dimensional time sequence group; analyzing the coupling degree of each environment parameter and the current sequence, and after locking the high coupling relationship between illumination and current, judging whether the current response has delay or not when the illumination suddenly changes; if the delay exists, further analyzing the current response sensitivity, if the sensitivity deviates from expectation, constructing an illumination-current prediction model, and calculating the current regulation quantity in advance by predicting the illumination abrupt change amplitude to realize pre-regulation; and finally, collecting the adjusted data iteration optimization model, and improving the dynamic response performance and stability of the assembly in a complex environment.

The invention relates to the technical field of intelligent production control, in particular to an aviation aluminum cutting fluid production control method and system.The aviation aluminum cutting fluid production control method comprises the following steps that an equipment number and other generation state series are extracted, changes are judged, control grades are adjusted, an intensity grade table is generated, a viscosity rate is calculated, a temperature difference change result is generated, and an injection opportunity is set to generate a rhythm ratio set; and judging a flow velocity track to generate a control result. According to the method, the state string is generated by collecting key identification data, periodic comparison is carried out, state change is rapidly identified, dynamic adjustment is achieved by combining trend deviation of control parameters, response sensitivity is improved, and regulation and control accuracy is improved through linkage of the viscosity change rate and temperature control response; the liquid injection rhythm coordination is enhanced through time difference value screening and flow velocity track intersection judgment, continuous and stable liquid supply is guaranteed, the whole scheme is based on multi-parameter trend analysis, a data-driven closed-loop regulation and control path is constructed, and efficient control and cooperation of the cooling and lubricating state are achieved.

The invention provides a charging facility and electric power spot transaction response method, which comprises the steps of generating a dynamic response sensitivity matrix by collecting electric power spot price data and fluctuation indexes in combination with historical charging behavior characteristics of a charging facility, and constructing a charging load period transfer strategy table. And converting the strategy table into a power adjustment instruction set, issuing the power adjustment instruction set to the charging pile, calculating a power execution deviation and updating a user response elastic coefficient weight factor library. Charging load time period distribution can be optimized, the charging cost is reduced, the operation safety of a power grid is guaranteed, and the operation efficiency and economic benefits of charging facilities are improved.

The invention relates to the technical field of semiconductor material photoelectric detection, and discloses a whole-course calibration method of a microwave photoconductive attenuation tester, which comprises the following steps: steady-state optical calibration: establishing reference mapping from light intensity to absolute carrier concentration; performing sweep frequency perturbation measurement at each concentration working point to obtain multiple groups of complex response data; separating an instrument transfer function from sample differential response sensitivity by adopting a system identification algorithm; an integral differential sensitivity reconstruction curve is converted into an absolute calibration model by using an anchor point; starting closed-loop feedback control to lock a specific phase, and verifying the model; and outputting an absolute calibration model and an instrument transfer function as a static reference and a dynamic operator. According to the invention, the absolute concentration anchor point is established through steady-state optical calibration, integral reconstruction is carried out on the differential response sensitivity decoupled by dynamic frequency sweep measurement, and finally, the anchor point is utilized to determine the integral constant, so that the accuracy and reliability of carrier concentration quantitative analysis are improved.

The invention relates to a fatigue limit rapid prediction method based on infrared thermal image energy dissipation characteristics. The method comprises the steps of sample preparation and pretreatment, fatigue loading stress level upper limit determination, infrared fatigue test and temperature rise data acquisition, thermal image data processing and temperature rise feature extraction, energy dissipation model construction, fatigue limit value prediction, error analysis and method verification. The method comprises the following steps: monitoring steady-state temperature rise rates under different stresses through a thermal infrared imager, establishing a relation curve between a dissipation rate and a stress amplitude, predicting a fatigue limit based on a least square method, and comparing the fatigue limit with an S-N test result for verification. Compared with a traditional method, the method has the advantages of high prediction precision, high response sensitivity, low loss, short test period and the like, and is particularly suitable for rapid fatigue limit prediction and strengthening effect judgment of the high-temperature alloy material subjected to laser-assisted ultrasonic rolling strengthening.

The invention discloses a pulse neural network conversion method based on output reordering and threshold optimization. The output reordering module is introduced to carry out weighted reordering on the output pulses, high feature information is transmitted preferentially, and the problem that deep features of the pulse neural network are gradually attenuated under the condition of short time steps is effectively relieved. According to the method, a threshold optimization mechanism is adopted, the distribution threshold of neurons without pulses is improved in time, the low-value input response is inhibited, and the response sensitivity and selectivity of the pulse neural network to high-value features are improved. According to the method, on standard data sets such as CIFAR-10, CIFAR-100 and ImageNet, the classification accuracy of the SNN model is remarkably improved, particularly, high precision is kept in a short time step, and the method is superior to existing similar technologies. The stable reasoning performance can still be maintained under the condition that noise interference is added, the robustness and practicability of the method in a complex environment are verified, and the adaptability is high.

The invention discloses a preparation method of a QCM formaldehyde gas sensor based on a UiO-66-NH2 / V2CTx composite material, and belongs to the technical field of nano gas sensors, V2CTx MXene is prepared by adopting an etching method, the UiO-66-NH2 / V2CTx composite material is synthesized in situ through a solvothermal method to obtain the UiO-66-NH2 / V2CTx composite material, then a composite gas-sensitive film is prepared on the surface of a QCM crystal oscillation sheet by utilizing a spraying method, and the UiO-66-NH2 / V2CTx composite material is used for preparing the QCM formaldehyde gas sensor based on the UiO-66-NH2 / V2CTx composite material. The QCM formaldehyde gas sensor based on the UiO-66-NH2 / V2CTx composite film is manufactured, the obtained sensor has good response sensitivity, the formaldehyde response / recovery speed is high, and the response sensitivity is obviously superior to that of a single material film.

The invention relates to the technical field of photovoltaic power generation monitoring, in particular to a power generation monitoring method and system for a photovoltaic panel, and the method comprises the steps: collecting the electrical parameters and environmental parameters of the photovoltaic panel in real time, and determining a preliminary voltage adjustment step length according to the electrical parameters and equivalent impedance characteristics of the photovoltaic panel; according to the change amplitude, the change rate and the historical stability of the illumination intensity and the temperature at the current moment, an environment influence factor is constructed, the initial voltage regulation step length is subjected to nonlinear correction through the environment influence factor, and the self-adaptive constraint and relaxation regulation of the step length on the severe environment change are realized. And on the basis, carrying out multiple symmetric disturbance operations on the voltage at the current moment, judging the adjustment direction through power difference, and carrying out dynamic adjustment in combination with the step length until the maximum power point is determined. The method improves the accuracy, stability and response sensitivity of photovoltaic power generation monitoring in a complex environment.

The invention relates to the technical field of disaster early warning, in particular to a soft soil area foundation pit inrush disaster early warning soft soil area foundation pit inrush monitoring and early warning system and method, and the system comprises an osmotic pressure fluctuation monitoring module, a deformation trend extraction module, an inrush disaster identification module, a threshold dynamic correction module and a disaster early warning output module. According to the method, the pore water pressure sequence and the free water surface height sequence are vertically grouped according to the depth of the measuring points, and the standard deviation of the water head difference values is calculated, so that the local water pressure disturbance abnormal region can be effectively identified, and the difference value fitting analysis of multi-source monitoring sequences such as horizontal displacement, structural strain and deformation curvature is combined; asynchronous response characteristics between a soil body and a support structure deformation rate are accurately extracted, a normalized disaster-inducing factor matrix is constructed based on multiple mechanical indexes, and the early warning level is determined through the combination of continuous duration and multi-factor overrun, so that the timeliness and classification accuracy of inrush disaster identification are improved, and the early warning efficiency is improved. And the response sensitivity and the field adaptability of the early warning system are enhanced.