231 results about "Response sensitivity" patented technology

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 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 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 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 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 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 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.

An electronic device includes a casing, a first sound-emitting device and a second sound-emitting device. The first sound-emitting device includes a basket, a vibration system and a magnetic circuit system. The vibration system includes a diaphragm and a voice coil. The magnetic circuit system includes a magnetic bowl, a magnetic unit and an auxiliary magnetic. The first sound-emitting device further includes an upper shell and a front cavity. The first sound-emitting device further includes a first elastic damping member. The diaphragm, the magnetic bowl, the basket and the first elastic damping member are sealed and enclosed to form a first rear cavity, and the first sound-emitting device, the second sound-emitting device and the casing are enclosed to form a second rear cavity. The sound-emitting device of the present invention with good vibration damping effect, high frequency response sensitivity and better acoustic performance.

The invention discloses a biosensor responding to glutaric acid concentration, and belongs to the technical field of biochemical engineering. According to the invention, regulatory protein and promoter elements are constructed on PSC101 series plasmids to obtain the biosensor responding to glutaric acid concentration. By performing point mutation on a-10 region and a-35 region of the response promoter PgcdH and optimizing expression of regulatory protein through the promoter and RBS engineering, the response sensitivity is improved, the response range is widened, and the application prospect in the field of glutaric acid high-yield strain screening is very wide.

The invention relates to the technical field of flexible sensing, in particular to a multi-stage pressure sensor for unconstrained monitoring and a preparation method thereof.The multi-stage pressure sensor comprises a flexible substrate, the flexible substrate comprises an upper-layer fabric and a lower-layer fabric which are arranged in a spaced mode, and the upper-layer fabric and the lower-layer fabric are connected through connecting wires; the static supporting unit comprises a plurality of supporting columns which are arranged in an array mode and fixed to the flexible substrate, and the height of the supporting columns is configured to be larger than the thickness of the flexible substrate; the flexible sensing unit is adjacently arranged on the periphery of the static supporting unit and comprises a conductive wire arranged on a flexible substrate, and the conductive wire and a connecting wire form a triboelectric sensing unit; the rigidity of the static supporting unit is larger than that of the flexible sensing unit. The purpose of the present invention is to improve response sensitivity to weak dynamic signals.

The invention discloses an information processing method, device and equipment for analyte concentration monitoring and a medium, and relates to the technical field of physiological signal analysis, applied to a computer device.The method comprises the steps that an original analyte response signal set generated by an analyte sensor based on a reference physiological fluid is obtained, the signal group comprises response sensitivity, a base line and response current; performing temperature compensation on the signal group according to the current temperature fed back by the temperature sensor, and determining the concentration value of the target analyte in the reference physiological fluid by using the compensated signal group; substituting the concentration value of the target analyte in the reference physiological fluid into a target model to obtain the concentration of the target analyte in the target physiological fluid; the target model is constructed on the basis of a regulation and control mechanism of the temperature to the transport relation of the analyte and a regulation and control mechanism of the temperature to the metabolic relation of the analyte, and the concentration of the target analyte in the target physiological fluid is determined as a concentration monitoring result. By means of the scheme, the accuracy of analyte concentration monitoring is improved.

The invention discloses an intelligent fatigue prediction method and system based on recreation facilities, and belongs to the technical field of intelligent operation and maintaining.The method comprises the steps that stress-strain data, environmental parameters and acoustic emission signals of a target recreation facility in multiple operation modes are collected, and an original multi-source data set is constructed; identifying microcrack initiation time nodes and fatigue early-stage features thereof based on change point detection and spectral analysis; a fatigue response sensitivity matrix is generated by constructing a microscopic response model fused with environmental factors; predicting a microcrack propagation path by using a time recursion model, identifying a fatigue risk point, and automatically generating a maintenance suggestion; if a frequency domain abnormal signal is found, carrying out residual life reevaluation and dynamically optimizing model parameters; according to the method, early fatigue recognition, trend prediction and adaptive optimization can be realized, and the safety and maintenance efficiency of recreation facilities are improved.

The invention relates to the technical field of agricultural machinery load control, in particular to a corn harvester operation load control system and method, and the system comprises a power extraction module, a speed regulation and control module, a trend analysis module, a parameter optimization module and a load closed-loop module. According to the method, frequency characteristics are extracted in a periodic and segmented mode through header power signals, load dynamic sensing is carried out, advancing speed adjustment is more real-time and prospective, response sensitivity and stability are improved in combination with PID control, equipment stably operates at multiple crop densities and plots, the torque increment of a main shaft is monitored in real time, and wavelet transformation multi-scale analysis is carried out; load fluctuation fine-grained identification is carried out, a roller gap and power output are dynamically adjusted to optimize parameters with a minimum mean square error, frequency and torque characteristic closed-loop comparison is constructed, control self-matching and continuous self-adjustment are carried out, error minimization and trend tracking feedback are fused, the intelligence of the harvester is enhanced, and load sudden change performance fluctuation and energy consumption loss are reduced.

The invention provides an electric energy meter operation error on-line monitoring and early warning method and platform, and relates to the technical field of electric energy meter monitoring, and the method comprises the steps: applying at least two different forms of metering equivalent perturbation in the metering calculation process of an electric energy meter, building a plurality of groups of corresponding perturbation metering response results, carrying out the alignment analysis, and carrying out the early warning of the operation error of the electric energy meter. Extracting relative offset characteristics, response consistency characteristics and response sensitivity characteristics among metering results under each perturbation condition, constructing error response fingerprints, inputting the error response fingerprints into an error recognizable degree calculation model, constructing a metering error recognizable degree index, performing time sequence tracking on the metering error recognizable degree index, and calculating the metering error recognizable degree according to the time sequence tracking result. And executing metering misalignment risk state identification by using a time sequence tracking result, and outputting an online early warning signal. The technical problem that in the prior art, operation errors cannot be timely and accurately recognized and early warned in the operation process of an electric energy meter is solved. The technical effects of real-time monitoring, accurate identification and on-line early warning of the metering error of the electric energy meter are achieved.

The invention discloses a self-adaptive vacuum filling and sealing device and method, and mainly solves the problems that the existing vacuum filling and sealing technology is difficult to realize accurate adaptation and optimal adjustment of multiple types of filling and sealing adhesives, and the filling and sealing consistency and the product reliability under complex working conditions are influenced. Comprising a vacuum box, a vacuum pump, a vacuum pressure digital display meter, a control panel and an air inlet valve, wherein a liquid flow meter is arranged at a glue injection and pouring outlet in the vacuum box, and instantaneous flow data of pouring sealant is detected and fed back in real time; a refrigerating device is arranged on the side wall to inhibit local temperature rise of the two-component pouring sealant caused by heat release of a chemical reaction; a heating device is arranged at the upper part to compensate the adverse effect of environment temperature reduction on the fluidity of the pouring sealant; and meanwhile, a fusion algorithm for self-adaptive adjustment of multiple types of pouring sealants is deployed in the microprocessor, so that self-adaptive temperature balance and regulation in the pouring process are realized. According to the invention, the automation degree and the response sensitivity of the potting process can be obviously improved, and the stability and the reliability of the potting process are improved.

The invention relates to the technical field of multi-modal registration, in particular to a neurosurgery visual operation navigation system based on multi-modal fusion, which comprises a feature fusion module, an image correction module, a deformation correction module, a layer labeling module and a navigation interaction module. According to the method, a DTI image and a CT image are aligned through three-dimensional pairing, white matter fiber and temporal bone edge directions are extracted, feature integrity is enhanced, edges are corrected through a structural similarity algorithm, space mapping precision is improved, a blood vessel center line and an ultrasonic image edge point track are combined, deformation is corrected, and drift and artifacts in an operation are restrained; the structure continuity of the blood vessel and the fiber is guaranteed, the positions of the white matter fiber and the blood vessel are integrated, a consistent directional diagram layer is formed, the navigation response sensitivity is improved through three-dimensional distance evaluation, a self-matching adjustment mechanism is set, the feature fusion and correction precision is enhanced, image deviation interference is reduced, and the navigation real-time performance and the analytic performance are improved.

The invention relates to the technical field of energy management, in particular to an energy management method and system based on an AI algorithm, and the method comprises the following steps: collecting terminal load data, constructing a periodic fluctuation curve, carrying out modeling, extracting change features, generating an adjustment table, recognizing the adjustment capability in combination with a disturbance threshold value, and labeling to generate a label set; the method comprises the steps of extracting slope and fluctuation characteristics based on periodic load data, identifying terminal adjusting capacity and generating capacity labels, obtaining a current load training generation priority queue, executing adjustment to extract response data to generate logs, comparing the logs to judge whether to rearrange or not, and updating adjusting table and label set output results. The priority queue is optimized and adjusted in combination with real-time load input, response time and change data are recorded to form a log after adjustment is executed, if a task deviates, the queue is requeued, a label and an adjustment table are updated, and multi-dimensional improvement of energy scheduling accuracy, system response sensitivity and terminal resource utilization efficiency is carried out.

The invention discloses a real-time data acquisition and analysis method in industrial intelligent manufacturing, and relates to the technical field of industrial automation and intelligent manufacturing, and the method comprises the following steps: air quality monitoring equipment carries out conventional detection on the concentration of harmful gas in the environment according to a pre-configured time sequence resolution, and obtains air quality parameter information in real time; according to the invention, the response sensitivity and intelligent identification capability of the air quality monitoring equipment to instantaneous high-concentration harmful gas leakage are improved, and the monitoring limitation of traditional fixed time sequence resolution is broken through. And through fusion of feature engineering and a machine learning model, accurate judgment of dynamic change of gas is realized. After the severe fluctuation state is identified, the system dynamically adjusts the sampling frequency and starts the cache, the concentration peak value is effectively captured, meanwhile, the storage pressure is relieved, the stability and the real-time performance of the system are enhanced, and the safety protection capability of the industrial environment is improved.

The invention relates to the technical field of imaging detection, in particular to a microscopic imaging system for detecting a microlens, which comprises a light source path establishing module, an initial image position acquisition module, a peak coordinate recording module, a stability offset analysis module and an imaging quality judgment module. According to the invention, the numerical aperture normalization processing is carried out on the direction of the light passing through the specific aperture area, so that the precise control of the spatial distribution characteristic of the light beam is realized, the spatial mapping of the focus position is completed on the basis of the light in the unified direction, the direction consistency and the imaging stability in the image forming process are improved, and the imaging quality is improved. By combining continuous peak coordinate search of a light spot focusing position, a light spot change trend can be dynamically obtained, imaging deviation caused by infinitesimal displacement can be effectively distinguished, and an accurate marking mechanism for light spot stability is constructed by means of absolute difference extraction of three-dimensional offset data and Rayleigh criterion tolerance judgment. And the response sensitivity and the analysis reliability of weak instability factors in the imaging performance of the micro lens are improved.

The invention relates to the technical field of location information utilization, in particular to an automatic docking station connection method with a wireless data transmission function, which comprises the following steps: collecting channel and identity information to generate an identification beacon, screening low-overlapping channels to generate a path set, comparing fields to generate a mapping chain, and judging voltage offset to generate a switching result. And binding and marking the channels to generate a connection result. According to the invention, identity verification is realized through feature matching of the electromagnetic waveform and the access time data, the identification precision of a connection source is improved, channels are screened by combining a channel delay value and a geographic mark overlapping degree parameter, the path independence and the response sensitivity are enhanced, and a mapping chain is established based on an equipment identity field and physical region information. The method is advantaged in that connection environment perception capability is improved, power supply path switching is realized through a voltage deviation trend, electric energy output stability and response efficiency are guaranteed, channel configuration is carried out in combination with input type and frequency band structure compatibility, and signal scheduling accuracy and automatic connection adaptability are improved.