1024 results about "Observation point" patented technology

The invention discloses a matrix layout illumination state detection method, device and equipment, and the method comprises the steps: collecting and analyzing a power supply bus current waveform and a single lamp feedback signal, and generating a node feature cluster; extracting a power consumption difference value of adjacent lamps, identifying an abnormal observation point and constructing a fault diffusion chain; marking a timestamp for the fault diffusion chain, determining a fault source, and marking current by injecting characteristics to delimit an influence domain; deploying monitoring anchor points at the boundary of the influence domain, collecting the light color parameter variation and predicting the residual life; extracting the oscillation envelope of the starting current peak, evaluating the health degree of the driver and generating a maintenance density field; and a three-dimensional detection field is constructed by fusing multi-dimensional detection data, singular points are searched, and coordinates of a fault lamp are accurately analyzed through fault positioning pulses. According to the invention, three-dimensional monitoring from electrical characteristics and optical performance to driver states is realized, a single lamp fault can be accurately positioned, a life trend can be predicted, fault propagation can be tracked, and technical support is provided for intelligent operation and maintenance of a large-scale illumination system.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

A slope monitoring method based on optical flow estimation and binocular vision belongs to the technical field of deformation monitoring and measurement, and adopts the technical scheme that a binocular stereoscopic vision system is built, and camera acquisition parameters are calibrated; transmitting the image to a server in real time through the Internet of Things; distortion correction and stereo matching are carried out to obtain a depth map; monitoring points are manually or automatically arranged on the corrected image, and dense optical flow estimation tracking pixel point movement is carried out; world coordinates of observation points and movement conditions of each frame are obtained, and displacement changes of slope monitoring points are obtained by combining coordinates before and after displacement; and obtaining a displacement rate by combining monitoring time and displacement, judging slope safety, obtaining a displacement track, and performing linear interpolation to obtain a slope deformation cloud picture, thereby realizing the monitoring purpose. The slope displacement monitoring method has the advantages that slope monitoring can be carried out more flexibly and efficiently, the safety of workers is improved, the slope monitoring cost is reduced, real-time slope displacement monitoring is achieved, and the intensive degree of slope monitoring points is greatly improved.

The invention relates to the technical field of geophysics, and particularly discloses a comprehensive inversion method and system based on bidirectional magnetic susceptibility weighted constraint, and the method comprises the steps: dividing an underground three-dimensional space, and building two different cuboid models; determining a total magnetic field observed on the ground and gradient anomaly thereof based on the observation point coordinates and the cuboid model position, and establishing a kernel function matrix; determining an inversion objective function reflecting the relationship between the total magnetic field and the gradient anomaly thereof and the physical property parameters; adding the bidirectional magnetic susceptibility weighted constraint function into an inversion objective function; the optimal solution of the comprehensive inversion target function is solved based on the conjugate gradient method, the high-level resolution capability of the magnetic gradient data and the deep detection advantage of the total magnetic field data are fused, the precision of fine depiction of the superficial anomalous body boundary and the reconstruction precision of the deep field source structure are remarkably improved, a vector cross multiplication regularization constraint mechanism is introduced, and the method has the advantages of high resolution and high resolution. The inversion multiplicity under the complex geological condition is greatly inhibited, and the reliability of the model result and the geological rationality are enhanced.

The invention provides a transformer substation unmanned aerial vehicle three-dimensional route real-time planning and autonomous obstacle avoidance system, and relates to the technical field of non-electrical variable control or adjustment systems. Through cooperative work of a navigation mode switching module and a near-field navigation control module, an electric field of equipment is used as a natural navigation beacon; and high-precision autonomous flight is realized in a strong electromagnetic interference area with RTK signal failure, and the dependence on external positioning is eliminated, so that the inspection blind area is eliminated. Meanwhile, a fusion environment map containing multi-physics field information is constructed, the value of observation points is scientifically quantified according to waypoint inspection efficiency indexes, and a path planning algorithm aims at optimizing a comprehensive target fusing energy consumption, risks and inspection efficiency. The design not only ensures that the unmanned aerial vehicle can safely get close to the core equipment, but also enables the planned route to be the most valuable path rather than the shortest path, and finally fundamentally improves the safety, the operation efficiency and the data validity of substation inspection.

The invention discloses a real-time dynamic trajectory tracking method and system for millimeter wave radar gesture recognition, and relates to the technical field of gesture recognition tracking, and the method comprises the steps: receiving an echo signal reflected by a gesture, extracting a potential target point cloud, and carrying out the clustering generation of a gesture point cloud sequence; establishing a multi-modal motion model library, dynamically selecting an optimal motion model by adopting graph matching, and generating a prediction state in combination with a gesture point cloud sequence; on the basis of a Poisson multi-Bernoulli hybrid filtering framework, according to the signal-to-noise ratio and spatial distribution of the current gesture point cloud sequence, dynamically adjusting the observation weight, optimizing the observation point cloud, and carrying out optimal association by combining Mahalanobis distance with dynamic time warping; a multi-hypothesis tracking strategy is adopted to maintain trajectory hypothesis, an optimal trajectory is selected through a trajectory scoring mechanism, and Kalman filtering smoothing processing is performed on the optimal trajectory. According to the method, high-precision and low-delay tracking of gesture motion is realized, gesture habits of different users and complex environment interference can be adapted, and meanwhile, relatively high track precision is kept.

The embodiment of the invention provides a multi-element deep learning correction method and device for numerical mode forecasting. The method is applied to the technical field of data processing, and comprises the following steps: marking observation points, and performing loss function construction on the positions of the observation points to obtain a model optimization target; carrying out iterative updating on model parameters, and carrying out training process optimization by adopting a dynamic learning rate adjustment strategy to obtain a forecast field correction model; and applying the prediction field correction model to target numerical mode prediction data acquired in real time, performing prediction effect evaluation, and performing standardization processing and anti-standardization processing to obtain a correction prediction result. According to the method, the information extraction capability of a deep learning visual algorithm model is utilized, fine deviation correction of data of a numerical mode forecast space grid by meteorological station observation data is completed, meanwhile, multiple types of meteorological elements are fused, the deep learning model is utilized to learn mutual influences and restrictions between related elements, and the prediction precision of the numerical mode forecast space grid is improved. And finer deviation correction is completed.

The invention discloses an oil reservoir production dynamic prediction method fusing discrete gradient information, and belongs to the technical field of oil reservoir development and artificial intelligence crossing, and the method comprises the steps: building a heterogeneous oil reservoir oil-water two-phase flow numerical simulation data set based on a numerical simulation method; designing a double-branch network structure and extracting spatial and physical characteristics of input field data in parallel, wherein the spatial and physical characteristics comprise a main characteristic coding branch and a differential operator branch; designing a backbone network to carry out deep nonlinear modeling; an efficient pressure and saturation field prediction neural network model is constructed based on a double-branch network structure and a backbone network, in a model training stage, spatial region observation points of part of time steps are used to participate in data item loss calculation, and meanwhile, physical control equation residuals are introduced into all time steps and a whole space to serve as physical loss items; a trained efficient pressure and saturation field prediction neural network model is obtained, and high-precision prediction of a full-time-sequence pressure field and a saturation field is achieved.

The invention discloses a dual-band InSar and GNSS fused high-precision three-dimensional deformation adaptive monitoring method, and the method comprises the following steps: constructing a dual-band observation equation, and decomposing the dual-band observation equation to obtain a dual-band InSAR resolving residual error; calculating the comprehensive weight of each GNSS observation point through an entropy weight method, selecting a plurality of GNSS observation points with high comprehensive weights as an optimal GNSS reference point combination, and converting the three-dimensional deformation observed by the optimal GNSS reference points into an LOS direction; expanding a two-dimensional radial basis function to a three-dimensional radial basis function, optimizing the number and distribution of the three-dimensional radial basis function according to the residual drop rate in the expansion process, and performing basis function encryption on the key area by combining the LOS direction result of the reference point; an original two-dimensional state equation in a Kalman filtering algorithm is expanded to be three-dimensional, meanwhile, a GNSS three-dimensional observation value serves as a strong constraint condition of the Kalman filtering algorithm, and a final three-dimensional deformation rate field is output through iteration and 3D-Va analysis.

The invention discloses a natural resource supervision method based on three-dimensional GIS scene and video fusion, and relates to the technical field of intelligent natural supervision. Comprising the steps of collecting a video stream through a preset observation point, extracting a continuous frame sequence, and calling a space parameter set; performing dynamic region detection on the video frame sequence to generate change feature data; mapping the change characteristic data into a topological structure to be updated based on the projection matrix; calling a pre-constructed three-dimensional GIS scene model, and positioning an update area; according to the type identifier of the change feature data, executing an operation of deleting the associated geometry or constructing and fusing a new geometry; adding a state attribute label to the updated geometry, and storing an update model; the resource change rate is calculated, and when the change rate exceeds a preset industry threshold value, an early warning instruction is generated and pushed to the control terminal. And the space accuracy, the model updating efficiency and the early warning reliability of resource change identification in a complex natural scene are remarkably improved.

The invention discloses a 3D GS cultural relic digital reconstruction method and system based on a block chain, and the method comprises the steps: collecting the RGB image data and depth perception data of a cultural relic, eliminating the influence of different shooting conditions through an illumination separation processing technology, and building a standardized image data set; recognizing a surface area suitable for reconstruction based on image analysis, determining feature point distribution by using kernel density estimation, and generating initial three-dimensional representation through Gaussian ellipsoid fitting; performing gradient calculation and feature extraction on the depth data, and combining with Gaussian representation to form a geometric constraint mechanism; self-adaptive encryption based on visual importance is executed for a sparse region, and a layered rendering effect is achieved through opacity parameter adjustment; the rendering characteristics and the conversion relation of different view angles are analyzed, key observation points are determined through stability analysis, and a smooth multi-view-angle display sequence is constructed; and integrating multi-view rendering information to generate volumetric representation, and completing right confirmation of the high-quality three-dimensional digital model through digital signature.

A light absorption probe includes an arm segment, a main segment located at one side of the arm segment, a testing segment connected to another side of the arm segment, and a light absorption coating layer. The main segment has a soldering end portion and an extending end portion respectively located at two opposite sides thereof along a predetermined direction. The testing segment has an upright shape along the predetermined direction and includes a pinpoint portion and an upright portion that connects the pinpoint portion and the arm segment. The light absorption coating layer covers the upright portion, and the pinpoint portion is exposed from the light absorption coating layer. Through the light absorption coating layer, the testing segment only forms an observation point at the pinpoint portion in an observation process of a detection apparatus.

The invention provides an illumination rendering method for a game scene and electronic equipment, and belongs to the technical field of global illumination. According to the method, the voxelization processing area is determined based on the observation point, only voxelization processing needs to be carried out on the scene in the limited range around the observation point, the calculation complexity can be greatly reduced, and memory resource occupation is reduced; tasks are distributed through a main thread, and a plurality of background threads execute voxelization processing in parallel, so that the utilization rate of a GPU can be improved, and blockage of the main thread can be avoided; by determining the processing priority of the voxel blocks, the voxel blocks which greatly influence the picture effect can be processed preferentially, real-time updating of the illumination effect of the central area of the visual field of a player is ensured, and delay or distortion of the key area of the picture is avoided.

The present invention provides a method, device, and system for tracing the source of atmospheric pollutant emissions. The method comprises: obtaining tracer observed concentrations and pollutant observed concentrations at multiple observation points within a predetermined time range; determining the atmospheric diffusion coefficient and ground reflectance coefficient of a target Gaussian model based on the tracer observed concentrations at the multiple observation points; generating an initial chromosome population, and determining the fitness of each initial chromosome in the initial chromosome population based on the atmospheric diffusion coefficient, ground reflectance coefficient, the pollutant observed concentrations at the multiple observation points, and the target Gaussian model; determining a target chromosome population based on the fitness of each initial chromosome, and determining the emission source location and / or emission rate of the target pollutant based on the target chromosome population. Embodiments of the present invention can effectively improve the accuracy of tracing the source of atmospheric pollutant emissions.

The invention relates to the technical field of illumination angle adjustment, in particular to a tea table illumination angle adjustment control method and system, and the method comprises the following steps: obtaining optical reflection characteristic data of a desktop area, three-dimensional geometric data of an article and user observation point information, and constructing a space model for illumination evaluation; when it is monitored that the desktop environment changes, the changes include that an object enters an unstable state, whether the illumination state touches a preset judgment threshold value or not is evaluated based on the current light source projection angle, and the judgment threshold value comprises a glare threshold value, an illumination uniformity threshold value and a secondary reflection avoidance threshold value; if any judgment threshold value is touched, re-calculation and adjustment of the current light source projection angle are started; and if any judgment threshold is not touched, the current light source projection angle is maintained. The problems of glare, uneven illumination, secondary reflection and the like are effectively solved, and the visual comfort of a user is improved.

The invention relates to the technical field of harmonic intelligent positioning, and discloses a coupling harmonic intelligent positioning method and system based on multi-modal fusion, and the method comprises the steps: obtaining the voltage amplitude time sequence data of each observation point, constructing a frequency distribution characteristic curve, and extracting a frequency center and a broadening parameter; parameter updating is realized in combination with priori information of power grid impedance, and an adaptive frequency analysis window is constructed on this basis to extract energy distribution characteristic quantity; the reliability weight of an observation point and the time sequence weight of harmonic source equipment are further introduced, and the dynamic characteristics of the space credibility and the operation state are comprehensively reflected; and finally, taking the two types of weights as weighting factors, carrying out weighted calculation on the harmonic propagation model deviation of the candidate nodes to obtain a comprehensive residual value, and positioning the node with the minimum comprehensive residual value as a coupling harmonic source node, thereby remarkably improving the accuracy of harmonic source positioning in a complex power grid environment.

The invention relates to the technical field of wind field construction, and provides a low-altitude three-dimensional real-time wind field construction method and system based on unmanned aerial vehicle data inversion, and the method comprises the steps: collecting the multi-dimensional data of a flight attitude, a power system, navigation positioning and the like in real time through an unmanned aerial vehicle group in the process of executing a distribution task; and extracting key indexes such as attitude change features, control response deviation features, energy consumption features and the like from the data, inputting the features into a deep neural network with a physical model as a constraint to carry out wind field inversion training, and finally carrying out multi-scale spatio-temporal interpolation on discrete observation points through a Gaussian process regression algorithm fused with the physical constraint. And generating continuous three-dimensional wind field distribution meeting the law of conservation of mass. Dynamic wind field monitoring under urban complex terrains is realized by utilizing the characteristics of high-frequency and wide-coverage operation of the unmanned aerial vehicle, and the problem of insufficient observation data of a traditional weather station in a building dense area is effectively solved.

The invention comprises a method for controlling a mobile service robot for monitoring the movements of a person, comprising identifying the person, associating the identified person with a stored monitoring profile, capturing the person's movements by means of a motion detection sensor, generating observation points for the captured person, evaluating the captured movements of the person, retrieving position data as coordinates of a map, and automatically outputting information to the captured and identified person via an output unit of the mobile service robot.

The invention discloses a new energy power system transient instability tracing method, device and equipment and a storage medium, and is used for solving the technical problem that a traditional simulation method is difficult to accurately judge transient stability of a power system, especially local transient stability. The method comprises the following steps: determining a traditional observation point of a target power system and a traditional observed quantity corresponding to the traditional observation point; performing fault-free simulation on a target power system by adopting the traditional observed quantity to obtain a simulation model; performing fault simulation on the target power system according to the simulation model to obtain a plurality of fault observation quantities corresponding to the traditional observation points; performing transient stability judgment according to the fault observed quantity; when the transient stability judgment result is unstable, obtaining new energy station observation quantity in the target power system; and determining a transient instability disturbance source according to the fault observation quantity and the new energy station observation quantity.

The embodiment of the invention provides a laser radar wind field inversion method based on a filtering speed volume processing algorithm, and is applied to the technical field of laser radars. The method comprises the following steps: obtaining observation data base data of each voxel of laser radar wind field inversion, the observation data base data comprising signal-to-noise ratios at observation points in the voxels for calculating an average signal-to-noise ratio in the voxels; when the average signal-to-noise ratio is greater than or equal to a preset signal-to-noise ratio switching threshold value, inverting a three-dimensional wind vector in the voxel based on a velocity volume processing algorithm; and otherwise, inverting the three-dimensional wind vector in the voxel based on a preset filtering speed volume processing algorithm. In this way, the speed volume processing algorithm can be adopted to invert the three-dimensional wind vector for the inversion of the near-field wind field data with the high signal-to-noise ratio so as to improve the calculation efficiency, and after the signal-to-noise ratio in the far field is reduced to the preset signal-to-noise ratio switching threshold value, the three-dimensional wind vector is inversed based on the preset filtering speed volume processing algorithm, so that the calculation efficiency is improved. Therefore, the laser radar wind field inversion precision is improved.

The invention provides an intelligent gas pipe network dynamic inspection path planning method, which is applied to a central control platform and comprises the following steps: acquiring pipeline fixed monitoring equipment data, external environment data, intelligent driving inspection vehicle state data and inspection data in real time in an inspection process according to an initial inspection path; if the inspection data does not include newly added hidden danger point information, dynamically updating an inspection path according to pipeline fixed monitoring equipment data, external environment data and state data of the intelligent driving inspection vehicle, and issuing the inspection path to the intelligent driving inspection vehicle; and if the inspection data comprises newly added hidden danger point information, generating an optimal reinspection path and a reinspection observation point position by taking the newly added hidden danger point as a center according to the external environment data and issuing the optimal reinspection path and the reinspection observation point position to the intelligent driving inspection vehicle, and after the intelligent driving inspection vehicle finishes reinspection of the hidden danger point, recovering the inspection path before reinspection.

The invention discloses a steady-state boundary convection-dispersion model parameter acquisition method based on a variable separation method, and the method comprises the steps: constructing a convection-dispersion mathematical model under a one-dimensional finite-scale steady-state boundary condition, and carrying out the dimensionless processing of an original mathematical model through a dimensionless factor; secondly, analyzing and solving the new mathematical model after nondimensionalization by adopting a separation variable method, systematically deriving an analytical solution expression of a one-dimensional steady-state boundary convection-dispersion model in combination with a groundwater dynamic theory and a solute transport mechanism, and constructing a standard curve capable of reflecting different permeability parameters and solute transport related parameters; an indoor one-dimensional convection-dispersion test is carried out, water level and solute concentration measured data at different observation points are obtained, and permeability parameters and solute transport related parameters of a test medium are efficiently obtained through wiring. According to the method, the accuracy and adaptability of the convection-dispersion model in the aspect of parameter identification under the limited scale condition are enhanced; and operation is simple, parameter identification quantity is large, and precision is high.

The application discloses a kind of bridge tower segment attitude intelligent prediction method and system based on multi-point cooperative positioning, method includes: S100, in the two adjacent sides of the segment that has been installed, select multiple observation points, using laser reflector obtains the space coordinates of observation point, using multiple total station instruments cooperates measurement;S200, based on rigid body transformation mathematical model, the attitude change prediction of segment to be installed, using rotation matrix and translation vector calculates the optimal attitude of segment to be installed, too much point redundant information optimizes attitude estimation, controls cumulative error;S300, BIM modeling is carried out to bridge tower structure, and high-precision three-dimensional rendering is carried out, complex monitoring data is converted into intuitive graphical interface, and segment state is shown in real time;S400, according to data comparison, combined with error analysis, if the error is larger than design data, then corresponding adjustment is carried out;S500, repeat the above steps, until all bridge tower segments are accurately positioned and installed. The precision and construction efficiency of bridge tower segment erection are improved.

The invention relates to an article searching method and device for a complex indoor environment, and the method comprises the steps: dividing objects in the complex indoor environment, obtaining environment articles with relatively fixed positions and dynamic articles with uncertain positions, building a semantic relation graph according to the prior relation between the environment articles and the dynamic articles, and searching the objects in the complex indoor environment according to the semantic relation graph. The search efficiency is improved by utilizing a prior relationship; a semantic layer map containing scene and environment article information is established based on the semantic relation graph, and the semantic layer map provides semantic understanding of the environment, so that the robot can recognize different scenes and articles; constructing a topological layer map containing the optimal observation point of the article and the scene connection point; the topological layer map provides structural information of the environment, so that efficient navigation and search are facilitated; when the robot detects a target object in the complex indoor environment, path planning is carried out according to the semantic layer map, the topology layer map and the semantic relation map, and a global path of the target object is obtained; therefore, path searching and rapid and accurate article positioning are realized.

The invention provides a method and a device for determining an optimal vegetation index of spartina alterniflora. The method provided by the invention comprises the following steps: acquiring hyperspectral image data of spartina alterniflora; determining a target wave band corresponding to each vegetation index based on the spectral characteristics of the hyperspectral image data in combination with the spectral characteristics of various vegetation indexes; for each observation point, respectively calculating each vegetation index under the observation point based on the reflectivity information under the target wave band; acquiring field sampling data of the spartina alterniflora; the field sampling data comprises vegetation coverage data of spartina alterniflora under a plurality of field sampling points; for each observation point, matching the vegetation index under the observation point with the vegetation coverage data under the corresponding field sampling point, and establishing a matching relationship; and for each vegetation index, calculating the correlation between the vegetation index under each observation point and the vegetation coverage under the corresponding field sampling point based on the matching relationship, and screening the optimal vegetation index based on the correlation.

A robot grabbing method based on a geometric topology constraint field and adaptive calibration comprises the following steps: constructing a manifold neighborhood enhancement field based on a target object prior point cloud; carrying out feature coding on an input real-time observation point cloud and a prior point cloud template, and embedding observation features into a manifold neighborhood enhancement field to realize geometric alignment; aiming at the RGB image and the real-time observation point cloud, establishing a preprocessing and symmetry sensing mechanism of a multi-modal feature; constructing a geometric feature re-calibration module, performing adaptive weighting on feature response through a channel attention and space attention mechanism, and performing adaptive calibration on deviation between observation features and prior manifold features; the 6D pose parameters of the target object are regressed based on the re-calibrated global features, and a corresponding geometric consistency measurement value is output; and according to the consistency measurement result, the pose result is subjected to iterative correction in the reasoning stage, and a robot grabbing instruction is output. According to the method, the space postures of different objects can be accurately modeled, and the grabbing precision of the robot in a complex environment is improved.

According to the dynamic correction method and device for the magnetic drive conveying production line, the electronic equipment and the medium, the magnetic drive conveying production line comprises a rotor, a machining unit and a mobile observation unit arranged on the rotor, and the method comprises the steps that the rotor is responded to move to a preset observation point along a preset observation path; driving the mobile observation unit to acquire a plurality of target equipment images of the processing unit; target feature points of the processing unit in the multiple target equipment images are determined through a preset target detection model; according to the target feature point and a preset coordinate conversion calibration parameter, calculating an actual three-dimensional attitude of the processing unit; comparing the actual three-dimensional attitude with a standard attitude template to obtain attitude deviation data; based on the posture deviation data, deviation correction parameters are generated, and the machining unit is corrected according to the deviation correction parameters. According to the invention, full-coverage monitoring and real-time dynamic correction of the production line processing unit can be realized by using the mobile observation unit carried on the rotor.

The invention relates to the technical field of settlement monitoring, in particular to a method for predicting a building settlement trend value, which comprises the following steps of: testing the settlement amount of a building in each period through settlement observation points so as to obtain a period test value in-time ordinal sequence of each settlement observation point; establishing a traditional GM (1, 1) model for the time sequence sequence to predict so as to obtain a time response function; constructing an equal-dimensional metabolism GM (1, 1) model through an iteration strategy; obtaining an optimal dimension according to the development coefficient of the time response function; and according to the isometric metabolism GM (1, 1) model under the optimal dimension, obtaining the ash action amount and the development coefficient, and obtaining the settlement trend value of the building. According to the method, the settlement trend value of the building can be effectively predicted, the defects of subjectivity and randomness of parameter selection in a traditional method are overcome, and reliable technical support is provided for engineering practice. The invention further provides a device for predicting the settlement trend value of the building.

The invention provides a multi-point displacement transmission-type laser monitoring system for a structure without stable observation points and a data processing method of the multi-point displacement transmission-type laser monitoring system. The system is mainly composed of a bidirectional laser transmission sensing unit, a laser transmitter and a synchronous control and data processing module. All the bidirectional laser transmission sensing units are arranged in a straight line, laser emitters are arranged at the two ends of each bidirectional laser transmission sensing unit, and emitted laser beams penetrate through all the bidirectional laser transmission sensing units. The bidirectional laser transmission sensing unit is internally provided with a semi-transparent / semi-reflective mirror window, a diffuse reflection target and a camera. The semi-transparent / semi-reflective mirror window reflects an incident laser beam to the diffuse reflection target to form a light spot, and the camera records and identifies the movement displacement of the light spot. In combination with the data processing method provided by the invention, accurate multi-point displacement monitoring of the to-be-measured structure can be realized under the condition of no stable observation point. The system is suitable for construction period deformation monitoring or engineering life cycle structure health monitoring of large civil engineering structures such as bridges, tunnels and high-rise buildings.

The invention relates to a hidden data center intelligent identification method based on multi-scale time sequence feature fusion. The method comprises the following steps: S1, obtaining time sequence data of a to-be-detected area and preprocessing the time sequence data; s2, based on the preprocessed time sequence tensor, an initial graph structure is established on the layers of an entity and an observation point, an edge weight is calibrated and sparsified through a data-driven adaptive graph learning mechanism, multiple time delay path sets between node pairs are learned, and optimized dynamic adjacency and time delay weights are obtained; s3, constructing a hidden data center intelligent identification model in combination with an ST-GNN model; s4, training the hidden data center intelligent identification model to obtain a trained hidden data center intelligent identification model; s5, acquiring a real-time detection probability and a confidence interval according to the real-time data stream; and S6, selecting an initial alarm threshold value through the cost sensitive curve, carrying out adaptive fine tuning, and obtaining a final alarm list according to the real-time detection probability. The method effectively improves the intelligent recognition efficiency of the hidden data center.