599 results about "Observation point" patented technology

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 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.

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.

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.

The invention relates to the technical field of mining face rock movement data analysis, in particular to a coal mine surface deformation monitoring method. The method specifically comprises the steps that a ground rock movement observation control network is established, and ground control points are arranged for combined measurement; a ground rock movement observation station is established, rock movement observation lines are set, and observation points are buried; the observation points are comprehensively measured before mining, and original observation data are obtained; during mining, collecting observation data in real time, and calculating and drawing deformation analysis after mining of the working face; and calculating surface movement parameters according to the observation data. According to the method, the technologies of GNSS-RTK observation, airborne LiDAR monitoring, InSAR monitoring and the like are utilized, modeling is conducted on the surface form before stoping, a database is established, along with stoping, real-time data and modeling data are compared, and the deformation amount is calculated. And large-area, all-weather and continuous observation of data can be realized, so that the research level of the surface movement rule of the mountainous area is further improved.

The invention discloses a method for calculating aerodynamic characteristics of a vertical axis wind turbine. The method comprises the following steps: establishing a computational fluid mechanics model and a free vortex wake model and forming interaction, segmenting a blade and establishing a two-dimensional local area, and initializing flow field and vortex wake parameters; calculating an induced flow velocity sum of each vortex element in the center of a local area, and loading the induced flow velocity sum and a far-field incoming flow velocity to a boundary node; the airfoil action is equivalent to point vortex and dipole flow at the circle center, and induced velocities of the point vortex and the dipole flow at boundary nodes are loaded; carrying out fluid mechanics parameter calculation on the flow in each two-dimensional local area; integrating the airfoil surface pressure to obtain a lift force, and calculating a circular rector to update the point vortex intensity; the dipole flow intensity is calculated and updated through the flow velocities of the three specific observation points. The method for calculating the aerodynamic characteristics of the vertical axis wind turbine is high in calculation efficiency and reliable in precision, the aerodynamic characteristics of the vertical axis wind turbine are accurately obtained with small calculation amount, and powerful support is provided for structural design and optimization of the vertical axis wind turbine.

The invention discloses a dynamic octree mapping method and system based on probability semantic fusion and a storage medium, and mainly solves the problem that a traditional octree map lacks semantic information and cannot support an advanced cognitive task. According to the implementation scheme, the method comprises the following steps: acquiring an RGB image, depth information and pose data through a sensor; extracting pixel-level semantic probability distribution by using a pre-trained semantic segmentation network; geometric information in the perception data is projected to a three-dimensional space based on the pose, and a three-dimensional observation point with semantic information is formed; performing ray projection on octree nodes, respectively updating geometric occupancy states of penetrated non-end-point nodes, and synchronously updating geometric occupancy states and semantic belief distribution of end-point surface nodes by using three-dimensional observation point data; semantic information is continuously optimized through Bayesian probability fusion, and dynamic pruning and map optimization are realized based on semantic entropy. According to the method, the map storage efficiency and the calculation speed can be remarkably improved while refined and real-time updated geometric and semantic information is given. The method can be used for map construction and storage of visual SLAM positioning, and fine three-dimensional object modeling and environment information description.

The invention relates to a calibration method and device for a visual mechanical arm and a medium, and belongs to the technical field of mechanical arms. The method comprises the steps that in response to a moving instruction of the visual mechanical arm, an observation point group indicated by the visual mechanical arm in the moving stroke and observation information of the observation point group are determined; wherein the observation information comprises expected joint poses of joints of the visual mechanical arm at all observation points and expected relative positions relative to a set reference object; when the visual mechanical arm is located at each observation point, the actual joint pose of the visual mechanical arm and the actual relative position of the visual mechanical arm relative to a set reference object are determined; determining calibration parameters according to the actual joint position and the actual relative position corresponding to each observation point; inputting the calibration parameters into a set resolving model to obtain resolving characteristics; wherein the resolving feature is used for measuring the positioning offset of the visual mechanical arm.

The invention discloses a visual sparse observation and physical model fused flexible body deformation tracking method, which comprises the following steps of: firstly, constructing a flexible body co-rotation finite element model considering geometric nonlinearity, and establishing a low-dimensional flexibility coupling model for describing a mechanical transfer relationship between a virtual actuating source and a characteristic observation point by utilizing a static condensation technology; in the real-time tracking process, a binocular vision system is used for collecting images and resolving three-dimensional coordinates of sparse feature points in a real physical world; constructing and solving a reverse optimization problem based on the low-dimensional flexibility coupling model, and calculating a virtual correction force vector capable of minimizing an observation error; and applying the virtual correction force vector to the finite element model to drive the finite element model to deform, and updating a full-field geometric node position and a tangent stiffness matrix in real time. Through deep fusion of a physical model and sparse visual observation, the problems of visual occlusion and virtual-real model deviation in flexible body assembly are effectively solved, and online real-time full-field high-precision deformation state estimation is realized.

The invention discloses a laser wind-finding radar and a data processing and assimilation method thereof. The method comprises the following steps: acquiring observation data of the laser wind-finding radar, atmospheric stability parameters and topographic relief feature data; a three-dimensional space weight distribution function is constructed based on the parameters, and initial influence weights of the observation points on surrounding grids are represented; dynamically adjusting the horizontal influence range and the vertical attenuation characteristic of the function according to the geomorphic stratification degree and the atmospheric vertical stratification change information, and generating dynamic weight distribution matched with the local atmospheric physical and topographic characteristics; the distribution serves as a space adjustment factor of an observation error covariance matrix and is introduced into a cost function, and the wind field analysis field and the dynamic weight distribution are synchronously optimized by minimizing the cost function; and outputting the optimized wind field analysis field for numerical weather forecast. According to the method, accurate and efficient fusion of observation information in three-dimensional variational assimilation is realized, and the authenticity and forecast reliability of wind field analysis under complex conditions are remarkably improved.

The invention provides a deep and far sea wind power plant area lightning dynamic parameter inversion method and device, and relates to the technical field of lightning protection, and the method comprises the steps: building a fan return stroke model based on current reflection caused by the wave impedance discontinuity of a fan top, a return stroke channel and a fan bottom grounding position, obtaining an initial propagation model of the electromagnetic field along the sea-land mixed path by combining the surface equivalent impedance of the sea surface and the land of the observation point zone; correcting the initial propagation model through electromagnetic field influence parameters to obtain a target propagation model; inputting a radiation source signal received by an array into the target propagation model for waveform time sequence inversion processing to obtain a corresponding time reversal signal; the time reversal signals in different directions are superposed and focused to obtain the peak azimuth, and the three-dimensional positioning of the lightning radiation source is carried out in combination with the positioning result of the double stations, so that the lightning development spatial-temporal distribution law is determined, and the measurement precision of the lightning leader development speed is remarkably improved.

The invention discloses a regional wind speed meteorological reference station representativeness evaluation method based on a correlation function field, a medium and a program product. The regional wind speed meteorological reference station representativeness evaluation method comprises the following steps: collecting multi-year day-by-day wind speed data of a high-density meteorological observation station in a target region and environment information of the observation point; selecting an independent sample of day-by-day wind speed data in each same time period in the region; establishing a correlation function field between every two stations, and fitting a relation model of correlation functions and distances; estimating a standardized observation random mean square error; calculating the relative standard deviation generated by replacing the target point wind speed truth value data with the reference station wind speed observation data; and evaluating the excellent grade of the spatial distance range represented by the wind speed of the reference station. The method can achieve the good representative evaluation of the reference station in the distance range, and has important practical values for the selection of a reliable meteorological reference station in the climate feasibility demonstration of wind power development site selection, urban and rural construction planning and the like, the evaluation of extreme climate risks and the calculation of the accuracy of engineering meteorological parameters.

The invention discloses a rapid forward modeling method for a gravity field of a rectangular coordinate system based on an isoparametric transformation global discrete grid, and belongs to the technical field of geophysical exploration. According to the method, a measuring network composed of dggrid grids is manufactured to cover the whole measuring area range by selecting the measuring network forming form and the side length, the exploration depth is selected as D, the dggrid grids extend to the ground by an n < z + 1 > layer to form dggrid units of an n < z > layer, and the corresponding thickness of the dggrid units is D / n < z >. And calculating the abnormal response of all the dggrid units at each observation point by utilizing isoparametric transformation based on a dggrid unit interpolation type function, an integral point and an integral weight coefficient obtained by a hexagonal prism integral formula. Compared with a square grid, the dggrid grid has excellent angular resolution, the discrete distance measurement of the dggrid grid is better close to the Cartesian distance, and the dggrid grid also has the advantage of displaying uniform and clear adjacency. Compared with conventional direct calculation, the rectangular coordinate system gravitational field forward modeling based on the isoparametric transformation global discrete grid has extremely high acceleration potential.

The invention discloses a display method and system of an electronic rearview mirror based on multi-mode risk field coupling. The display method comprises the steps that driving state data of a current vehicle and one or more target vehicles and observation point data of a current vehicle driver are obtained; constructing a coupling dynamic risk field model based on the driving state data, and generating a comprehensive risk value; generating an attention entropy and a visual search efficiency value based on the observation point data; generating a display control signal by the comprehensive risk value, the attention entropy and the visual search efficiency value through a visual information flow optimizer based on an optimal control theory; and according to the display control signal, simplifying the background region by adopting an anisotropic diffusion equation, enhancing the foreground target by utilizing a nonlinear rendering pipeline, generating an electronic rearview mirror synthetic picture, and displaying the electronic rearview mirror synthetic picture on an in-vehicle display screen. According to the invention, the prominent presentation of key risk information and the effective compression of redundant information are realized, the cognitive load of a driver is reduced, and the driving decision-making efficiency and safety are improved.

The invention discloses a high-precision bridge structure global strain measurement method and system, and relates to the technical field of civil engineering-structure health monitoring. The system comprises a video acquisition module which is deployed at a stable observation point of a bridge and acquires a standard frame rate video; the pixel domain preprocessing module constructs a space-time matrix and generates a weight matrix and a shielding mask; the multi-scale spatial adjacency graph module divides nodes according to first-level component-second-level segmentation-superpixel clustering, defines an edge weight and applies geometric prior constraints; the variation displacement solving module obtains a displacement field through a multi-constraint objective function and ADMM iteration; the adaptive baseline strain calculation module dynamically selects the baseline length and calculates the strain; the abnormal point processing module ensures data integrity through a discrimination-elimination-re-estimation process; and the cross-section synchronous constraint module reduces displacement deviation. The method solves the problems of large traditional calibration error, local measurement limitation and environmental interference, and guarantees the reliability of bridge health monitoring data.

The invention provides a seepage risk dynamic quantification method and device, and relates to the technical field of engineering prediction.The method comprises the steps that seepage parameter observation data of different observation points in a target area and geological environment factor data related to seepage parameters are obtained; constructing a multi-scale geographically weighted regression model based on the permeability parameter observation data and the geological environment factor data to obtain a deterministic space trend field of permeability parameters; calculating a spatial residual error of the penetration parameter observation value relative to the deterministic spatial trend field, and performing Kriging interpolation on the spatial residual error to obtain a random residual error field and uncertainty information; fusing the deterministic space trend field and the random residual field to obtain a permeability parameter space distribution field; and based on the seepage parameter space distribution field and the uncertainty information, utilizing a Bayesian assimilation algorithm to obtain a seepage risk dynamic quantification result. According to the method, the core defects of insufficient description of seepage parameter spatial heterogeneity and single risk prediction static state in a traditional method can be solved.

The invention discloses an active sonar target identification method based on message passing multi-hypothesis tracking, and belongs to the technical field of underwater target detection. The method comprises the following steps: firstly, processing single-cycle sonar data by using adaptive constant false alarm detection, obtaining observation points, and forming an effective observation set through hierarchical clustering; thirdly, extrapolating the existing target, calculating the association probability of the existing target and the current observation by adopting a message passing method, and generating a plurality of groups of association hypotheses; then, the target state is corrected through Kalman filtering, target rejection and new management are executed, and a high-probability hypothesis is reserved; and finally, estimating the motion speed based on the target multi-period position sequence, and distinguishing a static object from a suspicious motion target according to a speed threshold. Through the multi-hypothesis tracking and probability message passing mechanism, clutter interference and static target influence in a complex underwater environment are effectively overcome, and the stability of target tracking, the accuracy of speed estimation and the overall reliability of system identification are remarkably improved.

The invention relates to the technical field of intelligent agriculture, and discloses a traditional Chinese medicine organic planting management method based on big data analysis. A space coordinate system is established in a planting area, regular partitioning is carried out, a unique land parcel identifier is formed, a soil humidity sensor is arranged in the center of each land parcel, meteorological observation points are matched to continuously collect air temperature, wind speed and illumination, and an evapotranspiration potential index is generated through sliding window statistics and normalization. The system performs dynamic balance prediction and state correction according to the soil moisture content, the irrigation volume and the evapotranspiration loss, extracts a reference evapotranspiration rate by using an unirrigated sample, determines a threshold interval and a real-time deviation in combination with a historical quantile, and smoothly improves the stability through a sliding window. The threshold value is updated hour by hour within a feasible range, irrigation is triggered when the water content is lower than the threshold value, and the irrigation amount is limited within a reasonable range through depth and flow conversion. Structured logs are generated in the whole process, and irrigation whole-process trace reserving is achieved.

The invention discloses a multi-angle laser radar vertical two-dimensional wind field inversion method. The method comprises the following steps: firstly, setting a measurement center and sampling parameters of a measurement area; acquiring multi-angle radial wind speed data; introducing a multi-point local sampling strategy to each inversion center point, and establishing a local multi-constraint inversion equation set by selecting a plurality of observation points close to the spatial position of the center point and combining the observation angle and radial wind speed information; according to the method, the limitation that a traditional wind field inversion method depends on a large-range overall consistency hypothesis is broken through, a local linear modeling method is adopted, small-scale changes, local non-uniformity and turbulence disturbance existing in an actual wind field can be better adapted, and the adaptability and modeling precision of an inversion model to a complex wind field structure are improved; the anti-noise capability, the error control and the inversion convergence are remarkably improved; the invention further provides a system, equipment and a storage medium for implementing the method.

The invention discloses an ecological product value analysis method and system based on multi-source data fusion, and relates to the technical field of ecological product value analysis. Original ecological data are divided into two-dimensional space grid units according to a fixed scale through a space grid construction and index unit; according to the method, automatic mapping from an actual geographic coordinate system to an analysis grid unit is realized, so that multi-source data has a unified projection basis in space, the problems that multiple data source space references are inconsistent and results are difficult to transversely compare in original ecological evaluation are solved, and a strict space organization structure is provided for subsequent ecological supply modeling. In the process of mapping the ecological indexes to the grid units, the system designs a neighborhood interpolation-based mechanism for averaging and filling multiple observation points and invalid units, so that the problem of local deficiency caused by non-uniform distribution of the observation points or influence of shielding on remote sensing data in field sampling is effectively relieved, and the integrity and analysis reliability of a spatial expression matrix are improved.

The invention relates to a city domain vehicle envelope line generation method and device, computer equipment and a storage medium, and the method comprises the steps that vehicle body data submitted by a first user, bogie data submitted by a second user and track data submitted by a third user are acquired, and the vehicle body data comprise allowable tolerances of vehicle parts in the manufacturing and mounting process; according to the vehicle body data, the bogie data and the track data, constructing a vehicle-track dynamics model; a dynamic load is loaded in the vehicle-track dynamics model to simulate vehicle operation, a corresponding displacement set of the vehicle at each moment is obtained, and the displacement set comprises the displacement corresponding to each observation point; and according to the displacement set corresponding to each moment and the tolerance, determining a dynamic envelope line changing along with time.

The application relates to a distributed system fault delimiting method, device, equipment and medium, the method comprising: collecting observation data of observation points deployed on a plurality of network elements in a network, and associating a plurality of observation data belonging to the same session to generate a session view corresponding to the session; reconstructing a forwarding path of a data packet of the session in the network based on the timestamp in the session view and the TTL; querying a network topology database to obtain an expected forwarding path of corresponding observation data in a network element sequence from a source network element to a target network element; comparing the expected forwarding path of the data packet with the reconstructed forwarding path, if there is no missing network element, determining that there is no link fault, otherwise, based on the TCP flag bit of the observation data of the last observed network element before the missing network element, determining that the link is actively disconnected, normally terminated or abnormally interrupted; and based on the missing network element and the upstream network element of the missing network element, delimiting the link with abnormal interruption.

The present invention provides an information processing method, etc., that can suitably estimate solar radiation. [Solution] The information processing method acquires the amount of sunlight at an observation point and observation time information indicating the time of observation of the sunlight. Based on the acquired sunlight and observation time information, a computer performs a process to estimate the amount of solar radiation at the observation point. Preferably, the amount of solar radiation is estimated based on the acquired sunlight and observation time information using a regression equation that shows the relationship between the amount of solar radiation, the ratio of sunlight, and the observation time.

The application discloses a kind of unmanned aerial vehicle trajectory anomaly detection methods based on deep probability generation, belong to unmanned aerial vehicle intelligent management and data analysis technical field.First, the trajectory representation module of space perception is constructed, the unmanned aerial vehicle trajectory coordinates are mapped into grid sequence, and the surrounding space information is fused by the neighborhood aggregation mechanism of graph structure, to enhance the modeling ability to trajectory space correlation.Second, the prefix condition generation network is designed, the trajectory is divided into observation prefix and to-be-tested suffix by random truncation, the data distribution of alignment training and online inference is learned, and the trajectory conditional probability distribution under any historical prefix is learned.In the training stage, the generation evidence lower bound optimization is combined with the interval-based negative sample ranking loss, to improve the discrimination ability of unmanned aerial vehicle trajectory anomaly detection model to local deceptive anomaly.In the inference stage, the observation point reached is updated by recursive likelihood, to realize high accuracy and high real-time unmanned aerial vehicle online anomaly trajectory detection.

The utility model discloses a building wall crack high-position observation device which comprises a tripod seat, a telescopic rod assembly is installed on the tripod seat, the telescopic rod assembly comprises a bottom pipe rod, a middle section rod and a top end rod, and the top end rod and the bottom pipe rod are respectively provided with a first arc-shaped guide rod and a second arc-shaped guide rod which correspond to each other in position; sliding grooves are formed in the upper end face and the lower end face of the first arc-shaped guide rod and the upper end face and the lower end face of the second arc-shaped guide rod respectively, a first sliding block is connected to the sliding groove of the first arc-shaped guide rod in a sliding mode, and a second sliding block is connected to the sliding groove of the second arc-shaped guide rod in a sliding mode. According to the utility model, not only can high-position observation of the camera be realized, but also no complex electric transmission structure is needed, comprehensive observation at a crack observation point within a range of nearly 180 degrees can be realized under simple operation, the use is convenient and fast, and the potential safety hazard of conventional climbing observation is eliminated.

Embodiments of the present application provide a parking method, device, system, storage medium, vehicle and mobile terminal. The parking method comprises: acquiring real-time environment information of a vehicle; the real-time environment information comprises real-time environment images at multiple observation points of the vehicle; determining a real-time automatic driving state of the vehicle according to the real-time environment information; when it is determined that the real-time automatic driving state is an abnormal state, controlling the vehicle to stop moving, and sending abnormal reminding information to a mobile terminal, so that the mobile terminal displays the abnormal reminding information; when a viewing request of the mobile terminal for the abnormal reminding information is received, sending the multiple real-time environment images to the mobile terminal after being synthesized into a real-time panoramic image, so that the mobile terminal displays the real-time panoramic image. Embodiments of the present application optimize the hardware resources of the vehicle, effectively reduce the program lag probability in the automatic driving process of the vehicle, and improve the accuracy and safety of automatic parking.