113 results about "Adaptive interpolation" patented technology

The invention relates to the technical field of data fusion, and discloses an animal scene-oriented adaptive multi-modal data fusion method, which comprises the following steps of: extracting spatio-temporal characteristics from multi-source heterogeneous data such as visual sense, auditory sense and physiological sensing, constructing an animal-environment-group ternary spatio-temporal relation graph, and constructing an animal-environment-group ternary spatio-temporal relation graph; a pilot frequency sampling problem is solved through an adaptive interpolation algorithm, cross-modal projection alignment is completed in a public semantic space, unified space-time representation is output, and confidence coefficient weight is dynamically calculated based on uncertainty measurement of each modal feature. According to the method, accurate alignment of multi-modal data is realized through the cross-modal space-time attention network, the multi-modal feature alignment error is reduced compared with that of a traditional LSTM method, the training data volume of a federated element migration reinforcement learning framework is reduced compared with that of a traditional migration learning method, and the cross-species generalization performance of the model is improved. A multi-level causal inference engine quantitatively reveals causal association between environmental factors and animal diseases, and in combination with a dynamic decision tree visualization technology, the decision recognition degree is improved.

The invention relates to the technical field of aero-engine combustion chamber outlet temperature field monitoring, and discloses a multi-source data fusion method for an aero-engine combustion chamber outlet temperature field, and the method comprises the steps: unifying a multi-source data space-time reference through a standardized space grid and a precise time synchronization protocol; mapping the data to the same space-time grid by adopting a self-adaptive interpolation algorithm, and embedding a temperature gradient continuity constraint; the confidence coefficient weight is dynamically calculated based on a sensor drift error model and CFD residual feedback, and working condition self-adaptive weighted fusion is realized by using a fuzzy logic rule base; and the interpolation parameters and the weight distribution threshold values are optimized in a distributed mode through a federated learning framework, and fuel injection parameters are adjusted in combination with combustion chamber closed-loop control. The temperature field reconstruction precision and reliability are remarkably improved, the service life of turbine blades can be prolonged, the combustion efficiency can be optimized, and the method is suitable for real-time monitoring and regulation of the combustion state of an aero-engine.

The invention relates to the technical field of precision automatic lathe machining, in particular to a precision automatic lathe workpiece machining method which comprises the steps that a double-spindle synchronous machining mode is adopted to achieve seamless connection of rough machining and finish machining, clamping errors are reduced, a servo motor drives a tool shaft to achieve nanoscale feeding compensation, and traditional mechanical transmission precision limitation is broken through. The integrated infrared temperature measurement module monitors the temperature of a cutting area in real time and dynamically adjusts the spraying angle and flow of cooling liquid based on machining parameters, machining thermal deformation is effectively controlled, and a dynamic weight self-adaptive interpolation model is established to balance machining efficiency and contour precision. Main shaft load, tool vibration and workpiece deformation data are collected in real time through a multi-sensor data fusion system, axial following errors are corrected through an iterative learning control algorithm, contour consistency is improved, surface defects are eliminated through a nanoscale vibration compensation mechanism, machining efficiency is improved, and machining precision is improved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses a space-time sequence interpolation method and device oriented to heterogeneous deletion, and belongs to the technical field of space-time data processing. Aiming at random or continuous loss of the sensor network caused by faults and communication interruption, the method comprises the following steps: setting static space experts, dynamic space experts, short-term experts and long-term period experts in parallel in the same framework, and respectively capturing fixed geographical adjacency, time-varying space correlation, local continuous trend and long-period rules; spatial features are extracted through high-order diffusion diagram convolution and bidirectional gating circulation, time features are extracted through multi-layer space-time attention, a memory attention gating network is introduced to dynamically weight and fuse output of experts according to reconstruction errors, and node-level and time-step-level self-adaptive interpolation is achieved. Experiments show that compared with the prior art, the method has the advantages that under various real data sets and heterogeneous missing scenes, the precision and robustness are remarkably improved, and the method can be widely applied to scenes needing high-integrity spatio-temporal data, such as intelligent transportation, air quality monitoring and energy internet of things.

The invention discloses a path planning method and system for curved surface integrated conformal jet printing, and belongs to the field of additive manufacturing. According to the method, the geometric data of the non-deployable curved surface are obtained and converted into the discretized grid model, then the plane pattern is designed and mapped to the three-dimensional curved surface, the mapped pattern is locally optimized, and the complete printing area model is generated. Path planning is divided into initial jet printing path generation and five-axis linkage motion path planning, conformal characteristics are achieved through an adaptive interpolation algorithm and NURBS curve optimization, five-axis motion parameters are calculated based on an inverse kinematics algorithm, and the positioning relation between a jet printing head and a curved surface is kept through the RTCP technology. The method solves the problems of precision and path planning efficiency in complex curved surface manufacturing, and is suitable for manufacturing heterogeneous complex curved surface functional structures in the fields of aerospace, medical instruments and the like.

The invention relates to the technical field of artificial intelligence, and discloses a vision-guided automatic sorting and deviation rectifying method and system.The vision-guided automatic sorting and deviation rectifying system comprises a vision parameter processing module, a sorting strategy decision module and a deviation rectifying execution control module, a real-time judgment mechanism of dynamic and static resolution differences is established by setting a resolution collaborative correction end, and the deviation rectifying execution control module is established; a self-adaptive interpolation algorithm is adopted to adjust visual feature data, so that the system can eliminate imaging resolution deviation caused by ambient light and transmission vibration, the acquisition accuracy of material surface texture features is ensured, sorting identification errors caused by projection chromatic aberration are avoided, the environmental adaptability of the visual guidance system is improved, and the visual guidance system is more accurate in image quality. The position and posture coupling analysis end is arranged, the dynamic matching degree of the position and posture offset of the materials and the motion trail of the mechanical arm is monitored in real time, the grabbing angle offset is automatically corrected through a space coordinate compensation mechanism, and it is guaranteed that the relative space position of an end effector and the materials is in the optimal grabbing state all the time.

The invention discloses a multi-mode diabetes dynamic curative effect evaluation method, which relates to the technical field of data processing, and comprises the following steps of: unifying a time axis of multi-source data through a self-adaptive interpolation and alignment strategy, and marking credibility; peak values and abnormal fluctuations within one or two weeks are identified based on short-term window detection, and short-term variation and risk prompts are generated; multiple months of scale data are fused by applying multilayer attention or Transform, and comprehensive long-term curative effect indexes and key inflection points are extracted; a short-term quantification result and a long-term index are incorporated into a multi-target reward mechanism through a continuous learning and interactive real-time feedback module, model parameters are iterated online, and clinical intervention suggestions are output; external events are quantitatively marked, and recognition and response to emergency situations are enhanced in a bias injection mode. High-quality integration and short-term and long-term multi-level analysis of multi-source heterogeneous data are realized, and the accuracy and timeliness of dynamic monitoring and intervention of diabetes mellitus are remarkably improved.

The invention relates to the technical field of water quality monitoring and environmental protection, and discloses a water quality abnormity real-time early warning method and system based on machine learning, and the method comprises the steps: obtaining a high-quality original water quality time sequence data flow, and carrying out the fault diagnosis, adaptive interpolation and normalization processing; extracting time features, space correlation features and parameter interaction features, and performing feature splicing and PCA dimension reduction; performing isolated forest detection and semi-supervised recognition on the spatial-temporal feature vectors; carrying out LSTM prediction and risk assessment; performing flow velocity estimation and propagation prediction to obtain collaborative early warning information and a model updating result; calculating a dynamic threshold value and a Bayesian decision to obtain a graded early warning result and an early warning execution scheme; performing abnormal attribution analysis and structured early warning generation to obtain structured early warning information and a push result; according to the method, early warning can be given out in advance when the pollutant concentration does not exceed the standard, and the multi-parameter collaborative abnormal mode is accurately recognized.

The invention discloses a power distribution network fault type identification method based on big data, and relates to the technical field of power distribution network fault type identification. Multi-source data time axes are aligned on the premise of keeping original sampling precision through clock drift correction and a self-adaptive interpolation algorithm; the problem of misjudgment faults or missed judgment faults caused by asynchronous voltage / current signal time is thoroughly eliminated; low confidence is marked for communication delay data, participation in calculation is limited, and noise interference feature extraction is avoided; on the basis of a fault type association mechanism of a misjudgment sample, a network layer needing to be adjusted is accurately positioned, and resource overhead of full-model retraining is avoided; the scarce fault data is expanded by combining an adversarial sample generation technology, and the recognition robustness of complex scenes such as high-impedance grounding is improved.

The invention discloses a lightweight detection method for small defect targets in a complex industrial scene, and relates to the technical field of target detection technology improvement and deep learning computer vision. Spatial shift operation and a layer-by-layer interaction mechanism are introduced into a feature channel through the SS-CMM module, cross-channel and cross-space effective information fusion is achieved, and therefore the relation between local texture details and the global context is enhanced, and the perception ability of fine defects under the complex background is remarkably improved; the C2PSA-Topk sparse attention mechanism is combined, dynamic selection and weighting of important features are achieved on channel and space double domains, redundant background interference is restrained, discriminative response of tiny defects is highlighted, and the recognition precision of the model for small-scale targets and boundary fuzzy defects is effectively improved. The MS-KSU module introduces a multi-scale convolution and adaptive interpolation strategy in an up-sampling stage, so that a global structure and fine-grained details can be considered in a feature recovery process, and the continuity and integrity of a defect region boundary are ensured.

The invention discloses a multi-point non-stationary non-Gaussian random process efficient simulation method based on a spectral representation method, and belongs to the technical field of random process simulation. Aiming at the problem of low efficiency of existing potential Gaussian random process power spectrum matrix solving and matrix decomposition, the method comprises the following steps: determining a power spectrum matrix and non-Gaussian features of a target process, establishing a transfer equation by using a Mehler formula, calculating time and frequency interpolation points based on the amplitude of the power spectrum matrix, and performing matrix decomposition and orthogonal decoupling at the interpolation points. And generating a potential Gaussian process sample through interpolation and fast Fourier transform, and finally converting to obtain a target process sample. According to the method, time-frequency domain resource allocation is optimized through a self-adaptive interpolation strategy, so that interpolation points are concentrated in an area with high energy, redundancy decomposition is avoided, and the calculation efficiency is remarkably improved on the premise of ensuring the simulation precision.

The invention relates to the technical field related to automatic driving, in particular to a vehicle global positioning deviation correction method based on a roadside sensing result, and the method comprises the steps: obtaining the global pose of a vehicle through a 3D target detection network in which roadside equipment is fused with time sequence features, and carrying out the uncertainty quantification; meanwhile, a vehicle end adopts a tight coupling laser radar and IMU data to realize high-frequency local positioning, a time domain self-adaptive interpolation mechanism is designed for compensating time deviation aiming at the problem of roadside sensing time delay, roadside observation is constructed into a roadside sensing factor of self-adaptive covariance, and finally, through factor graph fusion optimization, a road-side sensing model is obtained. According to the present invention, by combining the IMU pre-integration factor, the laser radar odometer factor and the roadside perception factor, the accurate vehicle pose estimation of the accumulative error correction and the global coordinate system alignment is achieved, and the experiment shows that the method significantly improves the positioning precision and the robustness in the complex environment, and effectively solves the positioning drift problem when the GNSS signal is missing.

The invention relates to the technical field of vision, in particular to an industrial intelligent spraying tracing method, which comprises the following steps of: firstly, acquiring an original depth map of a curved surface workpiece by utilizing a depth camera, and obtaining original data containing depth information; next, edge feature enhancement preprocessing is carried out on the original depth image, edge pixels are accurately extracted through morphological gradient operation and a user-defined anisotropic diamond kernel, and an edge mask image is constructed; then, curvature region division and self-adaptive interpolation restoration are carried out, quadratic polynomial curved surface fitting is carried out on a hole pixel neighborhood, a local curvature value is calculated, a curved surface region is divided according to the curvature value, and corresponding interpolation strategies are adopted for different regions; afterwards, edge detail sharpening post-processing is carried out on the repaired depth image, and the edge contrast is enhanced by using a non-sharpening mask algorithm; and the repaired and sharpened depth map is converted into a three-dimensional point cloud, workpiece pose information is determined based on three-dimensional point cloud data, and spraying track planning is carried out.

The invention discloses an I SAR fusion imaging method, system and device based on centralized multi-station networking, and relates to the technical field of radar imaging, and the method comprises the steps: obtaining I SAR frequency domain echo data of each subsystem; performing frequency domain space matching on the frequency domain echo data based on the observation angle difference of each radar; performing frequency domain alignment processing on the matched frequency domain data; and fusion and imaging are carried out. According to the I SAR fusion imaging method, system and device based on the centralized multi-station networking, the imaging resolution is remarkably improved, multi-station frequency domain data are mapped to a unified local frequency space through a coordinate transformation matrix, non-uniform data are subjected to uniform reconstruction in combination with a self-adaptive interpolation algorithm, and the imaging resolution is remarkably improved. The frequency domain integration interval is effectively expanded, compared with a single-station I SAR which only covers a local area of the total rotation angle of the target, the range resolution is further optimized through multi-station bandwidth synthesis in the range direction, and high-fidelity reconstruction of the target in the complex shape is achieved.

The invention relates to the technical field of signal demodulation, and discloses an OFDM (Orthogonal Frequency Division Multiplexing) signal demodulation method of a signal analyzer. According to the invention, through joint optimization of a synchronous detection and frequency offset compensation mechanism, the symbol positioning precision in a low signal-to-noise ratio environment is improved, and subcarrier interference caused by a cyclic prefix pseudo peak value and frequency offset phase ambiguity is effectively suppressed; a time-frequency two-dimensional adaptive interpolation strategy is adopted to enhance the robustness of channel estimation to non-uniform pilot frequency distribution and a fast time-varying channel, and data subcarrier response distortion is reduced; in combination with minimum mean square error equalization and dynamic noise reduction processing, inter-symbol interference and a noise accumulation effect are greatly weakened, and the bit error rate performance of a high-noise scene is improved; the error control technology is updated through a closed-loop channel, a demodulation error propagation path is blocked, and cooperative improvement of system demodulation stability and precision in a complex multi-path environment is achieved.

The embodiment of the invention discloses a method and device for generating a dam surface settlement cloud picture based on a self-adaptive interpolation model. A specific embodiment of the method comprises the steps of performing feature extraction on multi-dimensional monitoring point data of a preset dam structure to generate a dam body scene portrait; according to the dam body scene portrait, generating an optimal interpolation algorithm; optimizing the optimal interpolation algorithm to generate an optimized interpolation algorithm; generating a non-oscillation cloud picture according to dam body physical attributes of a preset dam structure and an optimization interpolation algorithm; performing preset-size grid block division on the non-oscillation cloud picture to generate a cloud picture grid node set; performing parallel processing on each cloud picture grid node in the cloud picture grid node set to generate an accelerated interpolation result; and performing incremental updating on the accelerated interpolation result to generate an updated interpolation result. According to the implementation mode, the accuracy, efficiency and engineering applicability of dam settlement interpolation are improved.

The invention discloses an ocean terrain digital display method based on three-dimensional modeling. The ocean terrain digital display method comprises the following steps of 1, constructing a multi-source ocean data set; 2, performing space fusion by adopting a self-adaptive interpolation method based on position weight to generate a three-dimensional space density field; 3, performing contour surface extraction by adopting an improved Marching Cubes algorithm to generate an initial three-dimensional terrain model; 4, performing local modeling by adopting a Delaunay triangulation network reconstruction method, and constructing a complete three-dimensional terrain model; 5, obtaining a texture terrain model; step 6, constructing an LOD tree structure and realizing dynamic loading in combination with view cone cutting and a screen space error control strategy; and step 7, based on a dynamic loading result of the LOD tree structure, driving node switching through a time controller, and performing visual display. According to the method, three-dimensional modeling and an LOD tree structure are fused, and high-precision reconstruction and evolution display of the ocean topography are achieved.

The invention provides a ground-based laser radar near-surface wind field modeling method and system based on data fusion, and relates to the technical field of wind field modeling, and the method comprises the steps: firstly carrying out the multiple scanning of a target terrain region through a ground-based laser radar, so as to obtain an observation data set; acquiring land surface temperature data matched with the three-dimensional point cloud data in a space-time manner, and fusing the land surface temperature data with the three-dimensional point cloud data to generate a coupling data set; generating initial wind field feature vectors at different moments based on the coupling data set; the vectors are processed through a long-short-term memory network, and target wind field feature vectors are generated; inputting the target vector into a wind field inversion model constructed based on a high-order Taylor expansion polynomial, and calculating three-dimensional wind field data and turbulence characteristic data; and finally, constructing a near-ground wind field model of the target terrain area according to the data through a self-adaptive interpolation algorithm. According to the invention, the precision and accuracy of complex terrain near-surface wind field modeling are improved.

The invention discloses a power distribution overhead line icing thickness detection method, system and device and a storage medium, and relates to the field of icing thickness detection.The method comprises the steps that multi-source detection data of a power distribution overhead line is collected, self-adaptive interpolation completion is conducted based on data distribution characteristics, for example, bilinear interpolation is conducted on a two-dimensional image, linear interpolation is conducted on sequence data, and the detection result is obtained; complementing point cloud data in combination with information such as wind direction; performing multi-source icing thickness detection on the detection data set, and calculating a detection sequence based on image pixel gradient, point cloud intensity and the like; performing multi-source fusion by adopting Bayesian inference to obtain an accurate detection value and a confidence interval; predicting an icing trend in combination with historical data, and performing real-time monitoring and early warning; according to the invention, image edges and ice layer boundaries can be protected, so that interpolation better conforms to physical phenomena, and the detection precision is improved; icing thickness features are described from different aspects, posterior probability distribution can be updated by combining prior and real-time information, the icing thickness is accurately estimated, uncertainty is quantified, and powerful support is provided for decision making.

The invention belongs to the technical field of robot path planning, and particularly discloses a fixed margin removal robot grinding and polishing process and track synchronous planning method and system. Comprising the steps that a robot smooth end face grinding and polishing material removal profile is constructed; the machining allowance of the workpiece is determined, and the curved surface of the surface of the workpiece is subjected to curved surface reconstruction based on the maximum tolerance limitation; with the machining residual height and the smoothness of the technological parameters as targets, force-speed-position machining information of robot grinding is planned on the reconstructed curved surface, and discrete machining point positions and the technological parameters are obtained; expanding the reconstructed curved surface to a four-dimensional form, obtaining a composite track fusing a position component and a process component, and carrying out adaptive interpolation on the composite track to meet the process precision and the motion precision of a processing point; and obtaining a normal vector of the interpolated composite trajectory, and optimizing a tail end redundancy degree of freedom to obtain process parameters and processing poses of synchronous planning. According to the method, matching and optimization of a track field and a process field in robot grinding are achieved.

The invention provides a mine equipment intelligent operation and maintenance system based on digital twin driving, and relates to the technical field of industrial intelligence and equipment operation and maintenance. According to the system, a digital twin model is established through a twin construction module, and virtual sensing nodes are arranged. And the environment perception correction module reconstructs credible signals through multi-source data adaptive interpolation by comparing virtual and real sensing node deviations under a severe working condition. The virtual-real dynamic correction module adjusts model parameters in real time based on state differences, and identifies sudden and slow differences to extract early fault features. And the fault evolution prediction module constructs a time-varying characteristic link in the corrected data space, and predicts a hidden fault evolution path by analyzing a response trend and an energy transfer rule. And the intelligent decision execution module generates a grading response strategy according to the result, realizes maintenance scheduling optimization and operation condition adaptive adjustment, verifies the decision effect through digital twinborn feedback, and significantly improves the reliability and the intelligent level of mine equipment operation and maintenance.

The invention belongs to the technical field of path planning and robot motion planning, and particularly relates to a multi-strategy fusion improved RRT * sampling planning method and device and a storage medium, and the method comprises the steps: constructing a dual-search tree through a starting point and a target point in a three-dimensional working space, and selecting a to-be-expanded search tree through a random mode; halton-Bridge mixed sampling is adopted to generate sampling points, so that the global coverage uniformity is improved, and the narrow channel sampling capability is enhanced; calculating the dynamic step length of the exponential decay based on the local obstacle proportion; adopting a hierarchical guidance expansion strategy, preferentially expanding towards a target, introducing an improved artificial potential field to guide expansion when the expansion fails, and taking the bottom with random expansion; performing RRT * neighborhood father node reselection and reconnection optimization on the new node; greedy pruning, self-adaptive interpolation and cubic B-spline smoothing are carried out on a path after the double trees are connected, and an executable smoothing track is output; according to the scheme, planning efficiency, success rate and path quality are improved.

The invention relates to the technical field of data processing, and discloses an energy consumption loss data verification and correction method and system, and the method comprises the steps: carrying out the data cleaning of original energy consumption data, and obtaining standard energy consumption data; according to data distribution in a normal working condition in the historical energy consumption data set, generating a dynamic verification threshold range matched with the load type in a current time window; collecting data points exceeding a dynamic verification threshold range in the standard energy consumption data into a preliminary abnormal data point set of the standard energy consumption data; determining a target abnormal data segment of the preliminary abnormal data point set according to context information features of the preliminary abnormal data point set; based on the time sequence similarity of the standard energy consumption data, performing adaptive interpolation on the target abnormal data segment to obtain a corrected data set of the target abnormal data segment; outputting the corrected data set passing the integrity verification as a target verification report of the original energy consumption data; according to the invention, the efficiency of verifying and correcting the energy consumption loss data can be improved.

The invention discloses a CBCT metal artifact suppression method and system based on preoperative prior guidance, and the method comprises the steps: taking second CBCT image data, obtained in real time in an operation, of a target tissue as a reference, and carrying out the rigid registration of first CBCT image data, obtained before the operation, of the target tissue; deforming the first CBCT image data to obtain third image data; the relative positions of the tissue structures in the third image data and the second CBCT image data are consistent; based on the third image data, performing coarse segmentation on the metal implant in the second CBCT image data by adopting a digital subtraction technology; segmenting to obtain first mask data and background prior data of the metal implant; performing true and false positive judgment on the first mask data and performing false positive suppression processing to obtain second mask data; carrying out data restoration on the background prior data by adopting a self-adaptive interpolation grid to obtain projection domain data; and reconstructing by using an FDK reconstruction algorithm to obtain a CBCT image of the target tissue without metal artifacts.

The invention discloses a power grid fault-oriented multi-level spatio-temporal data processing and visualization method, comprising spatio-temporal reference unification, including sliding window adaptive interpolation and interpolation result optimization; performing spatial reference normalization, including spatial quantization alignment and power grid-meteorological coupling modeling; data governance, redundancy elimination and missing repair, including dynamic time warping de-duplication, principal component analysis dimension reduction and missing data repair; the visual architecture design comprises a physical power grid layer, an alarm signal layer and a disposal process layer. Through the spatio-temporal data fusion technology, the operation state and fault characteristics of the power grid equipment are accurately captured, and timely and accurate fault diagnosis support is provided; a hierarchical visual architecture is designed, so that a user can go deep into a microscopic equipment level from a macroscopic power grid state layer by layer, fault positioning and analysis are helped to be more accurate and efficient, understanding of power grid dispatchers on fault conditions is enhanced, and rapid response and decision making are achieved.

The invention discloses a control method and system for system positioning and position feedback of a laser engraving machine, and the method comprises the steps: collecting the rotation position data of a motor in real time through a magnetic encoder, eliminating a direction error in combination with a forward and reverse calibration algorithm, and achieving the high-reliability communication through an RS485 bus and a Modbus protocol. The problem that positioning of a traditional laser engraving machine is not accurate due to accumulative errors of a stepping motor, external interference and manual misoperation is effectively solved; through 20kHz high-frequency sampling and arithmetic average filtering and in combination with a dynamic position synchronization mechanism, the position deviation is controlled within a + / -0.5 stepping precision range; an interpolation algorithm is introduced to optimize free path carving, and track smoothness is improved through step length self-adaptive interpolation; laser power PWM closed-loop control and light path focusing design are integrated, nonvolatile calibration parameter storage is matched, hierarchical engraving precision and anti-vibration interference capacity are achieved, and system stability and finished product percent of pass under a precision machining scene are remarkably improved.

The invention discloses a loess mechanical parameter spatio-temporal interpolation prediction model construction method based on big data, and relates to the technical field of geotechnical engineering data analys.The loess mechanical parameter spatio-temporal interpolation prediction model construction method comprises the following steps that loess mechanical parameter data, environmental factor data and geologic structure data are collected through a multi-source sensor, and spatio-temporal unified multi-modal data are constructed; designing a collaborative kernel function fusing spatial heterogeneity and time dynamics on the basis of multi-modal data, and quantifying relevance between mechanical parameters and space-time positions and environmental factors; according to the collaborative kernel function, dynamically optimizing a space weight, a time attenuation coefficient and a multi-source data fusion weight, and generating a parameter-adaptive interpolation model; carrying out distributed training and calculation acceleration on the interpolation model by adopting a block matrix approximation and parallel calculation architecture; through the trained model, a mechanical parameter prediction value of a target space-time position is calculated in real time, and the calculation efficiency is improved through a tensor expansion algorithm.

The invention discloses a self-adaptive interpolation restoration device and method for automatic driving continuous time-space data transmission, belongs to the technical field of automatic driving data transmission and processing, and solves the problems of high bandwidth occupation, insufficient restoration precision, poor scene adaptability and the like of an existing transmission scheme. The device comprises a data acquisition module, a vehicle end edge calculation module, a self-adaptive interpolation reduction module and an error verification module. The method comprises the steps that a vehicle end collects multiple types of spatio-temporal data and adds identifiers, dynamic characteristic parameters are extracted to divide low, medium and high dynamic driving scenes, a starting and ending point interval is adaptively configured, data are packaged in a customized mode, a cloud end restores the data according to a scene matching interpolation algorithm, error verification is conducted through original spatio-temporal data cached by the vehicle end, and the data are stored in the cloud end. If the threshold value is exceeded, triggering supplementary transmission and reducing the interval between the starting point and the ending point. According to the method, the data transmission quantity and bandwidth occupation are greatly reduced, the restoration precision and the data reliability are improved, the vehicle end energy consumption is reduced, and the real-time data interaction requirements of all working conditions of automatic driving are met.

The invention belongs to the technical field of computer vision and artificial intelligence, and discloses a handwritten mathematical formula primitive feature enhancement method based on a self-adaptive symbol strategy, which comprises the following steps of: 1, dynamically scaling primitive scale matching: realizing undistorted scaling by keeping the aspect ratio of an original image, only fixing the height and dynamically calculating the width to obtain a primitive scale matching result; adaptive interpolation is carried out; step 2, pixel disturbance of the morphological filtering chain: isolated noise points are gradually eliminated through the multi-stage morphological filtering chain, broken strokes are repaired, and large-area background interference is removed; and the optimal structure elements are adaptively selected for different symbol types. According to the method, dynamic scaling of geometric constraints is combined with intelligent morphological filtering, so that the core problems of symbol deformation, noise interference, multi-source data difference and the like in handwritten mathematical formula recognition are solved, and the accuracy of a recognition system is remarkably improved. And an efficient and reliable preprocessing scheme is provided for education AI, intelligent correction and other scenes.