611 results about "Computational Technique" patented technology

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention discloses a digital twin three-dimensional scene modeling method based on a webGPU, and relates to the technical field of three-dimensional scene modeling and graphic computing, multi-source sensing data are asynchronously sampled from sparse point cloud, a video texture sequence, a scene semantic tag graph and a structure boundary tuple, and a six-dimensional structure unit group is generated through a normalization operator; constructing a structural unit atlas with nodes representing component entities and edges representing constraint relations, and introducing a tension balance mechanism and multi-scale constraints to generate a modeling path prior model; constructing a graph calculation and graph rendering dual-channel assembly line in the WebGPU, and executing parallel texture mapping and boundary fitting operation; a dynamic sensing module is used for capturing scene disturbance and driving atlas response, and incremental reconstruction of the model is achieved; and finally, mapping the model to a Web terminal, and supporting microscopic semantic query and multi-layer data linkage. According to the method, the response speed, semantic consistency and structural adaptability of three-dimensional modeling in a complex environment are improved.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention provides a neuromorphic visual target tracking method and system based on image processing, and relates to the technical field of neuromorphic calculation, and the method comprises the steps: fusing the input of an event camera and a conventional image sensor, constructing a combined input tensor, and introducing a multi-scale convolution and synaptic event driving mechanism. Quick response and stable feature extraction of a high-speed moving target are realized, and coding block mistaken deletion and tracking loss caused by quick movement of the target are effectively avoided, so that system delay is reduced. Meanwhile, in combination with significance entropy difference evaluation and a dynamic brightness enhancement mechanism, the target judgment accuracy under low illumination and complex backgrounds is improved; redundant noise blocks are screened out through a significance weight function and confidence calculation, the redundancy calculation burden is relieved, and the lightweight characteristic of the system is guaranteed. A memory trajectory tensor and dynamic template adjustment mechanism based on a recurrent neural network is further introduced, time sequence consistency verification and self-adaptive updating are achieved, and the stability and robustness of the tracking process are enhanced.

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The invention discloses a power grid energy storage capacity demand determination method and system based on multiple time scales, relates to the technical field of power grid energy storage capacity demand calculation, and aims to solve the problem of inaccurate energy storage capacity demand calculation. By generating multiple scenes, quantitatively screening key scenes and incorporating various uncertain factors, energy storage capacity calculation focuses on high-influence scenes, the coping capacity of the scheme to actual risks is enhanced, decision scientificity is improved, ultra-short-term to long-term multi-time scales are divided, core contradictions of all the scales are captured in a targeted mode, limitation of a single scale is avoided, and energy storage capacity calculation efficiency is improved. According to the method, capacity requirements and equipment distribution are integrated, operation rules are defined, visual documents are generated, multi-scale energy storage cooperative operation is achieved, the stability of a power grid is guaranteed, meanwhile, cost is reduced, scheme landing performance and operation efficiency are improved, multi-time-scale power grid state prediction is carried out based on a dynamic database, and historical rules and real-time data are combined, so that the power grid state prediction efficiency is improved. And the prediction coordination is ensured through multi-dimensional verification.

The invention discloses a cross-platform virtual-real fusion scene construction method and system based on AI space calculation, and relates to the technical field of artificial intelligence and space calculation, and the method comprises the steps: carrying out the multi-scale feature fusion based on a received cross-modal conversion instruction set, and generating an initial image sequence; carrying out implicit field coding on a target object by combining a three-dimensional reconstruction algorithm to obtain an initial parameterized model; performing space-time alignment on the multi-view video stream, loading a digital scene asset package in combination with physical sensing data and a preset spatial index structure, and establishing a bidirectional data channel between a virtual scene and a physical sensor; performing rendering and illumination parameter adjustment on the initial parameterized model to obtain an optimized parameter model; performing differential coding processing on the optimization parameter model to obtain a target virtual-real scene fusion model; and distributing the target virtual-real scene fusion model to a preset terminal. The invention provides a virtual-real fusion construction method for end-to-end collaborative optimization, which is suitable for cross-platform live broadcast or dynamic interaction scenes.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention provides a ship video concentration and intelligent retrieval method and system based on edge nodes, and belongs to the technical field of edge computing, and the method comprises the steps: S1, collecting ship video data; s2, an edge node generates a concentrated video and metadata through an improved Gaussian mixture model, an improved DeepSORT algorithm and a dynamic concentration proportion adjustment algorithm; s3, synchronously concentrating the index information of the videos and the metadata by each edge node through a publishing-subscribing mode; s4, training a lightweight intelligent retrieval model through a knowledge distillation algorithm; s5, calling the lightweight intelligent retrieval model to match the corresponding concentrated video clip, and returning a retrieval result; and S6, collecting feedback information of the user on the retrieval result, and performing incremental optimization on the lightweight intelligent retrieval model through an elastic weight consolidation algorithm. According to the invention, efficient monitoring management is realized through edge localization processing, cross-node cooperation and closed-loop optimization architecture, and the actual requirements of ship safety monitoring and operation management are met.

The invention discloses a light guide plate dot density detection method, system and device and a storage medium, and relates to the technical field of light guide plate dot density detection, and the method comprises the following steps: converting a historical light guide plate image into a historical light guide plate binary image based on a graying processing method and a binary processing method; obtaining a standard dot area and a dot area range based on the historical light guide plate binary image; obtaining a detection normal fitting area and a detection incomplete fitting area based on the standard dot area, the dot area range and the detection light guide plate binary image; calculating and obtaining the dot density of the detection light guide plate based on the detection normal fitting area and the detection incomplete fitting area; the method is used for solving the problem that errors are generated in dot density calculation due to the fact that edge incomplete dots are not analyzed and calculated in an existing light guide plate dot density calculation technology.

The invention discloses an emotion analysis method, system and device based on multi-modal information and a large language model and a medium, belongs to the technical field of natural language processing and multi-modal calculation, and aims at solving the technical problem of how to overcome the defects of data distribution difference, modal credibility deviation and low generated data quality in the prior art. According to the technical scheme, the method comprises the following steps: collecting multi-modal data; multi-modal data preprocessing and feature extraction: preprocessing the text data, the image data and the audio data respectively and extracting corresponding features; feature alignment: mapping features of different modes of texts, images and audios to a unified potential space by adopting a cross-modal alignment algorithm, and eliminating semantic gaps among the modes; weight distribution: adopting a dynamic weight distribution mechanism to adaptively and dynamically distribute weights of different modes; performing cross-domain attribute-level sentiment analysis; evaluating the credibility; and calibrating the confidence coefficient.

The invention discloses a heterogeneous computing power cooperative scheduling system and method for mixed precision training, and belongs to the technical field of artificial intelligence computing. The system comprises a computational graph analysis and operator portrait module which is used for analyzing and dividing a model computational graph and extracting operator features; the heterogeneous hardware capability sensing and matching module is used for managing performance files and real-time states of heterogeneous hardware in the cluster and matching optimal execution hardware for each calculation partition; and the data flow coordination and pipeline parallel controller is used for generating a global execution plan, managing cross-device data dependence and communication and calculating overlapping optimization execution efficiency through communication. According to the method, the problem of low scheduling efficiency of mixed precision training in a heterogeneous environment is solved, automatic and accurate mapping from a calculation task to heterogeneous hardware is realized, the training speed is remarkably improved, the training cost is reduced, and the overall resource utilization rate of a cluster is improved.

The invention discloses an integrated information automatic supervision system and method for an edge device, and relates to the technical field of edge computing, an edge node management module of the system accesses the edge device, generates basic attributes and carries out containerized application deployment; the real-time monitoring and diagnosis module is used for collecting equipment operation data, identifying abnormity, positioning a fault type and triggering an alarm; the automatic strategy generation module is responsible for generating a customized repair strategy; the MESH communication and repair execution module constructs an edge device ad hoc network, realizes networking and offline strategy transmission and executes a repair instruction; the solution knowledge base module stores equipment fault types, historical repair cases and strategy templates, and updates strategy matching logic; the application deployment management module packages application programs through container mirror images, and distributes the application programs to edge nodes in batches according to scene templates; and the centralized management and control module visually displays the equipment state, the fault alarm and the strategy execution record, and provides manual intervention and global strategy configuration.

The invention provides a parking lot license plate detection method and system based on exclusive learning, and belongs to the technical field of computer vision and edge computing. The method comprises the following steps: deploying a lightweight detection main model at each entrance and exit edge device to carry out real-time detection and screen low-confidence difficult case samples; after the sample is uploaded, the central processing end generates a high-quality pseudo label by using a high-performance teacher model, and trains an exclusive student model by using a mixed loss function fusing detection loss, distillation loss and difficult case concentration loss; and finally, calculating a weight difference value and quantizing to generate a lightweight incremental update package, and directionally pushing the lightweight incremental update package to edge equipment to complete non-perception hot update. The system correspondingly comprises an edge processing unit and a central processing unit. According to the method, the problem that a general model cannot adapt to a multi-gateway differentiated scene is solved, unification of personalized accurate optimization and efficient lightweight updating of the model is realized, and the accuracy and reliability of license plate detection in a complex scene are remarkably improved.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.

The invention discloses a three-dimensional scene reconstruction and monitoring method based on multi-view fusion and deep learning. The method comprises the following steps: acquiring image data from different viewpoints through a plurality of cameras, and carrying out geometric calibration; denoising, correcting and enhancing the image; constructing a multi-scale convolutional neural network and a variational auto-encoder model, and extracting multi-level features; carrying out weighted fusion on the features, and carrying out three-dimensional reconstruction through sparse coding and a graph neural network; performing feature optimization by applying a dynamic graph convolutional network and a double attention mechanism, and performing model updating based on adversarial gradient descent; anomaly detection is carried out through multi-scale analysis and an adversarial variational auto-encoder, preliminary processing is carried out at a camera end by adopting an edge computing technology, and further analysis is carried out at a central server end through a heterogeneous graph neural network and sparse subspace clustering. According to the method, high-precision and intelligent three-dimensional scene reconstruction and monitoring are realized, and the method has a wide application prospect.

A facial expression recognition method based on grid attention and pyramid segmentation attention belongs to the technical field of deep learning image processing and emotion calculation, and comprises the following steps: introducing a grid attention mechanism and a pyramid segmentation attention mechanism on a ResNet101 large model, capturing local expression detail features through a grid attention module, establishing multi-scale global feature association by using a pyramid segmentation attention mechanism; hierarchical segmentation is carried out on the backbone network, and information from multiple hierarchies is effectively fused; and dynamically balancing the contribution degree of each level of features through learnable parameters, and integrating the extracted features to judge the expression category. Experiments show that the model achieves the recognition accuracy superior to that of a traditional model on a public data set in natural scenes such as complex illumination and posture change. According to the method, a solution with high robustness is provided for facial expression recognition in a complex environment, and the method has important application value in the fields of intelligent human-computer interaction, mental health assessment and the like.

The invention discloses an unmanned aerial vehicle autonomous inspection system and method based on AI identification, the system comprises an unmanned aerial vehicle body carrying a high-power optical zoom lens, an edge computing device and a function module, and the autonomous inspection of a power distribution tower is realized by fusing front-end AI identification and edge computing technologies. A Yolo structure is adopted to construct a lightweight tower recognition model, a dynamic route planning module is combined to realize single-point reference route generation and three obstacle crossing modes, and real-time coordinate correction in an RTK-free environment is supported. The edge computing device integrates a semi-supervised learning engine and a multi-sensor data fusion module, meets miniaturization design, and supports breakpoint continuous flight control and precise landing. The method covers automatic route generation, visual tracking, zoom cooperative control and self-adaptive task scheduling, solves the problems that a traditional unmanned aerial vehicle depends on manual operation, the data quality is poor and the efficiency is low, realizes whole-course automation of tower inspection in a complex environment, and is high in inspection efficiency, and the picture definition reaches the pin level.

The invention discloses a construction site three-dimensional modeling method based on multi-sensor fusion. The method comprises the following steps: synchronously acquiring data by using a laser radar, a camera, an infrared sensor, a GPS (Global Positioning System) and an IMU (Inertial Measurement Unit); the method comprises the following steps of: performing denoising and filtering operation on collected original data, aligning a timestamp and a space of the collected data, fusing the collected multi-source data, and aligning and fusing the processed multi-source point cloud data through a point cloud registration and fusion algorithm to generate a complete three-dimensional point cloud model; generating a grid model by using a surface reconstruction algorithm based on the point cloud data, and performing texture mapping in combination with texture information collected by a camera; the model is simplified and the precision of the model is improved through a model optimization technology, and a reliable three-dimensional visualization foundation for construction management and analysis is formed; real-time detection and modeling of dynamic targets such as mobile equipment and workers on a construction site are realized, and the real-time response capability of the system in a complex construction site environment is ensured through combination of a lightweight neural network architecture and an edge computing technology.

The invention provides a low-cost visual field large model for visual multi-modal information processing, and the model comprises an image encoder module which is used for converting an input image into low-dimensional feature representation; the feature extraction module is used for extracting multi-scale visual features based on a hierarchical multi-task learning strategy and reducing redundant calculation through a cross-modal parameter sharing mechanism; the task specifying module is used for designing a lightweight sub-model for image classification, target detection and image generation tasks, and integrating pruning and quantification technologies to optimize calculation efficiency; the reasoning optimization module is used for reducing model reasoning complexity and energy consumption by adopting a low-rank decomposition LoRA and mixed precision calculation technology; and the multi-modal fusion module is used for integrating vision, text and sensor data through a cross-modal attention mechanism to generate cross-modal joint feature representation so as to improve task robustness in a complex scene. According to the method, through the design of model architecture, parameter quantity and calculation optimization, the requirement for hardware resources is remarkably reduced.

The invention discloses an end-to-end optical computing chip based on multi-mode analog signal fusion processing, and belongs to the optical computing technology. Comprising a multi-mode input fusion front-end module, an optical fiber input interface and an end-to-end reasoning module. The multi-mode input fusion front-end module can convert different types of original analog signals such as images, spectrums and radio frequencies into unified broadband spectrum input signals. The end-to-end reasoning module constructs a deep optical neural network, and the deep optical neural network comprises a sensing-convolution integrated unit realized by an arrayed waveguide grating (AWG). The end-to-end reasoning module further comprises a photoelectric non-linear-pooling integrated unit, the average pooling function and the non-linear activation function are achieved at the same time, and light path loss is effectively compensated through injection of the supply light source. And finally, integrating the signals subjected to multi-layer processing by a full connection layer, and outputting a classification result by an output layer. According to the chip architecture, direct and efficient processing of multi-mode analog signals is realized.

The invention relates to the technical field of computer vision and emotion calculation, in particular to an emotion real-time recognition and long-term monitoring method based on facial expressions. According to the method, an emotion recognition result is obtained by recognizing a high-definition facial image, and the emotion recognition result, environment information and physiological state data are fused to obtain time-space aligned multi-modal data; performing emotional causal analysis based on the multi-modal data, and judging emotional causes by combining a rule engine and a machine learning model: outputting a real-time emotional state recognition result and a periodic emotional report according to the emotional causes, and performing differentiated feedback according to the emotional causes. According to the method, through multi-source data fusion and a causal inference mechanism, the accuracy and interpretability of emotion recognition are effectively improved, and the technical span from passive recognition to personalized active intervention is realized.

The invention provides an edge-collaborative point cloud data modeling and building design collaborative management method and system, electronic equipment and a storage medium, and relates to the technical field of edge computation.The method comprises the steps that city multi-source point cloud data is collected through a mobile scanning device, and time-space reference synchronization is conducted through an edge node dynamic transmission channel to generate a unified data set; detecting terrain contour features based on the environment sudden change index, triggering to re-collect updated data and generating scene constraint parameters; density self-adaptive compression is executed, a compression data set with adjustable partition precision is generated in combination with vegetation and temporary building distribution, and spatial topological features are extracted; inputting a building rule base to start distributed collaborative optimization to generate a design model; the model boundary is compared with the topographic change, the space conflict is solved through geometric structure adjustment, the closed-loop cooperative management adaptive to the environment contour is realized, the closed-loop cooperative control of the building design and the actual topographic data can be realized, and the space conflict is effectively avoided.

The invention relates to the technical field of endoscope video processing and intelligent edge computing, in particular to an endoscope video enhancement processing intelligent edge computing system. Comprising a data acquisition module which is used for acquiring an original video frame sequence of edge endoscope equipment in real time; the feature extraction module is used for determining an instantaneous feature vector representing the dynamic change of the operation scene; the criticality quantification module is used for quantizing and generating a surgical event criticality score; the tuning logic module is used for generating a discrete and stable calculation normal form switching instruction; the assembly line switching module is used for responding to the calculation normal form switching instruction and executing an asynchronous weight preheating strategy; the utility evaluation module is used for constructing a dynamic utility function to evaluate system performance; and the threshold value correction module is used for performing closed-loop correction on the high-criticality threshold value by adopting a gradient rising strategy. According to the method, the stability of system decision making is enhanced, the smooth transition of the video processing flow among different calculation paradigms is ensured, and the robustness of the system is improved.

The invention relates to the field of rock mass structural surface recognition, in particular to a high and steep slope rock mass structural surface intelligent recognition method based on double clustering. According to the method, high-precision image data of a high and steep slope is obtained through an unmanned aerial vehicle approaching photogrammetry technology, and a three-dimensional digital model of the high and steep slope is constructed by utilizing a three-dimensional reconstruction and calculation technology. And calculating normal vectors of all triangular surfaces based on the triangular surface vertex information of the model, and performing clustering analysis to divide structural surface groups with similar occurrence. And further performing clustering analysis according to the surface center coordinates of the triangular surface, and dividing to obtain the structural surface. And finally, calculating the inclination angle, inclination direction and trend data of the structural surface through an accurate mathematical model. According to the method, the flexibility and the safety of high and steep slope structural plane measurement operation are enhanced, a measurement area of any scale can be completely covered, objective, accurate, rapid and low-cost structural plane intelligent identification and attitude data acquisition are realized, and the method has important significance in engineering application related to high and steep slopes.

The embodiment of the invention discloses an application thermal migration method, computing equipment and an application thermal migration system. Relates to the technical field of computing. The efficiency of application thermal migration can be improved. The method is applied to a target computing device, and comprises the following steps: reading a memory mirror image applied on a source computing device from a first memory space of a computing fast link CXL memory device; wherein the first memory space is a memory space shared by the source computing device and the target computing device; writing the memory mirror image into a second memory space of the CXL memory device; the second memory space is a memory space exclusively occupied by the target computing device; and recovering the application according to the memory mirror image of the second memory space.

The invention discloses an optical computing system, a data processing method, a product, equipment and a medium, and relates to the technical field of optical computation.The method comprises the steps that two input and output modules and an optical computing module are arranged, a parameter matrix in the optical computing module is updated according to a to-be-processed feature matrix, and in each iteration, the parameter matrix of the optical computing module is updated; the optical signal modulated by the first input and output module is positively input to the optical calculation module and calculated with the parameter matrix, and the optical signal modulated by the second input and output module is reversely input to the optical calculation module and calculated with a transpose matrix of the parameter matrix, so that an output vector of current iteration is obtained; the key feature information of the feature matrix is determined according to the output vector obtained by the last iteration at the end of iteration, so that the problems of high processing complexity and low processing efficiency caused by processing data through electric calculation are solved, efficient calculation of the parameter matrix and the transpose matrix is completed through optical calculation, the processing complexity is reduced, and the processing efficiency is improved. And the treatment efficiency is improved.

The invention belongs to the technical field of optical calculation, and discloses an error-adaptive optical diffraction neural network in-situ training method, which comprises the following steps: based on the phase of each phase modulation layer, calculating the light field complex amplitude Uk of the input sample complex amplitude on the rear surface of each phase modulation layer and the light field complex amplitude Uo of the input sample complex amplitude on an output plane after forward propagation; calculating an error light field P based on Uo, carrying out three-dimensional central symmetry on a back propagation light path of the error light field P without changing the phase distribution of each phase modulation layer, and calculating the light field complex amplitude of the complex amplitude of the symmetrical error light field on the front surface of each phase modulation layer after forward propagation; the method comprises the steps of obtaining the light field complex amplitude Un + 1-k'of P back propagation to the rear surface of each phase modulation layer, calculating the gradient related to a loss function based on Uk and Un + 1-k 'so as to carry out updating, and carrying out the next round of training until a preset training round or loss convergence is reached. The method can adapt to errors existing in an optical system.

The embodiment of the invention relates to the technical field of population activity monitoring, and particularly discloses a population activity quantity extraction method and system based on cloud computing and big data utilization. According to the embodiment of the invention, multi-source population data of a target monitoring area is collected and uploaded; based on a cloud computing technology, performing data standardization and fusion processing on the multi-source population data; predicting the number of regional population, and performing dynamic visual display; and determining a plurality of population early warning rules, carrying out abnormal risk judgment on the number of regional population, and carrying out early warning pushing and linkage response when an abnormal risk exists. According to the method, a dynamic fusion mechanism of an emotion-flow coupling anomaly thermodynamic diagram and a flow mutation grid map with conflict indexes is constructed, so that silent abnormal points and false abnormal points are accurately captured.