322results about How to "Reduce computing pressure" patented technology

The invention provides a concrete production management method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: collecting the health monitoring data of concrete production equipment in real time through a plurality of sensors, and collecting the full life cycle event data of the concrete production equipment; preprocessing the health monitoring data at an edge end, extracting state features of the equipment, performing real-time diagnosis by using a pre-trained fault prediction model based on the state features of the equipment, and constructing an equipment multi-level digital twinborn model at a cloud platform; the method comprises the following steps: fusing a pre-constructed physical degradation model and a data driving model based on a multi-level digital twinborn model, constructing an equipment residual life prediction model, and dynamically adjusting a concrete production strategy in the digital twinborn model according to an obtained prediction result to obtain an optimal concrete production scheduling scheme. And the information is converted into an equipment control instruction, so that automatic issuing and execution of a concrete production scheduling scheme are realized.

The invention discloses a classroom teaching quality auxiliary evaluation method, device and system based on cloud edge collaboration, and the method comprises the steps: obtaining video image data, collected by a camera, of students in a classroom in real time, and transmitting the video image data to an edge device; and in the edge device, performing student classroom behavior identification and tracking on the received video image data by using a pre-trained student classroom behavior identification model, and fusing the identification results of adjacent multiple frames with the identification result of the current frame in the tracking process to obtain the final identification result of the current frame. Evaluating the classroom teaching quality according to the student classroom behavior recognition result, wherein the student classroom behavior recognition model is obtained by training based on a multi-label target detection algorithm and by adopting a knowledge distillation structure in advance; and sending a student classroom behavior identification result and an evaluation result to a cloud video server for storage. The method has the advantages of being simple in implementation method, low in cost, high in real-time performance and precision, high in robustness and the like.

The invention provides an intelligent industrial quality inspection method and system based on AI visual identification, and relates to the technical field of visual identification, and the method comprises the steps: inputting an industrial product image collected at an edge end into a pre-trained defect region detection model, and obtaining a defect region detection result; extracting defect area features according to a defect area detection result, carrying out dimension reduction processing to obtain compressed defect area features, and deciding to unload a defect classification task to a cloud end or directly carry out defect classification at the edge end according to an adaptive task unloading strategy in combination with working data of the edge equipment; if the defect classification task is determined to be unloaded to the cloud, transmitting the compressed defect area features to the cloud through a network, performing defect type identification on the cloud by adopting a pre-trained defect classification model, outputting a classification result of the defect area, returning the classification result to an edge end, and combining a defect area detection result and the classification result of the defect area to obtain the defect classification task. And generating a defect visualization effect picture and a quality inspection report.

The invention discloses a rule engine scene linkage method and system based on an edge gateway, and relates to the technical field of scene linkage. The rule engine scene linkage method based on the edge gateway comprises the following steps: adding equipment linkage rule management according to a preset rule through a cloud platform; wherein the equipment linkage rule management comprises rule addition, rule deletion, rule query and rule modification; acquiring operation state information of the equipment end through the edge end, generating an equipment linkage fact, and adding the equipment linkage fact into a fact library of a preset rule engine; and matching a first equipment linkage rule below the equipment linkage rule management with the equipment linkage facts based on a preset rule engine. According to the invention, the computing pressure of the cloud server side can be reduced, and the purpose that the edge gateway independently analyzes the application rule and performs scene linkage when the edge gateway is disconnected from the cloud server is achieved.

The invention relates to the technical field of semiconductors, in particular to a photoelectric chip packaging structure which comprises a substrate, a signal transmission layer is arranged on the upper surface of the substrate, and a signal processing layer is arranged on the signal transmission layer; the signal transmission layer comprises a first electric chip which is provided with a first surface and a second surface, the first surface faces the substrate, and the second surface faces the signal processing layer; the signal processing layer comprises a signal conversion area and a data storage area; the signal conversion area is correspondingly provided with an optical chip, the optical chip comprises N * M photon calculation units, each photon calculation unit comprises an optical waveguide layer and a modulation layer located on the optical waveguide layer, and the modulation layer is a phase change material layer or comprises a phase change material layer; the data storage area is correspondingly provided with a second electric chip which is used for receiving and storing calculation data. The problems that an existing packaging structure is high in preparation difficulty, poor in heat dissipation performance and the like are solved.

The invention discloses an industrial park net load prediction method and system based on a multilayer perceptron, and the method comprises the following steps: dividing historical industrial park operation data according to an audio frequency collection region, and obtaining region historical operation data corresponding to the audio frequency collection region; performing clustering analysis on the historical operation data of each region based on the audio frequency fluctuation, and obtaining audio frequency-aging correlation according to a clustering result; training by using historical industrial park operation data, historical distributed energy operation data and audio frequency-aging correlation to obtain an industrial park net load prediction model; and obtaining industrial park net load prediction data according to the current industrial park operation data, the current distributed energy operation data, the industrial park load net prediction model and the audio frequency-aging correlation. The method has the beneficial effects that an aging model does not need to be constructed for each industrial device, the overall model structure is simplified, and meanwhile, the prediction efficiency and prediction accuracy of the load of the industrial park are improved.

The invention provides a cloud edge collaborative anomaly detection method, system and equipment for a virtual power plant, and is applied to the technical field of data mining. The method comprises the following steps: performing anomaly detection on real-time operation data of each distributed resource in a virtual power plant based on a global anomaly detection model obtained from a cloud server; sending the detection result to monitoring equipment associated with each distributed resource corresponding to the edge end equipment; the global anomaly detection model is obtained by the cloud server through fusion processing according to local anomaly detection models trained by the edge end devices respectively. The problem that abnormal data generated in the data transmission process of the virtual power plant system affects the accuracy and reliability of energy management and decision making is solved.

The invention discloses a coal mine carbon emission prediction method and system based on digital twinning and LSTM, and the method comprises the steps: building a carbon emission source information coding standard based on a business demand and a digital twinning model of a mining area in combination with a production link and an auxiliary production link of the mining area; performing carbon emission edge calculation on the carbon emission energy consumption data of the carbon emission source by using a coal mine carbon emission accounting method to obtain carbon emission data; establishing a corresponding mapping relation between the coal mine digital twinborn model and a database of carbon edge computing equipment by utilizing a carbon emission source information coding standard; according to the mapping relation, mapping the carbon emission data of each link to a coal mine digital twinborn model one by one; and establishing a carbon emission prediction model by using an LSTM algorithm, inputting historical actual carbon emission data into the model, and predicting carbon emission data of the current day. According to the method, the current carbon emission situation of each link of coal mine production and auxiliary production can be known, the carbon emission trend of each emission point is mastered in real time, and the emission reduction strength of enterprise energy-saving measures is quantified.

The invention discloses a virtual power plant adjustable potential comprehensive evaluation method, system and equipment giving consideration to communication and action time delay. According to the method, a communication system time delay model, an equipment action time model and a communication reliability model are constructed by constructing a cloud edge collaborative network system framework for regulating and controlling a virtual power plant. And then, establishing a virtual power plant maximum adjustable potential evaluation model based on timing delay step length search by using a k-shortest path algorithm. And finally, providing a method based on a near-end strategy optimization algorithm to solve the optimization model. According to the method, comprehensive evaluation of the adjustable potential of the virtual power plant considering the influence of the communication system and the resource action time is accurately described, a decision basis is provided for the virtual power plant to participate in primary and secondary frequency modulation and peak regulation of the power system, and frequency stability and power and electric quantity balance of the power system can be supported.

The invention discloses a power plant electrical automation control system and method based on hierarchical management, and relates to the technical field of power plant electrical control, and the system comprises a dynamic resource distribution coordination mechanism, a fault self-healing mechanism, and a local optimization control and energy efficiency optimization strategy based on edge calculation. The resource output of the dynamic resource allocation coordination mechanism is used as the optimized resource input of the local optimization control based on the edge computing, and the energy efficiency optimization mechanism is used as a dynamic interaction layer of the dynamic resource allocation coordination mechanism and the local optimization control based on the edge computing. The fault self-healing mechanism is used as a result execution mechanism of the energy efficiency optimization strategy. By designing a dynamic resource allocation coordination mechanism, the resource load dynamic allocation function of the power plant equipment is realized, the problems of unbalanced resource utilization, equipment overload or low-efficiency operation and incapability of quickly responding to resource requirements in a traditional power plant are solved, resource allocation is optimized, and the operation efficiency of the whole system is improved.

The invention belongs to the technical field of data analysis, and particularly relates to an information analysis method for bidding documents. The information analysis method comprises the following steps: S1, obtaining a newly added bidding and tendering file, and sending the file into a preprocessing module; s2, the preprocessing module outputs corresponding preprocessing data to a classification module; s3, the classification module divides the content in the current preprocessed data into valid tables, core blocks and independent blocks, and then the valid tables, the core blocks and the independent blocks are sent into a table analysis module, a core analysis module and an independent analysis module respectively; and S4, the table analysis module, the core analysis module and the independent analysis module respectively output the first analysis data, the second analysis data and the third analysis data to an integration module for integration to obtain core information and output the core information. According to the method, the core information of various bidding and tendering files can be accurately and efficiently analyzed.

The invention discloses an image down-sampling method and an image processing device, belongs to the technical field of image processing, and can solve the problems that an existing down-sampling method cannot realize rapid down-sampling, the algorithm efficiency is relatively low, and the resource consumption of a processor is relatively large. The method comprises the following steps: S1, acquiring an image to be sampled and a downsampling requirement, and storing the image to be sampled in a processor cache according to bytes; s2, determining dimension parameters of an EDMA transmission mode according to the to-be-sampled image and downsampling requirements; the dimension parameters comprise an ACNT parameter, a BCNT parameter and a CCNT parameter; and S3, performing byte extraction on the to-be-sampled image stored in the processor cache according to the dimension parameters to obtain a sampled image meeting the downsampling requirement. The method is used for downsampling of the image.

The invention discloses an obstacle-aware cloud-side-end cooperative industrial wireless network channel prediction method, and relates to the technical field of wireless communication. The industrial terminal equipment acquires channel state data in real time, preprocesses the channel state data by using a data preprocessing unit, and transmits the preprocessed channel state data to the edge server; based on the preprocessed channel state data, the edge server adopts a long-short-term memory network to carry out obstacle detection and classification, generates obstacle category information, and uploads the obstacle category information and the preprocessed channel state data to a cloud server; and an obstacle sensing channel conversion architecture model is established, and the cloud server obtains a channel state prediction result through the obstacle sensing channel conversion architecture model based on the received obstacle category information and the preprocessed channel state data. The method is high in accuracy, higher in adaptability in a complex environment, and good in reliability and expansibility in practical application.

The invention provides a medical internet of things data processing system and method based on a marginal container and electronic equipment, and relates to the technical field of computers, the system is deployed in the marginal container, and the system comprises a data input module, a data processing module, a logic analysis module and a data distribution module; the data input module is configured to receive medical Internet of Things data, sent by edge medical equipment, of a target patient; the data processing module is configured to determine an analysis rule corresponding to the edge medical equipment, and perform analysis processing on the medical Internet of Things data according to the analysis rule to obtain patient analysis data; the logic analysis module is configured to perform alarm analysis on the patient analysis data according to an alarm rule corresponding to the target patient to obtain alarm information; and the data distribution module is configured to send the medical Internet of Things data and the alarm information to a platform system deployed at a cloud end. According to the invention, the real-time performance of alarm information processing can be ensured while the calculation pressure of a platform system is reduced.

The invention provides a data query method and device, electronic equipment and a storage medium, and relates to the technical field of databases, the method comprises the following steps: in response to an obtained query instruction, determining a query tag corresponding to the query instruction; determining a corresponding target field in a plurality of local field sets based on the query tag; the target field is a related field of a target data set; wherein the plurality of field sets are attribute information sets of the data set periodically updated from the server; fields representing the same service attribute in the multiple field sets correspond to the same label; and sending the target field to the server, and receiving a target data set fed back by the server in response to the target field. According to the method, the efficiency of querying data from the server by the terminal is improved, and the accuracy of querying the data is improved.

The invention discloses a remote control anti-delay video transmission method based on a future scene generation network. The remote control anti-delay video transmission method comprises the following steps: synchronizing time references of a remote control vehicle and a remote cockpit; original video stream data of the environment where the remote control vehicle is located is collected; adding a camera number and timestamp information into the original video stream data; preprocessing the decoded video stream data to generate a plurality of paths of future scenes; generating multiple paths of future scene video stream data; camera numbers and timestamp information are added to the multiple paths of future scene video streams; decrypting, decoding and distributing the data; performing packet loss and time delay detection on the original video stream data, and performing visual quality optimization on the future scene video stream data; and a complete and continuous video stream is formed. According to the method, the delay in video transmission is greatly reduced, and the video content at the future moment can be predicted and generated in advance in future scene generation, so that the video blank possibly occurring due to transmission delay or data packet loss is effectively filled.

The invention relates to the technical field of picture rendering, in particular to a media picture rendering optimization method and system based on artificial intelligence. The method comprises the following steps: acquiring a terminal video stream; performing RGB format conversion on the terminal video stream to generate an RGB type frame image set; pixel partitioning is carried out on the RGB type frame image set, and RGB pixel matrix blocks are generated; performing color space acceleration conversion on the RGB pixel matrix block to generate a YUV cache data matrix; the YUV storage data matrix is sent to a DSP internal processing unit for key frame selection, and YUV key video frame data and YUV non-key video frame data are obtained; and performing intra-frame coding on the YUV key video frame data so as to generate YUV key frame coded data. According to the method, the media picture rendering efficiency is improved by optimizing data processing, transmission and rendering and combining user visual focus analysis.

The invention provides a remote sensing image slicing system and method based on an FPGA (Field Programmable Gate Array). The system comprises a CPU (Central Processing Unit) module and an FPGA module, the CPU module is used for decoding an input original image and transmitting decoded image data and a corresponding first configuration parameter to the FPGA module; the FPGA module comprises an image slicing module and a memory module; the image slicing module is used for segmenting the original image into a plurality of slice images based on the first configuration parameter, and the memory module is used for storing the original image and the generated slice images. A CPU module and an FPGA module are combined, a plurality of image slices can be processed at the same time by means of the parallel computing capacity of an FPGA, and the processing speed and efficiency are improved; and the method has relatively low processing delay and flexibility.

The invention relates to the technical field of battery car charging management, in particular to a battery car charging integrated management system based on edge calculation, which acquires battery parameters of a battery car and state parameters of a charging pile in real time through a battery car end acquisition module and a charging pile end acquisition module, and performs local processing through an edge calculation unit. The real-time performance of data processing is improved, and abnormal conditions can be quickly responded. Charging behaviors are analyzed through deep learning, abnormal conditions are judged, and data are distributed to a load balancing unit or a risk assessment unit. The load balancing unit utilizes a reinforcement learning model to optimize charging load distribution according to the power grid load, the battery car demand and the charging pile state so as to avoid power grid overload. The risk assessment unit establishes a model through historical data, monitors the risk in real time and gives an alarm, and meanwhile, through edge calculation and local deployment of a classification model, the dependence on the cloud is reduced, and the calculation efficiency and the management efficiency are improved.

The invention discloses a large language model training method based on efficient fine tuning of parameters. The large-scale language model training method comprises the following steps: aiming at a large-scale language model of a training object, dividing the large-scale language model into a client pre-training model and a server pre-training model; the client pre-training model and the server pre-training model are combined for parallel cooperative training, and in the training process, the client pre-training model and the server pre-training model are subjected to parallel training by adopting an efficient parameter fine tuning technology; after training is completed, combining the client pre-training model and the server pre-training model by adopting a model aggregation method; and repeating the steps until the large language model serving as the training object is converged. The large language model training method is based on the segmentation federal learning framework, and combines the parallel training advantage of the FL and the model segmentation advantage of the SL, so that the training efficiency is greatly improved.

The invention belongs to the technical field of electromagnetic exploration, and particularly relates to a key parameter optimization method for ground well electromagnetic exploration simulation calculation, which is based on solved irregular layered background medium Green function response, utilizes the plane symmetry property of a union-vector Green function and adopts a fast Fourier transform algorithm to optimize the key parameters of the ground well electromagnetic exploration simulation calculation. Only calculating and researching half of the grid point and vector Green function of the horizontal section diagonal line of the area, and reconstructing the distribution of the complete horizontal section grid point and vector Green function through a spatial interpolation algorithm; and solving the three-dimensional ground well electromagnetic field integral equation set under the background of the three-dimensional irregular layered medium by adopting a stable biconjugate gradient-fast Fourier transform algorithm, so as to obtain the total electromagnetic field for researching the center point of the subdivision grid in the three-dimensional region. The primary field and the scattered field of the electromagnetic field at each observation point in the ground well electromagnetic shaft are efficiently calculated.

The present invention discloses a bulk material volume measurement method based on image features and three-dimensional point clouds, including: fixing a three-dimensional laser radar and a digital camera on a forklift to obtain the overall information of the bucket and its load; collecting and preprocessing point cloud data, optimizing the collected point cloud data through filtering and clustering algorithms; fusing image and point cloud information, calibrating parameters between the laser radar and the digital camera, projecting the point cloud to a pixel plane, fusing the straight line features of the image and the point cloud information; obtaining a reference plane; triangulating the bulk material surface point cloud, dividing the downsampled point cloud into triangular grids through greedy triangulation; calculating the volume: calculating the volume of the oblique truncated triangular prism corresponding to each triangular grid, and then accumulating to obtain the final bulk material volume. The method of the present invention has fast calculation speed, simple steps and accurate results; by collecting the point cloud and image information of the bucket, real-time volume calculation is realized, which is suitable for real-time monitoring in complex industrial and mining environments such as construction sites.

The invention discloses a power grid load scheduling method and device and a storage medium, and the method comprises the steps: collecting first target load data for electrical equipment under the jurisdiction of an edge computing node in a power grid in the edge computing node, and collecting second target load data for electrical equipment under the jurisdiction of the edge computing node in the edge computing node; and inputting the load data into a preset short-term load prediction model, predicting future second target load data, generating a preliminary load scheduling scheme in the edge computing node according to the first target load data and the second target load data, transmitting the preliminary load scheduling scheme to a cloud computing center, and transmitting the preliminary load scheduling scheme to the cloud computing center. The initial load scheduling scheme is corrected into a global load scheduling scheme according to the operation state of the power grid, and the global load scheduling scheme is transmitted to each edge computing node to be executed in the cloud computing center so as to schedule the load of the power grid, so that the load scheduling efficiency and stability of the power grid are improved; and balanced distribution of the whole network load is realized through dynamic adjustment.

The invention particularly relates to a GIS mass data drawing method and system. According to the GIS mass data drawing method, a ServiceWorker thread is started in the background, a GIS data interface is intercepted through the ServiceWorker thread, and GIS total data is intercepted; selecting part of data according to a user-defined standard, pushing rendering data to the main thread, and rendering the rendering data as initial data; dividing the remaining total data into a plurality of data sets according to a user-defined preset rule, and putting the data sets into a queue to be rendered; pushing the to-be-rendered data to a main thread in batches by utilizing an incremental updating mode; and after the main thread receives the data, calling a rendering method to render the data to a page. According to the GIS mass data drawing method and system, the calculation pressure of a main thread can be relieved, long-time blank waiting and long-time occupation of a rendering thread can be avoided in an incremental rendering mode, and therefore instant huge pressure brought by direct rendering of mass data to equipment is avoided.

The invention relates to the technical field of greening management, in particular to an urban greening state monitoring method and system based on digital mirror image information, and the method comprises the steps: collecting the real-time live-action data of a designated city, combining the real-time operation rule of the city, and generating a virtual city update mirror image through a digital twinborn technology; extracting digital mirror image information in a specified time period from the virtual city updated mirror image to obtain greening state mirror image data, and generating a virtual city greening state by analyzing the mirror image data; further detecting the greening state of the virtual city, generating greening state early warning information, and feeding back the greening state early warning information to the real city to determine a monitoring area and a monitoring index; and performing live-action monitoring on the designated area of the real city, collecting and analyzing monitoring data to obtain an actual monitoring result of the alarm area, and synchronizing the actual monitoring result to the virtual city update mirror image. Dynamic monitoring and bidirectional feedback of the greening state are realized, and technical support is provided for fine management and scientific decision-making of urban greening.

The invention discloses a multi-sensor edge system and method based on cross-modal dynamic attention. The method comprises the following steps: S1, acquiring environment data based on multiple sensors and multi-modal input data based on an interaction module; s2, preprocessing the acquired environment data and the multi-modal input data, and performing feature extraction to obtain multi-modal feature data after feature extraction; s3, carrying out space-time dimension enhancement on the multi-modal feature data; s4, dynamically optimizing the configuration of each modal resource based on the enhanced multi-modal characteristic data obtained in the step S3; and S5, inputting the dynamically optimized modal resource information into the generative model to generate an output result. According to the method provided by the invention, the data processing flow is optimized, the data transmission delay is reduced, efficient integration and utilization of information are realized through a cross-modal dynamic attention mechanism, and the accuracy and robustness of decision making are enhanced.

The present invention proposes a valve control method and system based on the Internet of Things. It is characterized in that the method includes: installing a sensor at the pipeline valve, collecting pipeline-related data through the sensor, and transmitting the collected data to the edge device, and pre-processing the relevant data through the edge device; transmitting the relevant data pre-processed by the edge device to the cloud, the cloud stores the relevant data in segments, and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results; comparing the analysis results with preset thresholds, and formulating corresponding control plans based on the comparison results; and transmitting the control plan to the valve controller through the Internet of Things. By installing sensors at pipeline valves, performing preliminary processing through edge devices, and transmitting data to the cloud for processing through Internet of Things technology, the processing speed can be improved and the computing pressure on the cloud can be reduced.

The invention discloses an internet-based online gynecological disease intelligent consultation system, and relates to the field of data processing, and the system comprises the steps: generating a public and private key pair of a patient through employing an RSA asymmetric encryption method; a BERT model based on deep learning is adopted to process the decrypted inquiry information, processing is carried out on local equipment, and structured patient condition description is obtained; the encrypted patient condition description is uploaded to a server, a system private key is used for decryption on the server, diagnosis is carried out by using a reasoning method based on a knowledge graph according to the patient condition description, and a disease candidate list is obtained; transmitting the encrypted candidate list to local equipment of the patient, decrypting by using a private key of the patient, and reasoning by adopting a CART decision tree according to the disease candidate list to obtain a final diagnosis result; aiming at the low precision of online gynecological disease consultation diagnosis in the prior art, asymmetric encryption, knowledge graph reasoning, a CART decision tree and the like are adopted, so that the diagnosis precision is improved while the privacy of a patient is protected.

The invention relates to the technical field of cloud edge collaboration, in particular to an edge computing cloud edge collaboration method, system and device, and the method comprises the steps: obtaining a real-time monitoring video shot by each AI camera in a single target area, dividing the real-time monitoring video into an effective video segment and an invalid video segment, the effective video segment is divided into a dominant clear frame group and a hidden clear frame group according to the definition, a method for obtaining target information is determined according to the type of the clear frame group, the edge filtering module receives the target information of the dominant clear frame group and the clear enhanced frame group, all the target information of the single effective video segment is determined according to the time sequence relation, and the target information is obtained. And filtering to generate effective target information, and determining traffic information of each time period according to the effective target information. According to the method and the system, the dependence on cloud resources is reduced through a processing mode of cloud edge collaboration and cloud centralization removal, and the optimal configuration of the resources is realized.

The invention discloses a positioning data intelligent management system and method applied to an Internet of Things product, and relates to the technical field of positioning data management, and the system comprises a distribution area analysis module, a distribution task distribution module, and a distribution unit management module. The distribution area analysis module is used for acquiring a distribution area image and map data, dividing a distribution area into path nodes and path units, marking the path nodes and the path units, and constructing a distribution path node graph; the distribution task distribution module analyzes the distribution exchange rate of the shortest distribution path of each distribution task, carries out the least grouping decision of each distribution task, and plans the distribution path of the distribution unit of each group. And the distribution unit management module determines the path units where the distribution units are located according to the real-time positioning data of the distribution units, and sets obstacle avoidance strategies when the distribution units advance according to the calculation results of the environment change rates of the path units.