69results about How to "Reduce data transfer" patented technology

The invention discloses a data transmission method and device, equipment and a storage medium, and relates to the technical field of data transmission. The method comprises the following steps: preprocessing collected initial data to obtain data to be transmitted, and monitoring network quality in a data transmission process in real time to obtain a real-time network condition; compressing the to-be-transmitted data according to the data characteristics of the to-be-transmitted data and the real-time network condition to obtain candidate data; performing feature extraction on the candidate data according to data features to obtain target feature data; and determining a data transmission strategy according to the network topology structure, the data characteristics and the real-time network condition of the edge device, and transmitting the target characteristic data to the target device based on the data transmission strategy. According to the technical scheme, data compression, feature extraction and transmission strategy determination are dynamically carried out according to the real-time network condition, the data transmission problem caused by the unstable network environment can be reduced, and the data transmission efficiency and reliability are effectively improved.

The invention relates to a fermentation equipment cooperative control method based on a distributed control architecture. Comprising the steps that each fermentation equipment control unit broadcasts node information of the fermentation equipment control unit through a distributed protocol including Zigbee or LoRaWAN, and the node information includes equipment types, control capability and state parameters; the master control node receives and integrates the broadcast information, dynamically generates a distributed control topological graph, and marks the roles of each device including a master control, an auxiliary and a terminal; the master control node formulates a global cooperative control target according to a production target comprising a metabolite concentration or fermentation time; each node monitors fermentation parameters in real time and uploads the fermentation parameters to the master control node through a distributed data transmission protocol; the master control node generates a globally optimized control parameter set by using a collaborative optimization algorithm including a Lagrangian relaxation method; each distributed node adjusts the operation state of the equipment according to the issued parameter set; each node collects fermentation environment data including temperature mutation and pH value fluctuation in real time, and rapid analysis is carried out through an edge calculation unit.

The invention provides an intelligent switch networking method and device based on a Bluetooth mesh, and the method comprises the steps: obtaining initial feature data according to the hardware capability information, the power type information and the geographic position information of each intelligent switch connected to a Bluetooth mesh network; according to the initial feature data, performing type analysis on each intelligent switch to obtain switch type data; according to a preset network distribution algorithm and the switch type data, an initial network topology structure is obtained, and the initial network topology structure comprises backbone switches and edge switches; performing networking communication according to the initial network topology structure, collecting single load data in the networking communication process, and performing multi-dimensional evaluation according to the single load data to obtain a comprehensive load index; and when the comprehensive load index exceeds a preset threshold value, performing role upgrade evaluation on the edge switch to obtain a role adjustment decision, and obtaining an optimized network topology structure according to the role adjustment decision and the initial network topology structure.

The invention discloses transmission and distribution line strong wind disaster monitoring equipment based on a smart power grid, belongs to the technical field of smart power grid transmission and distribution line monitoring, and is used for solving the problems that strong wind disaster monitoring equipment is fixed in sensor acquisition frequency and low in resource utilization rate, and lacks an effective grade evaluation mechanism when coping with the influence of strong wind disasters on wire tension. Comprising a sensor module, a data acquisition module, a data processing module and a disaster detection module, the sensor module is provided with various environmental sensors at transmission and distribution line towers and wires to collect environmental parameters; the data acquisition module divides environmental parameter risk intervals based on historical data, assigns values, and intelligently adjusts the acquisition frequency according to a real-time total risk value; the data processing module integrates the environmental parameters into a detection data packet, determines and sends a disaster detection packet according to a total risk value and verifies the integrity of the disaster detection packet; the disaster detection module analyzes the environment parameters of the disaster detection package, obtains a disaster evaluation value, compares the disaster evaluation value with a threshold value, evaluates the disaster grade and adjusts the wire tension.

The invention provides a multi-microgrid load frequency control method based on dynamic event triggering, which can be applied to the technical field of intelligent microgrids. The method comprises the steps that a local sampling error of the micro-grid system is acquired, the local sampling error is obtained based on a global neighborhood error and a local neighborhood error of the micro-grid system, and the local neighborhood error is used for representing a state difference between the micro-grid system and a neighbor micro-grid system at a sampling moment; under the condition that the local sampling error meets a dynamic event triggering rule, updating an initial weight matrix of the action-evaluation neural network based on the local sampling error to obtain a target action-evaluation neural network; and determining a load frequency control strategy of the micro-grid system based on the system state of the micro-grid system at the sampling time and the target action-evaluation neural network. The invention further provides a multi-microgrid load frequency control device based on dynamic event triggering.

The invention discloses a vehicle environment data uploading method and device, equipment and a storage medium, and relates to the technical field of data processing, and the vehicle environment data uploading method comprises the steps: obtaining sensor data and an environment change flag bit; when the change frame number of the environment change flag bit reaches a preset frame number threshold value, a yaw angle change value and a pose change value are obtained according to the sensor data; when the system performance, the yaw angle change value and the pose change value all meet preset conditions, caching the sensor data; and when the sensor data reaches a preset data threshold value, uploading the sensor data. According to the method, efficient acquisition and uploading of the environment data are realized through judgment of the environment change mark monitored by the continuous frame vehicle in real time and modification of the key frame screening threshold value by the system resource information. The data acquisition efficiency and the system resource use efficiency are improved, the acquired data transmission and storage pressure is reduced, and the resource processing consumption of the cloud server is reduced.

The invention provides a neural network hardware implementation method and system based on an incomplete specified function, and the method comprises the steps: carrying out the quantification of the input data and weight of a neural network, and obtaining a position index of a non-zero weight; traversing the training set, and obtaining a plurality of data sets related to the non-zero weight according to the position index of the non-zero weight based on an incomplete specified function; performing logic minimization on the basis of the plurality of data sets, generating a Boolean logic expression based on an incomplete specified function, and determining a flash memory logic array scale and internal port connection lines according to a cube of the Boolean logic expression and logic variables in the Boolean logic expression; the flash memory logic array is constructed according to the scale of the flash memory logic array and the internal port connecting line, and a peripheral circuit of the flash memory logic array is constructed, and the peripheral circuit is mainly used for processing output data of the flash memory logic array. According to the invention, power consumption and time delay in a neural network hardware implementation process are reduced.

The invention discloses a passive field adaptive image recognition method for difficult sample division processing. The method is characterized by comprising the following steps: firstly, not accessing source domain data and training a source domain model; and then constructing a target domain model, training an optimal feature generator of the target domain, designing mutual information maximization loss and a weighted clustering algorithm to learn feature representation of the target domain, and implicitly aligning cross-domain features. And finally, the difficult samples are divided and processed, the target domain samples are divided into simple samples, low-confidence samples and high-confidence samples, and an effective weight distribution mechanism is designed to be combined with a strategy for promoting feature learning so as to realize excellent domain migration ability. The invention aims to provide a high-precision passive field adaptive image recognition method, which solves the problems of data security and privacy protection of existing field adaptation, solves the problem of dependence of deep learning on a large amount of annotation data, reduces sample annotation cost and hardware implementation cost in image recognition, realizes cross-field knowledge migration, and improves image recognition efficiency. And high-precision image recognition of the target domain is completed.

The invention provides an Internet of Things data acquisition and abnormity early warning method and system, and relates to the technical field of Internet of Things data processing, and the method comprises the steps: constructing a digital twin model for a physical entity at a cloud end, and issuing prediction data to an edge node; acquiring sensor data by an edge computing node at a basic frequency, and computing a residual error between real-time data and predicted data and an information entropy of a residual error sequence; dynamically adjusting the data acquisition frequency of the edge node based on a two-dimensional decision space formed by the residual error and the information entropy; and when the residual error and the information entropy both exceed the threshold values, potential abnormity is judged, an edge side abnormity analysis program is triggered, and abnormal event information is uploaded. According to the invention, through cloud edge cooperation and intelligent decision making, the acquisition frequency is reduced in a stationary period, resources are saved, the frequency is improved when an abnormal symptom appears, monitoring is enhanced, and optimal balance between resource consumption and monitoring precision is realized; double criteria are adopted to effectively filter noise interference, and the false alarm rate and the missing report rate are reduced.

The invention discloses a ship-shore data out-of-order synchronization method and device, electronic equipment and a storage medium, and the method comprises the steps: sending the compression information of a to-be-synchronized compression package to a shore end through a data synchronization request sent to the shore end, so as to enable the shore end to carry out the data synchronization detection based on the compression information, and obtaining a full bitmap of the data synchronization condition of the shore end; performing breakpoint detection on the data blocks based on the total bitmap to obtain breakpoint data; when the number of the breakpoint data is greater than the first number, retransmitting the breakpoint data to the shore end; otherwise, synchronizing the data blocks after the target block to the shore end in sequence; periodically acquiring a block receiving condition of a shore end, and updating the total bitmap based on the block receiving condition; and returning to execute the step of carrying out breakpoint detection on the data blocks based on the total bitmap to obtain breakpoint data, and carrying out a new round of data synchronization iteration until all the data blocks of the compressed packet are synchronized. The method can efficiently realize ship-shore data synchronization, and can be widely applied to the technical field of data processing.

The invention relates to the technical field of data storage, and discloses a blockchain-based trade platform data credible storage method and system, which comprises the steps of calculating a storage key coefficient to screen important platform sub-data, reducing unnecessary data transmission, and then calculating a correlation degree value to obtain a credible storage result of the trade platform sub-data. According to the method, the storage position of highly-associated platform sub-data is optimized, the access efficiency is improved, the energy consumption of cross-storage node transmission is reduced, finally, Q data storage strategies are designed and optimized, and the data storage process is optimized by using a natural heuristic algorithm, so that the total energy consumption corresponding to the data storage strategies is minimized, and the data storage efficiency is improved. Through data segmentation, storage optimization and energy optimization management of a data storage process, the problem that energy consumption is too high due to the fact that each node in an existing block chain needs to synchronize all data is effectively solved.

The invention discloses a storage and calculation integrated acceleration macro supporting multimode convolution, a chip and equipment, the storage and calculation integrated acceleration macro comprises an input cache (1), a column address decoder (2) and 64 multiply-accumulate operation arrays (3), each multiply-accumulate operation array (3) comprises a row address decoder (31), a multimode adaptability controller (32), an SRAM array (33), a multiplier (34) and an adder tree network (35), and the 64 multiply-accumulate operation arrays (3) share one common column address decoder (2) and are connected to the same input cache (1). According to the method, the inherent flexibility and expandability of the digital CIM are utilized, efficient control logic is designed under the condition that the original CIM storage density is not sacrificed, various kinds of convolution are supported with the minimum extra area cost, and high calculation efficiency is kept.

The invention discloses a self-adaptive event triggering cooperative tracking control method for an unmanned cluster system under resource limitation. The method comprises the following steps: establishing a dynamic model of a leader and a follower for a heterogeneous unmanned cluster system; constructing a distributed observer for the follower; determining a state measurement error and an internal dynamic variable of the follower, determining a dynamic event triggering condition of the follower, and constructing a self-adaptive event triggering controller of the follower; when the state measurement error meets or does not meet the dynamic event triggering condition, the adaptive event triggering controller adopts different means to obtain the system observation state of the neighbor node and updates the adaptive event triggering controller; and the follower realizes cooperative tracking control on the leader based on a system observation state. According to the method, the communication burden is effectively reduced, and an efficient and reliable solution is provided for cooperative control of the heterogeneous unmanned cluster system in a complex environment.

The invention relates to the technical field of cloud mobile phone multi-terminal data synchronization, in particular to a cloud mobile phone multi-terminal data synchronization system and method. According to the technical scheme, the system comprises a cloud server, a plurality of terminal devices and a data synchronization module; the cloud server combines a distributed architecture and an edge computing technology and is composed of a plurality of data centers distributed at different geographic positions, and each data center is provided with a high-performance server cluster and a large-capacity storage device. According to the cloud mobile phone multi-terminal data synchronization method, a distributed architecture is combined with edge computing, advanced technologies such as artificial intelligence, quantum encryption and deep learning are applied, efficient data synchronization, high security, adaptive interface adjustment and personalized synchronization strategies are achieved, the method has high reliability and stability, the real-time synchronization requirement and security guarantee of a user are met, and the method is suitable for large-scale popularization and application. And user experience is improved.

This invention relates to the field of flight scheduling technology, specifically providing a method, system, device, and storage medium for collaborative scheduling of low-altitude aircraft. The method includes: edge nodes receiving real-time data from a swarm of drones and generating corresponding scheduling data using a pre-deployed scheduling model; if the edge nodes confirm that the real-time data is environmental mutation data, they upload the environmental mutation data to a regional cloud; the regional cloud matches the environmental mutation data with historical scenario data; if the match is successful, scheduling data is generated; if the match fails, the environmental mutation data is uploaded to a central cloud; the central cloud generates scheduling data based on the received environmental mutation data and returns the scheduling data to the regional cloud, which then distributes the scheduling data to the edge nodes. This invention constructs a low-altitude traffic management system that combines real-time performance, reliability, and self-evolution capabilities.

The invention belongs to the technical field of Internet of Vehicles communication, and discloses a heavy diesel vehicle remote monitoring method and system based on edge computing, and the method comprises the steps: constructing a rule engine according to a predefined transmission rule, and deploying the rule engine to an edge computing node; the data stream of the heavy diesel vehicle is transmitted to an edge computing node, a rule engine is used for evaluating each data field in the data stream, a corresponding transmission level is distributed, and the transmission level comprises a first-level field, a second-level field and a third-level field; and matching a preset transmission strategy according to the data field with the allocated transmission level, executing differentiated data processing, and transmitting a processing result to an enterprise cloud platform through a network transmission layer. According to the method, low-value data is filtered in real time through edge calculation, the invalid data uploading amount is reduced, and data transmission is reduced; in addition, through a high compression ratio and a delay transmission technology, the bandwidth occupation of low-value data is reduced, and the utilization efficiency of network resources is optimized.

The present application provides a 5G information conversion method, device, equipment and storage medium, which relates to the field of communication technology. The method includes: detecting whether the target terminal is a 5G terminal; if the target terminal is the 5G terminal, obtaining the network status of the target terminal; combining the network status of the target terminal and the reception data of the historical 5G information, evaluating the receiving ability of the target terminal to 5G information; obtaining the information characteristics of the 5G information to be sent to the target terminal; combining the custom receiving mode of the target terminal, the information characteristics and the receiving ability of the target terminal to 5G information, converting the 5G information to be sent to the target terminal, obtaining the target information, and sending the target information to the target terminal. The technical effect of the present application is: improving the efficiency of information transmission.

The embodiment of the invention discloses an auxiliary focusing method and device, equipment and a storage medium. The method comprises the following steps: transmitting acquired preview image data to a pre-added peak focusing node, wherein the peak focusing node is a node pre-added in a CHI of a Camx of a hardware abstraction layer; calling an OpenCL library integrated in the peak focusing node to carry out peak focusing processing on the preview image data on a GPU (Graphics Processing Unit) to obtain image data after peak focusing; and sending the image data after peak focusing to an image preview node so as to display an image after auxiliary focusing. According to the embodiment of the invention, the efficiency and accuracy of peak focusing can be improved, and extra overhead and data transmission are reduced.

The invention relates to a data updating method and device applied to large language model training, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining to-be-processed data and a target data model; dividing the target data model into a plurality of model partitions; wherein each model partition comprises a part of model parameters of the target data model; for each model partition, calculating the to-be-processed data according to the model parameters contained in the model partition to obtain a partition gradient; and obtaining a global gradient according to the plurality of partition gradients, and updating the model parameters according to the global gradient. By adopting the method, frequent transmission and synchronization of the data among the nodes can be avoided, so that the communication overhead is reduced, the model training efficiency is improved, and the hardware resource utilization rate of computer equipment in the model training process is improved.

The present application relates to a kind of pneumonia detection model training method, specifically to a kind of hierarchical topology pneumonia detection model based on collaborative learning system and method.It overcomes the difficulty of existing pneumonia detection model training system, low efficiency and the problem that local detection node cannot achieve optimal detection performance.First, each node uses local pneumonia data to pre-train respective pneumonia detection model locally, then, center node is based on the feedback data of local model to build hierarchical topology collaborative learning system;After that, each cluster node and child node uses local pneumonia data to train respective pneumonia detection model locally;Finally, collaborative training is carried out, including intra-cluster collaborative training and inter-cluster collaborative training;Intra-cluster collaborative training includes cluster node aggregation training and child node random interactive training.The present application has small data transmission, storage and calculation pressure, and is easy to implement;At the same time, different client is provided with different model parameters, so that each hospital detection node achieves local detection performance maximization.

The invention discloses a large power grid security and stability control system remote virtual simulation method and system, and the method comprises the steps: building a first communication link for a first object, and forwarding test information; and a test master station simulation result is collected and packaged into a first transmission message, and the first transmission message is input to the first communication link and is dispatched to the test terminal unit. And analyzing the first transmission message at the test terminal unit to obtain an analysis result. According to the remote virtual simulation method and system for the security and stability control system of the large power grid, the bandwidth utilization rate, the packet loss rate, the transmission delay and the reliability are comprehensively considered, and the high efficiency and the reliability of data transmission are ensured. The link state information can be periodically updated according to the change of the network state, and the path weight and the optimal path are recalculated, so that the flexibility and the adaptability of the system are improved, and the dynamic adjustment capability is realized. Moreover, unnecessary data transmission is reduced, and the test efficiency and the resource utilization rate are improved.

The invention discloses a high-efficiency automatic data distribution system for solid waste monitoring based on an MQTT protocol, and relates to the technical field of solid waste monitoring, the system uses enterprise codes in data as dynamic subscription topic, data is analyzed in real time and a target topic is matched, automation and intelligence of data distribution are realized, and the distribution efficiency and flexibility are remarkably improved. Meanwhile, a lightweight MQTT protocol is adopted, massive topic and large-scale concurrency are supported, new data sources and monitoring objects can be flexibly accessed, good expandability is achieved, and interconnection and intercommunication with a heterogeneous system are facilitated. The method has remarkable advantages and innovations in the aspects of data distribution automation, routing efficiency, system decoupling, subscription granularity and the like, and the problems of insufficient flexibility, poor real-time performance, low expandability and the like in a massive heterogeneous data distribution scene in the prior art are well solved; the efficiency and the quality of data distribution can be remarkably improved, and the method has a wide application prospect.

Embodiments of the present invention provide a wireless communication method, a terminal, and an access network device. The method includes: receiving, by an access network device, a first downlink data flow that includes first downlink data and that is sent by a core network device, where the first downlink data flow includes a first flow identifier; and sending, by the access network device, a second downlink data flow that includes the first downlink data to a terminal, where the second downlink data flow includes a second flow identifier, a length of the second flow identifier is less than a length of the first flow identifier, and the first flow identifier corresponds to the second flow identifier. Air interface data transmission overheads can be reduced by using the foregoing method.

This invention relates to the field of robot control technology, specifically to an end-to-end closed-loop control method and system for robots based on multimodal perception fusion. It includes: a perception access layer for standardized access to data from multiple sensor types; a fusion processing layer for spatiotemporal alignment and multimodal fusion processing of the standardized access data; a decision control layer for generating execution instructions based on the fusion processing results; and an execution feedback layer for feeding back the execution status to the fusion processing layer to form closed-loop control. This technical solution can solve the problems of standardized access and real-time fusion of multi-source heterogeneous data, and construct a closed-loop control architecture with high fault tolerance and low latency.

The invention relates to a leg-foot robot digital twinning system and an operation method thereof, the leg-foot robot digital twinning system comprises a physical entity system, a digital twinning body system, a data information system and a service system, and all the systems are clear in labor division and highly integrated; through high-fidelity real-time digital twin bodies driven by physical and data information and in combination with a virtual-real synchronization method of robot dynamic behavior state transition, the interaction process of the leg-foot robot and a complex environment is realized, a mature and feasible scheme based on physical model dynamic behavior transition is provided, the data transmission and communication difficulty is reduced, and the data transmission efficiency is improved. Real-time display is facilitated, interaction service is provided, and virtual-real interactivity, model accuracy and feasibility, reliability and superiority of the established digital twin system are improved.

This invention discloses a generative image detection method and system for protecting facial privacy. The method first prepares face-text paired data and fine-tunes the diffusion model. Secondly, based on the fine-tuned diffusion model, latent vectors are extracted and clustered, and a dictionary is formed by combining text prompts with the cluster center vectors. Finally, based on the obtained dictionary, pseudo-images and labels are obtained through the fine-tuned diffusion model, and the detection results are output after passing through a classifier. This invention combines latent space clustering with conditional diffusion generation, achieving a balance between privacy protection and data diversity, and significantly improving the security and generalization ability of fake face image detection.

The invention discloses an intelligent report generation system and method based on knowledge graph enhancement, and belongs to the technical field of natural language processing and knowledge graph fusion. In order to solve the problems of insufficient accuracy, low efficiency and opaque reasoning process in the existing structured report generation only depending on large model intrinsic knowledge, the method comprises the following steps of: constructing a knowledge graph containing entities, attributes and relationships, and converting a user natural language problem into a structured query statement by utilizing a first large model; after an entity relationship result is obtained in the knowledge graph, a report with a clear basis is generated by the second large model; and adaptation can also be realized through query languages. By means of structured knowledge and efficient query of the knowledge graph, the accuracy and generation efficiency of the report conclusion are improved, the reasoning process is traceable, meanwhile, the flexibility and universality of the scheme are enhanced, and the method is suitable for structured report generation scenes such as enterprise data analysis and industry research.

The present invention relates to the field of information processing technology, and in particular to an information processing method and system for an electric power data collector, the method comprising: collecting electric power data; classifying the electric power data and determining the data level, and sending the classified data to corresponding data containers respectively; comparing the data volume with the standard total data volume and allocating the data, and comparing the data transmission speed with the standard data transmission speed and calculating the designated collection time, and calculating the adjustment coefficient of the designated collection time according to the data transmission speed change rate and adjusting the designated collection time of the data collector; issuing a control instruction to the collection module according to the processing result and switching the data collection mode. The present invention improves the data collection efficiency by determining the designated collection time of data collection according to the data transmission speed, and further determining the adjustment coefficient of the designated collection time according to the data transmission speed change rate and controlling the data collection mode.

The present application relates to the field of satellite positioning technology, and discloses an inertial navigation RTK measurement system, comprising: a centering pole provided with a bracket, a smart handbook fixed to the pole body of the centering pole by the bracket, and a GNSS antenna provided at the top of the centering pole and connected to the smart handbook, the GNSS antenna being used to receive GNSS satellite signals; the smart handbook being used to obtain GNSS observation data, CORS differential data and inertial raw observation data, perform RTK calculations based on CORS differential data and GNSS observation data, and simultaneously perform combined navigation calculations using inertial raw observation data and RTK positioning results, and generate high-precision position information of a point to be measured in combination with the smart handbook and the GNSS antenna at the position of the centering pole; the bracket being used to fix the smart handbook to ensure that the smart handbook and the GNSS antenna remain relatively fixed. The present application can simplify measurement operations, reduce production and maintenance costs, and reduce waste of resources.