159results about How to "Reduce data transfer" patented technology

The invention provides a meteorological monitoring method based on a GRU neural network and differential privacy federated learning. The meteorological monitoring method comprises the following steps: collecting local meteorological data to generate a meteorological time sequence data sequence; selecting a proper model structure according to the heterogeneity of a client, carrying out local meteorological monitoring model training based on a GRU neural network, and carrying out parameter updating on a local model through an LPDP-FedProx federal learning algorithm; the central server performs model aggregation and updating according to a global model weighted average aggregation algorithm after acquiring model parameters of the client; and the client receives the updated global model parameters and carries out the next round of local training until an optimal monitoring model is obtained. According to the method, the personalized differential privacy algorithm and the FedProx federated learning algorithm are combined, the LPDP-FedProx federated learning algorithm is provided, a federated learning framework capable of effectively processing heterogeneous data is constructed, the privacy of local data is effectively protected, and the security of federated learning is improved.

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 the technical field of networks, and discloses a method for determining position information of a routing node, computer equipment, a storage medium, a program product and a network general system.The method comprises the steps that a first code and a second code of a first routing node are obtained, and a third code and a fourth code of a target routing node are obtained; determining whether the first routing node and the target routing node belong to the same network subsystem or not according to the second code and the fourth code; when it is determined that the first routing node and the target routing node belong to the same network subsystem, performing XOR operation on the first code and the third code to obtain a target code; determining the number of target numerical values included in the target code; and determining position information of a next routing node corresponding to the first routing node according to the number of the target numerical values, the first code, the second code, the third code and one or more elements in the target code. The invention can improve the network transmission efficiency.

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 discloses a preset time encircling control method for privacy protection under an event trigger mechanism. The method comprises the following steps: firstly, establishing a multi-robot system comprising a plurality of leaders and a plurality of followers, and defining a preset time encircling control problem under privacy protection constraints; designing a privacy protector based on an output mask function; a preset time privacy protection surrounding controller is further constructed, so that all the follower robots can enter the convex hulls of the leader robots on the premise of protecting the privacy of the follower robots; then, constructing an event trigger function without a sesame behavior, and designing a privacy protection preset time encircling controller based on an event trigger mechanism; according to the method, an event triggering mechanism and a finite time theory are combined, the convergence performance of privacy protection surrounding control of the multi-robot system is improved, resource consumption of algorithm calculation is reduced, privacy protection, communication broadband constraint and convergence speed are integrated, and the application scene of the algorithm is further expanded.

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 belongs to the technical field of energy management, and particularly relates to a regional energy management optimization method based on edge computing and multi-objective optimization, which comprises the following steps of: determining edge equipment with qualified processor performance based on processor performance parameters of the edge equipment, establishing an edge equipment site diagram based on geographical sites of the qualified edge equipment, applying the initial multi-objective optimization scheme to the regional energy simulation model, obtaining an energy efficiency evaluation value and an energy efficiency comprehensive difference value, comparing the source efficiency comprehensive difference value with an energy efficiency comprehensive difference standard value stored in a database, matching a pre-selected management optimization scheme, and applying the pre-selected management optimization scheme to the regional energy simulation model, and based on the optimal management optimization scheme, adjusting an optimization scheme decision according to a real-time evaluation result. According to the invention, the flexibility of overall regional energy management can be improved, and sustainable development of long-term regional energy management can be maintained.

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 provides a vector data slicing processing method and device in a big data set, and belongs to the technical field of map data processing.The vector data slicing processing method in the big data set comprises the steps that vector data in the big data set is preprocessed; dividing the preprocessed big data set into a top-layer grid comprising a plurality of areas, and subdividing the top-layer grid to form a plurality of sub-grids; dynamically adjusting the granularity of grid division according to the interaction behavior of the user and the map zooming level; data processing in each grid unit after dynamic adjustment is used as an independent task, and the independent task is allocated to different computing nodes or threads for independent processing; and combining the processed data in each grid unit to generate vector slice data and outputting the vector slice data. The vector data slice processing method in the big data set is better in performance, higher in processing speed, lower in resource consumption, higher in precision and better in expansibility.

The invention belongs to the field of fault detection and diagnosis of rotating machinery, and discloses a real-time detection method for an early fault of a rolling bearing. The method comprises the following steps: determining priori knowledge; acquiring N groups of periodic synchronous single-point sampling data; calculating to obtain fault features; accumulating weak fault information contained in the fault features to obtain a bearing fault index; judging whether an early failure occurs or not; and realizing adaptive data acquisition. According to the method, weak fault information contained in fault features is accumulated by using accumulation and CUSUM technologies, the reliability and sensitivity of early fault detection are improved, the data acquisition amount is effectively reduced under the condition that the fault detection reliability is ensured, and then the data transmission, processing and storage costs are reduced.

The invention relates to the field of data processing, in particular to an intelligent data acquisition method and system for an industrial edge gateway, and the method comprises the steps: obtaining industrial data of each industrial device, carrying out the preprocessing, and carrying out the data compression through employing an LZ78 algorithm, and obtaining a compression result; meanwhile, performing statistics on equipment parameters and data volume of the edge gateway, and calculating data acquisition pressure of the edge gateway and data acquisition burden indexes of various types of equipment; and according to the data acquisition burden index of each device and the compression result of each device, adjusting the data acquisition frequency, and performing data acquisition on the device accessed by the edge gateway at the adjusted acquisition frequency. According to the invention, the data acquisition frequency is dynamically adjusted, and the data flow is intelligently managed according to the data acquisition burden index and the compression result, so that unnecessary data transmission is reduced, and the consumption of network bandwidth and storage resources is reduced.

The invention relates to the technical field of power plant inspection, and discloses a comprehensive safety inspection method for a smart power plant, which comprises the following steps: S1, arranging various sensors and pulse signal devices on each power device in the power plant, regularly collecting operation data and pulse reflection signal frequency spectrum of each power device, and establishing a normal operation state set of the devices; and S2, generating a visual three-dimensional map of the power plant according to the data of the sensor and the pulse reflection signal spectrum, marking an image model and a number of each device in the map, and mapping the accurate position of the device model in an actual scene to the map in an equal-proportion splicing mode. By introducing the equipment health index and residual life prediction model, the running state of the equipment is evaluated in real time, potential faults are predicted in combination with historical data, the service life of the equipment is prolonged, fault prediction is more accurate, predictive maintenance tasks are generated in advance, and serious faults are avoided.

The invention belongs to the field of fault detection, and discloses a motor bearing fault real-time detection method based on adaptive data acquisition. The method comprises the following steps: determining mounting positions of a vibration sensor and a rotating speed sensor, and setting detection parameters; acquiring N groups of periodic synchronous single-point sampling data Y; standard deviations of N groups of periodic synchronous single-point sampling data in the data Y are calculated respectively; whether a fault is generated or not is judged; and adaptive data acquisition is realized. According to the invention, through adaptive data acquisition, under the condition that the fault detection reliability is ensured, the data acquisition amount is reduced, and the data acquisition, data transmission and data processing costs are reduced.

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 provides a radar data processing method, a program product, electronic equipment and a storage medium, and the method comprises the steps: comparing collected radar data with a base value, and obtaining a difference value; if the difference value is smaller than a threshold; if yes, reporting the radar data according to the first data reporting frequency; if the difference value is not smaller than the threshold value, reporting the radar data according to a second data reporting frequency; updating the base value according to the base value and the threshold value; the second data reporting frequency is higher than the first data reporting frequency. The appropriate reporting frequency is selected based on the comparison result of the difference value and the threshold value, automatic adjustment of the reporting frequency is achieved, unnecessary data transmission can be reduced for non-abnormal conditions, and network bandwidth and storage resources are saved. And for abnormal conditions, the second data reporting frequency with higher frequency is adopted for data reporting, so that the problem of data omission is improved, and the accuracy of reported data is improved. By dynamically adjusting the base value, the real state of the radar data is reflected more accurately, and false alarms caused by a fixed base value are reduced.

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 behavior recognition method and device, equipment, a storage medium and a product, and relates to the technical field of behavior recognition, and the behavior recognition method is applied to an edge end, and comprises the steps: dividing a captured video clip into a plurality of video units; performing feature extraction on the video unit, and determining a to-be-reasoned video unit according to a feature extraction result; and determining a sampling frame rate according to the to-be-reasoned video unit, performing frame sampling on the to-be-reasoned video unit according to the sampling frame rate, and sending a sampling result to a cloud for behavior recognition. According to the invention, the collected video clip is divided into a plurality of video units, the to-be-reasoned video unit is determined based on the video units, the frame sampling is carried out from the to-be-reasoned video unit, and the sampling result is sent to the cloud for behavior recognition, so that the behavior recognition efficiency is improved compared with the existing mode that all collected videos are sent to the cloud for behavior recognition. According to the method, the analysis time delay can be reduced, and data transmission is reduced.

The invention relates to the technical field of electric vehicle leasing, in particular to a communication information transmission system and method in an electric vehicle leasing process, and the system comprises a user module which obtains actual user information; the analysis module receives and matches the user information, obtains historical lease information, and calculates a lease credit score based on an analysis result to determine a user lease level; the vehicle management module determines an initial rental vehicle according to the user rental level; the adjusting module collects real-time state parameters, calculates the ratio of abnormal state parameters, preliminarily judges and adjusts preset communication parameters, and transmits a plurality of real-time state parameters; the vehicle management module further comprises the steps that the receiving state parameters are obtained and analyzed, the communication effect is judged to calibrate the initial communication parameters, and a repair strategy is made based on the judgment result of the receiving state parameters. According to the invention, the communication stability in the electric vehicle leasing process is improved.

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.

The invention provides a transformer substation maintenance data management method and management platform. The method comprises the following steps that a background server receives a data uploading request sent by a PC client; the background server verifies the data uploading request based on the PC terminal user login information and the PC terminal user permission information; if the verification of the data uploading request is passed, the background server receives an uploading data packet corresponding to the data packet linked list information, and stores the uploading data packet to a distributed storage database; the background server receives a data downloading request sent by the mobile client; the background server verifies the data downloading request in combination with the mobile terminal user login information, the local data type information and the local key data hash value; and if the verification of the data downloading request is passed, the background server pushes the corresponding target data packet to the mobile client based on the local data type information. The method has the effects of efficiently and safely storing and accessing the data.

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 present application relates to the technical field of building modeling, and specifically to a lightweight loading method, computer equipment and readable storage medium for a linear engineering model. The method of the present application includes the following steps: extracting component information from a linear engineering model and constructing a model structure tree; generating a node data file for each node in the model structure tree and a data organization file for the model structure tree; the rendering engine loads the data organization file and the node data file of the root node to initialize the linear engineering model; calculating the screen space error SSE of all classified nodes; traversing all classified nodes for processing, if the screen space error SSE of the current classified node is less than a preset screen space error threshold, then loading the node data file of the current classified node, otherwise unloading the node data file of the current classified node. The technical solution of the present application can dynamically load or unload node geometry data according to changes in the visible range, avoiding crashes caused by excessive memory usage and CPU overload.

The present invention discloses an interface conversion circuit, a chip, a chip testing system and a method. The interface conversion circuit is placed inside the chip under test, and includes: a data module, a write module, a read module and a comparison output module. The input end of the data module is connected to the main control device; the data module is used to obtain test stimulus data and expected response data according to the test vector sent by the main control device; the input end of the write module is connected to the first output end of the data module, and the output end is connected to the input end of the test access port of the chip under test; the input end of the read module is connected to the output end of the test access port, and is used to collect the read data fed back by the chip under test; the first input end of the comparison output module is connected to the second output end of the data module, and the second input end is connected to the output end of the read module; the comparison output module is used to obtain and output an error signal according to the read data and the expected response data. The embodiment of the present invention can improve the flexibility and efficiency of chip testing.