16 results about "Distributed source" patented technology

The application provides a communication system based on forward error correction coding modulation, at a sending end, a uniformly distributed source sequence is subjected to Polar code coding, and then bit selection and redundancy insertion are performed on a polar code word through a BCDM algorithm, so that the symbol occurrence probability is adjusted, and shaping gain is obtained.The code word after the Polar code coding is sent to an 8-PAM modulator, so that three continuous bits are mapped into one 8-PAM symbol; and the modulated symbol with a target probability distribution is transmitted through an AWGN channel.At a receiving end, a demodulator demodulates the symbol probability into a bit Log-Likelihood Ratio (LLR); meanwhile, the inverse transformation of the BCDM algorithm is used to delete the redundant information in the bit LLR sequence, and a Polar code decoder corresponding to the Polar code encoder is used for decoding and estimating information bits; and the provision of the system guarantees the shaping gain of the coding modulation scheme, and realizes a low bit error rate.

The invention discloses an equipment access and control method and system for an off-grid micro-grid supporting plug and play and rapid power regulation. The method comprises the following steps: unifying information of distributed source and load end equipment, and standardly describing type characteristics, operating parameters, communication interfaces and control capability of the distributed source and load end equipment; the method comprises the following steps of: actively initiating a registration process after distributed source-load end equipment is accessed to an off-grid micro-grid, completing equipment identification, function registration and topology updating, and constructing a dynamic self-adaptive communication network at the same time; and when the system is disturbed, the distributed source-load end equipment cooperatively executes two local control mechanisms of frequency-power droop control and virtual synchronous power generation control according to modeling information and communication feedback so as to adjust the output power of the distributed source-load end equipment. The device plug-and-play capability, the system adjustment response speed and the operation stability of the off-grid micro-grid are effectively improved, and the method is suitable for off-grid micro-grids such as remote areas, novel energy islands and electric vehicle energy complementing stations.

Various embodiments of the present technology generally relate to systems and methods for providing a data preparation engine for curating secure and compliant data collections from distributed storage systems. In an aspect, a data preparation engine receives a query from a client device and determines files from one or more distributed sources based on the query. The data preparation engine determines sensitive data within the files and anonymizes the sensitive data while preserving context and integrity of the underlying information. The data preparation engine generates a data collection including the files with anonymized sensitive data. The data collection may then be deployed to downstream applications or workflows, such as used to generate curated data sets for training of artificial intelligence applications. Once deployed, the data preparation engine may continuously monitor the distributed sources for changes to data within the files and automatically update data collections in real-time.

The invention belongs to the technical field of power systems, and discloses a power quality situation deduction model evaluation method, system and device and a medium. The method comprises the following steps: acquiring power quality monitoring data, and forming a data set containing reference data and to-be-predicted data; constructing a deep learning situation deduction model based on the obtained data set, and constructing a performance evaluation framework of the deep learning situation deduction model based on a Taylor graph; performing skill score calculation on the performance indexes of the obtained performance evaluation framework to obtain skill score values, sorting the skill score values, and determining a deep learning situation deduction model with the highest score; and based on the deep learning situation deduction model with the highest score, evaluating the influence of the distributed source load power fluctuation on the power quality index of each node in the power distribution network system. According to the invention, accurate reverse traceability of the power quality problem and accurate positioning of the key source load are realized, and scientific decision support is provided for prevention and control and responsibility division of the power quality problem of the power distribution network.

The application discloses a kind of source network load flexibility resource integration's hybrid energy storage optimization method and system, it is related to distributed source network load storage planning field, for solving the problem that centralized energy storage planning ignores terrain hydrology and distribution network difference causes site distortion and scale expansion, is by first in terrain and water body information screen out with upper and lower reservoir conditions pumped storage candidate point, again mapping distribution and transmission network and according to energy path and tidal flow sensitivity cutting access position, so that energy storage is located close to load concentration area rather than limited to a few nodes in transmission level, from source to inhibit long-distance transmission loss and invalid expansion demand;On this basis, pumped storage undertakes slow change power movement, electrochemical energy storage undertakes fast change peak valley regulation, and the apportioned result is embedded in power generation, line opening, demand response and other multi-side joint model, so that spatial constructability and operation flexibility synergize in the same optimization framework;Therefore, avoid the expansion of energy storage capacity caused by ignoring flexible resources.

The invention discloses a distributed source-load collaborative strategy optimization method oriented to supply and demand mutual assistance of production and consumer of a micro-grid. The method comprises the following steps: constructing a production and consumer source-load physical state and energy storage time sequence constraint model; constructing a mutual aid power physical boundary; defining a virtual potential energy state quantity; constructing a delivery and disappearing person supply and demand mutual aid collaborative decision-making model; performing linearization processing on the alternating current power flow equation; solving a node voltage amplitude and a line power flow disturbance vector; converting the node voltage amplitude disturbance vector into a mutual aid power matrix; establishing a network physical constraint feasible region; establishing a global mutual aid matching model; constructing an asynchronous driving and joint optimization loop; virtual congestion potential energy is calculated; performing robust sensing extreme scene verification and cross-scene residual quantization on the mutual aid power matrix, and tightening the upper bound of a network physical constraint feasible region; and when the indexes converge, outputting a mutual aid power matrix and a scheduling instruction. According to the method, the problems that existing network constraint embedding lags behind, asynchronous negotiation convergence is insufficient, and cross-scene feasibility of extreme scenes is lacked can be solved.

This invention discloses a hybrid distributed source DOA estimation method based on the rank deficiency principle. First, a distributed source model is established where circular and non-circular signals are simultaneously incident on an array antenna. Second, the received signal is augmented using the conjugate of the received signal, and its covariance matrix is ​​calculated. Next, eigenvalue decomposition is performed on the covariance matrix to obtain the noise subspace under hybrid source conditions. Finally, based on the rank deficiency principle, a cost function is derived, and direction-of-arrival estimation is completed through one-dimensional spectral peak search. This method is applicable to scenarios where both circular and non-circular sources exist simultaneously in space. Compared to algorithms requiring two-dimensional search, this algorithm effectively reduces computational complexity while maintaining good angle estimation performance.

Dynamic Digital Immune Systems / Processes (DDIS) improve data integrity and quality in Explainable AI (XAI) systems. Operating in real time, it monitors data from distributed sources during federated training, detecting and flagging corrupted, poisoned, or low-quality data with dynamic anomaly detection algorithms. The system includes automated data cleaning to remove redundant and irrelevant data, enhancing AI model performance. It features dynamic encryption that adapts to data sensitivity, ensuring robust security. Swarm intelligence-based task distribution allows decentralized auditing of data integrity, while a traceability matrix tracks data lineage for transparency. The DDIS also incorporates a self-healing mechanism that automatically corrects identified anomalies, ensuring continuous reliability of AI outputs. This comprehensive system protects against data corruption, enhances processing efficiency, and safeguards the accuracy and integrity of XAI systems in high-stakes environments.

The invention discloses a space-time aggregation and collaborative optimization method and system based on distributed source network load storage, belongs to the technical field of power dispatching, and solves the problem of how to improve the optimization efficiency of renewable energy access to a power distribution network. Comprising the following steps: processing multi-source heterogeneous data of the distributed source network load storage system based on a space-time convolutional neural network, outputting a system state prediction result in a future time period, dynamically dividing physical resources into a plurality of virtual organizations by adopting a dynamic topology aggregation analysis method, and establishing an aggregation optimization model containing topology constraints; the topological community is taken as an optimization unit, the system state prediction result is taken as an input parameter, a C-ADMM algorithm is adopted to carry out distributed solution on the aggregation optimization model, information interaction is carried out on each region by virtue of a Lagrange multiplier and an auxiliary variable, and finally collaborative optimization and resource sharing of the multi-region source network load storage system are realized. And the optimization efficiency and the comprehensive optimality of the renewable energy access power distribution network are improved.

The embodiment of the invention provides a multi-level autonomous management and control system for distributed multi-region cooperative operation, and relates to the technical field of cross-region cooperative operation of a power distribution network. The distributed source-load interactive acquisition module provides quantitative state perception of a second-to-minute-level time scale for the system, and the hysteresis of centralized acquisition is overcome. The collaborative optimization and predictive control module can respond to rapid fluctuation of new energy and load, and the adaptability and economical efficiency of a traditional static optimization strategy are improved. And the resource interaction and dynamic aggregation module realizes real-time collaborative mutual aid of flexible resources in multiple regions. And the multi-level autonomous control and boundary optimization module can quickly respond and maintain the overall operation safety when local disturbance occurs. The system formed by sequentially connecting the modules is constructed, so that the dynamic collaborative optimization capability, the operation economy and the system robustness of the multi-region power distribution network in coping with new energy fluctuation and randomness are integrally improved.

Methods and systems for processing data from geographically distributed data sources are disclosed. The data may be processed by an application system that receives the data from geographically distributed data sources. Before arriving at the application system, the data may be affixed with a timestamp by a trusted local network component. The application system may reorder incoming data in a buffer based on the timestamp. Data may be then extracted from the buffer and processed based on the reordering.

The application relates to a data comparison method and device, computer equipment, a computer readable storage medium and a computer program product, relates to the technical field of databases, and can ensure normal business during data comparison. The method comprises the following steps: determining to-be-compared data; when the to-be-compared data comprises a data table of a target end database, determining a first to-be-compared sharding table in a plurality of sharding tables contained in the data table; the data table is obtained by performing data migration on sharding tables respectively provided by a plurality of database nodes in a distributed source end database, and the first to-be-compared sharding table is part of the plurality of sharding tables; a second sharding table is acquired in a database node of the distributed source end database providing the first sharding table, and the second sharding table and the first sharding table are compared.

The application belongs to the technical field of virtual power plants, and relates to an energy storage and load aggregation method and system based on a comprehensive virtual generator and battery model. The method comprises the following steps: according to an aggregated model of a comprehensive virtual generator and a virtual battery, aggregating multiple energy devices in a plurality of distributed source-grid-load-storage energy systems to obtain an aggregate; for undetermined parameters in the aggregated model, solving the parameters according to a constructed parameter robust optimization solving model; the parameter robust optimization solving model is divided into two-stage constraints, and a second-stage constraint condition is established based on a distributed source-grid-load-storage refined mathematical model; performing approximate linear summation on the aggregated model according to a Minkowski approximate linear summation method to solve an adjustable power domain of the aggregate, and realizing resource aggregation of the plurality of distributed source-grid-load-storage energy systems. The aggregation processing and solving method optimizes resource configuration in the plurality of distributed source-grid-load-storage energy systems, and improves the economy and flexibility of the system.

The application discloses a multi-base station cooperative communication optimization method based on reinforcement learning and belongs to the technical field of wireless communication processing; through 7-2 layer function segmentation, beamforming is sunk to a remote unit, and local beamforming weight is calculated by using global precoding auxiliary information fed back by a central unit at a previous moment, so that high-dimensional antenna signals are reduced to low-dimensional user data streams; a joint quantizer based on Wyner-Ziv distributed source coding is adopted, spatial correlation between data streams of different remote units is used for compression, and a compression codebook and a global precoding matrix are jointly designed, so that mutual information between a quantization noise covariance matrix and a user interference channel is minimized; a multi-user channel matrix is reconstructed based on compressed data streams, and a reinforcement learning intelligent agent is introduced, an adaptive cooperative precoding strategy is output through intelligent decision-making, and differential pulse code modulation and adaptive quantization are adopted to efficiently feed back the global precoding matrix.

The invention relates to the technical field of power distribution networks, and discloses a distributed source-load-storage aggregation adjustable capability correction method and system. The method comprises the following steps: determining a power-energy feasible region of each distributed source-load storage terminal in a power distribution network in a future scheduling time domain, and superposing the power-energy feasible regions of the terminals to obtain a theoretical aggregation feasible region; shrinking the theoretical aggregation feasible region based on a predefined conservative coefficient to obtain an actual aggregation feasible region applied to the current scheduling period; decomposing a target power curve sampled from the actual aggregation feasible region to each terminal, and taking a power deviation obtained after decomposition as a decomposition error of the current scheduling period; and updating the conservative coefficient according to the decomposition error, and applying the updated conservative coefficient to the next scheduling time domain so as to perform rolling correction on the aggregation adjustable capability of the distributed source load storage. According to the invention, real-time online correction of the aggregation adjustable capability is realized.

Dynamic Digital Immune Systems / Processes (DDIS) improve data integrity and quality in Explainable AI (XAI) systems. Operating in real time, it monitors data from distributed sources during federated training, detecting and flagging corrupted, poisoned, or low-quality data with dynamic anomaly detection algorithms. The system includes automated data cleaning to remove redundant and irrelevant data, enhancing AI model performance. It features dynamic encryption that adapts to data sensitivity, ensuring robust security. Swarm intelligence-based task distribution allows decentralized auditing of data integrity, while a traceability matrix tracks data lineage for transparency. The DDIS also incorporates a self-healing mechanism that automatically corrects identified anomalies, ensuring continuous reliability of AI outputs. This comprehensive system protects against data corruption, enhances processing efficiency, and safeguards the accuracy and integrity of XAI systems in high-stakes environments.