3933 results about "Source system" patented technology

The invention discloses a supply chain full-process traceability system based on digital twinning and block chains, and belongs to the technical field of digital twinning and block chains, and the system comprises a data collection layer which obtains original data from each link in real time, and carries out the preprocessing of the original data through an edge computing device; the twinborn model building layer is used for building digital twinborn models corresponding to all links of the supply chain respectively and generating link-level optimization parameters through simulation analysis; the block chain network layer is used for verifying and storing shared data and key traceability information among the digital twin models; the collaborative optimization module is used for dynamically adjusting a cross-link collaborative strategy based on a multi-agent reinforcement learning algorithm; and the data traceability layer responds to an external traceability query request and generates a full-link visual traceability map. By means of the collaborative algorithm based on multi-agent reinforcement learning, automation and intellectualization of information interaction among all link models are guaranteed, and personalized and multi-terminal traceability service can be provided for users.

The invention discloses a multi-modal knowledge graph construction and intelligent fault diagnosis method and system for a communication power supply system. The method comprises the steps of multi-modal knowledge graph construction, graph increment updating and intelligent fault diagnosis. The multi-modal knowledge graph construction adopts a unified data acquisition semantic specification and a heterogeneous data fusion strategy, a dynamic evolution heterogeneous graph is established, and equipment full life cycle state perception and causal link modeling are realized; the efficient, atomicity and consistency updating of the atlas is realized by the hierarchical atlas increment through shadow composition, structural difference rate calculation and a subgraph replacement mechanism; according to the intelligent fault diagnosis, an alarm propagation sub-graph is constructed, a path convergence and multi-dimensional attribute scoring mechanism is adopted, and deep joint verification is carried out by using a multi-modal evidence fusion network; and in combination with a reinforcement learning optimization strategy embedded based on a graph structure, adaptive scheduling and diversity constraint of a diagnosis path are realized, and the fault positioning accuracy and the system intelligence in a complex scene are remarkably improved.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a self-adaptive data processing optimization method, device and equipment and a medium, and the method comprises the steps: collecting operation data, host performance data, network state data and historical task data of a target data source, and constructing an analysis model in combination with recovery parameters and strategy preference, predicting a task load state, resource consumption and execution duration, generating a task execution strategy, completing task scheduling and execution monitoring, and collecting execution feedback data for iterative optimization of the analysis model. According to the method, an analysis model is constructed by fusing multi-source system data and historical task information, a task execution strategy is generated in combination with a dynamic prediction result and a strategy weight, intelligent task scheduling and process monitoring are realized, and feedback data is used for model iterative optimization. The task execution efficiency is improved, the resource use rationalization is realized, and the model adaptive capability is enhanced.

The invention discloses a material batch full-process traceability system based on a production process, particularly relates to the field of production and manufacturing traceability, is used for solving the problems of continuity and accuracy of material batch full-process traceability, and is characterized in that batch tracking coordinates are generated by constructing a process mapping matrix and an assembly topological index; detecting circulation holes in real time and inserting placeholder marks; judging broken chain credibility based on path integrity and structural complexity; compressing traceable chain segments; executing field dynamic alignment to generate batch circulation patches; a material batch whole-process continuous tracing view is established; and furthermore, the abnormity positioning time is remarkably shortened, the real-time performance is improved, the influenced batches are accurately locked, the recall range is reduced, the production line decision-making efficiency is optimized, and efficient and reliable technical support is provided for quality management and batch tracking in the production process.

The invention discloses a logistics traceability system and method based on a block chain technology, and relates to the technical field of logistics traceability, and the method comprises the steps: constructing an event graph structure containing a plurality of behavior event nodes, and calculating a structure closure signature value; judging whether the original data of the behavior event node is written into the block chain or not according to the responsibility structure weight value; constructing a verification path mapping graph based on the structure closure signature value and the responsibility structure weight value; when a traceability request is received, calling the verification path mapping graph, performing consistency verification on the original data of the target behavior event node and the structure mapping hash value, and generating a traceability verification result; according to the method, the credibility of the behavior event data structure is improved, meanwhile, the on-chain storage cost is effectively reduced, and the technical contradiction between full-uplink data and non-uplink data is solved.

The invention relates to the technical field of CT induction power supplies, in particular to a CT induction power supply system based on cooperative control and distributed optimization and a control method. According to the technical scheme, the CT induction power supply control method based on cooperative control and distributed optimization comprises the following steps: S1, deploying a multi-mode sensing array, and synchronously collecting a three-phase current harmonic spectrum of a power supply node, an iron core magnetostriction vibration waveform and a radiator surface infrared thermal image; generating a digital twinborn body with space-time relevance through multi-physics field coupling modeling; and S2, constructing a data fusion engine driven by a space-time attention mechanism at an edge computing node, and carrying out LSTM feature extraction on the output of the step S1 and a historical operation log under physical constraint. According to the method, through collaborative innovation of multi-mode federated learning and physical embedded edge calculation, a multi-physical field dynamic modeling mechanism under privacy protection is constructed, and the correlation precision and information security of electromagnetic-vibration-thermodynamic data are effectively balanced.

The invention discloses an intelligent livestock breeding traceability method and system, and the method comprises the steps: building an overall framework of the intelligent livestock breeding traceability system based on a micro-service architecture, and employing Spring Cloud Al ibaaba as a micro-service technology stack; livestock physiological data and environmental parameters are collected through Internet of Things equipment, and a livestock electronic file is established; an AI intelligent analysis engine is constructed, and livestock health state evaluation, oestrus prediction and abnormal behavior recognition are achieved; the whole life cycle data of livestock from breeding to selling is recorded based on the block chain technology, and a full-chain traceability system is constructed. The method further comprises the functions of integrating a financial service interface, designing a multi-terminal collaborative application platform, constructing a matchmaking transaction platform and the like. According to the invention, the problems of data islanding, incomplete traceability system, low intelligent level, insufficient system expandability and the like in the prior art are solved, and the accuracy, traceability and system stability of livestock breeding are improved.

The invention discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention discloses an energy management three-dimensional visualization method and system based on digital twin and Internet of Things, and relates to the technical field of energy management visualization, and the method comprises the steps: obtaining multi-source heterogeneous energy data of an energy management object; constructing a virtual mirror image model corresponding to the physical energy system based on the multi-source heterogeneous energy data and the digital twin model; generating a three-dimensional visual scene based on the virtual mirror image model so as to map the geographic position distribution of the physical energy system, and presenting the real-time energy consumption state, the energy flow transmission path and the environmental parameter change of the equipment; receiving a user interaction instruction, carrying out interaction operation on the target area or equipment, calling the associated multi-source heterogeneous energy data, and carrying out superposition display on the associated multi-source heterogeneous energy data; energy consumption abnormity early warning information or an equipment regulation and control strategy is generated in combination with the energy management optimization rule, and the equipment regulation and control strategy is input into the physical energy system to be executed. The invention provides an energy management method capable of realizing multi-source data fusion and accurate mapping of a physical system.

The invention provides an energy-saving and carbon-reducing multi-objective optimization method and device for a cold source system of a high-speed rail station, and the method comprises the steps: building an air-conditioning cooler system load prediction model and a human body comfort model based on the historical data of an air-conditioning cooler system, the historical data of the internal and external environments of the high-speed rail station, and the thermal comfort evaluation historical data of the internal environment of the high-speed rail station; a multi-objective optimization algorithm is established with minimization of the sum of the air conditioner refrigerator system load and the human body comfort as an optimization objective, and the weight of the air conditioner refrigerator system load and the human body comfort can be adjusted according to real-time data of the internal environment and the external environment of the high-speed rail station; and a multi-objective optimization algorithm is solved, and cold machine optimization control is carried out according to the air conditioner cold machine system load solution value and the air conditioner cold machine system load current value, so that the reliability of energy-saving and carbon-reducing multi-objective optimization of the high-speed rail station cold source system is effectively improved.

A data management system establishes connections with access control systems which are delegated by a domain to control data access and maintain data access history associated with the domain. The system receives a group access permission of a set of data resources to a group of named entities and heterogeneous sets of metadata related to the data access history and generates graph objects. The graph objects include named entity nodes and resource nodes. The named entity node represents a named entity associated with an organization, and the resource node represents a data resource. The system traverses access paths that connect the named entity node and the resource node determines a utilization level of a set of access paths. Based on the utilization level, the system revokes the set of access paths, thereby revoking an access permission of the set of named entity in the group to the specific data resource.

The invention relates to the field of energy storage power supply management, in particular to a power supply management method, system and equipment based on a household energy storage power supply system. The method comprises the following steps: collecting a household power utilization monitoring data stream, carrying out equipment use behavior preference mining and multi-scale state transition deep learning, and generating a user equipment behavior prediction model; mining power utilization behaviors of power utilization terminals one by one based on the household power utilization monitoring data flow, performing multi-time-point power utilization demand prediction based on a user equipment behavior prediction model, and constructing a multi-time-point power utilization demand prediction map of the user; and obtaining meteorological environment parameters of a current user family area, performing climate disturbance demand adjustment on the power demand prediction maps at the multiple time points based on the meteorological environment parameters, and constructing a climate disturbance power demand prediction map. According to the invention, through dynamic user demand changes, the energy efficiency management level and the energy allocation precision of long-term operation of the energy storage system are improved.

The invention discloses a block chain-based support steel member full life cycle quality traceability system, and relates to the technical field of block chains, and the method comprises the steps: deploying sensors at each stage of a steel member, collecting data, and unifying the format; dynamically calculating a weight based on timeliness and confidence, and generating a comprehensive quality score; generating a block by hash encrypted data, and triggering an intelligent contract by a preset engineering rule; full life cycle data are retrieved through UID, and BIM model three-dimensional display is integrated; and analyzing a contract log to locate a responsible party, and optimizing parameters to form closed-loop management. Through a dynamic weight distribution algorithm, the system can dynamically adjust the weight of each data source according to the timeliness and confidence of the sensor data, and the objectivity and accuracy of quality evaluation are improved.

The invention provides a deep sea energy system life prediction method, and relates to the technical field of life prediction, and the method specifically comprises the steps: collecting the electrical characteristics of a battery and deep sea environment stress, and carrying out the standardization processing; constructing a deep sea environment degradation model, and constraining the physical characteristics based on the deep sea environment model; inputting the data into a physical branch network to extract consistency constraint features and physical initial priori; the standardized data, the physical enhancement features and the consistency constraint features are input into a lightweight data branch network, and deep residual information formed after the lightweight data branch network is pre-trained is transmitted to a physical branch network; and the RUL priori estimation output by the physical branch network and the data correction item output by the data branch form a final prediction, the final prediction is input into a Bayesian framework to derive posteriori distribution, and uncertainty quantization is carried out. According to the technical scheme, the problems of low service life prediction accuracy and relatively high computing resources of the deep sea energy system in the prior art are solved.

The invention provides a power distribution network fault recovery method and device, a storage medium and computer equipment. The method comprises the steps of obtaining operation state data of a power distribution network; determining a fault detection result according to the operation state data in combination with a neural network algorithm and digital twinborn simulation; based on the fault detection result, after a fault area is determined, an isolation measure corresponding to the fault area is applied; and based on the topological information of the power distribution network, calculating a load transfer path, and according to the load transfer path, carrying out load transfer by adopting a flexible interconnection system and a household energy management system so as to recover power supply of a fault area. Therefore, cooperative scheduling of the power distribution network level flexible interconnection system and the user side household energy system is realized, the self-healing capability and the operation efficiency of the power distribution network are effectively improved, and the technical effects of rapid fault isolation and power supply recovery are achieved.

The invention relates to the technical field of power supplies, and discloses a high-precision power supply control method based on a digital signal processor, which comprises the following steps of: acquiring data such as voltage, current, temperature, load parameters and the like of a power supply at a high frequency of kHz-level sampling frequency by using a high-precision sensor, transmitting the data to a DSP (Digital Signal Processor), and removing noise interference by applying wavelet transform. Sample quality is improved through high-frequency data acquisition and wavelet denoising, nonlinear and time-varying characteristics of a power supply system are identified by using a lightweight CNN and an LSTM, a reinforcement learning dynamic optimization control strategy is combined, self-adaptive switching between traditional control and an intelligent algorithm is realized through working condition identification, and the power supply system control method based on the LSTM is realized. The problems that noise interference influences data quality, system characteristic recognition is inaccurate, complex working condition control strategy optimization is insufficient, and output precision is unstable due to the fact that a control mode cannot be switched in a self-adaptive mode in an existing power supply can be effectively solved, and control precision, efficiency and device loss can be considered. And the stable operation requirement of the high-precision power supply under multiple working conditions is met.

A system establishes connections with a data resource system and connections with a security monitoring system. The system receives metadata related to data access history of the data resources from the data resource system and risk related signals associated with data access activities from the security monitoring system. The system generates an access graph comprising graph objects that are connected by access paths signaling access levels of the data resources controlled by the data resource system. The system aggregates the metadata from the data resource system, the risk related signals from the security monitoring system, and data associated with the access graph to generate normalized risk signals and identifies a cybersecurity risk-related instance associated with an access paths in the access graph. The system generates an alert which allows a user to adjust access privilege of a data resource associated with the cybersecurity risk-related instance.

The invention relates to the technical field of data management, and discloses an automatic collaborative decision-making method, system and equipment for enterprise resource planning, a medium and a product, so as to solve the problem of low automatic decision-making efficiency of an existing enterprise resource planning system. The method comprises the following steps: constructing a multi-agent reinforcement learning model, designing a global reward function and a local reward function, constructing a potential function of the local reward function, and training based on training data to obtain a collaborative decision model; collecting work information of each collaborative workflow to obtain business data; calling a collaborative decision model to perform collaborative decision on the business data according to the global reward function and the local reward function, and performing reward shaping on the local reward function based on the potential function to generate a business strategy; generating a decision instruction based on the business strategy according to the labor division content of each actuator in the enterprise resource system; and each actuator executes the corresponding decision instruction, monitors the execution process in real time, records the execution result, generates feedback information, and updates the work information.

The invention discloses an impedance matcher, a radio frequency power supply system and a plasma source system. The impedance matcher comprises an impedance matching module, a control module and a plurality of adjustable capacitor modules which are connected in parallel, the first end of each adjustable capacitor module is electrically connected with the first end of the impedance matching module, and the second end of each adjustable capacitor module is grounded; each adjustable capacitor module comprises an adjustable capacitor unit and a switch unit which are connected in series; the control module is used for controlling the switch unit in the at least one adjustable capacitor module to be switched on and controlling the switch unit in the at least one adjustable capacitor module to be switched off when the load is in the current impedance; and the capacitance value of the adjustable capacitor unit in the adjustable capacitor module where the at least one disconnected switch unit is located is adjusted according to the next impedance of the load. According to the invention, impedance matching between the radio frequency power supply module and the load can be realized while the impedance of the load is changed, and impedance matching can be quickly realized.

The invention provides a multi-parameter cold source intelligent control system and method based on big data, and the system comprises a data collection and control module which is used for collecting related parameters of a cold source system; the comprehensive energy consumption standardization module is used for converting the energy consumption parameters into unified standardized energy consumption indexes; the comfort degree algorithm construction module is used for generating a dynamic comfort degree index; the association rule mining module is used for mining a multi-dimensional association rule from historical data; the intelligent decision module is used for generating an optimal control strategy based on the index, the index and the association rule; according to the embodiment of the invention, based on a multi-parameter fusion cold source intelligent control technology of deep reinforcement learning and an improved FP-Growth algorithm, through deep mining and intelligent decision making of multi-source data, the optimal control strategy is converted into equipment regulation and control parameters, and the equipment regulation and control parameters are obtained. And high efficiency and energy conservation of the cold source system and accurate regulation and control of indoor environment comfort are realized.

The invention provides a multi-tenant data pushing method, and relates to the technical field of computer software and information, and the method comprises the steps: achieving unified identity authentication through single sign-on, and dynamically distributing a pushing authority based on an authority strategy; defining conversion rules and tasks by utilizing a visual interface of the low-code configuration center, and generating a standardized data packet; and centralized scheduling is carried out in a multi-tenant management and control center, and the stability of the system is guaranteed by combining a current limiting and dynamic retry mechanism. Source system data are collected in real time, and format conversion is completed through a rule engine after grammar, permission and event state verification. And pushing in an asynchronous mode, and recording full-link information including a tracking ID, a timestamp, a data fingerprint and a response state. And when pushing fails, a compensation mechanism is triggered, degradation or retry is carried out according to a preset rule, final consistency of data is ensured, and the reliability and maintainability of the system are improved.

The invention relates to the technical field of power supply system health assessment, in particular to a health assessment method and system of a power supply system based on artificial intelligence. The method comprises the following steps: acquiring data of a power supply system, estimating initial performance parameters of the power supply system, evaluating an initial running state of the power supply system, and acquiring initial state data of the power supply system; performing power supply equipment load operation simulation based on the initial state data, and predicting a fault evolution trajectory; the transmission quality attenuation degree of the power supply system is estimated by analyzing the fault evolution trajectory of the power supply system, the loss increase trend of a transmission line is detected, and the performance decrease trend is evaluated in combination with the attenuation degree; evaluating health attenuation data of the power supply system according to the performance reduction trend; transmitting the data to an artificial intelligence model for deep learning training to obtain accurate power supply system health state evaluation data; according to the invention, health analysis is carried out on the power supply system, so that the power supply system is safer and more stable.

The invention provides an mbse-based system full life cycle management method and system, and relates to the technical field of data synchronization, and the method comprises the steps: obtaining to-be-synchronized data of a source system, constructing a semantic graph structure, generating a mapping strategy based on implicit association between semantic matching learning systems, executing transactional data transmission, and constructing a time sequence traceability tree to track change propagation. And adaptive reconstruction is carried out when a constraint conflict is detected. According to the method, the problem of data synchronization among heterogeneous systems is solved, and the model consistency maintenance capability and the adaptive conflict processing efficiency are improved.

An AI-powered HR recruitment platform system that aggregates job postings in real time using API and web scraping technologies, classifies roles using NLP, and ranks candidates using trust-weighted AI models based on blockchain-verified qualifications, thereby achieving increased transparency, data protection, and verifiable fairness over traditional HR systems. The components can be used individually or in any combination without departing from the scope of the invention.

A modular data center and a method of building a modular data center. The modular data center includes a site microgrid including at least one power converter connected to a direct current (DC) bus and a digital power system. The digital power system includes at least one digital power transmitter, a set of transmission lines, and at least one digital power receiver. The modular data center design uses an adiabatic cooling system to cool the modular data center.

The invention relates to the technical field of computer data processing, and discloses an automatic format conversion and incremental synchronization method for cross-platform heterogeneous document data. The method comprises the following steps: capturing an atomic data change event from a source system transaction log; aggregating into logic transaction units according to the transaction identifiers; converting into a unified intermediate normal form; generating a target system operation instruction set based on the metadata mapping rule; the synchronization sites are executed and persisted in a transactional manner. According to the method, through log-level capture and transactional synchronization, data integrity, sequence and high consistency are ensured, and efficient and reliable data synchronization between heterogeneous systems is supported.

The invention discloses an energy-saving economic dispatching method for a data center energy system based on a prediction model. The method comprises the following steps: establishing a digital twinborn model of the data center energy system; dividing the data center into a plurality of energy consumption partitions, and installing a temperature acquisition device in each energy consumption partition; after a cooling capacity demand prediction model of the data center under each time period partition is established, target combination parameters and target energy efficiency indexes PUE of the chilled water temperature and the air supply temperature of a refrigeration subsystem under each time period are obtained through calculation; establishing a data center energy efficiency index PUE prediction model, and establishing an upper-layer refrigeration subsystem optimization scheduling model by taking the minimum deviation between an energy efficiency index PUE prediction value and a target energy efficiency index PUE, the minimum deviation between actual combined parameters of chilled water temperature and air supply temperature and target combined parameters and the minimum cold capacity supply and demand deviation of each partition as targets; and establishing a lower-layer data center optimization scheduling model by taking the minimum power utilization operation cost of the system and the minimum load migration cost of the IT equipment as a target.

The invention discloses a tire production process quality data traceability system based on a multi-modal data space. The system is composed of a data layer, a processing layer and an application layer. In the data layer, multi-source data acquisition is carried out for core process links such as internal mixing, molding and vulcanization of tire production, entity recognition and relation extraction are carried out through ETL processing, and an entity triple is constructed and stored in a graph database. In the processing layer, hierarchical visualization processing and intelligent question and answer system construction are conducted on knowledge data in the graph database, a time axis hierarchical algorithm and a process step hierarchical algorithm are adopted in a hierarchical visualization engine, and the intelligent question and answer system comprises a model local deployment module, a cue word construction module and a knowledge verification module to generate structured response. Finally, the data layer and the processing layer are used for an application layer, the application layer is a Web visual interface, the application layer is used for quality tracing analysis and node query and is associated with an intelligent question and answer box of the interface, and the interface further comprises layered atlas display for visualization of the processing layer.

A system, non-transitory computer readable medium, and method include entering redundant oscillators and a cascaded oscillator of a spoofing resistant system into an initialization state. All but one of the redundant oscillators are disciplined to a time-and-frequency external input into normal disciplining state with the remaining one of the redundant oscillators in a holdover state. When all but one of the redundant oscillators have reached the normal disciplining state, placing all but one of the redundant oscillators into the holdover state, disciplining the remaining one of the redundant oscillators to the time and frequency external input, and disciplining the cascaded oscillator to one of the all but one of the redundant oscillators now in the holdover state. When the remaining one of the redundant oscillators and the cascaded oscillator have reached the normal disciplining state, transitioning from an initialization stage to a steady state management stage.

The invention relates to the technical field of emergency communication, in particular to a Beidou short message-based multi-mode emergency communication terminal, which comprises a Beidou communication module, a multi-source positioning unit, an information coding processor and a main control unit, the Beidou communication module is used for realizing short message communication with a Beidou satellite and supporting a BDS-3 protocol; the physical SOS button is provided with a mistaken touch prevention structure and comprises a mechanical pressing assembly with a triggering pressure threshold value larger than 2N and continuous pressing time verification logic; the multi-source positioning unit is used for acquiring position information including longitude, latitude and height; the information coding processor is configured to compress the collected help-seeking information to be within the capacity limit of a Beidou short message and generate a standard message of an emergency communication protocol; a standby power supply switching circuit is arranged in the main control unit, the main control unit is used for coordinating the work of each module and managing a power supply system, and the power supply system comprises a replaceable lithium thionyl chloride battery and a solar charging panel; and the low-power-consumption main control unit controls the Beidou communication module to transmit information to a Beidou communication satellite.