2823 results about "Source system" patented technology

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 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.

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 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 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 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.

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 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.

The invention discloses a broadband random power quality adaptive monitoring system, which relates to the field of power grid monitoring and comprises a broadband signal acquisition module, a synchronous phasor measurement module, an edge calculation module, a cloud edge collaborative analysis module and a local traceability module. The broadband signal acquisition module performs high-frequency sampling on a power grid signal and ensures integrity; the synchronous phasor measurement module is used for realizing time synchronization of multiple monitoring points and generating data with time marks; the edge calculation module is used for studying and judging disturbance and early warning through deep learning, and uploading data to the cloud; the cloud edge collaborative platform aggregates data, deduces regional power quality and issues an optimized AI model; the local traceability module quantifies harmonic responsibility and positions a broadband disturbance source. The system innovatively adopts an ensemble empirical mode decomposition and variational mode decomposition parallel strategy, combines a spectrum kurtosis maximization algorithm to determine the number of modes and accurately separate transient and steady components, solves the problem of feature confusion of a traditional method, and realizes the crossing from single analysis to multi-mode fusion research and judgment.

The invention relates to the technical field of aircraft power supply systems, and discloses an intelligent power distribution and fault diagnosis method for an aircraft power supply system, which comprises the following steps: acquiring a real-time current signal and load working condition information of an aircraft power supply bus, a double-domain composite entropy feature is constructed by extracting a time domain entropy transition index representing the energy domain stability of a current signal and a phase synchronization index representing the time structural domain stability; according to the method, dual inspection of the energy domain and the time structure domain is carried out on the current signals, and cooperative judgment is carried out in combination with the load working condition, so that sensitive capture of real fault forebodes is realized; and meanwhile, tolerance is dynamically shown on normal loads in the airplane operation process, the restriction between safety and usability in the prior art is effectively avoided, and predictive health management is made possible.

Disclosed is a system and method for standalone automated patient referral management. The system comprises an intake module that accepts and processes referrals from diverse referral communication channels as entry points for patient referrals. A processing module undertakes the analysis of patient information to pinpoint a healthcare provider based on a healthcare need, a preference, and a schedule availability. A scheduling module confirms the appointment, and engages in dynamic three-way communication between the patient, the referring entity, and the healthcare provider. A feedback module dispatches feedback to the referring entity, offering a report on an outcome of the appointment. A natural language processing (NLP) module extracts information from conversational speech. The system utilizes algorithms to match patients with the healthcare providers based on criteria including specialty, availability, and patient preferences. A feedback module communicates with the originating EHR systems and provides updates to maintain continuity of care.

The invention discloses a context-aware-based gap containerization TLS compatible method, which comprises the following steps of: distinguishing a simple TLS variable and a complex TLS variable in a source gap system, and establishing a TLS variable source feature; the method comprises the following steps: constructing a dynamic telescopic TLS memory pool matched with a variable access permission in a target system, expanding a kernel state TLS segment descriptor, constructing a bidirectional mapping table of musl TLS offset and a memory pool address, and optimizing and constructing a clustering structure aligned with a CPU cache line for a hotspot variable to improve the access efficiency; tLS variable access tracks are recorded through a dynamic binary translation engine, TLS segment descriptors are updated, the descriptors are read during thread switching to recover TLS context and refresh cache to guarantee consistency, address and offset conversion is completed according to target system alignment requirements by combining source features and a bidirectional mapping table for TLS variables of nested structures, pointers and array types, and the TLS variables of the nesting structures, the pointers and the array types are aligned. And completing dynamic conversion execution of the source system application program in the target system.

The invention provides a standard adaptive execution method and equipment based on data standard management, and belongs to the technical field of data management. The method comprises the following steps: in response to a standardization request for a service system from a user, generating a corresponding mapping relation library based on a preset root library and a source system field; wherein the mapping relation library at least comprises a mapping relation, established on the basis of a preset semantic matching model, between the source system field and the standard field. And converting each source data value of the business system according to the mapping relation library, and executing real-time verification based on a preset multi-level value domain enumeration constraint before the source data values are written into the target library, so as to intercept conversion abnormal data according to a verification result and generate an abnormal snapshot. And when it is determined that the data standard attribute change event exists or the abnormal snapshots meeting the preset conditions exist, determining a corresponding update strategy type based on the change content of the data standard version change or the root cause classification result of each abnormal snapshot, so as to perform standardized execution according to a corresponding update strategy.

The invention provides an intelligent charging pile control system based on a new energy automobile. The intelligent charging pile control system comprises a communication module, a data processing module, a charging power adjusting module, a safety protection module, a user interaction module, a cloud management platform, a power supply checking and recording module, an information backup and transmission module and a second-hand power supply monitoring module. The power supply checking and recording module is connected with a new energy automobile power supply system, detects and records parameters such as battery capacity and internal resistance, generates a power supply state report, realizes comprehensive detection and recording of the power supply parameters, provides data support for battery maintenance and fault diagnosis, assists an automobile owner to know the battery condition, and improves the safety of the automobile. A basis is provided for reasonable use and maintenance of a battery, basic data is also provided for subsequent battery management, the information backup transmission module locally backs up a power supply state report and transmits the report to a vehicle merchant service platform, the module realizes backup and transmission of power supply information, and a vehicle merchant can timely obtain detailed information of a vehicle power supply.

The invention discloses a radio frequency voltage sensor and a plasma source system. The radio frequency voltage sensor comprises a signal acquisition module and a signal processing circuit. The signal processing circuit comprises a rectangular wave pulse detection module, a signal conversion module, a peak holding module and an operation output module; the input end of the rectangular wave pulse detection module receives a pulse synchronization signal; the signal conversion module is connected with the output end of the signal acquisition module; the first input end of the peak holding module is connected with the output end of the signal conversion module, and the second input end of the peak holding module is connected with the output end of the rectangular wave pulse detection module; and the operation output module is connected with the output end of the peak holding module. According to the radio frequency voltage sensor and the plasma source system provided by the invention, the peak voltage corresponding to the single-stage pulse can be accurately detected, the peak voltage corresponding to the multi-stage pulse can also be stably detected, the output is prevented from jumping to zero during the pulse closing period, and the stability and practicability of measurement are improved.

The invention relates to a physical-data dual-drive heat supply system heat demand prediction and optimization control method and system, and belongs to the technical field of intelligent regulation and control of regional energy systems. A multivariable time sequence is given, the forward LSTM and the backward LSTM respectively process the multivariable time sequence, and bidirectional hidden state splicing is output; constructing a decoder layer by using a multi-head self-attention mechanism, and parallelizing attention heads; the re-attention module dynamically adjusts attention distribution based on physical constraints derived from fluid thermodynamics, realizes deep learning to pay attention to related key physical parameters in a prediction process, and generates a physical vector P through heat flux Q and inertial delay tau; and fusing and outputting the predicted temperature of the secondary return water. According to the method, physical knowledge and depth sequential modeling are combined, prediction precision and physical interpretability are combined, and the interpretability, robustness and generalization of thermal dynamics of a heat supply system in an actual area are enhanced.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.

The invention discloses a financial product intelligent matching system based on multi-source data integration, and relates to the technical field of finance, and the system comprises a data access module which is used for collecting enterprise heterogeneous data from an industrial end and a multi-source system; the data cleaning and structuring module is used for cleaning, integrating and normalizing the heterogeneous data and generating enterprise label data according to a data system; the rule configuration module is used for configuring and maintaining an access rule based on the financial product and converting the access rule into a computable label; the intelligent matching module is used for realizing bidirectional matching by calculating the similarity between an enterprise and a product based on an enterprise label and a product access rule, and the business opportunity management and collaboration module is used for carrying out business opportunity scoring and level-to-level management on a matching result. According to the application, various data systems such as ERP, tax affairs, credit investigation, supply chains and the like are seamlessly connected, and integration and real-time acquisition of heterogeneous data such as enterprise operation data, credit rating, cash flow, asset information and the like at an industrial end are realized, so that data sources are more comprehensive and timely.

The invention relates to the technical field of energy systems, and discloses an energy system intelligent decision-making method, which comprises the following steps: acquiring four-flow data of an industrial energy system, and constructing a four-flow parameter model based on a production flow topological graph; constructing a multi-agent reinforcement learning environment, taking the four-flow parameter model as a state space of the multi-agent reinforcement learning environment, and determining an action space and a reward function of each agent; performing domain adjustment on the large language model based on the industrial energy system knowledge base; through combination of a multi-agent reinforcement learning model and a large language model, a decision strategy is dynamically adjusted according to real-time data feedback, and decision feasibility verification is performed based on a physical mechanism model. According to the method, the four-flow integrated parameter model is constructed, and a multi-agent reinforcement learning and large language model cooperation mechanism is introduced, so that the problems of data splitting and the like in a traditional energy management system are effectively solved, and the intelligent level of energy system decision making is improved.

An online system receives a request to access an interactive geographical map of sources from a client device associated with a user. The system retrieves data describing a geographical location associated with the user. The system identifies one or more sources within a threshold distance of the location and retrieves data including information describing items available at each source. For each source, the system accesses and applies a machine-learning model to predict a user engagement score indicating a likelihood of one or more interactions by the user with a set of items available at the source if the source is included in the map. Based on the score for each source, the system selects a set of sources and generates the map, in which the map indicates the geographical location of each selected source. The map is then sent to the client device, causing the device to display the map.

The invention relates to the technical field of radar data processing, in particular to a method which is suitable for a multi-external radiation source and multi-station system, carries out single-frame and two-frame multi-target measurement value matching under the condition of no bistatic distance information, has good adaptability to the multi-target positioning problem of external radiation source systems of different systems, and is high in positioning accuracy. The bistatic joint multi-target positioning method based on the external radiation source system is beneficial to the processes of multi-target positioning, real-time tracking, track generation and the like, and comprises the following steps: matching multi-source and multi-station angle measurement values to obtain candidate association, and estimating the intersection point position and the speed state of the angle measurement values; approximately constructing a linear equation of bistatic speed and angle measurement value errors, establishing measurement value error estimation of candidate associations and a real target, and removing wrong candidate associations by using an association hypothesis decision; and establishing a cost function according to the measured value error estimation of the candidate association between the two frames and the state mahalanobis distance, and removing the error candidate association between the two frames by using an association hypothesis decision.

The invention belongs to the technical field of component prefabrication, particularly relates to a digital twinning-based fabricated building prefabricated component production process, solves the problems of insufficient quality and low construction efficiency in the prior art, and constructs virtual mapping of a design-production-construction whole process through a digital twinning technology. The method realizes data-driven intelligent decision making and flexible production, and specifically comprises the following steps: an intelligent design cooperation stage: a complete quality guarantee system is constructed through arrangement of structures such as an AI visual detection system and a block chain traceability system, the AI visual detection system adopts a multispectral imaging and deep learning technology, and the AI visual detection system adopts a multi-spectral imaging and deep learning technology; various defects on the surface of the component can be accurately identified, a visual thermodynamic diagram is generated, and a quality closed loop is realized by matching with an automatic repairing function of a mechanical arm; the block chain traceability system links the whole process quality data in real time through an encryption technology, and when a detection value does not reach the standard, the intelligent contract automatically triggers a rectification process and locks a problem component to form a complete quality prevention and control chain.

The invention discloses a regulation and control process for a nitridation gradient permeable layer of a die-casting special-shaped die, and belongs to the technical field of die nitridation, and the regulation and control process comprises the following steps: S1, carrying out three-dimensional quantitative analysis on the die to obtain three-dimensional information of the die, and dividing the surface of the die into regulation and control areas of four grades of A, B, C and D; s2, regulation and control area parameter mapping: calling a corresponding basic process parameter set from a preset process parameter mapping table according to different grades of the regulation and control area; s3, mold partition ion nitriding; s2, an ion nitriding furnace with multiple paths of independent control anodes and partitioned gas conveying nozzles is arranged, the mold is placed in the nitriding furnace, the process parameter sets, obtained in the S2, of all the regulation and control areas are distributed to a partitioned gas conveying control system and an anode power supply system to conduct permeation on the mold, and the mold is cooled to the room temperature; the industrial problems that due to the fact that geometrical characteristics of a special-shaped mold are complex, the infiltrated layer thickness is not uniform, and the structure property difference is large are solved, and the infiltrated layer with the enough thickness can be obtained in areas difficult to infiltrate such as deep cavities and narrow slits.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

A method for API connectors includes receiving from a user account a first API request formatted for a source system and comprising an intent, selecting a first action corresponding to the intent, generating a first prompt based on the first action, providing the first prompt to a first large language model (LLM) and receiving a response having a structured description of the intent, using the structured description of the intent to select a second action, generating a second prompt based on the second action, providing the second prompt to a second LLM and receiving a response having a second API request formatted for a destination system, providing the second API request to the destination system, receiving from the destination system a response to the second API request, and providing the response to the second API request to the user account.

To provide a power supply system capable of improving followability of power supply to a demand change without reducing power generation efficiency.SOLUTION: A power supply system 1000 comprises a plurality of power generation units 100 operated in a parallel state to a commercial power source system 500 and a system controller 18 which supervises control states of the power generation units 100. The power generation unit 100 includes a power generation module 20, a power conditioner 16, and a local controller 17. The system controller divides the power generation unit 100 into any one of a first group of maximum output fixed machines, a second group of minimum output fixed machines, a third group of output adjustment machines, and a fourth group of output standby machines and controls total output power of the system by selecting a combination of groups on the basis of purchase power information from the commercial power source system 500.SELECTED DRAWING: Figure 3

The invention belongs to the technical field of automatic control, and particularly relates to an improved linear gain digital control method for a power supply system. The problems that a traditional fixed linear gain digital controller is poor in control precision, robustness, response speed and stability, prone to overshoot and oscillation, heavy in calculation burden, large in hardware consumption and the like in a complex dynamic environment and a nonlinear or strong coupling system are solved. According to the method, an operation state and environment parameters are obtained and processed, and system dynamic characteristics are intelligently evaluated; calculating an optimal control gain factor in combination with a gain scheduling rule or an adaptive learning model, and applying the optimal control gain factor to a linear digital controller for dynamic adjustment; and non-linear compensation processing is carried out on the adjusted control quantity, so that accurate closed-loop control is realized. The control precision can be obviously improved, the system response can be accelerated, the external interference suppression capability can be enhanced, the hardware dependence can be reduced, overshoot and oscillation can be eliminated, and high-precision, low-power-consumption and wide-adaptability digital control can be realized.