5048 results about "Source system" patented technology

The invention provides a big data platform scheduling task and data collaborative smooth migration method and system, and relates to the technical field of big data, and the method comprises the steps: constructing a digital twin environment of a target system, collecting task operation state data of a source system, extracting task features through a deep neural network, and carrying out the clustering and grouping. And deploying the task group to a digital twin environment, analyzing a task association relationship and a resource competition relationship based on a graph neural network, and generating a task stability score in combination with a task operation index. And constructing a migration risk prediction model to perform quantitative evaluation on the migration risk, and determining a migration priority and an execution sequence according to the quantitative evaluation. And making corresponding resource configuration, data synchronization and fault recovery plans for each task group, carrying out migration drilling in a digital twinning environment, and optimizing a migration scheme by utilizing a reinforcement learning algorithm. The migration risk can be effectively reduced, the smoothness and stability of the migration process are guaranteed, and the migration efficiency is improved.

A method for conditioning and maintaining power with predictive load compensation using an uninterruptible power supply system is disclosed. The method includes measuring an electrical input from a primary power supply using analog sensors and transmitting the input through an impedance to introduce a controlled delay. During the delay, input parameters and downstream system effects are determined based on telemetry sensor data. Digital identities are generated for the electrical input and the downstream load and compared to a stored digital identity representing historical conditions. Using the comparison, a system simulation is performed with analog twinning to predict downstream system responses. The predicted response is used to adjust a power converter parameter, modifying the electrical input to an intermediate output that compensates for voltage fluctuations and harmonic distortions. A secondary power supply with a bidirectional high-discharge battery supplements or stores energy as needed, ensuring stable power delivery to the downstream load.

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 an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

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 discloses a control method and device of a building cold and heat source system self-adaptive to load requirements. The control method comprises the following steps: acquiring historical operation data, physical characteristics and equipment system data of a building, and establishing a building load prediction model; generating the hourly load curve, and predicting the cooling or heating demand of each time period in the future according to the curve; dynamically adjusting the refrigerating or heating load demand, and generating a corresponding cooling or heating curve; and constructing a machine learning algorithm based on an equipment mechanism model, optimizing a control strategy of the cold and heat source system based on the historical operation data of the equipment, and outputting control parameters of the equipment. According to the technical scheme, the load of the building cold and heat source system is intelligently predicted and adjusted, the energy efficiency of the system is improved, invalid energy consumption is reduced, energy utilization is optimized, control parameter optimization is achieved through a machine learning algorithm, efficient operation is ensured, indoor comfort is kept, and the purpose of saving energy is achieved.

The invention provides a rebalance dynamic adjustment method applied to a multi-source system and a service system, and relates to the technical field of smart energy, and the method comprises the steps: collecting a real-time operation data set of the multi-source energy system, covering energy supply, load demand and system operation state features, carrying out the dynamic supply and demand deviation feature extraction, and carrying out the dynamic adjustment of the dynamic supply and demand deviation features; a feature set containing supply, demand fluctuation and system stability deviation is generated, then a pre-trained multi-source collaborative prediction model is called, and a multi-source collaborative prediction result containing energy supply and load demand trend prediction features is generated based on the feature set; and dynamic adjustment strategy matching is carried out according to a multi-source collaborative prediction result and real-time operation data, and a strategy set of energy output adjustment, energy storage charging and discharging, load priority distribution and the like is obtained. And finally, executing an adjustment strategy and obtaining feedback data, optimizing parameters of the multi-source collaborative prediction model based on the adjustment strategy, triggering closed-loop adjustment operation, and realizing rebalance dynamic adjustment of the multi-source system.

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 data communication method and system based on information security, and belongs to the technical field of data communication, and the data communication method comprises the stages of preparation, transmission, intrusion detection and defense, receiving verification and cleaning ending. The two parties authenticate and exchange keys during preparation; encrypting data by using a symmetric key and an AES algorithm; automatic defense is performed after intrusion detection; the receiver verifies the data; and cleaning the resources at the end, and performing log optimization on the system. According to the invention, through innovatively improving the intrusion detection and defense method, based on real-time network flow monitoring, intrusion analysis based on machine learning and an automatic defense mechanism, the detection accuracy and efficiency are greatly improved, and false alarms and missing alarms are reduced; defense capability and flexibility are enhanced, attacks are intelligently blocked and isolated, and cooperative emergency response is rapid; data communication security technology progress and expansion are promoted, and network security is powerfully guaranteed.

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 information screening method and system for a human resource system, and relates to the technical field of human resource systems. The problem that a traditional matching method is difficult to deeply mine semantic association and cannot accurately measure the matching degree of candidates and posts is solved. According to the method, through multi-step intelligent data processing and optimization, the candidate screening efficiency and accuracy are remarkably improved, a semantic analysis technology is adopted to construct a multi-layer topic tree and an emotion analysis model, and deep semantic matching of candidate resumes and position requirements is achieved; meanwhile, candidates are intelligently screened by using a bidirectional semantic matching network, the screening effect is continuously optimized by mining potential high-matching candidates and historical data feedback, bidirectional recommendation is realized through a block chain communication link, the security and efficiency of data transmission are guaranteed, complex enterprise recruitment requirements can be effectively adapted, the recruitment period is shortened, and the recruitment efficiency is improved. The man-post matching accuracy is improved, and the human resource management cost is greatly reduced.

The present invention relates to the technical field of new energy vehicles, and provides a system control method for hybrid electric vehicles based on deep reinforcement learning. [Solution] For a plug-in hybrid logistics light truck, the method includes step S1 of acquiring multidimensional aisle situation information during the historical driving process, step S2 of creating a full-vehicle power system model, step S3 of performing initial optimization processing for two electric motors, step S4 of performing dynamic programming calculations to generate a state transfer dataset, step S5 of identifying state variables, operation variables and incentive functions, step S6 of initially training a network of critics and actors using the state transfer dataset, step S7 of constructing an ambient intelligence model and recovering and training an energy management policy using a deep reinforcement learning algorithm, and step S8 of applying the model.

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.

An apparatus may include a platen, a light source system, an ultrasonic receiver system and a control system, the light source system being configured for providing first light and second light having first and second wavelengths to a target object on an outer surface of the platen and including one or more light guides configured to convey light parallel to a surface of the platen. The control system may be configured to: cause the light source system to provide the first light and the second light to the target object at a first time and a second time, respectively; receive first and second ultrasonic receiver signals from the ultrasonic receiver system corresponding to photoacoustic responses of the target object to the first light and the second light, respectively; and estimate one or more blood vessel features based on at least one of the first or the second ultrasonic receiver signals.

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.

The invention discloses a pair of blind person intelligent glasses based on binocular vision. The glasses comprise a glasses terminal and a cloud end. A data acquisition and transmission module, a voice interaction and feedback module and a power supply system are arranged in the glasses terminal, and the glasses terminal is used for acquiring and processing data and receiving and feeding back voice instructions; the cloud integrates a sensing module, a task planning module, an action module and a path planning and navigation module, the sensing module analyzes instructions and environment information, the task planning module extracts natural language intentions and generates task structures, the action module dynamically generates task templates to execute tasks, and the path planning and navigation module achieves fine-grained obstacle avoidance navigation. The environmental perception precision is improved through binocular vision and GPS fusion, the use threshold is reduced through natural language interaction, the complex task processing capacity of the system is improved through dynamic task decomposition, and the obstacle avoidance accuracy is improved through hierarchical path planning.

Systems and methods are provided relating to power systems, such as a power grid, including for providing control to a power system by utilizing available flexibility in charging electric vehicles (EVs). The system generates control information for controlling the power system based on predicted power demand in the system during a target time period and based on predicted EV charging curtailment information, which relates to a predicted flexibility in charging EVs while meeting charging goals of the EVs during a target time period. The generated control information includes EV charging scheduling information that utilizes the predicted flexibility in charging EVs by scheduling charging of EVs to curtail or to increase an aggregate charging load of the EVs during the target time period.

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 present invention relates to the field of voltage control technology, specifically to an output voltage and mains adaptive synchronization system. In the present invention, by collecting the amplitude and phase data of the mains voltage, and combining the disturbance information on the load side, a disturbance weight set is established, which can realize the quantitative analysis of the impact of the mains fluctuation, thereby improving the perception of the mains change trend, so that the regulation strategy can be adaptively adjusted for specific disturbance events. Based on the weight set, the disturbance probability of the future short period is calculated, and the mains fluctuation range is predicted in combination with the UPS output data, so that the amplitude or phase compensation strategy can be adjusted in advance, which improves the response speed of the power supply system to the mains fluctuation and reduces the impact of sudden fluctuations on the load. The amplitude and phase of the UPS output are sampled at multiple frequencies, and converted to a unified reference system using a coordinate transformation method, which can ensure that the amplitude and phase information of each frequency component are compared under the same coordinate reference.

The invention provides a multifunctional controller for a new energy automobile and the new energy automobile, and relates to the technical field of new energy automobile control. The multifunctional controller for the new energy automobile comprises a data collection module, a data processing module, a prediction module, a storage system, a communication system, a user interface, a software system, a battery management system, an auxiliary driving system, a safety system, an emotion recognition module, a power supply system and a continuous learning system. Through the prediction model and time sequence analysis, the charging and discharging strategy of the battery is optimized, the service life of the battery is prolonged, the energy efficiency is improved, a power system is optimized according to real-time data and a prediction result, the performance and efficiency of the vehicle are improved, and thermal management of the vehicle is optimized according to weather forecast and real-time traffic information. The optimal performance can be kept under different weather conditions, and the system can automatically adjust the internal environment, such as temperature and music playing, of the vehicle by analyzing the emotional state of the driver so as to adapt to the emotion of the driver.

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.

An apparatus may include a platen, a light source system, an ultrasonic receiver system and a control system, the light source system configured for providing first light of a first wavelength and second light of a second wavelength to a target object on an outer surface of the platen. The control system may be configured to: cause the light source system to provide the first light to the target object at a first time and to provide the second light to the target object at a second time; receive first ultrasonic receiver signals and second ultrasonic receiver signals from the ultrasonic receiver system corresponding to photoacoustic responses of the target object to the first light and the second light, respectively; and estimate one or more blood vessel features based on at least one of the first ultrasonic receiver signals or the second ultrasonic receiver signals.

A control switch incorporating a 1:2 demultiplexer is used to control timing for a concurrent switching, break-before-make and make-before-break power multiplexing, and is configurable to link a plurality of the control switches into a control chain to perform sequential charging, sequential discharging, parallel charging, parallel discharging, and concurrent sequential charging and discharging for a plurality of batteries coupled to the control chain in a power system.

Techniques can include: performing asynchronous replication for a source storage object and a target storage object; tracking writes tagged with a tracking identifier associated with a replication related snapshot of the source storage object; receiving a request at a source system to create identical user snapshots of the source storage object and the target storage object; creating a user snapshot of the source storage object; and storing a record in metadata of tracked writes denoting a time order of the request relative to tracked writes tagged with the tracking identifier; determining, in accordance with said tracking, first tracked writes tagged with the tracking identifier that are received before the request; replicating, to the target system, data changes from the replication related snapshot corresponding to the first tracked writes; and creating a user snapshot of the target storage object after data changes are applied to the target storage object.

The invention provides a self-adaptive algorithm optimization control system and method for intelligent energy management. The system comprises a data acquisition unit which acquires operation data of various energy devices in real time; the model prediction unit predicts renewable energy output and load demand within a preset time based on a machine learning algorithm by using historical data and operation data collected in real time to obtain a prediction result; the optimization engine unit constructs a multi-objective optimization model according to the prediction result and the operation data, solves the multi-objective optimization model by adopting a heuristic intelligent algorithm, and generates an optimal energy scheduling strategy; the adaptive control unit analyzes the optimal energy scheduling strategy into a control instruction which can be executed by each energy device, issues the control instruction to an execution unit, and monitors the execution state and system response of the energy device at the same time; and the execution units adjust respective operation states according to the control instruction, and cooperatively realize optimal scheduling of the whole system. The method can dynamically adapt to changes of an energy system, and the energy utilization rate is increased.