416 results about "Control layer" patented technology

The invention provides an energy efficiency optimization and predictive regulation and control method for a building electromechanical system based on digital twinning. The method comprises five layers of architectures including a physical entity layer, a data acquisition and interaction layer, a digital twinning model layer, an energy efficiency analysis and optimization layer and a predictive regulation and control layer. The dynamic digital twinborn body interacting and synchronizing with the physical electromechanical system in real time is constructed, fundamental transformation from passive lag control to prospective predictive control is achieved, a high-fidelity virtual model is used as a nondestructive testing platform, countless operation strategies can be safely and rapidly simulated and evaluated in a virtual space, and therefore accurate optimization is achieved, and the real-time dynamic digital twinborn body is obtained. The overall energy efficiency of the system is remarkably improved, the dilemma that the traditional method makes consideration and loses others is eliminated, and the leap-type improvement of the comprehensive operation quality is achieved.

The invention relates to the technical field of intelligent control of injection molding machines, in particular to an injection molding machine closed-loop mold adjustment control system and method based on multi-sensor fusion and a self-adaptive algorithm, and the system comprises a sensing layer which adopts a multi-sensor fusion closed-loop feedback architecture and comprises a pressure sensing array, a displacement sensing unit and a temperature sensing array. The data acquisition module is used for synchronously acquiring mold clamping force, position and temperature data of a mold, constructing a three-dimensional error compensation model and realizing dynamic correction of the parallelism of the mold through space force field analysis; the execution layer comprises a servo motor, a belt wheel and a ball screw and is used for adjusting the position of the template; a double-closed-loop control module is arranged in the control layer, the double-closed-loop control module comprises outer ring mold clamping force control and inner ring position control, mold clamping force deviation serves as input of an outer ring, position correction is generated through a self-adaptive PID algorithm, the inner ring converts the position correction into a servo motor rotation angle instruction, and therefore precise control over the mold clamping force is achieved.

The invention provides a flow and form decoupling cooperative processing method, and relates to the technical field of business flow management, and the method comprises the steps: constructing a layered architecture, separating a flow control layer from a form service layer, defining a flow node jump rule by the flow control layer based on a BPMN standard, and dynamically defining a form structure by the form service layer through JSON Schema; a form identifier is declarated in the process definition file, a corresponding form template is matched through a dynamic mapping service according to context information, and the context information comprises a process instance state, a user role, a device type and a service type; establishing a data sandbox isolation mechanism, generating a unique data space identifier for each process instance, and managing data access authority through a multi-dimensional isolation strategy of a tenant level, a service level and a process instance level; deploying an event bus communication mechanism, triggering two-way communication between a process engine and a form engine through a predefined event type, and ensuring the consistency of cross-system operation by adopting a transaction compensation mechanism.

The invention provides a trailing suction dredger loading operation parameter optimization real-time recommendation system and method, and relates to the technical field of trailing suction dredger dredging engineering.The system comprises a data collection layer, a real-time calculation layer, an intelligent recommendation layer and a feedback control layer, the data collection layer is used for collecting trailing suction dredger loading operation parameters in real time, and the real-time calculation layer is used for calculating the real-time calculation layer; the real-time calculation layer can calculate energy consumption and yield in real time based on edge calculation equipment, the intelligent recommendation layer can generate a Pareto optimal solution set through a multi-objective optimization algorithm, and the dynamic weight adjustment module adjusts energy consumption and yield weight coefficients in real time according to the construction stage. The feedback control layer issues the collected loading operation parameters of the trailing suction dredger to an execution mechanism in real time through a PLC, and monitors the operation state of equipment to update a historical database, so that collaborative optimization of energy consumption and yield can be achieved, and the optimization precision is continuously improved through historical data.

The invention relates to the technical field of medical informatization and intelligent scheduling, and particularly discloses a ward nursing process prediction and dynamic scheduling method and system.The ward nursing process prediction and dynamic scheduling method comprises the steps that firstly, a digital twinborn simulation environment synchronized with a physical ward in real time is established, and a nursing process is simulated through a multi-agent network; carrying out multi-time sequence deduction based on the environment, generating a prospective scheduling instruction set, and issuing and executing the prospective scheduling instruction set; calculating a prediction mismatch degree index by collecting actual execution data and comparing the actual execution data with a plurality of dimensions of an expected state; when the index exceeds a dynamic threshold value, a meta control layer is triggered to carry out self-adaptive adjustment on simulation environment parameters, and the self-adaptive adjustment comprises reconstruction of an agent behavior logic weight or switching of a scene deduction mode; and deducing and generating an updated instruction set again based on the adjusted environment to form closed-loop optimization.

The invention provides a tire crane automatic control system and method based on embedded edge calculation, and relates to the technical field of port automation, the tire crane automatic control system comprises a sensing and detecting layer used for collecting motion states and operation environment information of a cart, a trolley and a lifting appliance of a tire crane in real time; the core control layer is used for processing sensor data in real time and generating a control instruction; the action execution layer comprises a PLC gateway, a main control PLC and a driving motor, the PLC gateway communicates with the core control layer and is used for receiving a control instruction, and the main control PLC drives the motor to execute corresponding actions of a cart, a trolley and a lifting appliance; and the task management layer comprises an intelligent remote control console, is in communication connection with the core control layer, and is used for issuing the operation task instruction to the core control layer and receiving the system state data from the core control layer. Unmanned operation of the tire crane can be achieved, and a complete technical base is provided for port automation.

The invention discloses a multi-tenant management and control system and method for a big data platform, and relates to the technical field of data management, and the system comprises a data basic management and control module layer and a data processing collaborative management and control layer. The data basic management and control module layer comprises a big data component management module, a tenant management module, a unified login authentication module, a project management module, a data resource authority management module and a computing resource management module; the data processing collaborative management and control layer comprises a component health monitoring sub-module, a resource prediction distribution sub-module, a dynamic desensitization encryption sub-module, a task collaborative management sub-module and a data quality management and control sub-module; the problems of weak tenant basic management and control, low resource allocation efficiency and insufficient component reliability and data security management and control in the existing multi-tenant management and control technology are solved, and the defects of lack of cross-tenant cooperative capability, lack of task and data quality management and control and insufficient data security management and control caused by the problems are overcome.

The invention discloses a multi-mode perception collaboration and real-time synchronization system, which breaks through the limitation of vision / hearing in the prior art, covers vision, hearing, smell and movement, and realizes more accurate cooperation through a perception layer, a control layer and an execution layer. According to the invention, an immersive experience space full of shock and reality can be created, synchronous output of visual, auditory, tactile and olfactory signals is realized by means of high-precision equipment, and a series of cross-sensory signal superposition schemes are designed on the basis of response characteristics of human physiology and psychology to various sensory stimuli. Therefore, the overall perception of the experiencer to the space atmosphere is enhanced. Meanwhile, through weight distribution and conflict resolution, the system can flexibly adjust the sensing mode according to the environment and the user state. The multi-device cooperation error can be controlled at millisecond level and millimeter level, and the natural fusion of virtuality and reality is realized.

The invention provides a display module and a display device, and the display module comprises a display panel which comprises a plurality of pixel units arranged in an array; the light control device is arranged on the light emitting side of the display panel, the light control device comprises a first regulation and control layer and a second regulation and control layer which are sequentially arranged on the display panel in a stacked mode, the first regulation and control layer comprises a first refraction layer and a second refraction layer covering the first refraction layer, the first refraction layer comprises a plurality of lenses arranged in an array mode, and the second refraction layer comprises a second refraction layer covering the second refraction layer; the orthographic projection of each lens on the display panel at least covers two pixel units, the refractive index of the first refraction layer is larger than that of the second refraction layer, when the display module is in a three-dimensional display state, liquid crystal molecules in the second regulation and control layer do not deflect, and when the display module is in a two-dimensional display state, liquid crystal molecules in the second regulation and control layer do not deflect. And the second regulation and control layer is equivalent to a plurality of lens units arranged in one-to-one correspondence with the lenses. The display module provided by the embodiment of the invention can reduce view crosstalk in a three-dimensional display state.

The invention discloses a cigarette cut tobacco batch production circulation control system and method based on single-point condition judgment, and the system comprises a work order parameter verification module which is used for receiving a work order issued by a scheduling system, and carrying out the work order information integrity verification, process parameter compliance verification and equipment suitability verification; the regular expression configuration module is used for providing a visual interface and configuring a regular judgment rule set of equipment starting conditions, ending conditions and abnormal ending conditions; and the single-point judgment execution mechanism is embedded into the PLC control layer, acquires equipment parameters in real time, dynamically evaluates regular conditions based on a finite-state machine model, and triggers an equipment start-stop instruction. According to the system, the cigarette cut tobacco batch switching time is remarkably shortened, and the work order circulation efficiency is improved; the manual intervention frequency is further reduced, and the misoperation rate is further reduced; and the problem of production interruption caused by disconnection of the equipment state and the process parameters is solved.

The invention relates to the technical field of industrial compressed air system intelligent control, in particular to a digital air compression station intelligent control method and system based on AI self-learning, and the system comprises a data collection layer, an AI prediction layer, an optimization control layer and an execution layer. The data acquisition layer realizes accurate acquisition and preprocessing of multi-dimensional data through a three-stage high-precision sensor network and an edge computing node; the AI prediction layer adopts an XGBoost and LSTM mixed model to be combined with an online learning mechanism to realize accurate prediction of the gas consumption demand and the energy efficiency ratio within 1-24 hours; the optimization control layer completes air compressor operation combination optimization and pressure dynamic control based on an improved NSGA-II algorithm and self-adaptive PID adjustment; the execution layer realizes control instruction landing through frequency converter and cluster cooperative scheduling. Meanwhile, an equipment health degree monitoring module, a multi-source data fusion correction module and a waste heat-gas consumption collaborative optimization module are innovatively introduced, and the problems that a traditional air compression station is low in energy efficiency, poor in self-adaption, insufficient in prediction precision and lack of equipment management are solved.

The invention discloses a shield construction full-process management and control system and method based on intelligent scheduling, and relates to the technical field of tunnel construction, the system comprises a data acquisition layer, a data fusion and intelligent scheduling layer, an execution control layer and a man-machine interaction and visualization platform. The data acquisition layer acquires multi-source data of shield construction in real time; the data fusion and intelligent scheduling layer performs fusion processing on multi-source data, generates a comprehensive scheduling scheme based on an intelligent algorithm, and dynamically manages task priorities and security constraints; the execution control layer receives and executes the scheme, and cooperatively controls tunneling, transportation, assembly and vertical transportation equipment; the man-machine interaction and visualization platform realizes construction state visualization, scheduling display, abnormity alarm, remote control and multi-work-point data access and authority management. According to the invention, through a multi-layer collaborative architecture, full-process intelligent management and control of shield construction are realized, the data-driven decision-making capability, the equipment collaborative efficiency and the multi-project scheduling level are improved, and the construction efficiency is improved.

The invention provides a supply and demand matching degree-based multi-target collaborative optimization scheduling system for a light storage building group, and the system comprises a sensing layer which is used for collecting the physical operation state data of the light storage building group in real time; the network layer is used for transmitting data to the control layer; the control layer is used for generating a global scheduling plan by taking maximization of the comprehensive supply and demand matching degree as an optimization target according to the supply and demand matching degree prediction information of the first scale and the time-of-use electricity price information of the power grid; according to the supply and demand matching degree prediction information of the second scale and the physical operation state data, performing rolling correction on the global scheduling plan to generate a correction instruction; according to the supply and demand matching degree prediction information of the third scale and the physical operation state data, performing local autonomous judgment on the correction instruction, and triggering a local response when supply and demand fluctuation is detected; and the application layer is used for displaying the data acquired by the sensing layer and / or the output data of the control layer, so that the self-balancing capability of the system is fundamentally improved.

The invention discloses an engineering construction digital intelligent supervision management and control system, and relates to the technical field of engineering construction supervision. The system comprises an information acquisition module which is used for acquiring equipment state, image and environment data; the data fusion layer module is used for carrying out cleaning, alignment and Kalman filtering fusion on the multi-source data to generate a multi-modal construction data flow; a progress calculation and prediction unit, a deviation judgment unit and a digital modeling unit are arranged in the analysis monitoring module, and the analysis monitoring module is used for updating a progress state based on an XGBoost model, analyzing a node dependency relationship by using a graph neural network, calculating a deviation propagation risk coefficient and generating a hierarchical deviation signal in combination with three-level judgment conditions; and the management and control layer module executes hierarchical response according to the level of the deviation signal, and feeds back execution effect data to the front-end module to realize model optimization and closed-loop iteration. According to the invention, intelligent perception, risk prediction and adaptive management and control of the construction process are realized, and the real-time performance, accuracy and autonomy of project supervision are improved.

The invention relates to the technical field of remote monitoring, in particular to a TBM remote monitoring method based on a multi-level collaborative management architecture, which is applied to a remote comprehensive monitoring platform. The platform comprises a construction operation layer, a construction management and control layer and a decision support layer, each monitoring scene forms a local block chain ring by a plurality of construction surface nodes, and an integral block chain ring is integrally formed. The method comprises the steps of collecting construction state data, performing standardization processing, and inputting the data to an equipment risk identification model and a geological evaluation model for construction state analysis; based on local anomaly detection, deducing an anomaly propagation path, calculating the reverse coverage capability of each non-abnormal local circle, and determining a decision block chain circle; and performing node locking and decision switching through the abnormal isolation control smart contract, and generating a decision instruction based on a multi-objective optimization algorithm. According to the method, the abnormal self-healing capability and the dynamic decision-making efficiency of the TBM construction monitoring system are improved.

The invention discloses an intelligent sensing socket based on equipment feature recognition and an energy consumption management method thereof. The method comprises the steps that a sensing layer collects various feature parameters and environment data of connecting equipment in an omnibearing mode; the control layer is used for deeply processing and analyzing data acquired by the sensing layer and executing equipment identification, strategy management and communication coordination; the platform layer provides far-end service support and comprises an equipment management module, a data analysis module, a user interaction module, an energy consumption management module and a core algorithm module; the original data collected by the sensing layer is subjected to feature extraction and identification analysis of the control layer, and the result is uploaded to the platform layer. According to the invention, through a multi-modal feature fusion identification technology, extremely high identification accuracy can be maintained in various use scenes; the potential risk can be identified before the fault occurs through the trend analysis of the electrical parameters and the temperature change.

The invention belongs to the technical field of financial science and technology, and discloses a multi-agent collaborative document-driven quantitative research and transaction self-evolution system and method. The system adopts a layered architecture, comprises a data and factor service layer, an execution engine layer, an agent layer, an interface service layer and a front-end control layer, and aims to solve the core problems that research and engineering implementation are separated in quantitative strategy research and development, code generation lacks interface constraints and strategies lack self-evolution ability. According to the system, through cooperative work of a research agent, a factor agent and a coding agent, a strategy idea described by a natural language is automatically converted into a standardized document, configuration and executable code; through a closed loop formed by self-monitoring, self-diagnosis and self-improvement intelligent agents, automatic diagnosis and optimization of problems of strategy overfitting, factor attenuation and the like are realized, continuous self-evolution of the strategy is driven, and full-life-cycle automatic management from strategy conception, factor design, automatic coding and real disk back-testing to feedback autonomous optimization is realized.

The invention provides a photovoltaic-building group load space-time matching degree prediction system, and the system comprises a sensing layer which is used for collecting photovoltaic side output data and building group side load data; the network layer is used for transmitting the acquired data to the control layer; the control layer is used for constructing a photoelectric-building load coupling model fusing time and space dimensions based on the photovoltaic side output data and the building group side load data, executing multi-time scale collaborative prediction, and calculating a time matching degree, a space matching degree and a comprehensive space-time matching degree based on a prediction result; and the application layer is used for visually displaying the prediction result, the time matching degree, the space matching degree and the comprehensive space-time matching degree. By constructing the photovoltaic-building load coupling model fusing the space-time dimension and executing multi-scale collaborative prediction, accurate prediction of the future state of the photovoltaic output and the building load can be realized, the matching degree of the photovoltaic output and the building load in time and space is quantitatively output, and the defects that prediction is independent and the space-time matching state cannot be evaluated are overcome.

The invention provides an archive management model based on digital twinning, and relates to the technical field of archive management. The archive management model based on digital twinning comprises a physical layer, the physical layer is connected with a data layer, the data layer is connected with a digital twinning layer, and the digital twinning layer is connected with an intelligent control layer. According to the technology, an archive management model is constructed by utilizing digital twinning, a virtual mirror image of an entity storehouse is created through a building information model and an internet of things data stream, millimeter-level precision three-dimensional modeling is realized, archive management is more accurate and intuitive, a weight sensor can monitor the weight of the archive in real time, the deviation can be compared during returning, mistakes and omissions are avoided, and the integrity of the archive is guaranteed; the intelligent control layer generates a three-dimensional navigation path, combines a virtual reality terminal, simplifies the archive searching and returning process, reduces manual errors, tracks an administrator and identifies archives in real time through an ultra wide band positioning module and a radio frequency identification reader-writer, optimizes borrowing and returning efficiency, achieves intelligent management of an archive repository, reduces cost and improves efficiency.

The invention discloses an active power distribution network multi-target cooperative control system and method based on digital carbon flow sensing and dynamic optimization, and the system comprises a hardware layer, an algorithm layer, a control layer, and a visualization layer: the hardware layer is a distributed carbon sensing and execution network, and is used for collecting the full-link carbon flow, power, and price data of an active power distribution network; the source-network-load-storage carbon information full coverage is realized; the algorithm layer comprises a dynamic carbon cost coefficient calculation model and a multi-objective optimization model, and is responsible for carbon cost quantification, optimization objective solution, carbon cost coefficient output and output plan optimization; the control layer is used for executing a'day-ahead robust optimization-intra-day rolling optimization-real-time rapid adjustment 'three-time-scale cooperative scheduling strategy based on the result of the algorithm layer; the visualization layer is used for constructing a'carbon flow-power flow 'double-flow visualization interface of the active power distribution network by adopting a WebGIS (Web Geographic Information System); according to the invention, accurate quantification of dynamic carbon cost, improvement of distributed new energy consumption rate and real-time traceability and visualization of carbon flow are realized.

The invention discloses an industrial intelligent safety risk early warning and management and control platform, and relates to the technical field of industrial safety monitoring and management and control. The industrial intelligent security risk early warning and management and control platform comprises a full-scene perception layer, a data management layer, an intelligent risk identification layer, a hierarchical management and control layer and a global operation and maintenance layer, and each layer and an edge computing node construct a distributed data interaction network through an industrial Ethernet; the scene sensing layer is used for realizing all-region and all-time security data acquisition of industrial production and comprises a multi-mode sensing terminal array, an equipment protocol adaptation module, a personnel state monitoring unit and an acquisition scheduling module; according to the industrial intelligent safety risk early warning and management and control platform, through a multi-mode terminal array and a dynamic acquisition scheduling mechanism of a full-scene sensing layer, full-area and full-time-period automatic monitoring of industrial production is realized, dependence on manual inspection is thoroughly eliminated, the problems of low manual inspection efficiency and missing inspection of hidden risk points are effectively solved, and the industrial production safety risk early warning and management and control platform is suitable for popularization and application. And the monitoring coverage rate is improved to 100%.

The invention provides a self-adaptive control method for improving new energy power generation grid-connected stability of a micro-grid, and the method comprises the steps: carrying out the description according to a corrected phase change trend, fusing the real-time feedback data of voltage deviation, generating a reactive compensation instruction sequence segmentation adjustment scheme, and determining a preliminary compensation capacity dynamic distribution strategy; the microgrid system is regulated and controlled in real time through the optimized control instruction sequence in combination with the power grid stability constraint condition, the actual execution effect of the compensation action is obtained, and whether the system voltage deviation meets the stability requirement or not is judged; according to the stability requirement judgment result of the system voltage deviation, the execution effect is fed back to a micro-grid control layer, a closed-loop mechanism is adopted to carry out iterative updating on the instruction sequence segmentation adjustment scheme, and a final reactive compensation regulation and control scheme is determined.

The invention discloses an industrial intelligent control system and method, relates to the technical field of industrial automatic control, and mainly aims to solve the problems of low data cross-layer interaction efficiency, system global optimization, poor safety, poor expansion capability, difficulty in function upgrading and difficulty in fusion of AI control and traditional control in the existing industrial intelligent control scheme. The system comprises a basic control layer, an intelligent I / O layer and an intelligent control layer. The basic control layer comprises an extended I / O cabinet, a common controller and a main control network A / B; the intelligent I / O layer comprises an intelligent I / O cabinet and an intelligent I / O switch; the intelligent control layer comprises an intelligent controller, a credibility regulator, a hyper-fusion module and an intelligent middle table, an end-edge-cloud intelligent collaborative architecture is constructed, the computing power of a DCS is enhanced, the development collaborative safety capability of an intelligent algorithm of the system is improved, and safety fusion and smooth transition of AI intelligent control and traditional control are promoted.

The invention provides an intelligent optimization system and equipment for coking production and a storage medium, and the system comprises an intelligent sensing layer, an intelligent control layer, an AI decision-making layer, and an interactive application layer. Wherein the intelligent sensing layer is used for acquiring production data in a coking production process; the intelligent management and control layer is used for performing multi-objective optimization control and fault diagnosis based on an AI model and coking production process knowledge in combination with production data to obtain candidate control parameters and fault diagnosis data; the AI decision-making layer is used for controlling coking production equipment and monitoring the production process in real time based on the candidate control parameters and the fault diagnosis data; and the interactive application layer is used for providing a three-dimensional visual platform and an interactive interface, the three-dimensional visual platform is used for displaying the operation state of the coking production equipment in combination with the three-dimensional model of the coking production equipment, and the interactive interface is used for displaying production data and controlling the coking production intelligent optimization system. According to the scheme, intelligent management and control of coking production can be realized.

The invention provides an embedded layer access collaborative acceleration system and method of a neural network model, a processing unit layer, a control layer and a routing layer cooperatively work, and the control layer can predict a candidate item of a next lexical element before a processing unit by querying a lexical element statistical table. A routing layer reads word embedding vectors of candidate lexical elements from a memory bank in advance and stores the word embedding vectors in a prefetching buffer area corresponding to the memory bank, and after a processing unit inferes a next lexical element by using a neural network model, if the next lexical element hits the candidate lexical element, the next lexical element is not hit by the candidate lexical element. If not, the word embedding vector of the hit candidate lexical element is read from the prefetching buffer area, and compared with the mode that the processing unit inferes the next lexical element and then reads the corresponding word embedding vector from the storage bank, lexical element prefetching processing is increased, and the access speed of an embedding layer of the neural network model can be increased.

The invention discloses an automatic alarm system of an air cushion type belt conveyor. The automatic alarm system comprises a sensing layer, a control layer and an execution layer. The sensing layer constructs a multi-dimensional sensing network through an air film thickness sensor, an air pressure sensor, a belt state sensor, an air chamber airflow audio analysis module, a motor power fluctuation detection module and a distributed optical fiber sensing module. The control layer adopts an edge-cloud computing collaborative architecture, an edge computing node is responsible for real-time data preprocessing, the fog computing layer runs a digital twin model to complete data fusion and state simulation, and a cloud computing center stores historical data and optimizes an algorithm and is provided with a three-level early warning and adaptive sensing mechanism. The execution layer executes parameter adjustment, sound-light alarm or emergency shutdown according to instructions of the control layer. Comprehensive monitoring, intelligent diagnosis and automatic regulation and control of the operation state of the air cushion type belt conveyor are achieved, and the operation reliability and the maintenance efficiency of equipment are remarkably improved.

The invention discloses a denitration system capable of automatically adjusting an ammonia spraying strategy, which comprises a data sensing layer and an execution layer, the data sensing layer comprises a plurality of measuring devices arranged at an inlet and an outlet of an SCR (Selective Catalytic Reduction) reactor, the execution layer comprises a plurality of ammonia spraying grids arranged along the section of a flue in a partitioned manner, and each partition corresponds to an independent ammonia spraying branch pipe; an adjustable flow control valve is arranged on each ammonia spraying branch pipe; the control layer is in communication connection with the data sensing layer and the execution layer, and the control layer comprises an advanced controller and is configured to receive real-time data of the data sensing layer and optimize the real-time data on the basis of a preset denitration model and an optimization algorithm; calculating the distribution uniformity index of the NOx concentration at the outlet of the SCR reactor and the target ammonia gas flow required by each partition of the plurality of ammonia spraying grids; and the distributed PID controller is connected with the advanced controller and is configured to receive the target ammonia gas flow of each partition and generate a control signal to drive the corresponding flow control valve, and the total ammonia consumption can be remarkably reduced under the same denitration efficiency.

The invention discloses a user dynamic access control method and system based on multi-dimensional data, and belongs to the technical field of software. The invention aims to solve the problems of user access control policy jitter, security vulnerability and difficulty in explaining compliance caused by static threshold, module independent decision and non-explicit modeling user experience cost and compliance risk cost in the prior art. Therefore, the invention provides a dynamic access control method, which comprises the following steps of: constructing a session-level multi-dimensional access context; dividing risk signal detection according to a visual angle; carrying out risk evidence combination and mode recognition; performing risk trajectory and strategy hysteresis control based on a state machine; unifying action arrangement and conflict resolution of the strategy center; strategy execution and session consistency control are carried out; and effect evaluation and strategy evolution based on multi-dimensional indexes are carried out. Compared with the prior art, multi-view evidence combination, session-level trajectory analysis, state machine and constraint solution and other means are introduced, multi-layer integration from a data layer, a logic layer, a time layer to a control layer is achieved, strategy jitter is effectively relieved, user experience is improved, service continuity is guaranteed, and compliance interpretability is enhanced.

The invention belongs to the technical field of data analysis and processing, and particularly relates to a joint review and judgment method for territorial space planning, compiling and reviewing full-chain business, which comprises the following steps of: 1, reading a project declaration text, a reply text, a planning layer and a management and control layer, planning polygon elements and control polygon elements are read from the planning layer and the control layer, and corresponding enclosing rectangles are generated; 2, inputting a project application text marking sequence into the pointer network model, and performing polygon superposition operation on a project plot polygon and a retrieval result to form a spatial superposition unit set; and step 3, reading the territorial space planning management and control rule base, and generating a project joint review opinion list including a modified point location list and a review conclusion. According to the method, the examination efficiency can be remarkably improved, the manual operation amount is reduced, the examination consistency is improved, and high-precision and high-reliability intelligent support is provided for territorial space planning management.

The invention discloses an offshore wind plant monitoring system based on a digital twin and grid-connected architecture and a design method thereof. The system comprises a field layer, an intermediate network communication layer and a station control and centralized control layer, the field layer comprises a fan monitoring subsystem, a booster station monitoring subsystem, a video monitoring subsystem, an environment monitoring subsystem, a power prediction subsystem and a submarine cable fault monitoring subsystem; the intermediate network communication layer comprises an SDH communication node and a longitudinal encryption device; the station control and centralized control layer comprises an integrated intelligent monitoring management platform and a digital twinborn model, the digital twinborn model is constructed based on collected data of the field layer and covers multi-dimensional information and functional sub-models of the wind power plant, and the integrated intelligent monitoring management platform stores a functional component library and can call the functional component library to realize monitoring and control functions; and the system can be tested and optimized. The system has the advantages of being high in integration level, intelligent and efficient in operation and maintenance, adaptive to different grid-connected frameworks, capable of fusing static design and dynamic operation and the like.