263 results about "Distributed intelligence" patented technology

The invention provides a network attack detection method and system based on a distributed intelligent probe, and the method comprises the steps: receiving real-time flow data synchronously collected by the distributed intelligent probe at each node of a network, carrying out the inter-node interaction relation modeling processing of the real-time flow data, recognizing a flow communication mode between different network nodes, and carrying out the detection of the network attack. Generating a flow association map containing the node connection relationship and the communication frequency; carrying out abnormal communication path mining based on the flow association map, and extracting a node communication sequence with an abnormal mode by analyzing the deviation degree of a node connection relationship and the fluctuation characteristics of communication frequency; performing pattern matching processing on the node communication sequence and an attack behavior template in a preset attack feature library, calculating sequence matching similarity and generating an attack matching degree score set; and determining a network attack type and attack source node positioning information, and generating a network attack detection result. According to the invention, the practicability and effectiveness of network attack detection are improved.

The invention discloses a multi-dimensional computing power dynamic perception routing decision-making method and system based on SRv6 driving. The method comprises the following steps: firstly, screening an optimal target computing power node based on a service demand and a node real-time load; and then, based on the dynamic network topological graph fusing the service sensitivity, calculating an optimal network path reaching the computing power node by using an improved SPFA algorithm, and realizing a global collaborative decision of selecting the most suitable computing point and searching the most efficient connection path. Meanwhile, based on an SRv6 driving service chain dynamic generation and closed loop execution method, a target computing power node and necessary network functions are abstracted into a programmable SID, an SID sequence (service chain) is dynamically constructed according to a double-stage decision result and is packaged in an SRH head, second-level path issuing and state monitoring are achieved through a programmable controller, and a real-time monitoring result is obtained. According to the method, the problems of poor real-time performance, single dimension, weak cooperative capability and the like in the prior art are solved, and efficient, stable and intelligent development of a future-oriented distributed intelligent computing network system can be promoted.

The invention provides a distributed intelligent cooperative energy-saving control method and device for an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the field of data processing application. The method comprises the following steps: acquiring industrial air conditioner operation related data including environment parameters and equipment state data; an intelligent cooperative control mechanism is constructed based on multi-device cooperation and hierarchical adjustment requirements, and the intelligent cooperative control mechanism comprises a multi-device linkage strategy for dynamically distributing refrigeration resources according to regional cooling and heating loads and a hierarchical adjustment algorithm of a chilled water system and a cooling system, and a control framework for achieving global optimization is formed; processing the environment parameters and the equipment state data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis result and the dynamic control strategy; and all operation data and control effects generated based on the target energy-saving control information are integrated, a high-energy-consumption link optimization scheme is generated, energy consumption data credible tracing is achieved, and the distributed air conditioning system is aggregated to participate in power grid frequency modulation.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention relates to a source-containing power distribution network multi-target collaborative voltage optimization control method based on an FACMAC algorithm, and belongs to the technical field of photovoltaic inversion control. According to the technical scheme, a power distribution network physical system is composed of a plurality of feeder lines, a transformer, a line and a plurality of grid-connected photovoltaic inverters, and each inverter can measure operation information such as local voltage and current in real time; the data acquisition and communication system is used for acquiring node operation data and realizing low-delay communication; the multi-agent reinforcement learning control system is composed of a plurality of distributed agents and factorization centralized Critic modules, and whole-network voltage optimization decision can be carried out in training and execution stages. And the execution unit adjusts the reactive power output of the inverter in real time according to the control instruction. According to the method, the whole-network cooperative regulation and control capability is improved, the training efficiency bottleneck in a high-dimensional scene is relieved, the expression capability on a complex nonlinear coupling relationship is enhanced, and efficient, stable and extensible power distribution network voltage optimization control is realized.

The invention relates to the field of power distribution control, and particularly discloses a self-healing control system and method based on multi-agent collaboration, and the system comprises a regional agent and a local agent which are disposed in a power distribution network. Wherein the local intelligent agent is configured to obtain real-time operation data of a monitoring point in the power distribution network, and perform rapid fault detection on the real-time operation data; the regional intelligent agent is configured to fuse the fault analysis information of the plurality of local intelligent agents to determine fault position information when the power distribution network has a fault, generate a self-healing control strategy according to the fault position information, and send the self-healing control strategy to the local intelligent agents for execution; through cooperative work of the distributed intelligent agents, the problems of low response speed, low positioning precision, weak autonomous capability and the like in traditional power distribution network fault management are solved. In addition, the system does not need to depend on centralized control of a master station, can still operate autonomously when communication is interrupted, and improves the efficiency and reliability of fault processing through cooperation of multiple agents.

According to the lightweight helicopter fault diagnosis method and device based on cloud edge cooperation and the medium, a dynamic cooperative distributed intelligent diagnosis system is constructed between the cloud end and the edge end, so that efficient lightweight reasoning of the edge end and centralized optimization training of the cloud end are realized; therefore, an aviation health management platform with self-learning, self-adaption and online updating capabilities is constructed. The whole system adopts a cloud-edge-end three-level architecture, wherein the end side is responsible for data acquisition and preprocessing; the edge side undertakes real-time diagnosis and lightweight model reasoning; and the cloud is responsible for global training, model scheduling and strategy optimization. Cyclic interaction of model parameters, task instructions and diagnosis results is achieved between the cloud and the edge through a secure communication link, and a closed-loop intelligent updating mechanism is formed. According to the method, the real-time performance, the computing power efficiency, the model generalization ability and the system adaptability of a fault diagnosis system are remarkably improved.

The invention relates to the technical field of on-load voltage regulation, in particular to a transformer on-load voltage regulation control method. According to the technical scheme, the transformer on-load voltage regulation control method comprises the following steps that S1, a transformer cluster running in parallel is decomposed into distributed intelligent nodes, and each node executes local voltage prediction and constraint condition management; s2, adopting a distributed model prediction control framework, and cooperatively solving a voltage regulation optimization target through an edge calculation unit; s3, a harmonic disturbance signal with a preset amplitude is injected into the transformer, equivalent impedance is dynamically identified based on response data, and virtual impedance compensation is triggered; and S4, establishing a hierarchical cooperative control architecture, and realizing clock synchronization, dynamic weight distribution and multi-objective optimization decision among nodes. Through a cooperative mechanism of distributed predictive control and dynamic impedance compensation, the response speed and the regulation precision of the on-load voltage regulation system to the power grid voltage fluctuation are remarkably improved, and the voltage out-of-limit problem under the complex working conditions of new energy grid connection, load sudden change and the like is effectively solved.

The invention discloses an automatic UI self-healing test method and system based on multi-agent AI, and belongs to the technical field of automatic UI tests.The automatic UI self-healing test method comprises the steps that an original test script is semantized through a test reasoning interpretation class, and an expected intention is output; when the test fails, synchronously collecting multi-source data and extracting multi-modal features, constructing a fault propagation graph according to an expected intention and the multi-modal features, and performing fault propagation analysis by using a graph attention network to obtain a fault diagnosis result; according to a fault diagnosis result, a deep Q network, a greedy strategy and a Byzantine fault-tolerant algorithm are adopted to negotiate and decide a repair strategy through a distributed agent cooperation mechanism, and feedback is obtained; optimizing and dynamically adjusting the repair strategy and the test reasoning interpretation class through a meta-learning algorithm based on feedback; a test intention is deeply understood by constructing a test reasoning interpretation class, and fault diagnosis and automatic repair are cooperatively completed by adopting a distributed intelligent agent, so that the semantic understanding capability, the diagnosis accuracy and the repair success rate are improved.

The invention discloses a power supply and distribution control system based on a virtual power plant, and relates to the technical field of power distribution systems. The system comprises five core modules: a digital twin simulation control closed loop module constructs a complete closed loop of data acquisition, simulation calculation, control execution and model iteration, and realizes real-time synchronization of a physical system and a digital mirror image and accurate issuing of a control instruction; the multi-agent source-load-storage optimization module improves the source-load-storage collaborative scheduling efficiency through distributed agent collection and reinforcement learning; the multi-scene energy storage adaptive control module generates a scene-based energy storage strategy based on meteorological, load and electricity price multi-dimensional perception and model prediction control; and the multi-type energy storage ratio linkage module realizes multi-type energy storage cooperative operation based on an energy storage characteristic database and scenarized weight optimization. All the modules work cooperatively, the system response speed, the control precision and the power supply stability are effectively improved, and the system is suitable for industrial parks and other complex power supply and distribution scenes.

The invention provides a distributed intelligent multi-machine cooperative operation system and method, a deployment device and equipment, belongs to the technical field of industrial automation and intelligent operation system crossing, and aims to solve the problems of low reliability, rigid static task distribution, poor compatibility of heterogeneous equipment and insufficient fault-tolerant capability caused by a centralized architecture in the prior art. The operating system comprises a communication module, a task scheduling module, a heterogeneous equipment adaptation module, a fault-tolerant and self-healing module and a human-computer interaction module. The operating method comprises the following steps: S1, establishing an equipment communication network; s2, equipment state visualization and task instruction issuing; s3, dynamically allocating tasks and scheduling the tasks; and S4, carrying out equipment fault monitoring and self-healing recovery strategies.

The invention relates to the technical field of data management, in particular to an intelligent flow scheduling method for a high-performance oil way of a metal plate pipe network. The specific implementation process comprises the following steps: establishing a distributed intelligent executor and a resource optimization model based on a sheet metal pipe network comprising a schedulable unit and production management information comprising a historical performance baseline and a production plan; the distributed intelligent executor obtains and evaluates the operation data of the schedulable unit and generates real-time state data; combining the real-time state data and the historical performance baseline to analyze the drift trend and generate performance degradation data; based on the production plan and the performance degradation data, quantifying the comprehensive operation cost as a target function to calculate and generate a resource allocation strategy and an operation instruction; and the distributed intelligent executor combines the operation instruction, the real-time state data and the performance degradation data to execute dynamic compensation management to generate a scheduling instruction. According to the method, resource allocation is optimized by evaluating the real-time and long-term states of the equipment, so that risk avoidance and cost dynamic optimization are realized.

The invention discloses a distributed intelligent power distribution method and system for a power network, and belongs to the technical field of power distribution control, and the method comprises the steps: collecting multi-dimensional electrical parameters of a power generation node in real time, and carrying out the dynamic power monitoring at a feeder terminal of a transformer substation through an edge calculation node; executing line loss calculation and load prediction in the power distribution unit, generating a demand feedback instruction to a feeder terminal node, and triggering an adaptive power distribution strategy; and on the basis of a digital twin system of a regional dispatching center, credibility verification is carried out on operation decisions of all edge nodes, and global optimization compensation control is executed. The method has the remarkable advantages in the aspects of optimizing power distribution, reducing line loss, improving system stability and safety and achieving efficient energy utilization, and intelligent management of a power network becomes possible.

The invention discloses a vehicle-mounted distributed intelligent cooperative reasoning method. The method comprises the following steps: acquiring natural language input of a user in a vehicle; analyzing the natural language input through the vehicle-mounted large model to obtain a user intention; generating a plurality of reasoning tasks according to the user intention, and calling a plurality of reasoning models based on the plurality of reasoning tasks; generating a plurality of preliminary reasoning results based on the plurality of reasoning tasks through the plurality of reasoning models; the plurality of reasoning tasks are in one-to-one correspondence with the plurality of reasoning models; and generating a target reasoning result based on the plurality of preliminary reasoning results by using the vehicle-mounted large model. According to the method, the results after independent reasoning of each module are integrated to the vehicle-mounted large model in a centralized manner, and deep fusion and final decision making are performed on multi-system data by means of the vehicle-mounted large model, so that a cross-module collaborative reasoning mechanism is constructed, the user operation complexity is remarkably reduced, and the system response efficiency is effectively improved.

The invention provides a demand side response power consumption management method based on a power gap architecture, and relates to the technical field of information, the power gap architecture especially relates to a distributed intelligent communication framework oriented to a novel power system, and multi-source power consumption and environment data are collected through power gap edge computing nodes of a transformer substation; through cleaning, feature extraction and clustering, classification results of three types of power consumption subjects of industry, business and residents are obtained, and the nodes carry computers and auxiliary equipment repair adaptation modules to guarantee operation; a classification result is encrypted and transmitted to a trusted execution environment, a power consumption behavior model is constructed by fusing multiple factors, a dynamic electricity price strategy and a response rule are generated and pushed to a terminal, and a regional response strategy is optimized by combining a computer and an auxiliary equipment repair intelligent operation and maintenance mechanism; a final management instruction is generated through load balancing judgment, optimized data are obtained after execution, and the model and the strategy are dynamically updated. Accurate regulation and control of power utilization are realized, the power grid load is balanced, and the power utilization efficiency is improved.

The invention relates to the technical field of artificial intelligence service development and intelligent operation and maintenance, and discloses a system for developing and designing an AI service, and the system comprises a multi-dimensional target configuration module which defines a business target and a KPI, and generates an initial configuration parameter; the life cycle layered modeling module is used for receiving parameters and constructing a digital twinborn model in a layered (macroscopic, mesoscopic and microscopic) manner; the global prediction optimization module is used for executing multi-objective optimization based on digital twinning and generating a global control strategy; the distributed intelligent execution module is used for executing a global strategy in a microcosmic layer by an intelligent agent and performing local autonomous optimization; the intelligent resource management module is used for dynamically distributing system resources according to strategies and task requirements; and the monitoring module collects execution and resource data and is used for updating the digital twinborn model and adjusting a global strategy. According to the method, AI service accurate modeling, global intelligent optimization, efficient cooperative execution and continuous closed-loop self-adaption are realized.

The invention relates to the technical field of water resource scheduling, in particular to a refined multi-dimensional collaborative scheduling optimization method and system for a cascade reservoir group. According to the method and the system, macroscopic prediction and real-time dynamic correction are combined, an intelligent gate actuator with local autonomous adjustment and fault processing capabilities is introduced, sensing, decision making, execution and feedback capabilities are dispersed to each gate unit, and a distributed intelligent scheduling architecture combining global collaborative optimization and local intelligent strain is formed. The invention provides a refined multi-dimensional collaborative scheduling optimization method and system for a cascade reservoir group, so as to solve or at least alleviate the problems of signal transmission lag, execution blind areas, insufficient system toughness and the like caused by centralized control in the existing cascade reservoir group scheduling method. Finer water volume scheduling, quicker emergency management and higher system operation reliability are realized, and the scheduling efficiency and the disaster prevention capability are improved.

The invention provides a multi-Agent-based workshop management system and a cross-workshop migration method and system. The multi-Agent-based workshop management system comprises a sensing layer, an intelligent layer, an execution layer and a communication layer, a data acquisition module of the sensing layer is used for acquiring sensing data in a physical scene, and a sensing Agent of the sensing layer generates a corresponding virtual mirror image platform according to the sensing data and outputs an analysis result; the intelligent layer comprises a decision-making Agent and a coordination Agent, the decision-making Agent dynamically generates a global or local decision-making result according to an analysis result, and the coordination Agent monitors and evaluates the decision-making result through a preset mechanism and outputs an optimization strategy; the intelligent layer transmits the decision result and the optimization strategy to the execution layer; an execution Agent of the execution layer deploys a lightweight model through an edge calculation module, and analyzes a decision result and an optimization strategy into an equipment control signal to control equipment operation; and the communication layer provides an information interaction channel for the Agent of each layer. The limitation of a traditional layered architecture is broken through, and a self-organized, self-adaptive and self-optimized distributed intelligent ecological architecture is constructed.

The invention discloses an intelligent diagnosis system for power utilization abnormal information acquisition, particularly relates to the technical field of power system monitoring, and is used for solving the problems of waveform distortion and broadening caused by transmission line distribution parameters in the long-distance wired transmission process of an existing system. The system collects abnormal characteristic waveform data through a distributed intelligent terminal, selects a characteristic harmonic component in the power frequency voltage of a power line to construct an inherent test signal, extracts the envelope form similarity of a response signal and the test signal through envelope analysis as a first distortion evaluation index, and obtains a second distortion evaluation index after statistical significance judgment. Performing instantaneous frequency stability analysis on the signal with significant distortion to obtain a second distortion evaluation index, and finally generating a corresponding waveform compensation parameter based on double indexes to realize real-time compensation correction; the integrity of the abnormal characteristic waveform in the transmission process is effectively maintained, and the accuracy and reliability of power consumption abnormity diagnosis are remarkably improved.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage treatment facility distributed intelligent monitoring and early warning system based on the Internet of Things, which comprises distributed intelligent monitoring nodes, a region edge computing gateway and a central cloud platform. The monitoring node is integrated with a state self-sensing module, and is used for quantitatively evaluating self corrosion, power supply and environment health degree in real time and locally warning; the edge gateway carries out spatial correlation diagnosis on the multi-node data, and distinguishes node fault false anomalies and real water quality events; and the cloud platform performs macroscopic situation analysis and early warning decision scheduling. According to the invention, dual reliable monitoring of water quality parameters and node health states is realized, and the credibility of monitoring data and the reliability of the system are improved.

The invention relates to the technical field of biomechanical detection and automobile safety evaluation, and discloses an automobile collision dummy abdomen safety test device and method. The testing device comprises a human body feature modeling module which is used for establishing a target population abdomen parameterized model based on statistical analysis, determining a key size design domain covering target population percentile and constructing a target population metrology database; the abdominal mechanical property simulation module is used for obtaining a human tissue relative density distribution function based on the hyperelastic constitutive model, taking the density distribution function as an input parameter for manufacturing a gradient honeycomb structure and simulating human abdominal mechanical properties through relative density gradient distribution in the thickness direction; the multi-mode sensor integration module adopts a distributed intelligent sensing network architecture and is composed of a sensing layer, a transmission layer and a processing layer. By integrating pressure, deformation, acceleration and tissue stress sensors and combining a real-time feedback algorithm, collision parameters are dynamically optimized, and the test precision and the biological fidelity are improved.

The invention provides an intelligent control system and method for an industrial dust collector, and relates to the technical field of industrial automation. According to the system, real-time operation data of a pipe network and an execution mechanism are obtained through an on-site sensing and collecting module, a safe operation envelope boundary of a current working condition is dynamically calculated through a multi-dimensional safety situation evaluation module, and a performance degradation trend is analyzed through an equipment health decline compensation module to generate an aging drift compensation coefficient. The distributed intelligent control module calculates a basic control quantity in combination with a target process parameter, performs gain correction by using an aging coefficient, and performs forced amplitude limiting according to a safety boundary to generate a composite speed regulation control signal. And finally, the frequency conversion execution module receives the signal and drives the negative pressure fan motor to accurately operate in the corrected safety interval. The problems that in the prior art, a safety protection mechanism is rigid, the self-adaptive compensation capability for equipment performance degradation is lacked, and the multi-dimensional data cooperative control level is low are solved.

The invention relates to the technical field of industrial automation and intelligent manufacturing, and discloses a flexible packaging forming system and method based on a modular station robot, and the system comprises a central control module, a station robot module, a flexible material conveying module and a distributed intelligent detection module. The station robot module integrates functional units such as gluing, positioning and attaching, edge folding and frame erecting, foam scraping and flattening, precise gluing and assembling and cleaning. The method comprises the following steps: the central control module analyzes an order into a control instruction; the flexible material transmission module establishes a dynamic logistics network according to the instruction; the station robot module executes a full-process automatic forming process based on force control, visual compensation and impedance control algorithms; and the intelligent detection module feeds back quality data in real time to realize closed-loop correction. The problems that a traditional rigid production line is difficult to adapt to multi-variety small-batch production, the key process precision is insufficient, and accessory assembly depends on manual work are solved, and the flexibility level and the yield of packaging production are improved.

The invention discloses a distributed intelligent deduction system and method for multi-source data fusion and dynamic scheduling, and relates to the technical field of deduction simulation. The deduction preprocessing module is used for carrying out task stage division on military scenarios and setting decision points, generating branch tasks according to tactical rules and carrying out model classification; the deduction management module is used for promoting deduction from an initial state based on a simulation engine, generating branch tasks at a decision point according to a real-time state, distributing initial weight coefficients by combining historical efficiency parameters and model types, and realizing parallel deduction and dynamic model switching; the deduction optimization module continuously updates a weight coefficient through a branch efficiency parameter, screens an optimal deduction path and iterates a state snapshot in a closed loop; the resource regulation and control module collects data in real time to support efficiency evaluation, resource distribution is dynamically optimized according to task loads, the deduction efficiency and accuracy are remarkably improved, and intelligent resource scheduling and system self-adaptive optimization are achieved.

According to the task cooperative processing method and device applying the distributed agent network and the medium, the task processing request sent by the user terminal is received, hierarchical semantic analysis and task dependency relationship analysis are carried out to generate the structured atomic task set, the structured atomic task set is input into the distributed agent network, and the task cooperative processing efficiency is improved. And carrying out collaborative decision making and execution intention confirmation on the atomic task units, constructing a dynamic task graph according to a decision making result and a dependency relationship descriptor, and executing the corresponding atomic task units in parallel according to a task distribution relationship in the dynamic task graph. The method comprises the following steps: exchanging task execution state information and intermediate data output in real time through a preset publishing and subscribing communication mode, generating a distributed execution track based on an exchange result, carrying out association fusion processing on execution results of atomic task units, calculating a global consistency weight, carrying out weighted aggregation on the execution results based on the global consistency weight, and generating a distributed execution track; and generating a global task processing result and sending to the user terminal. According to the invention, the task cooperative processing accuracy can be improved.

The invention discloses a distributed waterlogging and ponding digital map prediction system based on edge calculation, and relates to the technical field of urban flood prevention intelligent decision making. The system comprises a distributed intelligent acquisition module, an edge integrated processing module, a multi-stage collaborative analysis module and a visualization and decision output module. Collecting multi-source heterogeneous data through multiple types of sensing equipment; calling a local digital elevation map slice and a lightweight hydrological model, generating local waterlogging state information and self-calibrating model parameters; key information is screened based on double threshold values of information entropy change and accumulated water depth change rate, multi-monitoring-point data and a drainage pipe network topological relation are fused, and regional-level inland inundation situation analysis and accumulated water and recessive water trend prediction are achieved; and generating and updating a whole-region distributed ponding digital map, and outputting graded early warning and auxiliary decision information. According to the method, distributed acquisition and edge calculation are combined, the monitoring real-time performance and precision are improved, and the problems that traditional monitoring is high in delay, low in precision, fuzzy in decision and the like are effectively solved.

The invention belongs to the technical field of energy planning, and discloses a coal mining area green electricity direct connection planning method and system, and the method comprises the steps: integrating geographic space information, obtaining a three-dimensional geographic space information database, carrying out the processing through a land recycling evaluation model, obtaining a thermodynamic diagram, and carrying out the quantitative analysis of the thermodynamic diagram, and obtaining the power generation amount. Processing and fusing by using a space-time convolutional neural network and a distributed intelligent sensing model to generate a meteorological database; analyzing by using a wind power and photovoltaic space-time output model to obtain a photovoltaic combined sample; processing by using the load prediction model to obtain a load prediction curve set; a source-load time sequence mismatch quantitative model is input for analysis, and an energy storage collaborative configuration scheme is obtained; inputting a multi-target line optimization model for analysis to obtain a direct connection line topological structure diagram; and performing data-driven optimization decision optimization to obtain a source storage load collaborative optimization model, and performing coal mining area green electricity direct connection planning through the source storage load collaborative optimization model. The green electricity is obtained through direct connection, and the cost is reduced.

The invention discloses an operation alarm method and system for a power system, and belongs to the technical field of power system monitoring, and the method comprises the steps: deploying a distributed intelligent sensing network to collect data in a layered manner, and extracting basic physical characteristics; semantic annotation and hierarchical credibility evaluation are carried out on the basic physical features to generate a credible physical feature set; constructing a semantic causal graph and optimizing an edge weight through disturbance simulation to extract a dynamic risk factor; constructing a layered state vector and a dynamic health threshold vector, and comparing the layered state vector and the dynamic health threshold vector to generate an operation alarm state; and executing attribution analysis according to the operation alarm state to generate a threshold calibration signal for feedback. According to the method, the technical scheme of combining multi-source data fusion, hierarchical credibility evaluation, causal risk modeling and dynamic threshold adaptive calibration is adopted, so that accurate identification and prospective early warning of potential risks of the power system can be realized, and the accuracy and adaptive capability of alarm decision making are improved.

The invention provides a distributed intelligent control method and system for an integrated cupboard and the cupboard, and the method comprises the steps: dividing the integrated cupboard into a plurality of control nodes, collecting environment and behavior variables, such as temperature and humidity, oxygen concentration, carbon dioxide concentration, pressure difference, door opening and closing frequency, airflow direction and the like, and forming a state vector; inputting the state vector into a risk assessment model, calculating a risk rate and a quantile index thereof, and obtaining a risk trend in combination with baseline deviation and sliding statistics; based on the risk rate, the quantile and the slope condition, threshold judgment is carried out, and an atmosphere micro-pulse budget containing the number of times, the dose upper limit and the interval is generated; and executing atmosphere micro-pulse adjustment, including carbon dioxide injection, oxygen dilution and humidity linkage, under the condition that the safety constraint condition is met. According to the method and the system, intelligent mildew-proof and insect-proof control of the dry warehouse in the integrated cabinet can be realized while energy consumption is reduced, and the method and the system have the advantages of high adaptability, high safety and good robustness.

The invention discloses a redundancy control device and system for a valve actuating mechanism of a pneumatic isolating valve, and belongs to the technical field of industrial automation and process control. The device comprises a triple gas source supply system which comprises a main gas source, a standby gas source A and a standby gas source B adopting a high-pressure gas storage tank; the distributed intelligent control module comprises a main controller, a hot standby controller and a cold standby controller and adopts a 2oo3 voting mechanism; the triple modular execution mechanism system comprises an electro-pneumatic execution unit, a pure pneumatic execution unit and a mechanical standby execution unit; the invention discloses a self-adaptive fault diagnosis and recovery system. The control system comprises an intelligent self-learning system, an undisturbed switching control system and the like, and has a layered software architecture and a multi-level control strategy. Multiple redundancy is achieved, the reliability of extreme conditions is improved, fault prediction is accurate, response is fast, switching disturbance is small, the method adapts to working condition changes, and the safety and the intelligent level of the system are improved.