1608 results about "Equipment Safety" patented technology

The invention relates to the technical field of edge computing, in particular to a safety production monitoring method based on edge collaboration, which comprises the following steps: collecting multi-node clock pulses, calculating adjacent time differences to form a pulse difference parameter set, carrying out statistical distribution, setting a dynamic tolerance boundary, and constructing an effective deviation parameter set; the method comprises the following steps of: generating a weighted smooth deviation result by combining clock stability and link delay weighted normalization, sorting monitoring data according to a timestamp to identify duplication abrupt change exception, constructing a cross-node exception time sequence by associating nodes and time information, and judging a safety state transition result of equipment. A time difference parameter is constructed through multi-node reference pulse arrival time, a dynamic deviation constraint is formed by combining distribution characteristics, clock stability and link measurement weighted smoothing are introduced, cross-node time reference consistency is kept, operation data unified time sequence rearrangement is carried out, a sudden change event reliably reappears in a time window, and an abnormal identifier is associated with a node identity. And forming a cross-node event evolution link.

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention provides an oil and gas equipment leakage acoustic characteristic intelligent identification early warning method and system, and relates to the technical field of oil and gas equipment safety monitoring, and the method comprises the steps: arranging a sonic sensor array to collect signals, calculating a wavefront propagation path, optimizing the wavefront curvature, carrying out the multi-scale decomposition of a sonic signal, and extracting time-frequency characteristics; and reconstructing a sound field by using an acoustic propagation function to determine a leakage point space coordinate. The method can accurately identify the acoustic characteristics of oil and gas equipment leakage, improves the leakage point positioning precision, achieves the early warning of leakage, and guarantees the safe operation of oil and gas equipment.

The invention relates to the technical field of energy system control, and discloses a distributed cooperative fault-tolerant control method for a multi-energy-station cooling and heating system, and the method comprises the steps: enabling each energy station to measure and calculate a residual error based on a physical model and a sensor, forming a normalized health index, and carrying out the neighborhood broadcasting; carrying out robust anomaly judgment by utilizing a neighborhood median and a median absolute deviation, and isolating an abnormal site; cost coefficients are automatically generated for available sites according to equipment maneuverability, and control redistribution of minimum disturbance is solved and implemented through distributed consistency optimization; an exponential weighting updating mechanism of a residual error sequence is adopted to carry out online self-adaption on a noise baseline and a judgment threshold value so as to realize closed-loop self-calibration; when sensor abnormity, equipment errors or individual station faults occur in the multiple energy stations which work cooperatively, distributed rapid identification is achieved, faults are isolated, energy supply is redistributed with minimum disturbance, system control requirements are met, and equipment safety constraints are kept.

The invention belongs to the technical field of base station equipment management, and discloses a base station equipment management method and system based on big data. The method comprises the following steps: firstly, collecting security situation data of a physical layer, a network layer and an application layer, extracting time sequence behavior characteristics, performing cross-layer correlation analysis, and constructing an equipment security credibility dynamic evaluation model; then, identifying vulnerability indexes of control nodes according to the trust attenuation curve, and constructing a cascade risk conduction model in combination with a topological connection relationship; dynamically dividing a security isolation domain based on the model and generating protection parameters and control rules; further, a differentiated security policy is configured, and a cross-domain collaborative response channel is established; and finally, dynamically adjusting the isolation domain boundary and the security policy by monitoring the security event frequency and the interception success rate in real time. According to the invention, the conversion from passive defense to active prediction is realized, and the safety protection capability and operation stability of the base station equipment are improved.

The invention discloses an equipment safety early warning method and equipment based on multi-modal data and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: collecting industrial equipment multi-modal data, and preprocessing the multi-modal data to obtain standard multi-dimensional equipment data; performing feature extraction on the standard multi-dimensional equipment data to obtain a multi-dimensional equipment feature vector; based on joint semantic coding, mapping the multi-dimensional device feature vectors to a unified semantic space, and performing weighted fusion on the multi-dimensional device feature vectors by using a gating mechanism to obtain device fusion features; based on the equipment fusion features, constructing a risk association map to obtain a predicted risk conduction path; and based on the risk conduction path, triggering graded early warning, and generating a visual decision report. According to the method, the risk conduction path is predicted by constructing the risk association map, and the risk of the industrial equipment is accurately predicted and actively prevented and controlled.

The invention belongs to the technical field of power equipment state monitoring, and discloses a cable branch box operation state intelligent evaluation system based on multi-sensor fusion, which comprises a state acquisition module used for acquiring a partial discharge signal, a voltage fluctuation signal, a terminal temperature signal and a terminal contact resistance signal of a cable branch box in an operation process, constructing a multi-source operation state parameter set of the cable branch box through time synchronization and physical quantity normalization processing; the mutual coupling analysis module is used for performing transient correlation deduction on the partial discharge signal and the corresponding voltage fluctuation signal based on the multi-source operation state parameter set to obtain a partial discharge-voltage coupling amount; when the partial discharge-voltage coupling amount exceeds a preset partial discharge-voltage coupling amount threshold value, determining that partial discharge abnormity caused by insulation degradation exists in the cable branch box; comprehensive and accurate evaluation of the operation state of the cable branch box is realized, and reliable support is provided for safe operation and maintenance of equipment.

The invention belongs to the technical field of Internet of Things security, and particularly relates to a multi-level Internet of Things equipment security management and access control method, which comprises the following steps of: performing bidirectional verification on a digital certificate and a hardware fingerprint through an edge node when equipment is accessed; calculating credibility in real time and dynamically dividing security levels by using a hybrid evaluation algorithm based on equipment operation context data; a fine-grained access strategy is generated in combination with equipment attributes and environmental risks, and cross-level collaboration is realized through a lightweight protocol; abnormal behaviors are detected in real time in the access process, and high-risk operation is blocked within milliseconds; and strategy conflict resolution and adaptive optimization are realized through formalized verification and deep reinforcement learning. According to the technical scheme, the identity authentication reliability, the authority dynamic adaptability, the strategy consistency and the threat response speed of the Internet of Things equipment are remarkably improved, and a systematic security guarantee is provided for large-scale Internet of Things applications with high security requirements.

The invention discloses a paper-plastic mold positioning auxiliary system capable of monitoring hydraulic pressure in real time through an AI. The paper-plastic mold positioning auxiliary system comprises an AI control center, a hydraulic monitoring unit, an air pressure monitoring unit, a positioning auxiliary execution unit, an abnormity protection unit and a data acquisition and transmission module. The hydraulic monitoring unit is used for collecting hydraulic oil cylinder pressure, temperature, hydraulic oil viscosity and equipment operation duration data; the air pressure monitoring unit collects air pressure, flow and valve delay time data of a pipeline valve; the data acquisition and transmission module transmits data to the AI control center, and the AI control center analyzes abnormity through a model and calculates a correction amount; the positioning auxiliary execution unit coordinates the positioning of the servo motor and the hydraulic oil cylinder; and the abnormal protection unit receives the early warning and starts a protection action. The system solves the problems of hydraulic monitoring lag and the like, realizes hydraulic and air pressure cooperative monitoring and positioning compensation, ensures production coherence and equipment safety, and is adaptive to automatic production of paper-plastic molds.

The invention discloses a GIS ultrahigh frequency sensor ultrasonic cooperative detection system based on piezoelectric energization, and relates to the field of electric power detection, and the system comprises an excitation module which is used for transmitting a piezoelectric excitation signal with a specific frequency to a preset detection region in GIS equipment, so as to trigger the potential partial discharge acoustoelectric signal radiation of the region; the sensing module is used for receiving ultrahigh-frequency electromagnetic signals and ultrasonic vibration signals radiated by partial discharge in the GIS equipment; the excitation frequency, the filtering bandwidth and the sampling rate are dynamically optimized according to the detection result, the detection accuracy and efficiency are continuously improved, and reliable and effective guarantee is provided for safe operation and defect early warning of GIS equipment.

The invention relates to the technical field of power equipment management, in particular to a digital low-voltage cabinet full life cycle data management method and system, and the method comprises the steps: distributing a global unique digital identity identifier for a low-voltage cabinet and key components, and constructing a standardized digital mapping model based on an OPCUA framework; gathering full-process multi-source heterogeneous data to a cloud edge collaboration platform by taking the digital identity as an index to form a data consanguinity chain; creating a digital twinborn body, dynamically updating the model through real-time data and iteratively calibrating parameters; carrying out abnormity identification, health assessment and residual life prediction by using an AI algorithm, and generating an operation and maintenance instruction; and feeding back the instruction to an upstream system. According to the method, a data island is broken, predictive maintenance is changed into full-period optimization, and the equipment safety and the operation efficiency are improved.

The invention relates to the technical field of power equipment safety operation and maintenance, in particular to a power distribution network single-phase grounding positioning method and device based on multi-source information fusion. Heterogeneous sensing nodes deployed in a layered and distributed mode are adopted to synchronously collect steady-state and transient-state electrical quantity data of a power distribution network, and preprocessing is carried out to generate a standardized data set; multi-dimensional feature extraction is carried out; carrying out weighted fusion on the extracted multi-dimensional features based on a multi-feature fusion strategy of a dynamic weight adjustment mechanism and a D-S evidence theory, and calculating a line fault probability; when the calculated line fault probability meets a preset threshold value, the position of a fault point is locked through first-stage line screening, second-stage section division and third-stage accurate positioning in sequence. Therefore, through a multi-stage data fusion and hierarchical positioning algorithm, the fault positioning error is obviously improved from the line level in the prior art to the section level, and the fault positioning problem under the scenes of high-resistance grounding, intermittent faults and a complex power distribution network containing a distributed power supply is effectively solved.

The invention discloses an intelligent fire prevention and control and operation and maintenance optimization method and system for an electric power supply network, and the method comprises the steps: collecting temperature, humidity and smoke concentration data in real time through a sensor network, generating a comprehensive environment index data flow, and obtaining a fire initial signal through abnormality judgment; calling a medium selection model, and outputting an optimal environment-friendly insulating medium and release parameters thereof by querying an environment-friendly medium and insulating characteristic parameter library and performing matching calculation based on the equipment type; utilizing a response speed optimization algorithm to generate an accurate release instruction sequence containing a release path, a dose and a time sequence; controlling the medium atomization device to synchronously release the medium according to the instruction; and collecting equipment protection and power supply continuity data, performing risk assessment to generate an efficiency index, and updating the prevention and control strategy library to optimize a subsequent decision. Through intelligent decision making and closed-loop optimization, power supply continuity and equipment safety are guaranteed to the maximum extent while rapid and accurate fire extinguishing is achieved, and transformation from post-disaster disposal to operation and maintenance level prevention and control is completed.

The invention belongs to the field of safety monitoring of wind power equipment, and provides a multi-source data fusion and safety early warning system and method in the hoisting process of a wind turbine generator, and the system comprises a data collection and preprocessing module which collects and preprocesses multi-source sensor data such as a tower inclination angle and a suspension arm inclination angle; the state manifold construction module is used for mapping the preprocessed data to a multi-dimensional space to construct a state manifold and generating an ideal geodesic line; the trajectory analysis and risk assessment module is used for calculating the deviation degree between the real-time state trajectory and the ideal geodesic line and generating a risk score; the dynamic threshold generation module constructs a risk tensor field based on environmental parameters, and extracts a risk threshold curved surface after wind speed adjustment; the early warning decision module is used for comparing the risk score with a threshold curved surface to determine an early warning level and sending information; and the visual display module is used for displaying the state manifold, the track, the tensor field and the early warning information. By introducing a differential geometry theory, state manifold representation and geodesic deviation measurement are constructed, and accurate assessment of the hoisting process risk is realized.

The invention discloses an electric lightning arrester insulation shield capable of being installed without power outage and an installation method, and relates to the technical field of electric protection, the electric lightning arrester insulation shield comprises an insulation cover, an elastic automatic locking mechanism, an installation sheet and a reinforcing plate, the insulation cover is integrally formed, a turnover plate is connected to the lower ends of a left side plate and a right side plate, and the elastic automatic locking mechanism comprises a left locking module and a right locking module; the modules are connected through a pressure piece and a clamping piece through a connecting piece, a lock body part penetrates through a locking hole to achieve locking, during installation, the protective cover is driven by an insulating rod to be horizontally pushed into the top of the lightning arrester, a locking mechanism is extruded and triggered to achieve automatic locking, the protective cover is fixed to an exposed metal piece, and main insulation is not affected. Locking is reliable, the service life is long, operation is convenient, the device can be expanded to multiple fields, and safe operation of equipment is guaranteed.

The invention relates to the technical field of equipment compensation control, and discloses a medical suction device with an altitude compensation function and a control method thereof. The method comprises the following steps: reading ambient air pressure data of a first pressure sensor, reading molecular sieve tank differential pressure data of a second pressure sensor, collecting pressure data of an output end of a third pressure sensor, and determining a real-time altitude according to the ambient air pressure data; a breathing pressure fluctuation value is calculated based on the molecular sieve tank differential pressure data, when the breathing pressure fluctuation value is lower than a preset negative pressure threshold value, the current breathing frequency is recorded, and nitrogen removal time, pressure equalizing time and flushing time are determined to control an electromagnetic valve to work; and when the oxygen concentration is lower than a first concentration threshold value and the real-time altitude height is lower than a preset height, increasing the nitrogen removal time by a first preset proportion and increasing the target rotating speed by a second preset proportion, and when the oxygen concentration is lower than a second concentration threshold value, stopping the operation of the compressor and closing all the electromagnetic valves. According to the invention, the false alarm rate is effectively reduced, and the equipment safety is improved.

The invention relates to the technical field of biomass fuel processing, and discloses biomass fuel production and processing equipment, which comprises a granulation box body, a flat die rotationally connected in the granulation box body, a compression roller arranged above the flat die, a rotating assembly mounted at the bottom of the granulation box body and used for driving the flat die to rotate, and a control assembly mounted at the bottom of the granulation box body and used for controlling the compression roller to rotate, the telescopic assembly is mounted on the side wall of the granulating box body and is used for driving the compression roller to move up and down and adjusting the distance between the flat die and the compression roller; and the spacing control system is in communication connection with the control end of the telescopic assembly and is used for accurately controlling the spacing between the flat die and the compression roller. The distance control system obtains raw material characteristics, equipment states and particle quality data in real time, constructs a multi-dimensional evaluation model and dynamically adjusts the distance between the pressing roller and the flat die, the problems that manual adjustment is low in precision and high in machine blockage risk are solved, intelligent and accurate control is achieved, safe operation of equipment is guaranteed, and the particle quality is optimized.

The invention relates to the technical field of high-voltage commutation, and discloses a control method and system for a composite LC filter of a high-voltage converter station, and a storage medium. According to the method, on the basis of staggered solid / liquid-filled dielectric plates and series hollow inductors in a sealed oil tank, by obtaining a capacitance / inductance adjusting instruction, the filling and pumping volume and speed of insulating liquid and magnetic liquid are controlled according to the absolute value of an adjusting value, and continuous and accurate adjustment of the filtering frequency and reactive power is achieved. The method comprises multiple optimization: monitoring high fluctuation of liquid, synchronously filling, pumping and radiating, balancing efficiency and energy consumption according to an adjusting frequency adaptive circulation speed, and performing reverse linkage cooperative adjustment on a dielectric medium pump and a magnetic liquid pump; and magnetic liquid leakage and insulation abnormity are treated in stages, so that operation is guaranteed. The method solves a traditional adjustment dead zone, does not need a power electronic device, is low in cost, is small in loss, is adaptive to outdoors, and improves the electric energy quality and equipment safety of a converter station.

The invention provides a fault monitoring system for coal mine electromechanical equipment, and relates to the technical field of coal mine equipment safety monitoring. The system comprises a multi-source data acquisition module used for acquiring multi-mode operation data through a sensor on coal mine electromechanical equipment; the multi-modal feature extraction module is used for performing feature extraction on the multi-modal operation data to obtain an equipment operation state feature vector; the health trend evaluation module is used for determining an equipment health trend prediction result; the equipment fault prediction module is used for determining an equipment fault estimation result by combining the equipment fault prediction module with a preset expert knowledge graph and multi-modal operation data; and the visual simulation monitoring module is used for visually and dynamically simulating and displaying the equipment operation state and the fault influence range of the coal mine electromechanical equipment through the updated digital twinborn model. According to the technical scheme, the comprehensiveness and accuracy of equipment fault monitoring can be improved, and meanwhile, the adaptive capacity of the system in a complex coal mine scene is enhanced.

The invention discloses a substation equipment fault diagnosis and defect early warning system supporting cloud-side cooperation. The substation equipment fault diagnosis and defect early warning system comprises a data acquisition module, an edge calculation unit, a cloud processing center, a state evaluation module, an early warning generation module and a feedback control unit. According to the application, primary data processing and real-time analysis are realized through the edge computing unit, comprehensive evaluation is completed in combination with a cloud deep learning engine, and the diagnosis efficiency and accuracy are remarkably improved; the self-adaptive adjustment module dynamically optimizes resource allocation and enhances the response capability of the system; graded alarm and multi-channel notification ensure timely early warning; and the feedback control unit realizes remote and local linkage to guarantee safe operation of equipment. According to the invention, large-scale complex equipment management requirements are effectively met, and reliable support is provided for intelligentization of a power system.

The invention discloses an SVG valve hall multi-source monitoring data fusion method and fusion system, and belongs to the technical field of power system equipment monitoring, and the method comprises the steps: carrying out the abnormal value and missing value data cleaning of initial data, outputting the preprocessed monitoring data, carrying out the algorithm matching of the preprocessed monitoring data to complete the time-space unification, and carrying out the fusion of the SVG valve hall multi-source monitoring data. Extracting associated features, forming a multi-source feature parameter set, quantifying data source reliability differences and locking key features by taking the multi-source feature parameter set as a reference, forming a dynamic fusion weight distribution scheme and key feature data, calling the multi-source feature parameter set, and eliminating feature conflicts; and generating a comprehensive fault diagnosis result and a health state evaluation index of the valve hall equipment, outputting early warning information and maintenance decision suggestions, and completing the whole data fusion closed-loop application. And a more efficient monitoring scheme is provided for safe operation of valve hall equipment.

The invention relates to the field of special equipment safety management, and discloses a special equipment intelligent control method and system based on the Internet of Things. The method comprises the following steps: acquiring a facial image and fingerprint data of an operator through an industrial Internet of Things sensor, and matching by combining a preset biological recognition template to obtain preliminary verification information; personnel qualification grades and historical operation records are called from a database, and a comprehensive qualification score is determined through random forest algorithm processing; acquiring temperature and pressure parameters of the equipment in real time, and marking abnormity to generate a state label if the parameters exceed threshold values after detection; based on the state tag and the qualification score, verifying identity authenticity in a multi-dimensional manner, matching equipment operation qualification requirements, generating a matching degree, generating a temporary permission token when the matching degree reaches the standard, and otherwise, refusing to access; and the token is encrypted and transmitted to the equipment control module, and the operation range is adjusted in real time to complete dynamic permission configuration. According to the method, accurate authority control is realized, the operation safety and management efficiency of the special equipment are improved, and guarantee is provided for safe operation of industrial production.

The invention belongs to the technical field of metallurgical continuous casting equipment, and particularly relates to a pulling plate type free adjusting mechanism of a crystallizer foot roller, which comprises a lower flange, a plurality of groups of foot roller frames are fixed on the lower flange, and guide grooves are arranged on opposite sides of two adjacent groups of foot roller frames. The roller gap value is rapidly and accurately adjusted by rotating the pulling plate adjusting bolt, different production requirements are met, the production efficiency is improved, the foot roller can be continuously used through adjustment when the foot roller is slightly abraded, the replacement amount of the foot roller is reduced, the service life of the foot roller is prolonged, the cost is reduced, the use efficiency of equipment is improved, and the pulling plate can be pulled out. The roller assembly is convenient to adjust and replace, the workload of disassembling parts during overhauling is reduced, manpower and material resources are saved, the equipment maintenance cost is reduced, the downtime is shortened, meanwhile, the overall structural design is beneficial to precise control over roller gaps, safe use of equipment is guaranteed, and then the casting blank quality is improved.

The invention belongs to the field of artificial intelligence and energy power system cross control, and particularly relates to a coal-fired unit fused salt combined frequency modulation control method and device based on deep reinforcement learning. According to the method, a deep reinforcement learning agent model is constructed, and boiler fuel quantity, steam turbine valve position, fused salt heat charging and discharging power and pump starting and stopping signals are dynamically output based on state space vectors; and tracking precision, thermal stress, emission and coal consumption indexes are fused through a multi-target reward function, and strategy autonomous optimization is realized by adopting a double-Q network, experience playback and target network delay updating mechanism. After deployment, the boiler, the steam turbine and the molten salt unit can be coordinated on line, power space-time decoupling and closed-loop cooperation are achieved, on the premise that main steam parameter stability and equipment safety are guaranteed, the frequency modulation response speed and precision are remarkably improved, the thermal fatigue risk is reduced, and the unit economy and the power grid adaptability are enhanced. And a flexible adaptive adjustment capability is provided for a high-proportion new energy power system.

The invention discloses a multi-parameter fusion air suspension heat pump AI load prediction energy-saving control method. The method comprises the following steps: constructing a multi-source heterogeneous parameter acquisition system and preprocessing data; fusing the parameter spatio-temporal features through a multi-head spatio-temporal feature extraction module embedded with an attention mechanism; establishing a load prediction model integrating a long-short-term memory network and thermodynamic physical constraints; on the basis of a model predictive control framework, a future control instruction is optimized under the condition that comfort and equipment safety constraints are met; an online learning and updating mechanism based on prediction error triggering is introduced, and the model weight is dynamically and finely adjusted to adapt to system characteristic changes. According to the technical scheme, the load prediction precision and the control response speed under the complex working condition are remarkably improved, the real-time optimization of the system energy efficiency is achieved, and the long-term operation stability is guaranteed.

The invention discloses a vibrating screen control system based on a cloud platform. The vibrating screen control system comprises a data acquisition layer, an edge computing layer, a cloud analysis layer and an execution control layer. The data acquisition layer comprises a plurality of sensor groups, and each sensor group comprises a vibration sensor, a temperature sensor and an audio sensor which are arranged on a vibrating screen so as to acquire a vibration signal, a temperature signal and an audio signal in real time. And the edge calculation layer preprocesses the vibration signal, the temperature signal and the audio signal. And the cloud analysis layer executes a fault prediction and diagnosis step and outputs a fault result according to the preprocessed vibration signal, temperature signal and audio signal. And the execution control layer controls the vibrating screen based on the fault result of the cloud analysis layer. According to the method, the fault detection accuracy, the system robustness, the equipment safety and the operation and maintenance efficiency are comprehensively improved through multi-mode fusion sensing, cloud-side cooperative computing and intelligent control modes, and the defects of an existing vibrating screen monitoring and control technology are remarkably overcome.

The invention discloses an improved SAC-based intelligent arrangement system for a power-cut plan of a power grid. The system comprises an objective function construction module which establishes a multi-objective function of the power-cut plan of the power grid according to power demand data; the constraint condition construction module establishes a multi-dimensional constraint condition according to the safety operation data of the maintenance equipment; the Markov decision process modeling module constructs an intelligent learning framework by using a multi-objective function and a multi-dimensional constraint condition, wherein the intelligent learning framework comprises a state space, an action space and a reward function; the action cutting module is used for dynamically cutting the action space based on constraint conditions; the SAC network construction module learns an optimal strategy through intelligent agent and environment interaction to obtain a trained SAC network; and the SAC network application module deploys the strategy parameters in a power grid dispatching system to generate an intelligent arrangement scheme of the power-cut plan. According to the invention, the problems of low efficiency, difficult coordination, easy conflict and difficult global optimization caused by dependence on artificial experience in traditional power-cut plan arrangement are solved, and the intelligent level of arrangement and the decision-making efficiency are significantly improved.

The invention provides a water immersion and electric leakage monitoring method, device, system and equipment for power equipment, and relates to the technical field of power equipment safety monitoring. The method comprises the following steps: acquiring electrical monitoring data of a grounding wire of power equipment and water level monitoring data of an area where the power equipment is located; judging whether an electric leakage event occurs or not according to the electrical monitoring data; water immersion event judgment is conducted according to the water level monitoring data, when the water level monitoring data exceed a preset water level threshold value, visual review operation is triggered, and whether a water immersion event occurs or not is judged based on the result of the visual review operation; and determining different alarm levels according to a combination condition between the judgment result of the water immersion event and the judgment result of the electric leakage event, and executing alarm responses corresponding to the alarm levels. According to the method, the electrical monitoring data is obtained in a non-contact manner, and a dual verification mechanism of'water level threshold triggering + visual rechecking 'is established, so that the water immersion and electric leakage states of the electrical equipment can be quickly and accurately identified.

The invention relates to the technical field of safety operation and maintenance of power equipment, in particular to an active anti-condensation control method, system and equipment for a switch cabinet and a medium, which are applied to an active anti-condensation control system for the switch cabinet and used for preprocessing operation environment data of the switch cabinet acquired by a sensing layer based on a data acquisition unit; cabinet body surface temperature fusion and dew point temperature calculation are carried out based on the risk research and judgment unit, a dynamic condensation risk index and a global risk index are obtained, and a risk level is judged through the obtained dynamic condensation risk index and a preset threshold value; and according to the risk level and the global risk index, based on a cooperative control unit and according to a preset multi-mode control strategy, generating a control instruction to perform active condensation prevention of the switch cabinet. Therefore, the early condensation trend is pre-judged, and faults such as insulation reduction and metal corrosion caused by condensation formation are effectively avoided.

The invention relates to the technical field of safety monitoring of power transmission equipment, in particular to an intelligent early warning nut and a monitoring method thereof.The nut comprises a quantitative anti-interference sensing and dynamic filtering module, a verification parameter is dynamically calculated based on an interference-verification quantitative model by synchronously collecting loosening distance, vibration and electromagnetic interference data, and the verification parameter is obtained; a real loosening event is accurately identified; the environment coupling trend quantitative prediction module is used for fusing multi-environment factor dynamic weighting such as humidity, wind speed and temperature difference after real loosening is judged, and predicting a future loosening distance through an environment-loosening quantitative correlation model; and the scenarized risk grading response module is used for calculating a scenarized risk quantized value R and triggering grading early warning in combination with the prediction result and the structural importance weight of the bolt mounting position, and generating a strategy instruction containing a maintenance priority. According to the invention, the problems of high false alarm rate, incapability of predictive maintenance and short endurance in the prior art are solved, and accurate sensing, intelligent prediction and graded early warning of the bolt loosening state are realized.