2469 results about "Equipment Safety" patented technology

The invention discloses a special equipment life cycle supervision method and system based on characteristic parameter monitoring, and the method comprises the steps: collecting a multi-dimensional characteristic parameter data flow containing a real-time operation parameter, an accumulated damage parameter and a performance degradation parameter, and carrying out the trend analysis through employing a time sequence prediction model, and generating a trend deterioration early warning signal; an association rule mining algorithm is adopted to carry out association analysis to generate an associated fault early warning signal, then the two early warning signals are fused, inherent attribute data and historical operation and maintenance data are combined, and a real-time dynamic risk score is calculated through a dynamic risk portrait model; and finally mapping to a preset discrete supervision level and automatically executing a corresponding differential supervision instruction set. According to the method, the problems of risk identification lagging and strategy static solidification in traditional supervision are effectively solved, the transformation from passive response to active early warning and from average supervision to accurate strategy implementation is realized, and the foreseeability, pertinence and resource configuration efficiency of special equipment safety supervision are remarkably improved.

The invention relates to the technical field of intelligent power grids, and provides an intelligent operation centralized control center intelligent scheduling interaction method and system, and the method comprises the steps: generating a heat distribution gradient through environment noise filtering, reflecting the temperature field change of equipment, obtaining a voltage amplitude envelope through fundamental wave and harmonic wave separation, and monitoring the power quality; a current fluctuation coefficient is extracted by combining segmented deviation analysis to evaluate a line load state, and frequency fluctuation parameters are captured by using time domain statistics to track system stability. By constructing a dynamic association map, heterogeneous data such as thermodynamics, electrical quantity, load characteristics and synchronous states are mapped to power grid topological nodes, and a cross-dimension association relationship is established to realize whole-network state coupling analysis. An overload risk, a voltage out-of-limit probability and a system instability threshold are quantified through a hierarchical evaluation mechanism, a multi-target optimization constraint condition is formed, and finally a scheduling strategy considering equipment safety, economy and power supply reliability is generated. When the power system operates, intelligent scheduling is carried out according to the operation state of the power grid.

The invention discloses a power equipment safety state assessment method and system, equipment and a readable storage medium, and relates to the technical field of power equipment assessment, and the method comprises the steps: collecting a power equipment communication interaction data flow, carrying out the time synchronization and outlier elimination, and constructing a multi-modal data matrix; evaluating the exception probability score and an exception probability threshold to obtain an exception state flag, and generating a security event data packet in combination with the preliminary exception type and the power equipment communication interaction data stream; adopting a LiNGAM causal discovery algorithm to perform non-Gaussian test and time sequence lag analysis on an abnormal event time sequence in the security event data packet, and constructing a directed acyclic causal graph; dynamically adjusting the causal intensity of the directed acyclic causal graph according to the real-time equipment load rate and the communication quality index, and outputting a dynamic causal graph; according to the method, the accuracy and adaptability of abnormal propagation analysis are improved, and finally the real-time performance and credibility of safety assessment of the power equipment are enhanced.

The invention discloses a GIS disconnecting switch multi-state intelligent sensing system, and relates to the field of GIS disconnecting switch state monitoring. A self-calibration multi-mode sensor is deployed for multi-source data acquisition, and novel sensors including terahertz imaging and the like are included; in data preprocessing, deep learning noise reduction is applied, fuzzy entropy is used for dynamic weighted fusion, and abnormal values are processed by an improved algorithm; feature extraction is combined with a plurality of frontier algorithms to process vibration signals, and CRNN is used to analyze acoustic signals; the QPSO evidence theory is adopted for state fusion perception, the node relation is learned by means of GNN, and the weight is adjusted according to the information gain rate; state assessment and early warning are based on transfer learning, GAN and LSTM-attention mechanisms, and early warning priorities are ranked by FAHP. According to the invention, multi-mode accurate acquisition, intelligent data processing, deep feature mining, innovative fusion perception, accurate evaluation and early warning and efficient fault diagnosis and positioning are realized, the state of the GIS isolation switch can be comprehensively and accurately perceived, the system is self-learned and optimized, the equipment safety is guaranteed, the risk of a power system is reduced, and the power operation and maintenance benefits are improved.

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The invention discloses an acquisition and processing system and method for dry data of a transformer, and relates to the technical field of data processing. Comprising the following steps: step 1, multi-source data real-time acquisition and edge preprocessing; 2, constructing a drying end point prediction model; 3, constructing a composite objective function, and solving the composite objective function by adopting a multi-objective evolutionary algorithm; 4, performing real-time correction and feedback; according to the method, key parameters in the drying treatment process are monitored in real time, an accurate drying end point prediction model is automatically constructed, heating and air exhaust curves are dynamically optimized according to the real-time working condition and the production strategy, and traditional manual blind judgment and static presetting are replaced; and meanwhile, the operation energy consumption is remarkably reduced, the optimal balance of the energy consumption and the production efficiency is realized through a multi-objective evolutionary algorithm and an online Kalman filtering correction closed loop mechanism, the drying quality and the equipment safety are improved, the energy cost is greatly saved, and the dual requirements of modern intelligent manufacturing for flexibility, energy conservation and consumption reduction are met.

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 provides a robot intelligent inspection method and system based on multi-source data fusion, and relates to the technical field of intelligent inspection, and the method comprises the steps: carrying out the data collection of an inspection region based on an inspection robot; adjusting the enhancement parameter of the equipment appearance image, highlighting the equipment operation sound in the inspection area sound, correcting the equipment temperature distribution, and compensating the area gas concentration; extracting defect features in the equipment appearance image, capturing sound features in equipment operation sound, extracting temperature features in equipment temperature distribution, and learning gas concentration features corresponding to regional gas concentration; and performing space-time fusion on each feature to generate a space-time fusion inspection feature, outputting an equipment fault diagnosis result and adjusting a current inspection path of the inspection robot, thereby cooperatively collecting multi-dimensional information of the equipment, optimizing data quality through adaptive processing, mining associated features through space-time fusion, and adjusting the inspection path in combination with intelligent decision. Fault diagnosis reliability is improved, and safe and stable operation of equipment is guaranteed.

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 relates to the technical field of turret heat management, in particular to a power turret heat management optimization method and system based on intelligent cooling, and the method comprises the steps: obtaining the temperature distribution data and related working condition parameters of a power turret in real time, and building a heat state feature vector representing the dynamic relation between the heat generation and heat dissipation capability of the turret; constructing a cooling decision model, inputting the state vector into the cooling decision model, and generating an initial control strategy; the initial control strategy is simulated and executed in the digital twin environment, and a strategy reconstruction mechanism is triggered when the predicted temperature suppression rate is lower than a set threshold value; and performing real-time dynamic optimization on the verified initial control strategy according to the processing quality constraint and the equipment safety threshold, and superposing the thermal inertia delay compensation amount to generate a final control strategy. According to the method and the device, the problem of temperature control failure caused by thermal inertia time lag is effectively solved, accurate space-time matching of the cooling instruction and the thermal load peak value is realized, and thermally induced deformation is remarkably inhibited.

The invention belongs to the technical field of safety protection, and particularly relates to an energy storage equipment safety protection early warning system applied to an industrial park, which adopts a module-cluster-cabin three-level architecture to realize real-time monitoring and dynamic evaluation of thermal runaway risk, the architecture integrates multi-source data, and when the risk exceeds a preset threshold value, the energy storage equipment safety protection early warning system is used for early warning the thermal runaway risk. The system automatically starts graded fire extinguishing measures and a fire-fighting linkage mechanism, and meanwhile, secondary analysis is carried out on an early warning result through a cloud platform to optimize model parameters, so that the accuracy and the efficiency of the system are ensured; the system constructs an integrated monitoring, response and self-correction prevention and control process, and effectively overcomes the problems of early warning delay, high false alarm rate, single protection means, data islanding and the like in the prior art through a multi-parameter fusion early warning, accurate recognition, graded fire extinguishing and intelligent linkage mechanism. And a solid guarantee is provided for fire safety in a high-density energy storage environment.

The invention relates to a new energy charging pile dynamic scheduling method, system and device and a medium, and the method comprises the steps: collecting the real-time load data of a power grid, user charging request information, battery health state data and historical charging data, and carrying out the classification and aggregation of charging behavior characteristics, and generating behavior grouping labels; demand prediction and power safety boundary calculation are carried out based on a preset power prediction model in combination with the battery health state, and a power prediction result is formed; detecting power grid load conflicts and optimizing power distribution to generate a preliminary scheduling scheme; performing multi-target power optimization adjustment on the conflict points to generate a charging power scheme, and issuing and executing the charging power scheme; and finally, dynamically updating prediction model parameters according to charging pile feedback data. According to the method, the problems of neglect of battery health change, insufficient charging behavior distinguishing, response lag, weak collaborative optimization capability and the like in the prior art are solved, and collaborative optimization of power grid stability, equipment safety and charging efficiency is realized.

The invention relates to an intelligent construction method and system based on Internet of Things monitoring, and the method comprises the steps: obtaining real-time vibration spectrum data and historical equipment vibration data of target construction equipment, carrying out the environment noise suppression of the real-time vibration spectrum data based on the historical equipment vibration data, and obtaining equipment noise reduction state features; obtaining an operation vibration waveform of the impact equipment, and carrying out equipment health degree analysis on the noise reduction state characteristics of the equipment to obtain a health degree attenuation rate; carrying out equipment load reduction operation quantification on the health degree attenuation rate based on a preset construction plan, and carrying out equipment safety collaborative analysis to obtain an associated equipment collaborative scheduling strategy; and performing instruction construction on the target construction equipment according to the associated equipment collaborative scheduling strategy to obtain a load control instruction, and performing construction optimization on the construction plan to obtain an optimized construction plan. According to the invention, the equipment health degree attenuation rate can be dynamically analyzed based on the operation vibration waveform of the impact equipment, and real-time response to the equipment performance change is realized.

The invention relates to the field of equipment safety verification, in particular to a wifi router on-line upgrading safety verification method and a wifi router on-line upgrading safety verification system. A wifi router online upgrade security verification system comprises a firmware package construction module, a firmware package verification module and a guarantee activation module. According to the method, a firmware packet header protection logic closed loop is formed through an innovatively designed multi-order composite verification structural body and an anti-offset signature positioning framework; the system can detect the nanoscale tampering of an attacker to the length identification field in real time, cuts off the power supply of the flash memory controller and erases the data buffer area at the hardware fusing level response speed when verification fails, and compared with a traditional scheme, the response efficiency is 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 discloses a coal conveying belt fault early warning method and system, relates to the field of safety monitoring of coal conveying equipment, and solves the problems of lagging and high false alarm rate of traditional single-parameter early warning. The system comprises a multi-dimensional data acquisition layer, a dynamic feature extraction layer, a belt-material coupling analysis module, a fault judgment layer and an early warning output layer, wherein the acquisition layer acquires parameters such as belt tension, speed, material granularity and weight; noise reduction is carried out on the extraction layer, parameters are normalized, and features are extracted; the coupling module builds a dynamic coupling model to calculate a coupling coefficient, matches a fault feature library and outputs an abnormal signal; determining a layer fault type and a risk level; and the output layer gives early warning information and suggestions. According to the method, the system is applied, belt and material characteristic correlation early warning is achieved through sub-model construction and coupling coefficient calculation, the early warning accuracy and timeliness are improved, and the method is suitable for multiple scenes and low in operation and maintenance cost.

The invention discloses a distributed computing power dynamic scheduling method and device and a medium, and the method comprises the steps: packaging heterogeneous computing power resources of a home terminal and an edge cloud node through a lightweight containerization technology, and collecting the hardware resource state data of the home terminal and the load data of the edge cloud node in real time; generating a dynamic scheduling strategy according to the task calculation type and the real-time network state of the to-be-calculated task, and splitting the to-be-calculated task into a plurality of sub-tasks according to the dynamic scheduling strategy; distributing the sub-tasks to home terminals or edge cloud nodes according with a dynamic scheduling strategy, and aggregating calculation results of the sub-tasks; and performing end-to-end encryption and fragmentation verification on the cross-domain transmitted data stream, and dynamically adjusting the task allocation permission of the home terminal based on the equipment security score.

The invention relates to the technical field of power equipment safety monitoring, and provides a target detection early warning method and system oriented to power equipment safety active intervention. The method comprises the following steps: continuously monitoring the operating environment of the power equipment to obtain multi-source data; based on the multi-source data, identifying a potential safety hazard target through a target detection algorithm, when the potential safety hazard target is identified, extracting corresponding multi-source risk features from the multi-source data according to the type of the potential safety hazard target, and constructing a risk feature spectrum; based on the risk characteristic spectrum, the risk of the potential safety hazard target is quantified, and a quantified risk vector is obtained; and determining a risk level corresponding to the quantitative risk vector, and executing an early warning strategy corresponding to the risk level. According to the technical scheme provided by the invention, closed-loop management and control of electric power hidden dangers from intelligent identification and multi-dimensional risk assessment to graded early warning are realized, and the safety protection accuracy is improved.

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 present invention belongs to the field of multi-equipment safety management and control for personnel and equipment clusters at an underground coal mine driving working face, and solves the problem of safety hazards caused by human-machine collaborative operations as the mechanization level of underground equipment increases. Provided are a system and method for safety monitoring and early warning of human-machine collaborative operation at a working face. The system comprises a safety management and control system for mobile equipment at a working face, which is used for performing in real time position, orientation and environmental sensing of individual equipment and behavior analysis of drivers; working-face collaborative intelligent wearable devices, which are used for environmental sensing of operating personnel and individual equipment and acquiring in real time the motion postures of the operating personnel; and a working face output control subsystem, which is used for identification and management of operating personnel entry and exit, position monitoring of personnel and individual equipment, operating personnel behavior identification and monitoring and working environment monitoring, and generating corresponding alarm instruction information. The present invention can realize human-equipment and equipment-equipment safety management and control during multi-equipment collaborative operation at a driving working face.

The invention relates to the technical field of Internet of Things safety management, in particular to a special equipment Internet of Things informatization management system which comprises an assembly reading module, an inspection evolution module, a difference recognition module, a manufacturing hooking module and a credible screening module. According to the method, the initial value of the equipment identification chain is generated through the dynamic splicing assembly data and the installation record, the traceability of the full-life-cycle data of the equipment is enhanced, the dynamic characteristics of the multi-period connection parameters and the function state are extracted by adopting the long-short-term memory network, the running state jump of the equipment is identified, and the real-time performance and continuity of anomaly detection are improved; constructing a data comparison mechanism, analyzing the difference between a manufacturing timestamp and a functional state, forming equipment degradation path visualization, enhancing maintenance decision data support, fusing multi-dimensional data, breaking through the limitation of single-dimensional analysis, and improving the safety management predictive maintenance capability of special equipment through time sequence feature mining and data collaborative verification; and the systematic risk caused by state mutation is reduced.

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 relates to the technical field of new energy power system control, in particular to a distributed photovoltaic cluster intelligent control device, method, equipment and medium, and the device comprises a northbound communication unit which is configured to communicate with a power grid dispatching master station and is used for receiving and analyzing a dispatching instruction issued by the power grid dispatching master station; the southbound coordinated control unit is configured to communicate with photovoltaic cluster edge control equipment and is used for collecting operation data of each inverter in a photovoltaic cluster; the autonomous decision-making unit is used for collecting voltage, frequency and power flow data of the power grid in real time and generating a control strategy based on a dynamic priority algorithm; and the execution unit is used for executing the control strategy. The method has the beneficial effects that the intelligent control level of the distributed photovoltaic cluster can be remarkably improved, the stability of a power grid and the safety of equipment are enhanced, and the method has important significance in promoting large-scale application of distributed photovoltaic power generation.

The invention discloses a control method and system of automation equipment for industrial production, and particularly relates to the technical field of automation equipment control. The method comprises the following steps: extracting diversified samples of normal values, abnormal values and hidden defects from an existing data warehouse, labeling categories, and constructing a high-quality initial data set; evaluating the stability of the model by using a layered K-fold cross validation method, and testing and evaluating the consistency of the abnormal response capability and the historical mode through a high-load condition when the model is unstable; comprehensively analyzing the stability and the abnormal response capability of the model, and evaluating the capability of the AI for identifying error modes in historical data; for inaccurate identification, the dynamic time warping algorithm is used for detecting the deviation degree of the equipment operation parameters, an alarm is triggered in time, and the production process is paused to guarantee the safety of the equipment, so that the reliability and accuracy of the AI model in a dynamic environment are remarkably improved, and meanwhile, the risks of equipment damage and production safety accidents caused by optimization errors are effectively reduced.

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 relates to the technical field of intelligent manufacturing and industrial Internet of Things, in particular to an intelligent factory equipment safety management system and method, and the system comprises an identity authentication module, a dynamic safety interaction module, an intelligent monitoring response module and a self-adaptive optimization module. An identity authentication module; through fusion of multi-dimensional information authentication, block chain decentralized storage, intelligent algorithm behavior analysis, encrypted communication and fine-grained authority control, multi-source data threat detection and reinforcement learning driven strategy optimization, a whole-process security system is constructed. The method comprises the steps of identity authentication, security interaction, monitoring response and adaptive optimization. The problems that in a traditional industrial scene, equipment identity authentication is fragile, data transmission is not safe, threat response lags and strategies are staticized are solved, high-safety authentication, dynamic encryption communication, real-time threat response and strategy self-optimization are achieved, and the equipment safety protection capacity and the system intelligence level are improved.

The invention discloses an intelligent charging priority distribution system and method based on multi-device identification, and particularly relates to the technical field of charging priority distribution. Equipment identity and charging demand information are obtained through an intelligent identification technology, a priority score is calculated in combination with a preset weight parameter, the real-time performance of priority distribution is ensured by adopting a dynamic threshold adjustment strategy, power demand fluctuation in a short time in the future is predicted based on a load prediction algorithm of machine learning, power distribution is optimized in advance, and the power distribution efficiency is improved. The method comprises the steps of reducing instantaneous load impact, monitoring equipment power in real time in the charging process, comparing the equipment power with the maximum distributable power, and dynamically adjusting the charging power or switching the charging sequence through a self-adaptive power adjustment mechanism if abnormality is detected, so as to guarantee the stability of a power grid and the safety of the equipment, and the method can effectively improve the utilization efficiency of charging resources and reduce the energy consumption. Overload of a power grid is prevented, multi-device charging scheduling is optimized, and safety, reliability and intelligence of the charging process are ensured.

The invention discloses a speed reducer self-adaptive torque control method and system based on load identification, and relates to the technical field of speed reducer control. The method comprises the steps of collecting multi-source time sequence sensing data in real time when the speed reducer operates; performing time domain and frequency domain feature extraction on the data, and identifying current load features including load levels and types; predicting a future load change trend based on the load characteristics and historical data; a self-adaptive torque control instruction is generated according to the load characteristics, the change trend and the health state parameters, and the output torque is dynamically adjusted; and recording instruction execution efficiency data, and optimizing a load identification and torque control strategy by adopting a strategy gradient reinforcement learning algorithm. The system realizes the functions through cooperation of multiple modules. The problem that torque control of a traditional speed reducer cannot adapt to dynamic load changes is solved, precise self-adaptive adjustment of torque output is achieved, the operation efficiency and equipment safety are improved, and the service life is prolonged.

The invention relates to the technical field of power system equipment state monitoring and fault diagnosis, and discloses an arc light online monitoring method and system based on a spectrum sensing technology, and the method comprises the steps: arranging a first spectrum sensor and a second spectrum sensor corresponding to a detected target, and respectively detecting optical signals in different wavelength ranges; collecting optical data according to a set period, and identifying abnormal features according to a preset template; when the double abnormal features exist, the duration of the double abnormal features is calculated, and if the duration exceeds the corresponding reference duration, a duration reference value is generated; and performing weighted calculation on the time length analysis value, if the time length analysis value is within a preset range, giving a partial discharge abnormal prompt, and resetting the reference value. Multi-wavelength spectrum monitoring is utilized, non-contact and high-sensitivity detection of partial discharge is achieved, electrical equipment is prevented from being damaged, fault hidden dangers are found in time, and safe and stable operation of the equipment is guaranteed.