3037 results about "Equipment Safety" patented technology

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

The invention relates to the technical field of data management, in particular to an industrial control system remote monitoring data synchronization method, which comprises the following steps of: 1, generating a dynamic synchronization time window based on an equipment state sudden change frequency and a network fluctuation degree; 2, dividing transmission priorities according to the influence degree of data on equipment security control; 3, quality characteristics of available transmission paths are evaluated, data of different priorities are transmitted in a fragmented mode according to a differentiation strategy, and data streams are recombined at a receiving end based on time sequence identification; 4, after network disconnection is recovered, synchronous consistency guarantee is achieved through data segment cascade hash verification and control instruction logic conflict detection; and 5, optimizing a time window generation algorithm and a path evaluation rule according to the historical synchronization effect data. Through accurate control, priority division, network adaptability enhancement and consistency guarantee of data transmission, an efficient and safe industrial control system remote monitoring data synchronization scheme with adaptive capability is provided.

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 provides an intelligent mechanical safety protection system based on industrial Internet of Things equipment, which comprises a dynamic topology modeling module, a risk propagation analysis module, a dynamic protection generation module and a collaborative triggering execution module, the composite topological graph comprises a physical layer and a logic layer; the risk propagation analysis module is used for analyzing a propagation path of the risk along a physical connection edge and a logic dependence edge based on the composite topological graph, and generating a risk propagation full-link sub-graph; the dynamic protection generation module is used for calculating a minimum necessary protection set based on the risk propagation path and generating a hierarchical response strategy; and the cooperative triggering execution module is used for sending a cooperative control instruction to the equipment in the minimum necessary protection set based on the hierarchical response strategy. By adopting the system, a risk propagation path under a complex scene can be automatically identified, and the safety protection level of equipment is 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 network / security equipment security policy generation and evaluation method and system based on a large language model, and the method comprises the steps: carrying out the fine adjustment of a base model through employing a threat research and judgment data set and a policy generation data set, and obtaining a threat research and judgment LLM and a policy generation LLM; a threat research and judgment LLM performs threat research and judgment on the received abnormal log output by the log acquisition module, and outputs a threat information tetrad; the policy generation LLM generates an equipment security policy by calling a knowledge base Agent according to a threat information quadruple output by the threat research and judgment LLM; and the policy generation LLM call evaluation Agent evaluates the equipment security policy from three aspects of policy correctness evaluation, policy validity evaluation and policy suitability evaluation. According to the invention, the effectiveness and reliability of the equipment security policy are improved.

The invention relates to the technical field of energy storage power station combined operation and maintenance management, and provides a virtual power plant-oriented energy storage power station combined operation and maintenance management system, which comprises a dynamic sensing module for acquiring battery cluster temperature difference distribution, power converter switch transient characteristics and grid-connected point harmonic spectrum data of an energy storage power station in real time through a multi-dimensional sensor network; the collaborative decision-making module is used for dynamically generating a cross-station power interaction constraint rule based on the virtual power plant topological structure; the impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; the state balancing module is used for realizing charge state dynamic balance and power margin collaborative distribution among the multiple energy storage power stations; and the secure communication module is used for ensuring secure transmission of control instructions among the modules by adopting a layered encryption protocol. The operation stability, the power distribution flexibility and the equipment safety of the virtual power plant can be improved, and the service life of the energy storage equipment is prolonged.

The invention discloses an electronic cigarette temperature control algorithm with a preheating function, particularly relates to the technical field of electronic cigarette temperature control, and is used for solving the problem of out-of-control temperature caused by waste heat accumulation due to continuous smoking in the prior art. Precise temperature control is realized through multi-dimensional parameter collaborative analysis and a dynamic correction mechanism; according to the algorithm, a waste heat accumulation superimposed effect is evaluated based on time interval distribution characteristics of historical suction actions of a user and current suction interval time, dynamic interference intensity is quantified in combination with airflow rate deviation, and a waste heat attenuation stability level is judged by detecting a temperature drop rate deviation direction. According to the contact area proportion of the heating element and the tobacco tar, waste heat conduction efficiency grades are divided; the target temperature compensation value is calculated through the preset temperature compensation mapping table, the preheating target temperature is dynamically adjusted, the constant temperature control program is started, the preset atomization temperature interval is maintained through a real-time temperature feedback closed loop, temperature overshoot caused by waste heat accumulation is effectively restrained, and atomization taste consistency and equipment safety are improved.

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 relates to the field of industrial internet intrusion prevention, in particular to a computer network big data security protection method and system, and the method comprises the following steps: monitoring network flow, collecting user identity, equipment security state, access time and network behavior data, and generating a network behavior record. In the invention, a dynamic security assessment system is constructed through network flow monitoring, identity verification and behavior analysis, and compared with static strategy or fixed rule matching, the dynamic security assessment system can adapt to a continuously changing network attack environment. Authority dynamic adjustment is performed based on the user identity verification state, flexible management and control of the access authority are ensured, and the problem of long-term high authority abuse is avoided. Threat detection not only depends on historical mode matching, but also is combined with real-time behavior analysis, and accurate isolation is executed for potential threats, so that protection measures are changed from passive response to active intervention, and the delay of manual intervention is reduced.

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 discloses an equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis, and the system comprises a data collection module, a data processing and analysis module, an intelligent early warning and decision module, an execution and feedback module, and a behavior monitoring and management and control module. The safety behavior management and control capability in the operation and maintenance process of the industrial equipment is comprehensively improved, and the problems that an existing equipment monitoring system is poor in real-time performance, insufficient in prediction capability, much in manual intervention, lack of multi-dimensional collaborative management and control and the like are solved. According to the equipment operation and maintenance safety behavior management and control system based on intelligent monitoring and data analysis, through the technical means of real-time data acquisition, intelligent data analysis, an early warning mechanism, automatic management and control, a feedback mechanism and the like, management and control of equipment safety behaviors are effectively enhanced, and the safety, timeliness and accuracy of equipment operation and maintenance are improved.

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 path planning, and discloses a path planning method and system of an AGV (Automatic Guided Vehicle) for a silver electrolysis process workshop. The method comprises the following steps: carrying out environment characteristic acquisition and electronic map construction on a silver electrolysis process workshop to obtain a silver electrolysis process workshop electronic map; calculating an initial optimal path of the AGV from the current position to the target workstation; detecting the actual driving resistance in the execution process of the initial optimal path to obtain a path correction parameter; performing local adjustment to obtain an energy-saving safe path; and in the process that the AGV executes the energy-saving safe path, the front moving obstacle and the electrolyte leakage area are monitored in real time, and a temporary detour path is generated. According to the application, the path planning is more suitable for the actual environment characteristics of the silver electrolysis workshop, the emergency situation of the silver electrolysis workshop can be timely handled, the equipment safety and the production continuity are guaranteed, the no-load rate of the AGV is reduced, and the overall logistics efficiency of the silver electrolysis workshop is improved.

A method for managing a storage system includes initiating, by a hardware resource manager, a boot-up of a storage controller managing the storage system comprising a plurality of storage devices, making a determination, by the storage controller, that the storage controller is in a secured mode, based on the determination: identifying a security state of each of the plurality of storage devices, determining that a storage device of the plurality of storage devices is in an unsecured state, and based on the unsecured state, sending, by the storage controller, a security operation request for securing the storage device, obtaining a secure state response from the hardware resource manager corresponding to securing the storage device, and based on the secure state response, resuming operation of the storage controller based on the secure mode.

The invention discloses an anti-leakage primary and secondary fusion complete ring main unit, and relates to the technical field of ring main units, the anti-leakage primary and secondary fusion complete ring main unit comprises a ring main unit body and further comprises an air pipe, the two ends of the air pipe are communicated with an air inlet and an air outlet formed in the upper end of the ring main unit body respectively, and a plurality of cooling fans are fixed to the side, away from the air pipe, of the air outlet; the looped network box body is divided into a plurality of mounting chambers through a plurality of inner partition plates, and air ports are formed in the sides, close to the mounting chambers, of the air pipes; the interior of the air pipe is divided into a plurality of cavities through a plurality of outer partition plates, and each cavity corresponds to the corresponding installation chamber. The air pipe is sleeved with an airflow path switching assembly, the airflow path switching assembly comprises a sealing plate arranged on the lower surface of the air pipe in a sliding mode, and the sealing plate is provided with a plurality of corresponding holes matched with the air openings; the problems of metal corrosion, circuit board damping, sensor misjudgment and threat to equipment safety caused by internal condensation and water accumulation due to moisture permeation of heat dissipation holes of the ring main unit are solved.

The invention relates to the technical field of Internet of Things security, and discloses an Internet of Things control system and a security early warning method, and the method comprises the steps: firstly collecting equipment basis and real-time information in an Internet of Things system, then evaluating equipment risk, network security and data encryption conditions, setting an early warning threshold according to multi-aspect evaluation results and historical security data, and finally, carrying out early warning. And finally, real-time monitoring is carried out, and early warning is triggered when the risk index exceeds a threshold value. The method has obvious advantages, equipment aging and safety risks can be accurately evaluated, equipment maintenance is planned in advance, and the maintenance cost is reduced; network attacks can be effectively identified, and network security is guaranteed; the data encryption security is enhanced, and the data leakage risk is reduced; early warning is given out in time, and the system response speed is increased; and the equipment safety state can be dynamically updated, the system safety management is continuously optimized, the safety and reliability of the Internet of Things system are comprehensively improved, and the stable operation of the Internet of Things system is promoted.

The invention discloses a three-dimensional laser cutting method for a frame with an n-shaped section. The three-dimensional laser cutting method comprises the following steps that S1, a tool is clamped; s2, a part is preliminarily clamped, and a notch theoretical line is scribed; s3, alignment is carried out; s4, the cutting of all the round holes is compiled into a subprogram, and the cutting program of each notch is compiled into different bisection subprograms; and S5, the cutting track of each subprogram is preliminarily debugged, and each subprogram is adjusted as required according to the measurement result after the first workpiece is cut. The additional shaft of the machine tool controls the rotating table to rotate in a numerical control mode, so that the tool drives the part to rotate, it is ensured that each pair of notch cutting positions are located at the lowest point of the part clamping posture, cutting head movement is simplified into four-shaft movement, and interference of a rotating shaft is avoided; a small number of attitude control points are arranged to optimize the cutting attitude, three-dimensional five-axis laser cutting of the n-shaped section frame is achieved, the programming and debugging efficiency is improved, the equipment safety is improved, and the overlap ratio of notches is ensured.

The invention discloses an intelligent substation secondary equipment maintenance control method and system, and relates to the technical field of secondary equipment maintenance control, and the method comprises the following steps: obtaining first data of a substation to construct a static matrix, and generating a dynamic topological graph; determining the security level of the controlled equipment according to the dynamic topological graph, and establishing a priority table of mapping of the equipment type and the security level; detecting concurrent instruction conflicts through a priority table, identifying an N-order virtual loop based on a dynamic topological graph, generating a minimum isolation set, and outputting a virtual terminal isolation instruction sequence with timing constraint according to a signal level; and when the state of the maintenance pressing plate or the GOOSE link is changed, the affected equipment is positioned based on the static matrix, and incremental updating of the dynamic topological graph is controlled according to the instruction sequence. According to the method, the path optimization strategy based on the cross-interval coefficient is matched with the time window constraint, so that the maintenance efficiency is guaranteed, potential safety hazards caused by disordered operation time sequences are avoided, and the maintenance process is standardized and intelligent.

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 discloses a bottle washing machine temperature deviation adjusting method and system based on intelligent temperature control, and aims to solve the problems of temperature control precision, environmental adaptability and operation efficiency of a traditional bottle washing machine. The internal temperature, the water quality and the external environment of the bottle washing machine are monitored in real time through the data acquisition and monitoring module, the future temperature trend is predicted according to historical data by adopting a machine learning algorithm ARIMA, and the most appropriate control instruction is calculated in combination with a dynamic adjustment algorithm. After the control execution module receives the instruction, the working intensity of the heating or cooling device is adjusted through a controller and an actuator, and accurate temperature regulation and control are achieved. And the safety protection and self-adaptive optimization module ensures continuous improvement of equipment safety and production efficiency. Generally speaking, the intelligent temperature control bottle washing machine system remarkably improves the temperature control precision, and the operation efficiency and the safety guarantee level of the system are improved.

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.