140 results about "Life management" patented technology

The invention discloses an industrial and commercial energy storage EMS scheduling system based on multi-objective optimization, and relates to the field of intelligent control of energy storage systems. The method is used for solving the problems of battery life attenuation, electricity price fluctuation and insufficient load abrupt change adaptability in energy storage scheduling. The service life management module analyzes the ion migration state to generate a dynamic power safety envelope line as a layered optimization hard constraint; the feature extraction module constructs an electricity price association network, predicts photovoltaic / load in combination with quantile regression and extracts net load abrupt change features; the hierarchical optimization module compresses the discharge depth and activates load regulation in demand risk, and synchronously optimizes economy, life loss and power grid constraint through taboo search multi-objective evolution; the execution arbitration module monitors the distance between the power and the envelope line in real time, and when the distance is lower than a threshold value, rolling optimization is started to improve the life weight and feed back the lithium precipitation characteristic to drive the envelope line to update, and closed-loop protection is formed.

The invention discloses an intelligent life prediction and optimization system and method for a steam turbine rotor welded joint. The system comprises a multi-source data acquisition module, a digital twin modeling module, a health state evaluation and life prediction module, a risk early warning module and an operation collaborative optimization module. The multi-source data acquisition module acquires the multi-dimensional physical quantity of the rotor welding joint in real time. The digital twin modeling module establishes a high-fidelity virtual model and realizes real-time synchronization and correction of a physical entity and a digital model. And the health state evaluation and life prediction module is used for calculating a damage accumulation rate and a health index based on fusion of a physical model and an LSTM neural network so as to realize residual life estimation. And the risk early warning module performs graded early warning according to the dynamic trend of the health state. And the operation collaborative optimization module adaptively adjusts operation parameters and optimizes the unit efficiency based on a reinforcement learning algorithm. All the modules are interconnected through an industrial network to form a closed loop, and real-time monitoring, intelligent evaluation and active service life management of the rotor welding joint are achieved.

The invention discloses a machine tool cutter service life monitoring method, a machine tool is provided with multiple sets of cutters, and the method comprises the steps that the expected cutting times of the cutters are adjusted according to the cutting speed, the cutting depth and the cutting direction of the cutters, and the cutting time threshold values of the multiple cutters are obtained one by one; monitoring the number of times of executing accumulated cutting of the cutter, and when the number of times of executing accumulated cutting is equal to a cutting number threshold value, triggering an alarm so as to perform replacement operation of the corresponding cutter; and in response to the replacement operation of the cutter, executing zero clearing of the accumulated cutting times, and re-monitoring the service life of the cutter. According to the invention, the problems of large life prediction error, strong manual dependence, poor data continuity and the like in the prior art are solved through dynamic threshold adjustment, automatic monitoring and a zero clearing mechanism, the life prediction precision is improved, the labor cost is reduced, the production stability is enhanced, and intelligentization and automation of cutter life management are realized.

The invention relates to the technical field of digital twinning, in particular to a distributed photovoltaic power generation system optimization control method based on digital twinning, and the method comprises the steps: constructing a photovoltaic module-level digital twinning body through multi-source data fusion, generating a digital mapping body through the training of historical fault data, and achieving the simulation of the operation state of a photovoltaic module; a multi-agent cluster is introduced, a multi-target optimization architecture framework coordinated with the multi-agent cluster is constructed, and balance and collaborative optimization among multiple targets are achieved through power generation efficiency optimization, equipment life management, power distribution coordination and other agents; and based on the candidate strategy after multi-target collaborative optimization, the improved grey wolf algorithm is used for further optimization processing, an optimal scheme is obtained, system optimization operation is realized, and power generation benefits and stability are improved.

The invention discloses an active power and frequency coordination control method for a wind storage combined system, and belongs to the technical field of electric power system operation and control. The problems that in the prior art, due to wind power and load uncertainty, the frequency control effect is poor, energy storage life management is insufficient, and long-term and short-term control instructions are disjointed are solved. The problem is solved by constructing a three-layer collaborative architecture of a predictive optimization layer, a real-time coordination control layer and an execution layer: the predictive optimization layer randomly optimizes and generates a reference state track and a frequency modulation capacity instruction of an energy storage unit based on a scene method; the real-time coordination control layer utilizes a deep reinforcement learning coordination controller, synthesizes frequency deviation, SOC compensation signals and frequency modulation capacity reservation instructions, and dynamically distributes active power of a fan and energy storage; and the execution layer realizes rapid issuing of the instruction. The method is mainly used for active power coordination and frequency support of a wind storage combined system in a high-proportion new energy power system, and can effectively delay energy storage life attenuation while stabilizing frequency fluctuation.

The invention belongs to the technical field of mechanical structure health monitoring and life prediction, and particularly relates to an engineering machinery bearing structure fatigue life intelligent monitoring method, which comprises the steps of 1, building a high-fidelity geometric model, 2, building a finite element model, 3, building a finite element simulation data set, 4, building a neural network agent model, and 5, building a neural network model. 5, working parameters and loading history are obtained; and 6, a visual operation and maintenance platform is integrated. According to the intelligent monitoring method for the fatigue life of the engineering machinery bearing structure, the evolution process of fatigue damage of the structure can be intelligently deduced based on the working parameters and the loading history of the structure, dynamic calculation of the residual life and real-time early warning of the service risk are achieved, calculation resources are saved, the fatigue life evaluation efficiency is improved, and the engineering machinery bearing structure fatigue life monitoring method is suitable for popularization and application. The problems of dependence on off-line simulation, response lag, discontinuous evaluation and the like in traditional structure life management are solved, the intelligent operation and maintenance level of the engineering machinery is effectively improved, and safe and efficient operation of the engineering machinery is guaranteed.

The invention discloses a lightning arrester insulation protection method, and relates to the technical field of electric power insulation protection. The method comprises the following steps: generating a comprehensive insulation risk index based on collected insulation state parameters; determining repair permeability according to material structure characteristics, and constructing an electric field compensation factor in combination with running electric field distribution; performing periodic data accumulation and analysis on the comprehensive insulation risk index, the repair permeability strength and the electric field compensation factor to generate a long-term life prediction result covering the operation cycle, the material sub-region and the sampling point; and the service life prediction result corresponds to an insulation protection management module, and partition-level service life management and operation risk early warning are realized. Precise life prediction is realized through multi-dimensional data fusion, sub-region differential protection is supported, and the consistency of an operation period and a repair strategy is maintained, so that the reliability and stability of long-term operation of the lightning arrester are comprehensively improved.

The invention discloses a gas turbine airborne system-oriented multi-model fusion service state sensing method and device, and relates to the field of high-temperature equipment life management, and the method comprises the steps: designing a similar structural member, and carrying out the mechanical performance test; performing parameter fitting on the mechanical test data by using a multi-physical damage model in a fatigue-creep mutual combination manner; carrying out weight optimization and aggregation on the fitting result by adopting a multi-objective optimization method; mechanical test data is used as input, a physical damage model deviation factor is used as output, a classification task indicating whether the deviation factor is greater than 1 is used as correction of a deviation factor regression task, and a multi-task learning neural network is constructed and trained; and constructing a structural member service life prediction model by using the multi-physical damage model and the multi-task learning neural network, loading the structural member service life prediction model to an airborne system of the gas turbine, and performing local service life prediction on a to-be-tested part of the gas turbine. According to the invention, the accuracy and universality of life prediction of the to-be-tested part of the gas turbine are improved.

The invention discloses an IGBT life prediction method fusing time-frequency domain branches and an autoregressive neural network, and the method comprises the following steps: S1, obtaining Vce (on) historical data of an IGBT in a continuous thermal cycle test, the historical data comprising a Vce (on) value of an IGBT initial operation state; s2, carrying out preprocessing on the historical data; s3, respectively extracting time domain features and frequency domain features from the preprocessed data, and constructing an autoregressive neural network model fusing the time domain branches and the frequency domain branches; s4, training the autoregressive neural network model by adopting a dynamic teacher mandatory strategy; and S5, reasoning by using the trained autoregressive neural network model to obtain the life prediction of the IGBT. According to the method, the time-frequency domain features are fused, the lightweight GRU network is adopted, a dynamic teacher forced training strategy is innovated, the residual life of the IGBT can be accurately and efficiently predicted at low cost without complex modeling, and the convenience and generalization of life management of a power device are improved.

The invention discloses a mold life management system based on vehicle lamp production mold monitoring, which comprises a data acquisition module, a mold health assessment module and an early warning and decision module, and relates to the technical field of mold life management. According to the mold life management system based on vehicle lamp production mold monitoring, data are preprocessed, various characteristics of the temperature change rate, the pressure value, the strain amplitude and the vibration frequency are extracted for analysis and processing, and the extracted characteristic parameters are integrated to establish and form a mold life evaluation model; then real-time data is extracted and introduced into the mold life evaluation model to evaluate the health index of the mold, and the actual wear condition and performance change of the mold can be more accurately mastered by monitoring various physical quantity data of the mold in real time and utilizing an advanced algorithm and model to evaluate the life; and subjectivity and uncertainty of artificial experience judgment are avoided.

The invention relates to the technical field of life prediction, in particular to a high-voltage switch cabinet contact life management system based on digital twinning, which comprises a thermal behavior acquisition module, a conductive state construction module, a thermal loss dynamic evaluation module, a thermal disturbance response regulation and control module and a life mapping driving module. According to the method, temperature difference and time interval in an on-off period are collected, thermal stress change is calculated in combination with current density, thermal characteristics of materials are correlated, thermal behaviors of a contact are dynamically reflected, a conductive path boundary is reconstructed based on contact area and pressure offset, a through-flow state is accurately described, and heat accumulation and arc energy input are extracted. The method comprises the steps of analyzing a temperature trend under thermal diffusion lag, constructing a heat loss evolution curve, adjusting thermal conductivity distribution through a ratio of temperature rise to mass migration rate, improving local adaptability of thermal disturbance response, generating a life attenuation layer in combination with equivalent thermal mechanical load and fatigue tolerance, and enhancing dynamic nature and precision of evaluation. And accurate judgment and management of the degradation area are realized.

The invention relates to the field of start-stop control, and discloses a one-key start-stop control system of a single-shaft gas turbine, which is used for solving the technical bottlenecks of the traditional control method in the aspects of state perception, service life management and the like. Comprising the steps that a gas turbine state parameter set containing rotor thermal stress, combustion chamber temperature and exhaust temperature distribution is generated; and generating a life loss evaluation result and an operation efficiency evaluation result by adopting the life evaluation model and the efficiency calculation model, and generating a start-stop control prediction sequence through a dynamic programming algorithm. The service life loss and the operation efficiency are quantitatively evaluated in real time, so that the operation cost problem that the service life and the operation efficiency of the equipment are difficult to consider in the existing start-stop technology is solved, and the full-life-cycle operation cost is remarkably reduced while the long-term reliability of the equipment is guaranteed; maintenance cost can be effectively reduced, fuel consumption is saved, and power grid response economy is improved.

The invention relates to the technical field of production intelligent management and control of energy enterprises such as thermal power enterprises and nuclear power enterprises, and discloses a power plant material full-life-cycle intelligent management method and related equipment.The method comprises the steps that a power plant material full-life-cycle big data center is established, and the power plant material full-life-cycle big data center integrates multi-source data through a distributed architecture; unified data standard and interface specification are carried out on the distributed architecture integrated multi-source data; respectively carrying out life management, material management and equipment maintenance management on the unified data standard and interface specification to obtain dynamic multi-service data; dynamic multi-service data are butted and fused for data association analysis, material full-process penetration management, material full-life-cycle tracing, intelligent purchase decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and full-process visual monitoring. According to the invention, power plant material management is converted from passive response to active prediction and from segmented control to full-process collaboration, and the management efficiency is significantly improved.

The invention discloses a tool monitoring system for precision machining equipment, and the system comprises a sensor module which is used for collecting a tool cutting force signal, a vibration signal, a temperature signal and a main shaft power signal in real time; the data acquisition module is used for acquiring a cutting force signal, a vibration signal, a temperature signal and a main shaft power signal of the cutter in a machining process in real time; and the alarm module receives the alarm instruction of the data analysis module. According to the tool monitoring system of the precision machining equipment, the cutting force sensor, the vibration sensor, the temperature sensor and the main shaft power sensor are arranged, so that the tool can be conveniently detected in all directions, and the problems of tool missing, chip winding and cutting output change are avoided. And by means of the tool service life management module, the use duration and the machining frequency of the tool can be recorded, the service life of the tool can be predicted conveniently, and machining and use of the workpiece are facilitated.

The invention discloses an offshore wind turbine generator reliability distribution modeling and maintenance decision-making method under long-life management, and the method employs a multi-source heterogeneous data fusion and meta-transfer learning technology to improve the prediction accuracy and adaptability of the health condition and remaining service life of key parts of an offshore wind turbine generator. Based on the RCM theory and the system analysis component structure and influence factors, uncertainty in fuzzy theory processing evaluation is introduced, predicted health and degradation information is combined, an objective and quantitative reliability index distribution model of the key components is established, and the reasonable reliability index distribution weight of each key component is determined; according to the method, the maximum average reliability of an offshore wind turbine generator system and the minimum full-life-cycle operation and maintenance cost serve as targets, a long-life-period multi-target optimization model considering part degradation, the maintenance effect and multiple constraints is constructed, an intelligent algorithm is adopted for solving, and an optimal preventive maintenance plan is obtained.

The invention belongs to the technical field of power supply facility monitoring, and discloses a power supply facility diagnosis and early warning method and system for an intelligent power grid. The method comprises the steps that a platform end receives long-term operation data collected by a facility end; the platform end analyzes the long-term operation data, obtains analysis data and judges whether an abnormal instruction is generated or not; if the abnormal instruction is generated, the platform end receives real-time operation data collected by the facility end; the platform end analyzes the analysis data and the real-time operation data and judges whether a descending instruction is generated or not; if a descending instruction is generated, the platform end diagnoses the residual life of the power grid facility according to the analysis data and the real-time operation data; the platform end generates an early warning instruction, and sends the early warning instruction, the analysis data, the real-time operation data and the residual life to the corresponding facility end; according to the invention, deep learning and dynamic decision-making of power grid facility life management are realized, and the management efficiency and operation safety of the power grid facility are improved.

The invention discloses a life-based aero-engine maintainability design method, which processes an engine maintainability design process, gives design tasks and management activities included in each process node, and effectively solves the blindness problem of the design process. According to the technical scheme, the method comprises the steps of 1, receiving input data required by a maintainability design index; 2, formulating a part life policy; 3, calculating the service life of the key component; 4, evidence obtaining, development and testing; step 5, maintenance resource development; 6, the working range of the engine is set; step 7, life management; 8, whether airworthiness approval is met or not is judged, if yes, engine service operation is approved, and if not, the step 2 is executed again.

The application discloses a precision belt wheel mold life cycle management method and system, and relates to the technical field of mechanical manufacturing, comprising the following steps: in the whole life management process of the precision belt wheel mold, continuously collecting design version change data, processing process record data, running condition waveform data and maintenance disposal track data, and uniformly writing the collected data into a data chain of the same time reference for time sequence management, and generating a time alignment index at the end of the data chain. The application realizes continuous connection and anti-overlapping collection of multi-source data by constructing a data chain with time as the main line and introducing a precursor anchor point, a previous priority constraint and a time sequence traction control, ensures that the deterioration signal is continuously visible in the time dimension, accurately reflects the mold performance attenuation process, and improves the reliability of life prediction and maintenance decision.

The invention discloses a feed pump energy-saving optimization method and system based on variable frequency speed regulation, and relates to the technical field of industrial process control and energy-saving optimization, and the method comprises the steps: constructing a causal analysis model by extracting residual statistical characteristics, recognizing the main drive type of carbon emission change, quantifying the causal effect, and estimating the life loss in combination with sensitivity; energy consumption and service life target weights are dynamically adjusted according to causal labels, a dual-objective optimization function is constructed, a multi-objective genetic algorithm is adopted to determine control parameters of frequency adjustment, and frequency self-adaptive adjustment is achieved. The offset abnormal driving reason is effectively identified, and the accuracy of energy-saving control and the equipment service life management and control capability are improved.

The invention discloses a fuel cell management method and system based on life budget credit adjustment, and relates to the technical field of fuel cell life management. The method comprises the following steps: acquiring running state data of a fuel cell, and predicting a residual life budget value of the fuel cell at the current stage; calculating a lending value and a credit value of the fuel cell; if the absolute value of the difference between the credit values of the fuel cells in the current stage and the previous stage is smaller than a first threshold value and the absolute value of the difference between the lending values is smaller than a second threshold value, continuing to use the control strategy in the previous stage; if the absolute value of any difference value is larger than or equal to the corresponding threshold value, the control strategy of the current stage is adjusted. According to the invention, a self-adaptive adjustment mechanism based on life budget lending and credit rating is realized, the overall operation economy of the system is improved, the fuel consumption rate is reduced, and the maintenance period is prolonged, so that the fuel cell system can maintain the dual advantages of high energy efficiency and long life under different operation conditions.

The present application belongs to the technical field of mechanical structure health monitoring and life prediction, in particular to an engineering machinery load-bearing structure fatigue life intelligent monitoring method, comprising the following steps: step one, establishing a high-fidelity geometric model; step two, establishing a finite element model; step three, constructing a finite element simulation dataset; step four, constructing a neural network proxy model; step five, obtaining working parameters and loading history; and step six, integrating a visual operation and maintenance platform. The engineering machinery load-bearing structure fatigue life intelligent monitoring method can intelligently deduce the evolution process of fatigue damage based on the working parameters and loading history of the structure, realize dynamic calculation of the remaining life and real-time early warning of the service risk, save computing resources, improve the efficiency of fatigue life evaluation, and solve the problems of dependence on offline simulation, response lag, and discontinuous evaluation in traditional structure life management, thereby effectively improving the intelligent operation and maintenance level of engineering machinery and providing protection for safe and efficient operation.

1.The name of the design product: graphical user interface of the near-expiration product management system of electronic equipment. 2.The use of the design product: for interactive operation, display information. 3.The design points of the design product: the content of the graphical user interface. 4.The picture or photo that best indicates the design points: front view. 5.The design points of the design product are the content of the graphical user interface, and the rear view, left view, right view, top view, and bottom view are omitted. 6.The use of the graphical user interface: to display data information related to near-expiration product management in the sales management system and provide interactive operations related to near-expiration product management, including near-expiration product filtering, near-expiration product sorting, and new near-expiration product list related functions. 7.The change state description of the graphical user interface: the front view is the main interface of the near-expiration product management system of the sales cabinet; clicking "new near-expiration product" in the front view jumps to the interface change state diagram. XX in each interface represents the text content, and the gray box and white box areas in each interface are content pictures, not the shape of the content pictures. The gray box and white box areas can be replaced by text or pictures.

The invention discloses a method and a system for controlling the service life of a medical fabric based on multi-modal data, and relates to the technical field of methods for controlling the service life of the medical fabric, and the use damage level of the medical fabric is determined based on the use damage state and the area stress level of each use heavily-used area. The theoretical service life of the medical fabric is predicted according to the use damage grade and type of the medical fabric and the previous use environment of the medical fabric, and the preliminary accuracy of the theoretical service life of the medical fabric is improved. Therefore, multi-modal data is constructed according to the use damage grade and type of the medical fabric and the current use scene of the medical fabric, a plurality of data sets of different levels are determined according to analysis of the multi-modal data, and corresponding life influence factors are determined according to recognition of the data sets of different levels; the actual service life range is determined based on the multiple service life influence factors and the theoretical service life, and the accuracy of the actual service life range is improved.

The invention discloses a full-life intelligent management method and system for a power distribution automation terminal, and the method comprises the steps: constructing an equipment object fingerprint through equipment ledger data, operation data and parameter data, carrying out the evaluation of the continuity of an equipment object through the combination of an equipment life cycle event sequence and an equipment behavior feature sequence, and carrying out the evaluation of the continuity of the equipment object. Identifying a device object offset state; and when the offset state is identified, life cycle account section division is carried out at an offset detection time point, affiliation correction is carried out on related historical data, and an equipment life cycle management result is generated. According to the invention, the accuracy, continuity and traceability of the full-life management of the power distribution automation terminal can be improved.

The application discloses a kind of steel-concrete combined support active regulation and turnover evaluation method and system, it is related to geotechnical engineering deep foundation pit support and monitoring technical field.The method includes two big technical schemes: one, the equivalent time-varying parameter of combined support is determined, the real-time monitoring data of combined support is obtained, then the temperature action component in monitoring total axial force, time effect component is calculated, finally the real stress response is calculated and combined support is actively regulated;Second, based on the strain history of the whole service process, plastic damage component, fatigue damage component and time-varying stiffness damage component are calculated respectively, and a comprehensive damage index is constructed by weighted summation.According to the preset grading threshold, the component disposal suggestion is output.The application can effectively eliminate the interference of temperature fluctuation and time-varying effect on regulation decision, and provide objective quantitative damage evaluation basis for component turnover use.The application can realize the whole life management of combined support, and improve the safety and economy of component turnover use.

The present invention relates to a battery life management system and a battery charging method using the same. The charging method using the battery life management system for this purpose involves acquiring charging curve information required for each type of device including a battery to be charged. ;request Changes in charging power on the charging curve information rate is specified rate of change A time period where the change is more sudden than Predict Measurement ;prediction Measured When In the interval Noba Limit the charging power that can be used to charge devices containing batteries. ,time Change in charging power within the interval Rate This includes reducing the speed below a certain level.

The invention provides an electrical tree initiation characteristic characterization method and system, a storage medium and equipment. The method comprises the following steps: acquiring key material parameters in a high-voltage direct-current cable insulation sample under thermal aging; and inputting the key material parameters into a pre-constructed electrical tree average initiation voltage representation model, and outputting an electrical tree average initiation voltage predicted value. According to the method, the mathematical relationship between the average electrical tree initiation voltage and the key parameters is established, accurate prediction of the electrical tree initiation voltage is realized, a reliable theoretical basis and a practical means are provided for cable state monitoring and service life management, and the method has a good application prospect.

The invention provides a power equipment oil paper insulation sample moisture state estimation method and system, and belongs to the field of insulation performance monitoring and management.The method comprises the steps that the water content corresponding relation between oil and paper in the heating / cooling process is obtained based on an Oommen curve, and a heating and cooling asymmetric kinetic equation is obtained based on fitting of the water content corresponding relation between the oil and the paper; a core kinetic equation is constructed based on a kinetic equation with asymmetrical temperature rise and temperature drop, a temperature-dependent time constant equation and a rate-dependent equation. And substituting the temperature change at different moments and the water content change of the insulating oil into a core kinetic equation for calculation to obtain the water content in the insulating paper. And a technical support is provided for moisture state evaluation and equipment life management of the oil paper insulation system.

The invention discloses a working condition self-adaption-based guide vane connecting rod stress trend diagnosis method, and relates to the technical field of intelligent diagnosis of hydroelectric generating sets, and the method comprises the steps: constructing an optical fiber stress monitoring system, setting an optical fiber grating sensor for a guide vane connecting rod, collecting a stress signal in real time, and collecting operation parameters; the method comprises the following steps: collecting multi-working-condition data within one month of normal operation after overhaul of a unit, calculating a correlation matrix between guide vane connecting rod stress and operation parameters, and establishing a stress working condition self-adaptive analysis model; and calculating a maximum value, a minimum value, an average value and a standard deviation of stress based on rolling statistics, determining a normal range of stress fluctuation, inputting real-time acquired data into a diagnosis algorithm, judging a stress deviation trend according to the reference model, outputting an alarm level, and updating the reference model. According to the method, the accuracy and timeliness of anomaly recognition are improved, the pertinence and the performability of maintenance decision making are enhanced, and the operation safety and the service life management effect of the guide vane connecting rod of the hydraulic unit are improved.

The present application relates to the technical field of industrial automation control and equipment intelligent operation and maintenance, in particular to a mechanical and electrical equipment load operation and maintenance management method and system, comprising: collecting real-time state and total load demand of a group of mechanical and electrical equipment; mapping the state to thermodynamic credit by using a loss mapping model to calculate marginal loss cost; constructing a reverse bidding game model based on the cost, taking the minimization of total marginal loss of the group as the goal under the premise of meeting the demand, and solving the optimal load distribution ratio; generating control instructions accordingly to drive each sub-equipment to execute load adjustment; the present application eliminates the calculation deadlock between fine life management and real-time load response, ensures that the system can respond to the instantaneous mutation of the load within microseconds, and realizes high real-time of control decision.