178 results about "Life management" patented technology

The invention relates to a battery life prediction and health assessment system based on BMS expansion parameter analysis, and the system fuses deformation quantity and electrochemical parameters, and remarkably improves the modeling precision and life prediction capability of a battery health state. The acquisition module obtains battery expansion characteristics and operation parameters through a sensor or a modeling mode, and constructs a multi-dimensional time sequence data set; the processing module performs normalization and feature reconstruction on the data to form a health track sample for learning; the prediction module outputs residual life and degradation trend scores based on a deep regression model, and introduces an expansion feature sensitivity adjustment mechanism; the evaluation module evaluates the health level of the battery according to the prediction result and a threshold rule and generates a report; the regulation and control module dynamically adjusts BMS control parameters according to an evaluation result, and optimizes energy efficiency and a safety strategy; the method breaks through the dependence of a traditional BMS system on electrochemical characteristics, realizes more comprehensive and accurate intelligent sensing and life management of the battery health state, and is suitable for various BMS architecture scenes.

The invention relates to the technical field of equipment life management, in particular to a life prediction method and system based on an application analysis spindle, and the method comprises the steps: building a modal sensing network, collecting multi-source operation data in the operation process of the spindle, and generating a multi-source operation characteristic matrix; based on the multi-source operation characteristic matrix, establishing multi-application characteristic mapping, and generating a multi-scene characteristic vector for describing an equipment operation state; performing multi-dimensional coupling fatigue analysis on the multi-scene feature vector, and outputting a fatigue damage accumulated value of the main shaft; and according to the fatigue damage accumulated value, obtaining and combining historical motion data of the main shaft, establishing a different-parameter life prediction model, and outputting a residual life prediction value based on the different-parameter life prediction model. According to the invention, the problems of single data source, insufficient prediction precision under complex working conditions, difficult modeling of nonlinear fatigue damage and lack of dynamic feedback and correction capability in a traditional main shaft life prediction method are effectively solved.

The invention discloses a system and a method for predicting the fatigue life of a flange by utilizing ultrasonic flaw detection, and belongs to the field of artificial intelligence. Comprising an aggressive frequency domain spot feature extraction module, a microdefect enhancement recognition module, an acoustic echo database training module, a damage accumulation-stress wave mapping model construction module, an active echo disturbance injection module, a fatigue life progressive prediction module, a real-time feedback closed-loop control module and a multi-scale self-checking redundancy mechanism module. Spot feature extraction and defect identification share high-frequency phase data to generate a focusing map, and registration with historical defect features is carried out in a database; the mapping model constructs a stress-damage relation according to the stress concentration coefficient and the frequency domain characteristics; the disturbance injection module applies directional excitation to potential fatigue points, and the prediction module constructs a life evolution curve based on response; the feedback control module fits and corrects the database and the disturbance strategy; the self-checking mechanism improves the prediction robustness and self-adaptability through waveform deviation and mismatch correction. The beneficial effects are that the structure safety and the service life management intelligence are improved.

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 relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system and method based on fuzzy logic control. Comprising a renewable energy power generation unit, an electrolytic cell array, a multi-type energy storage device, a power divider and a fuzzy logic controller, the renewable energy power generation unit is connected with the electrolytic cell array and the power divider through a direct-current bus, and the output end of the power divider is connected with the storage battery pack and the super-capacitor bank. The fuzzy logic controller collects the bus voltage fluctuation ratio, the electrolytic cell temperature gradient and the energy storage charge state in real time to serve as input variables, and outputs a multi-target optimization instruction to the power divider. Wherein the system dynamically adjusts the start-stop combination and the energy storage charge-discharge priority of the electrolytic cell array. According to the scheme, the mindset that fluctuation suppression and life management are separated in the prior art can be broken through, and a new paradigm of multi-parameter cooperative control is established.

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 a grounding grid full-life management method fusing Lasso and an improved grey wolf algorithm, and the method comprises the steps: carrying out the corrosion diagnosis of a grounding grid through employing a preset corrosion diagnosis algorithm when the abnormal fluctuation of the operation of the grounding grid is detected, or the operation of the grounding grid is carried out for an inspection period time after the completion of the previous rectification; and generating a corrosion diagnosis report of the grounding grid according to the real-time detection data and the historical detection data so as to rectify the grounding grid, and according to the real-time detection data and the historical detection data, adjusting the next inspection period time of the grounding grid and predicting the residual life of the grounding grid. Based on a preset corrosion diagnosis algorithm, integration and closed-loop management can be carried out on whole-process data of design, detection, diagnosis, rectification, operation and maintenance of the grounding grid, an underdetermined equation set in grounding grid corrosion diagnosis is solved, the positioning precision is improved, non-excavation type accurate corrosion fault positioning is achieved, the diagnosis efficiency and the intelligent management level are improved, and the operation and maintenance cost is reduced.

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 provides a logistics storage life management method based on radio frequency tag identification, and the method comprises the steps: obtaining an article identifier and initial life data stored by a passive radio frequency tag attached to an article from a radio frequency tag reader-writer of a target storage partition in response to an article life evaluation request; corresponding environment parameters, signal intensity and position information are called from the storage module, and abnormal records are read; the calculation module determines a life information set based on the data and reports the life information set to a superior management system; and after verification is correct, generating a service life detection packet for the packaging environment parameters, the signal strength, the position information and the service life adjustment rule of each article, and transmitting the service life detection packet to a storage terminal through a wireless sensor network. According to the invention, precise and automatic management of the service life of the stored goods can be realized, the inventory turnover efficiency is improved, and the deterioration loss is reduced.

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 numerical control machine tool cutter management system which comprises a hardware layer, a system layer, a function module and a system interface. The hardware layer is composed of a numerical control machine tool, a tool presetting instrument, a tool recognition device, a tool setting instrument, a bar code scanning gun, an industrial computer, a server and the like and is used for data acquisition, transmission and storage. The system layer comprises tool management software, a database management system, data acquisition and monitoring software and a manufacturing execution system interface, and full life cycle management of tools is achieved. The system is provided with a tool basic data management module, an inventory management module, a service life management module, a circulation management module, an abrasion monitoring and compensation module, a purchase plan management module, a performance parameter management module and other functional modules, and operation and display are carried out through a system interface. The system can improve the tool management efficiency and precision, reduce the tool cost, improve the machining quality and the production efficiency, and is suitable for efficient tool management of numerical control machining enterprises.

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 technical field of community life management, in particular to an intelligent community life service recommendation system and method based on big data analysis, and the system comprises a resident demand analysis module, a provider service resource module, a matching degree acquisition and correction module, and a resident demand dynamic matching monitoring module. The resident demand analysis module comprehensively collects and analyzes individual demands of residents and provides a data basis for recommendation service; the provider service resource module effectively integrates and quantifies resource information of service providers, and matching and evaluation are facilitated; the matching degree acquisition and correction module calculates and analyzes the matching degree and corrects in combination with the position information to ensure efficient preliminary screening of the supplier and the demander; and the resident demand dynamic matching monitoring module realizes dynamic deep matching between resident demands and service providers by using real-time context awareness and data fusion technologies. The technical problem that the current community life management effect is poor is solved.

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 discloses an electric vehicle battery charging and discharging method for peak load shifting and a related device, and belongs to the technical field of electric vehicle charging and discharging. The method comprises the following steps: constructing a battery EQU life model, and calculating to obtain the daily energy storage loss cost of the battery; based on the daily energy storage loss cost of the battery, considering the comprehensive load and battery degradation cost of the electric vehicle, the peak-valley difference of the comprehensive load and the peak-valley fluctuation of the comprehensive load to establish a target function; establishing a power battery charging and discharging constraint, a power battery energy continuity constraint and a power battery EQU life model constraint; a peak clipping and valley filling multi-objective optimization model is constructed and obtained; and solving the multi-objective optimization model to obtain a battery charging and discharging strategy. The influence of charging and discharging at different depths on the service life of the battery is comprehensively considered, and more accurate service life management is realized. Through a three-objective optimization strategy of comprehensively considering the battery life cost, the load balance and the peak-valley difference, the system is ensured to realize the balance of economical efficiency and reliability while peak clipping and valley filling are performed.

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 invention relates to the technical field of aero-engines, and discloses a wheel disc low-cycle fatigue test load coefficient determination method, a wheel disc low-cycle fatigue test load coefficient determination device and application of the wheel disc low-cycle fatigue test load coefficient determination method. Based on stress characteristics and temperature characteristics of key parts of a wheel disc in an engine state, reasonable determination of a low-cycle fatigue test load coefficient of the wheel disc and conversion of the fatigue life of the wheel disc are realized by comprehensively considering the influence of a peak-valley value stress ratio of the wheel disc in a working state on the fatigue life of the wheel disc and a change rule of material performance along with temperature. The safety risk caused by the fact that the load coefficient deviates from the reality is reduced, and the design and use of the wheel disc are safer and more reliable; by correcting the load coefficient, the fatigue damage of the engine in the test state can be truly reflected, so that the safe life of the wheel disc is effectively determined, and support is provided for the life management of the wheel disc.

The invention relates to a nuclear turbine aging management method and a whole-life management platform. The method comprises the steps that an aging management table is created, the aging management table comprises a plurality of fields, and each field is used for storing equipment information of a specific type; each field of the table is configured. The method comprises the following steps: acquiring and analyzing a failure mechanism and a failure mode of an important part according to external experience, a maintenance / in-service inspection result, an abnormality / defect / fault and processing condition, operation / periodic test data, performance monitoring data and an equipment state / trend analysis / aging evaluation result, so as to obtain a predictive failure mode of the important part; and filling the data into corresponding fields of the table. According to the invention, efficient utilization of equipment information can be realized, and real-time and high-frequency aging management and state evaluation can be carried out on the equipment, so that predictive maintenance with higher economical efficiency is realized, and the equipment can be timely and accurately analyzed and processed based on a predictive fault mode when the equipment has a fault.

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.