128 results about "Plant management" patented technology

The invention relates to the technical field of power plant management and control, and discloses an intelligent power plant management and control system based on the Internet of Things, and the system comprises the steps: when a plurality of labels related to the same equipment or parameter exist in different subsystems, according to a similarity index between the labels and an equipment association relationship; on the basis of the detected conflict label group, combining equipment historical operation and maintenance data and an upstream and downstream parameter flow relationship, constructing a label semantic evolution graph, and performing reasoning analysis on label equipment through a fusion rule engine and a graph neural network; through mapping knowledge domain fusion and semantic embedding comparison, matching and clustering among conflict labels are completed based on structural similarity and semantic relevancy, and a label alignment rule is constructed; according to a label coordination result, designing a mapping rule of a data field; and performing inter-system synchronous verification on a result after structure conversion and label standardization processing, and writing a standardized label into a unified semantic database. The method has the advantage of improving data semantic consistency.

The invention relates to a cotton field carbon sink dynamic evaluation method and system based on multi-source data fusion. The method comprises the following steps: firstly, comprehensively collecting multi-source data of a cotton field area environment, and processing the multi-source data by using a time window alignment technology to generate a multi-source heterogeneous data set; based on the data set, key features are extracted through a data mining means, and a key influence factor weight table is generated; and then, inputting the key influence factor weight table and the multi-source heterogeneous data set into the trained optimization evaluation model together to obtain cotton field carbon sink space-time distribution characteristics. And finally, carrying out correlation analysis on the spatial-temporal distribution characteristics and meteorological continuity data, and generating a curve capable of visually displaying the dynamic change of the cotton field carbon sink along with time. The method overcomes the limitation of a single data source, greatly improves the accuracy and comprehensiveness of cotton field carbon sink evaluation, can accurately reflect the real state of cotton field carbon sink under different time-space conditions, and provides a key basis for agricultural managers to formulate scientific and reasonable cotton field planting management strategies.

The invention relates to the technical field of intelligent agriculture, in particular to a planting management method and system for intelligent agriculture, and the method comprises the steps: S1, obtaining multi-modal data through configuring an independent sensing module and a local control execution end for each micro-block; s2, recognizing a current crop variety in real time according to the multi-modal data by adopting a low-power-consumption edge computing device in combination with image recognition; when the system is used, each micro-block operates independently, maintenance of each plant can be refined, multi-source sensing data can be fused, the problems of data lag and weight deviation among different sensors are solved, full-automatic feedback regulation and control are achieved, the labor burden of workers is reduced, low-power-consumption and high-real-time recognition is facilitated, multi-source data such as images and fusion of temperature and humidity are achieved, and the system is suitable for popularization and application. According to the method, the identification accuracy is improved, the problem of crop confusion which is like a long variety can be solved conveniently, accurate management in multi-variety mixed cropping planting can be achieved conveniently, and the accuracy of fine management and control of different demands of multiple different crops in a small range can be improved.

The utility model belongs to the technical field of unmanned agriculture, and particularly relates to a covering plant management device for an unmanned farm, which comprises a C-shaped planting assembly, a first driving assembly and a second driving assembly, the C-shaped planting assembly comprises a plurality of C-shaped planting plates, corresponding C-shaped sowing grooves are formed in the C-shaped planting plates, cutting knives are arranged in the C-shaped sowing grooves, the cutting knives are in sliding fit along the inner walls of the C-shaped sowing grooves, supporting rods are vertically arranged on the upper end faces of the cutting knives, blocking pieces are arranged at the upper ends of the supporting rods, and the blocking pieces obliquely extend into the C-shaped sowing grooves; the first driving assembly is connected with the cutting knives; according to the application management device, the structure is simple, unmanned crop laying and covering can be achieved, the covering plants are pressed to the C-shaped inner side (the roots of grapes) of the C-shaped planting disc through the blocking pieces, it is ensured that the cut-off covering plants can cover the roots of the grapes, and covering and soil moisture conservation are conducted on the grapes.

The invention discloses a protection setting calculation and optimization method based on an intelligent power plant management platform, and relates to the technical field of power system protection. By constructing a platform and equipment communication link, integrally collecting and preprocessing internal and external parameters, and dynamically updating topology and model parameters in combination with a switch state, the problem that a traditional static model is difficult to adapt to operation changes is solved, the timeliness and accuracy of the model are improved, a multi-scene database is constructed in a classified mode, labels are labeled, and the efficiency is improved. Fault current distribution is accurately calculated, constant value adaptability verification is carried out, constant value optimization is carried out in combination with a multi-objective optimization algorithm and sensitivity analysis, limitation of single-scene constant values is avoided, constant value conflicts under different scenes are reduced, reliability of initial constant values is guaranteed, sensitivity analysis is superposed, it is ensured that the constant values are still reliable during parameter fluctuation, and the reliability of the constant values is improved. The risk of protection operation refusal and misoperation is reduced, a constant value is issued, the device state is monitored in real time, evaluation indexes are calculated, the constant value is controllable in the whole process, and stable operation of the device is guaranteed.

The invention discloses an integrated management system for a concrete mixing plant, and relates to the technical field of mixing plant management, a data acquisition unit is accessed to a raw material supplier system to acquire raw material supply data and concrete usage plans in different time periods, and a demand prediction unit introduces meteorological information and construction site states to predict the concrete usage plans in different time periods. And the dynamic optimization module substitutes the raw material supply data and the concrete dosage demand result into a dynamic adjustment model, the dynamic adjustment model dynamically adjusts the stirring progress, a stirring task queue is established, and the production sequence is intelligently sorted according to the material supply condition and the delivery priority. The management system not only can automatically generate a stirring task queue according to the material arrival condition and the construction usage priority, effectively avoid raw material waste and production plan conflicts, but also can adjust the delivery time, the loading sequence and the vehicle scheduling path in a linkage manner, break through the management breakpoint between production and transportation, and improve the production efficiency. And the overall cooperation efficiency and the customer response speed are improved.

The invention discloses a global-oriented multi-language multi-time-zone power station management method and system, and relates to the technical field of electric power digital management, and the method comprises the steps: collecting power station data, carrying out the standardized identification, and constructing a power station multistage coding identification main key; a multi-language mapping system is constructed by taking a multi-level coding identification main key of a power station as a drive, and time zone offset logic is constructed by using standardized identification; and performing hierarchical authority control based on the power station multi-level coding identification primary key prefix and generating a localized alarm prompt. According to the method, standardized identification of power station data is realized by constructing a unified power station multi-level coding main key; based on primary key driving multi-language mapping and time zone offset processing, accurate presentation and time consistency of data in a multi-language environment are guaranteed; authority control is achieved through primary key prefix division, multi-language alarm prompts are triggered in combination with signal coding, and a power station event management mechanism which is clear in alarm semantics, timely in response and controllable in boundary is achieved.

The invention discloses a crop target character accurate matching algorithm model, and belongs to the technical field of agricultural information, and the crop target character accurate matching algorithm model comprises a data and knowledge management unit, an intelligent decision-making unit and a closed-loop optimization mechanism. Constructing a domain knowledge base by integrating genetic, environmental and agronomic data, generating genotype-environment-character combined response virtual data, and training a character prediction model by using deep learning; small sample scene parameters are optimized in combination with transfer learning, a decision report containing genetic function annotations and environment adaptation analysis is generated after multi-dimensional rule verification, and a closed-loop optimization mechanism is formed through user feedback. The character matching precision is remarkably improved, the test cost is reduced, and the method is suitable for precision agricultural breeding and planting management.

The invention discloses a factory data management system and method based on digital twinning, relates to the technical field of factory management, and solves the problems that in the prior art, equipment operation parameters are collected mostly through a sensor, abnormity is judged based on a static threshold value, but coupling influence of environment variables on faults is not fused, misjudgment is easily generated due to environment fluctuation, and meanwhile, the fault rate is low. The comprehensive optimization of the production efficiency is neglected, so that the monitoring and early warning precision is not high; the method comprises the following steps: constructing a digital twinborn model of factory production equipment; the method comprises the following steps: acquiring simulation data during simulation operation of production equipment and environment data at a corresponding moment; calculating an evaluation coefficient based on the simulation data; based on the environment data, constructing a correction model to correct the fault evaluation coefficient, and obtaining a target fault coefficient; predicting the current simulation data based on a pre-trained fault prediction model to obtain a prediction result; and finally, whether early warning is carried out is judged based on a prediction result. The technical problem is solved.

The invention relates to the technical field of plant management, and discloses a chaenomeles speciosa remote management and data modeling method based on a cloud collaborative architecture. The method comprises the following steps: firstly, receiving begonia growth environment data collected by terminal equipment, and carrying out standardized preprocessing and multi-source data fusion analysis to obtain a growth feature entity candidate list and a non-feature data fragment; after a first to-be-modeled data entity is extracted from the candidate list, a plurality of matched candidate data entity entries are extracted from the cloud collaborative knowledge base, the candidate data entity entries and the non-feature data fragments are input into a collaborative disambiguation model, semantic-level collaborative aggregation analysis is carried out with the non-feature data fragments as contexts, semantic aggregation coding features are obtained and decoded, and the semantic aggregation coding features are obtained; and obtaining a disambiguation data entity. And circularly executing the steps to obtain a disambiguation data entity list, and further generating a remote management strategy. The method can improve data processing accuracy and management pertinence, and is suitable for large-scale and distributed planting scenes of begonia.

The invention discloses an artificial intelligence-based economic crop planting management method and system. The method comprises the steps of obtaining planting management original data, optimizing the original data, selecting target economic crops, predicting market prices of the economic crops, optimizing economic crop planting strategies and intelligently managing economic crop planting. The invention relates to the technical field of economic crop planting data processing, in particular to an artificial intelligence-based economic crop planting management method and system, which innovatively takes market price prediction as a guide and integrates a method system of target crop intelligent screening and full-cycle planting strategy optimization. The planting income stability of commercial crops is improved, price data signal decomposition processing is introduced, and the quality of commercial crop market price prediction input data is improved; a market price prediction model is innovatively designed, and the accuracy and stability of a model prediction result are improved; and an improved particle swarm optimization algorithm is introduced to realize efficient global optimization of the economic crop planting strategy combination.

The invention provides a palm oil plantation multi-modal decision management method, system and device and a medium, and belongs to the technical field of Internet of Things in the planting industry, and the method comprises the steps: building a heterogeneous federated learning framework for a palm oil plantation; the edge layer constructs a lightweight model, a planting industry heterogeneous terminal device is used for collecting data for local training, and asynchronous federal aggregation is performed in combination with the fog computing layer until requirements are met; the edge layer is used for extracting local features from data collected by heterogeneous terminal equipment in the planting industry; the cloud computing layer aligns local features of the edge layer by using a cross-modal space-time encoder, constructs a causal graph in combination with a Bayesian network, determines intervention measures, quantifies the influence of the intervention measures by using anti-factual reasoning, generates and executes a decision scheme, and solves layered Nash equilibrium through a distributed Actor-Critic algorithm, so as to improve the performance of the cloud computing layer. And task allocation and path planning are carried out on the planting industry heterogeneous terminal equipment. According to the invention, the utilization rate of planting management data, decision reasonability and equipment collaboration are improved.

The invention relates to the technical field of intelligent agriculture, and discloses a traditional Chinese medicine organic planting management method based on big data analysis. A space coordinate system is established in a planting area, regular partitioning is carried out, a unique land parcel identifier is formed, a soil humidity sensor is arranged in the center of each land parcel, meteorological observation points are matched to continuously collect air temperature, wind speed and illumination, and an evapotranspiration potential index is generated through sliding window statistics and normalization. The system performs dynamic balance prediction and state correction according to the soil moisture content, the irrigation volume and the evapotranspiration loss, extracts a reference evapotranspiration rate by using an unirrigated sample, determines a threshold interval and a real-time deviation in combination with a historical quantile, and smoothly improves the stability through a sliding window. The threshold value is updated hour by hour within a feasible range, irrigation is triggered when the water content is lower than the threshold value, and the irrigation amount is limited within a reasonable range through depth and flow conversion. Structured logs are generated in the whole process, and irrigation whole-process trace reserving is achieved.

The invention discloses an industrial configuration-factory affair management system and method based on data fusion and closed-loop feedback, and the system comprises a multi-source data fusion layer, a closed-loop collaborative optimization layer, and a configuration control and management execution layer. The multi-source data fusion layer associates data sources of different sources through a protocol conversion and data cleaning module and a feature extraction and semantic association module, establishes a data model and constructs a knowledge graph, and the closed-loop collaborative optimization layer performs model optimization on the constructed unified data model and knowledge graph through a multi-target optimization module. Then verification is carried out through an analogue simulation and execution verification module, an optimization strategy is generated, and the configuration control and management execution layer feeds back an execution result to the multi-target optimization module after execution so as to form a feedback learning closed-loop module; according to the method, the problem of real-time data fusion of multi-protocol equipment is solved, cross-level closed-loop cooperation of control instructions and service decisions is realized, and the problem of multi-target optimization conflicts in a dynamic environment is solved.

The invention relates to the technical field of power plant management, and discloses a virtual power plant management method based on multi-source data fusion, which is used for solving the problem of equipment response delay among equipment during virtual power plant management. Acquiring equipment response behavior characteristic parameters of each distributed energy equipment according to the initial equipment operation sequence library, evaluating according to the equipment response behavior characteristic parameters to obtain a response lag index, and judging whether each distributed energy equipment has an asynchronous response lag effect or not according to the response lag index, all response lag devices are screened out, delay compensation is carried out on all the response lag devices, an actual device operation sequence library is obtained, a fusion input matrix is obtained according to the actual device operation sequence library, main feature dimensions are extracted based on the fusion input matrix, a state feature vector sequence is constructed, and a time sequence prediction model is obtained through training. And the accuracy of virtual power plant management is effectively improved.

The invention relates to the technical field of disconnecting link maintenance and transformation, in particular to an isolation disconnecting link updating and transformation method. The method comprises the following steps: S1, performing inspection and quantitative analysis, performing equipment evaluation and problem diagnosis, and identifying operation problems and function deficiency; s2, performing model selection and material optimization, and setting a standard isolation switch and an electric operation assembly; s3, defining a transformation range, dividing transformation units based on the main wiring diagram, and covering the isolation disconnecting link and the grounding disconnecting link; s4, implementing a safety locking structure, adding an electrified locking and five-prevention system, and accessing whole plant management to prevent misoperation; and S5, accessing a monitoring system, and connecting an LCU screen and a computer monitoring system to realize remote centralized control. The contact resistance and temperature rise of the contact reach the standard, ablation is completely eradicated, the operating force and time are greatly reduced, jamming is returned to zero, the five-prevention locking success rate is extremely high, the misoperation drill can reach the zero penetration level, and the on-site actual effects of mechanical zero jamming, five-prevention zero penetration, remote control zero delay and old and waste utilization are achieved.

This invention relates to the field of plant management technology, and more particularly to a plant equipment monitoring system and method, comprising a tracking module, a recording module, an updating module, a monitoring module, and an early warning module. The recording module stores historical call data; the updating module generates standard call information based on historical data after a call is completed; the monitoring module analyzes the difference between the actual call information and the standard call information when a call fails, calculates the deviation value, and feeds back a call reference value to the calling end that failed; the early warning module can issue alarms for abnormal call situations at the calling end. By constructing a multi-module collaborative working mechanism, it can provide quantitative feedback on the current call status when a call fails. Users can analyze the controllability of waiting calls based on the feedback results, enabling real-time dynamic monitoring and refined management of the equipment call process, thereby effectively improving the automation level and scheduling response capability of plant equipment management.

The invention relates to the technical field of virtual power plant distributed resource management, in particular to a virtual power plant-oriented distributed resource regulation and control method and system, and the method comprises the steps: collecting authority operation parameters of candidate virtual power plant distributed resources which are captured periodically, and inputting a preset power plant management association matching model; obtaining a power plant management association relationship between the candidate virtual power plant distributed resources and the virtual power plant distributed resource main body, managing the virtual power plant distributed resource main body to select the candidate virtual power plant distributed resources, collecting to-be-corrected operation parameters, inputting a preset virtual power plant distributed resource type clustering algorithm, and correcting the to-be-corrected operation parameters. And obtaining to-be-corrected virtual power plant distributed resource type parameters. The method has the advantages that the method is applied to power plant management association matching and virtual power plant distributed resource type detection, better universality can be achieved for different targets and scenes, and the accuracy and the regularity also meet the distributed resource sensing requirements of candidate virtual power plants.

The invention discloses an intelligent interaction management method and system for a coal-fired thermal power plant, and relates to the technical field of coal-fired thermal power plant management, and the method comprises the steps: collecting multi-source heterogeneous data of the coal-fired thermal power plant; generating a structured data asset library; building an intelligent interaction core module; a layered intelligent control system is constructed, a production intelligent control layer performs parameter prediction and closed-loop regulation and control on production core equipment based on production related data in a structured data asset library, and an environmental protection intelligent control layer performs parameter optimization and linkage regulation and control on environmental protection processing equipment through a mechanism-data hybrid drive model based on production data and environmental protection monitoring data. The technical problems that in the prior art, a coal-fired thermal power plant lacks a collaborative management linkage mechanism, so that the operation efficiency is low, the environmental protection standard reaching stability is poor, and the management intelligence level is insufficient are solved, and accurate collaborative management and control of the coal-fired thermal power plant are achieved. And the operation efficiency, the environmental protection standard reaching stability and the management intelligence level of the factory area are improved.

The present application relates to the technical field of power plant management, and discloses a kind of intelligent power plant power factor intelligent regulation and control method and system, collect power plant equipment energy consumption data, environmental parameter, load demand data, equipment operating state data and historical power factor data, align multi-source data in combination with DTW algorithm, obtain time series data;GNN network is used to topologically model power plant equipment;Time series data is input into Transform model, and the result output by GNN network is combined to carry out feature extraction, and obtain future 24 hours load prediction result;Based on load prediction result, through the joint application of particle swarm optimization algorithm and DQN network, regulation and control scheme is generated;Regulation and control scheme is converted into regulation and control instruction, regulation and control instruction is issued, and regulation and control data is collected to carry out real-time feedback;The present application realizes the real-time prediction of power factor, and the response time of regulation and control is shortened compared with traditional manual regulation and control, reduces plant power factor, and improves response speed.

The invention discloses a steel plant management statistics intelligent data processing system and method. Comprising a converter original report module, a refining furnace original report module, a refining furnace production report module, a steelmaking production report module and a steelmaking three-shift report module. And each module is respectively provided with functions of data display, query, export and report updating. According to the invention, by integrating the related data in the production process of the steel plant and intensively displaying various types of data in different pages, unified management of the data is realized. Meanwhile, convenient data entry, modification and deletion operation and efficient query and export functions are achieved, the time and labor cost of data management are greatly saved, and the data management efficiency is improved.

The invention discloses a multi-dimensional perception-based dynamic analysis method for fuel coal input, consumption and storage and a related device, and belongs to the field of coal-fired power plant management.The method comprises the following steps of: acquiring storage data, furnace input data and inventory data of fuel coal of a thermal power plant, and performing data association in combination with an inventory batch; carrying out inventory structure analysis on the inventory data after data association to obtain the inventory and proportion of the coal types and the weighted average of the inventory index parameters, carrying out visual display, carrying out consumption trend analysis on the warehousing data and the in-furnace data after data association to obtain fire coal consumption trend data, and carrying out visual display; and calculating a storage plan amount according to the fire coal consumption trend data, and adjusting a fire coal purchase amount and the storage plan amount in real time in combination with the inventory amount and the proportion of the coal type and the fire coal quality level value, so as to realize dynamic analysis of fire coal input, consumption and storage. According to the invention, the problem that the fuel coal consumption cannot be accurately and dynamically monitored in the prior art can be solved.

The application relates to the technical field of power plant management, and discloses a virtual power plant management method based on multi-source data fusion, which is used for solving the problem that response delay occurs between devices when virtual power plant management is performed, and comprises the following steps: obtaining device response behavior characteristic parameters of each distributed energy device according to an initial device operation sequence library; obtaining a response lag index according to the device response behavior characteristic parameters; determining whether a non-synchronous response lag effect occurs for each distributed energy device according to the response lag index; screening all response lag devices; performing delay compensation on all response lag devices; obtaining an actual device operation sequence library; obtaining a fusion input matrix according to the actual device operation sequence library; extracting main characteristic dimensions based on the fusion input matrix; constructing a state characteristic vector sequence; and training a time sequence prediction model, so that the accuracy of virtual power plant management is effectively improved.

The invention relates to the technical field of smart power plants, and discloses a smart operation platform of a smart power plant, and compared with a traditional power plant management mode, the smart power plant system is higher in automation degree, can dynamically collect and process equipment data, can evaluate and optimize equipment and system protocols in real time, and improves the system reliability. And delay and cost increase caused by equipment incompatibility are eliminated. On the basis of the existing technical means, the platform further enhances the intelligence, automation and adaptability of the system through deep data analysis and protocol calibration, and improves the response speed, interconnection and intercommunication capability and overall operation efficiency of the power plant. Through automatic protocol conversion and real-time data feedback, the platform provides accurate decision support for operation of the power plant, energy management and resource configuration are optimized, and the power plant can quickly respond to changes and requirements of equipment protocols.

The invention relates to the technical field of seedling culture management, and discloses a crop planting management system based on three-dimensional digital twinning, and the system comprises a data collection module which obtains a three-dimensional time sequence of growth of each hole of a crop, environment light quality and root zone temperature data, and calculates growth state parameters to generate a threshold time field; the phase ridge determination module establishes a coordinate system according to the contour of the cultivation table top, calculates the derivative and curvature of a threshold time field, and determines a phase ridge curve and a normal derivative; the inversion module solves key sensitivity parameters and thermal boundary layer parameters based on ambient light quality and root zone temperature data; the first management module maps the normal guide number into a regulation and control execution parameter in combination with the parameters, and multi-hole growth progress synchronization is achieved; and the second management module calculates the distance from each hole to the phase ridge curve and the normal derivative, generates a transplanting moment and forms a continuous transplanting schedule, thereby realizing high-precision and full-process seedling digital management.

The invention provides a pogostemon cablin growth management and prediction method, system and device and a medium, and relates to the technical field of intelligent agriculture, and the method comprises the steps: obtaining environmental condition data, physiological index data, staged standard physiological data and staged environmental demand data related to pogostemon cablin; through regression analysis and feature processing, determining a weight matrix of the environmental condition data influencing the physiological index data and growth state features of pogostemon cablin; performing deviation analysis on the environmental condition data and the staged environmental demand data to obtain environmental condition deviation parameters; predicting the growth process of the pogostemon cablin according to the growth state characteristics, the environmental condition deviation parameters and the weight matrix to obtain a growth prediction result; and performing growth management on the pogostemon cablin according to the environmental condition deviation parameter and the growth prediction result. According to the method, environmental conditions and physiological indexes are comprehensively analyzed, the growth state of the pogostemon cablin is predicted through regression analysis and feature processing, and planting management of the pogostemon cablin is optimized.

The application relates to the technical field of booster station plant management, and discloses a booster station plant data acquisition self-control energy-saving management system. Through standardized collection and deep feature extraction of full-factor operation data of the booster station plant, an innovative combination of physical information neural networks and a Transformer architecture is adopted to establish a precise prediction system covering load changes, equipment efficiency attenuation and environmental coupling influence. Meanwhile, through data envelopment analysis and life cycle assessment methods, a dynamic evaluation mechanism in the double dimensions of energy efficiency and carbon emission is constructed. Based on the fusion of multi-objective optimization algorithms and model predictive control, a real-time control strategy considering economic operation and low-carbon constraints is formed, so that the dual goals of improving the overall energy efficiency level and the management accuracy of carbon emission of the system are achieved.

A tree and shrub identification tag device is provided. The device is comprised of a tag designed for efficient and adaptable plant identification while promoting environmental sustainability. The device comprises eco-friendly materials, including biodegradable and recyclable plastics, to ensure durability and weather resistance through UV protection additives and waterproof coatings. The tag body is available in various sizes and features a rubberized texture for enhanced grip. A notched slot at one end allows for secure attachment and easy removal, while perforations facilitate damage-free detachment from around a plant. The device may also be comprised of a color-coding system for categorizing plant types and includes a customizable surface for detailed labeling. Additionally, the tag may incorporate QR codes for accessing digital resources such as care guides and supplier information, enhancing overall plant management and user experience.

To predict results of processing performed by a chemical plant by reflecting characteristics of unit operation.SOLUTION: A chemical plant management device includes: a storage part which stores information of a plurality of different unit operations included in processing processes in a chemical plant, log data information related to the processing of the unit operations, and condition information related to a condition in each of the unit operations; an input part which inputs sensor information acquired from a sensor device related to a first unit operation in the chemical plant; and a calculation part which creates a prediction model for predicting results of the processing in the first unit operation based on the log data information and the condition information related to the first unit operation, and stores the prediction model into the storage part in association with the first unit operation.SELECTED DRAWING: Figure 4

This application proposes a quality management method and system for new energy power plants, relating to the field of power plant management. The method includes: receiving a list of component models and quantities for target energy storage units in a target new energy power plant; statistically analyzing the time-series matrix of energy storage impact attributes, energy storage unit samples, and workload time-series information; performing a redundancy lifetime assessment on the target energy storage units based on the workload time-series information to obtain a predicted redundancy lifetime value; and assigning temporary or default periodic quality inspection labels to the target energy storage units. This application solves the technical problem in existing technologies where quality inspection of new energy power plant construction relies solely on periodic monitoring, potentially leading to the omission of critical risk moments and the existence of operational hazards. It achieves the technical effect of conducting redundancy lifetime assessments based on the service data and workload time-series information of energy storage units, enabling targeted quality inspection labels, and effectively preventing operational risks.