244 results about "Entire life cycle" patented technology

The invention discloses a full-life-cycle-based medical equipment monitoring analysis system and method, and relates to the technical field of medical equipment monitoring, and the method comprises the steps: collecting medical equipment data in real time, and dynamically constructing a full-life-cycle digital twin model of medical equipment; constructing a medical equipment knowledge graph based on the equipment type, the function association and the spatial distribution; when the medical equipment node detects abnormal data, an early warning signal is sent to a full-life-cycle digital twin model associated with the medical equipment in the medical equipment knowledge graph in combination with the medical equipment knowledge graph; preliminarily judging fault causes and fault location, and generating an analysis report; performing multi-dimensional verification on the diagnosis result in the digital twin environment, comprehensively evaluating the risk coefficient of the scheme, and outputting an optimal maintenance strategy; in the maintenance process, the maintenance process is recorded in real time, and maintenance data and equipment state updating are synchronously fed back to the equipment full-life-cycle digital twin model.

The invention relates to a building material mining life cycle process carbon emission improvement potential quantification method comprising the following steps: constructing a full life cycle carbon footprint digital model covering mining, transportation, processing and restoration, and integrating a digital twinning and BIM technology dynamic correlation carbon emission factor library; key carbon emission influence factors are identified through Monte Carlo simulation and regression analysis; establishing a multi-objective optimization model, generating an optimal process parameter combination by adopting an intelligent algorithm, and quantifying the emission reduction potential; model parameters are dynamically calibrated in combination with real-time data, and a closed-loop optimization mechanism is formed; space-time distribution visualization and block chain evidence storage technologies are introduced, and accurate monitoring, intelligent decision making and credible tracing of carbon emission are achieved. According to the method, the problems of data lag and single optimization in a traditional method are solved, the accuracy of carbon emission and the feasibility of an emission reduction scheme are remarkably improved, meanwhile, carbon sink offset evaluation is supported, and a whole-process technical support is provided for mine green transformation.

The invention relates to the technical field of digital management, in particular to an offshore platform component processing full life cycle quality tracing system which comprises a component unique identification code generation module, a multi-source processing data acquisition module, a quality judgment and tracing code association module, a holographic digital archive construction module and a three-dimensional visual tracing module. Wherein the component unique identification code generation module is used for generating a unique identification code; the multi-source processing data acquisition module is used for acquiring processing process data associated with the unique identification code of the component in real time; the quality judgment and traceability code association module is used for generating a quality judgment result based on a preset quality rule base; and the holographic digital archive construction module is used for generating a full-life-cycle data chain of the component. According to the method, the whole life cycle data chain with the unique traceability code as the index is constructed and mapped to the BIM model, and structural management and space-time visual traceability of the quality information of the offshore platform component are achieved.

The invention discloses a battery full life cycle intelligent management method and system based on a large model, and the method comprises the following steps: S1, collecting the operation data of a battery, and carrying out the preprocessing; s2, inputting the pre-processed sample into a pre-trained Transform encoder model, and extracting a time sequence and cross-stage characteristics; s3, executing model reasoning, outputting a state index, and generating a battery state vector; s4, identifying an abnormal category and a position in combination with the working condition information; s5, generating a dynamically optimized battery management strategy based on the battery state index and the abnormity identification result; s6, deploying a lightweight model at the edge device, executing local reasoning and uploading data; s7, the cloud updates the model through self-supervised training and issues the model; and S8, repeatedly executing the steps S1 to S7, and carrying out optimized closed-loop management. According to the invention, large model modeling and an edge cloud cooperation mechanism are fused, and intelligent sensing and dynamic management of the whole life cycle of the battery are realized.

The invention belongs to the technical field of smart city construction and intelligent construction management, and particularly relates to an intelligent scheduling and cooperation system and method for the whole life cycle of city reconstruction. The system comprises an edge sensing layer, a digital twinning network module, a space-time brain module, a block chain collaborative trust layer and an XR collaborative interaction layer. The system collects multi-source data of a construction site in real time through a 5G edge device, realizes fusion modeling of BIM, GIS and TIN models by using a graph convolutional neural network, and performs multi-target scheduling optimization in combination with meta reinforcement learning and a dynamic graph neural network; and meanwhile, construction task verification and fund payment linkage is realized through a block chain smart contract, and immersive cooperation and visual acceptance are provided in cooperation with an AR / VR platform. According to the method, the problems of model splitting, scheduling response lagging, low data collaboration efficiency, insufficient credibility of the performance process and the like in the urban reconstruction project are solved, and the intelligence, transparency and automation level of the construction process is remarkably improved.

The invention discloses a carbon emission index evaluation method and system based on a full-life-cycle power transmission and transformation project, and the method comprises the steps: S1, collecting data in the full life cycle of the power transmission and transformation project, including material consumption, energy use and equipment operation data in the stages of design, construction, operation, maintenance and demolition; s2, decomposing and optimizing a carbon emission path of each stage by adopting a hierarchical constraint path optimization algorithm; s3, introducing a self-correction energy consumption curve algorithm, and dynamically correcting the carbon emission data according to historical and real-time energy consumption data; s4, monitoring the energy consumption fluctuation in the operation maintenance stage, and automatically correcting the carbon emission deviation caused by the external environment change; s5, integrating hierarchical constraint path optimization and energy consumption curve data, and generating carbon emission assessment; s6, generating a standardized carbon emission evaluation report containing optimization suggestions; and S7, outputting an evaluation report, and supporting low-carbon planning implementation. According to the invention, the carbon emission management and optimization efficiency of the whole life cycle of the power transmission and transformation project is effectively improved.

The invention provides an Internet of Things system rigid-flex board full life cycle carbon footprint accounting method and device, and the method comprises the steps: carrying out the feature extraction according to carbon emission activity data, and generating carbon emission activity features; according to different life cycle stages, in combination with a carbon emission factor method, through a pre-trained carbon emission prediction model, prediction is carried out according to the carbon emission activity characteristics, and carbon emission prediction results corresponding to the life cycle stages are generated; and according to the carbon emission prediction result and the global warming potential value corresponding to the life cycle stage, generating a carbon footprint accounting result corresponding to the life cycle stage, thereby generating a full-life-cycle carbon footprint accounting report, and realizing accurate accounting of the full-life-cycle carbon footprint of the rigid-flex board. The carbon footprint is scientifically managed, the accuracy of carbon emission data is improved, meanwhile, the efficiency of carbon footprint accounting is improved, a real-time monitoring and early warning function is provided, and a transportation route is optimized to reduce carbon emission in the transportation process.

The invention discloses a document review method and device based on the full life cycle of science and technology projects and a medium, belongs to the technical field of document review, and solves the problem of how to improve the accuracy of normative review of science and technology project documents. According to the method, project document content is sequentially decomposed into field type analysis, specification requirement analysis and content specification analysis on the basis of the thinking chain technology, each item of analysis depends on the result of the previous item of analysis, and a complete path of conventional review is constructed step by step; the method comprises the following steps: analyzing whether a conflict and a logical conflict exist between a current to-be-audited project document and a historical document or not by utilizing a retrieval generation enhancement technology, and obtaining a plurality of fragments associated with to-be-audited fragments of the document from the documents in different life cycles; and ranking of possible useful fragments is provided for the large language model, an examination result is given, logic conflicts and contradictions among the documents are identified and solved, and the examination accuracy is improved from the aspects of consistency and logicality of the documents to be examined and historical documents.

The invention discloses an intelligent networked battery full life cycle optimization system and method, and relates to the technical field of energy management, the system comprises a sensor array module, temperature data and voltage data in the battery operation process are continuously collected through a sensor array, an initial multi-dimensional time sequence data set is formed, and the initial multi-dimensional time sequence data set is stored in the sensor array module; the alarm and adaptive adjustment module is used for obtaining an alarm log record aiming at the initial time sequence data set, triggering the adaptive adjustment module to dynamically optimize battery operation parameters according to the alarm log record, generating a temporary control instruction, reducing the influence range of temperature or voltage abnormity and obtaining adjusted operation state data; according to the intelligent networked battery full life cycle optimization system and method, risk monitoring of the battery full life cycle is realized, and the battery operation safety and reliability are effectively improved.

The invention discloses a full-life-cycle intelligent operation and maintenance method based on a BIM model, and relates to the technical field of building intelligent operation and maintenance, and the method comprises the steps: collecting voiceprint data, and carrying out the preprocessing of the voiceprint data; setting an adaptive weight for the preprocessed voiceprint data according to the credibility of the equipment and the data updating frequency, and performing fusion according to the adaptive weight to generate a comprehensive voiceprint feature vector; performing real-time analysis on the comprehensive voiceprint feature vector through an anomaly detection model, and identifying abnormal voiceprint information; and according to the abnormal voiceprint information, combining operation and maintenance data of the whole life cycle of the building, generating an operation and maintenance plan, feeding back an operation and maintenance decision to the BIM model, and updating the operation and maintenance plan. According to the invention, through two core steps of dynamic adaptive weight setting and fusion and RNN and GRU-based anomaly detection model, efficient fusion and accurate anomaly recognition of multi-device voiceprint data are realized.

The invention provides a leasing equipment full life cycle monitoring method and system based on the Internet of Things, and belongs to the technical field of equipment operation and maintenance monitoring, and the method comprises the steps: obtaining real-time state data of leasing equipment in an operation process through a multi-source sensor; performing feature extraction on the real-time state data to obtain various feature vectors, and constructing a multi-dimensional feature vector set; inputting the multi-dimensional feature vector set into a random forest model, and judging whether the leasing equipment is in an abnormal state or not in combination with a space-time clustering algorithm; if yes, fault prediction is carried out on the leasing equipment in combination with an abnormal detection result, historical operation data and a baseline model, and a fault probability value of the leasing equipment is obtained; the future position of the leasing equipment is predicted based on the position data, an electronic fence boundary is set for the leasing equipment, if the future position exceeds the electronic fence boundary, an early warning notification of trajectory deviation is generated, and the system finally realizes trajectory and fault visual presentation in a thermodynamic diagram mode, so that the equipment operation and maintenance intelligence and management precision are improved.

The invention relates to the technical field of electrical operation safety management, in particular to an electrical operation full-life-cycle intelligent safety management and control platform which comprises a data acquisition module, a data processing and feature extraction module, a hidden fault mode recognition module, a hidden fault prediction module, an early warning module and a data storage module. The data acquisition module acquires equipment multi-mode operation state data in real time through a distributed sensor network, the data processing and feature extraction module preprocesses the data and extracts potential features of hidden faults, and the hidden fault mode identification module distinguishes a normal operation mode from a hidden fault mode. The hidden fault prediction module predicts the future operation state and fault risk of equipment, the early warning module generates risk prompts and maintenance suggestions through real-time monitoring and a multi-stage early warning mechanism, and the data storage module realizes classified storage of data; according to the invention, comprehensive monitoring, accurate prediction and intelligent early warning of the operation state of the equipment are realized, and the operation safety of the electrical equipment is improved.

The invention provides a solid waste recycling full life cycle environmental impact evaluation method, which belongs to the technical field of solid waste recycling, and evaluates environmental impact by constructing a system boundary and time dynamic list including solid waste collection, transportation, pretreatment, recycling and product use stages and applying a time weighted characterization model. A time discount rate is introduced to quantify future environmental influence, a multi-scene influence prediction function is adopted to construct a simulation matrix, and an environmental influence time distribution curve is calculated. A four-dimensional index system of resource recovery efficiency, energy substitution efficiency, pollutant emission reduction and environmental impact mitigation is established, a system dynamics and Envi rPred ictor deep learning model is combined to predict a solid waste long-term environmental behavior, an environmental impact comprehensive evaluation matrix is constructed, characterization factors are dynamically adjusted, an iterative optimization mechanism is formed, and the environmental impact mitigation efficiency is improved. Scientific evaluation on the solid waste recycling full life cycle environment influence is realized.

The invention discloses an asset management system based on a full life cycle, relates to the technical field of asset management, and comprises centralized management, real-time monitoring, intelligent analysis and scientific decision of asset data. The asset management system based on the full life cycle comprises an asset information management module, an Internet of Things data acquisition module, a data analysis and intelligent maintenance module, an intelligent decision support module, a visual management module, a risk early warning and compliance management module and a block chain evidence storage and traceability module. Through the asset information management module, basic information of assets is intensively recorded, updated, audited and cleaned, data formats are unified, meanwhile, the Internet of Things data collection module collects asset operation data in real time, the data are subjected to cleaning, conversion, feature extraction and other processing in the big data analysis and intelligent maintenance module, and the data are stored in the storage module. Effective integration of multi-source data is achieved, the system further provides an intelligent analysis function, and deep mining is carried out based on the integrated data.

The invention discloses a modulus-driven high-speed rail overhead line system full life cycle management method, and relates to the technical field of railway overhead line systems, and the method comprises the steps: analyzing the full life cycle of an overhead line system, and constructing a life stage block; collecting life stage data, and storing the life stage data into the corresponding life stage blocks; respectively constructing a representation model library, a data lake and a mechanism model library for the collected data according to a subject domain; establishing a collaborative periodic relationship based on the axial data relationship; building a digital-analog period integrated atlas relation; and performing operation processing to generate stage management information. The technical problems of unreasonable resource allocation, slow fault response and low management efficiency caused by the fact that information is difficult to share in real time and the intelligent level of full-life-cycle management is not high due to neglecting of contact and mutual influence of all stages of the full-life-cycle of the overhead line system in the prior art are solved. Concentrated storage and efficient management of data in all stages are achieved, and the technical effect of improving operation and maintenance management efficiency and power supply stability is achieved.

The invention relates to the technical field of geotechnical engineering, and discloses a soft soil foundation full-life-cycle intelligent electro-osmosis maintenance system which comprises a field control system. And the intelligent monitoring regulation and control system transmits a feedback signal to the power supply system, and the field control system and the spraying humidification system perform corresponding actions, so that active prevention and control of foundation diseases are realized. Dynamic management is carried out on the anode boundary through real-time monitoring data, regular or on-demand starting is carried out through intelligent decision, the physical and mechanical properties of a foundation soil body are dynamically improved through the electroosmosis effect of a field control system, and the durability and long-term stability of the foundation soil body are improved; according to the intelligent electroosmosis maintenance system, the efficiency of an electroosmosis reinforcement system can be improved, the environmental adaptability is enhanced, and long-term stable operation is achieved.

The invention relates to a monitoring method and system for the full life cycle of a transformer substation project, and the method comprises the following steps: deploying an unmanned plane automatic airport to collect environment data, and selecting a transformer substation project construction site according to the actual construction demands and the environment data; modeling the power generation and transformation project based on the monitoring data of the power generation and transformation project construction site, and constructing a hidden danger sample library; constructing an intelligent monitoring system which comprises a hidden danger recognition module, an expert rechecking module and a decision generation module; wherein the hidden danger identification module performs hidden danger identification by using an improved YOLOv8n network to obtain a hidden danger target; the improved YOLOv8n network is specifically characterized in that a convolution attention module and an adaptive feature fusion module are embedded in a backbone network; the hidden danger target passing through the expert rechecking module is input into a decision generation module, and a hidden danger decision is generated and issued to an unmanned aerial vehicle automatic airport; the unmanned aerial vehicle automatic airport receives and controls the unmanned aerial vehicle to execute the hidden danger decision.

The ground control system for multi-mode switching of a full-life cycle aircraft disclosed in the present invention includes a proximal part and a distal part, wherein the proximal part is close to one end of the full-life cycle aircraft, and the distal part is far away from one end of the full-life cycle aircraft; the proximal part is used to switch the working mode of the ground control system according to the received control command, wherein the working mode of the ground control system corresponds to the multi-mode of the aircraft; the distal part is used to receive the control command for presetting the aircraft operation and send the control command to the proximal part. It is possible to realize automatic and real-time supervision of the changing trend of control system degradation.

The invention belongs to the technical field of Internet of Things, and discloses an Internet of Things encrypted traffic accurate classification method. The whole life cycle of encrypted traffic data acquisition, processing, extraction and classification is throughout. The implicit correlation between the original bytes is mined, and the internal feature information of the flow graph is effectively extracted and fused by using the graph neural network. And meanwhile, the problem of class imbalance in the encrypted traffic data set is well solved through the balance loss function. According to the method, a simulation experiment is carried out on two encrypted traffic data sets, the validity of the method is verified when the two encrypted traffic data sets are compared with a plurality of reference models, and a certain guarantee can be provided for the security of the Internet of Things. By using a small amount of marked data and a large amount of unmarked data, better traffic identification performance and relatively high accuracy are realized, and the network security of the traffic of the Internet of Things is ensured by using a small amount of resources.

The invention discloses a shale gas full life cycle yield prediction method, and relates to the field of shale gas development. According to the shale gas full life cycle yield prediction method, the production stage of a shale gas well is divided into six stages of stable yield, continuous decreasing, intermittent production, pressurization decreasing, pressurization intermittent opening and fracturing effect receiving; in the stable production stage, the gas production rate is predicted by adopting a productivity coefficient method based on unstable linear flow, in the continuous decline stage, the gas production rate is predicted according to the ARPS decline model analogy partition decline rate, and in the intermittent production stage, the gas production rate is predicted by combining the ARPS decline model and the intermittent efficiency; in the pressurization decline stage, the gas production rate is predicted according to the gas well decline rate of the corresponding decline stage by adopting monthly production data; the gas production rate is predicted in combination with a decline model and the intermittent opening efficiency in the pressurizing intermittent opening stage; and predicting the gas production rate by using the effected ARPS model in the fracturing effected stage. According to the shale gas full life cycle yield prediction method, the corresponding yield prediction method is established according to the production rules of different development stages of the shale gas full life cycle, and the yield prediction efficiency and accuracy are improved.

The invention relates to the technical field of electric power iron tower full-life-cycle carbon footprint accounting, provides an electric power iron tower full-life-cycle carbon footprint accounting method and system, and aims to solve the problems of low efficiency and poor accuracy of electric power iron tower full-life-cycle carbon footprint accounting in the prior art. The method comprises the steps that an electric power iron tower component library is constructed, and a design stage carbon accounting benchmark is generated in combination with a building information model, a steel model, a welding process and anti-corrosion coating parameters; capturing assembly installation deviation and splicing gaps in real time, calculating material loss caused by construction errors, and converting the material loss into carbon emission increment; loading data by using a digital twinning technology, adjusting a material loss early warning threshold value, and updating design data; based on the corrected data, icing load deformation and corrosion expansion in the operation and maintenance stage are analyzed, and a full-life-cycle carbon footprint report is generated in combination with the updated stress distribution data. According to the technical scheme provided by the invention, the efficiency and accuracy of the full-life-cycle carbon footprint accounting of the electric power iron tower can be improved.

The invention discloses a full-life-cycle carbon footprint quantitative evaluation method and device for a sulfuric acid circulating symbiotic utilization system. The method comprises the following steps: 1) dividing subsystems in the full life cycle of the sulfuric acid circulating symbiotic utilization system and determining a system boundary of carbon footprint quantitative evaluation; 2) determining a carbon emission source of sulfuric acid in each subsystem according to a production process, and constructing a carbon emission list; 3) determining a carbon emission calculation method of each emission source, and constructing a carbon footprint quantitative evaluation model; 4, activity data and carbon emission factors of all carbon emission sources are established, and the carbon footprint of the sulfuric acid circulation symbiotic utilization system.By means of the method, the full-life-cycle emission reduction target function of the sulfuric acid circulation symbiotic utilization system can be determined, and a carbon footprint optimization scheme is formed for different circulation symbiotic utilization scenes.

The invention provides an energy storage battery full life cycle management method, system and device and a medium, and belongs to the technical field of energy storage battery management, and the method comprises the steps that an edge node collects energy storage battery operation parameters, power grid operation parameters and environmental data in an energy storage battery box and provides the parameters to a cloud node; the cloud node solves an energy storage battery charging and discharging power instruction and an energy storage battery thermal management set value by taking the minimum energy storage battery full life cycle cost function as a target and taking an energy storage battery SOC constraint, a power constraint and a power grid power balance constraint as constraint conditions, and issues the energy storage battery charging and discharging power instruction and the energy storage battery thermal management set value to the edge node; and the edge node adjusts the operation parameters of the energy storage battery according to the charging and discharging power instruction of the energy storage battery and the thermal management set value of the energy storage battery, inputs the operation parameters into the battery health state prediction model, and performs early warning and reminds operation and maintenance personnel when the energy storage battery is predicted to have a fault. The full-life-cycle closed-loop management of the energy storage battery is realized, the safe operation of the battery is ensured, and the operation efficiency is improved.

The invention provides a product full-life-cycle carbon footprint data acquisition and analysis system and method, relates to the field of data acquisition and analysis, and is used for performing data analysis and semantic understanding on product full-life-cycle data by introducing an artificial intelligence data processing technology, and calculating an initial total value of carbon emission of each stage of a product. Then, a carbon emission correction factor is obtained based on the semantic information of each stage and the relation between the semantic information and the relation between the semantic information and the relation between the semantic information and the relation between the semantic information and the relation between the semantic information and the relation, so that the difference in each processing process is reflected more accurately, and dynamic changes of different product processes and external conditions are better adapted; and the total value of the carbon emission in the whole life cycle of the product, which is closer to reality, is obtained. Therefore, the real environmental influence of the product can be reflected more accurately, more reliable product full-life-cycle carbon footprint data support is provided for enterprises, and effective emission reduction measures are formulated.

A device and a method of whole lifecycle multi-indicator synchronous detection of yarns or fabrics are provided. The device includes a hairiness detection unit, a first guide unit, a tension adjustment unit, and a friction unit. The device is used for circularly rubbing a closed-loop object to be detected with a preset length on the friction unit, and collecting hairiness data in real time through the hairiness detection unit. The tension adjustment unit is a free-falling tension adjustment unit used to automatically adjust tension of the object to be detected in real time to ensure the stability of friction and transmission of the object to be detected. By arranging the circulating friction closed-loop circuit and arranging the free-falling tension adjustment unit on the circuit, the friction stability is improved. The synchronous and rapid detection of multiple indicators of the object to be detected can be realized.

The invention discloses a data operation coding platform throughout the full life cycle of rail transit engineering, which is used in the technical field of constructional engineering and comprises a design module used for selecting and managing design-level components in a design stage and inputting coding information related to design; the construction module is used for selecting and managing construction-level components in the construction stage and inputting coding information related to construction; the operation module is used for selecting and managing maintenance-level components in an operation stage and inputting code information related to maintenance; the operation module is used for operating the coding information input in the design module, the construction module and the operation module to generate a coding system; and the transmission module is used for transmitting related data among the design, construction and operation stages. According to the invention, each component is coded and calculated, so that the data can be freely combined and transmitted, and the identification degree, standardization, automation and sharing of the data are greatly improved.

The present application belongs to the field of carbon emission statistics, and particularly relates to a carbon emission statistical tracking system based on the whole life cycle of products, which can effectively track the total carbon emission of each product in the whole life cycle after being sold out through the cooperation of a detection module, a remote recording module and a terminal receiving module; meanwhile, the time t less than the threshold t1 is not included in the total amount, so that the situation of carbon emission caused by the operator's accidental touch to start or the start for a few minutes and the discovery of problems requiring timely stop can be reduced, the accurate calculation of the carbon emission amount is improved; through the setting of the influence coefficient u, the problem of the increase of the carbon emission amount caused by the insufficient treatment of raw materials in the same use time due to the increase of the service life of the product, the aging of parts and the accumulation of residues in the product can be corrected.

The invention discloses a scientific and technological achievement full-life-cycle traceability tracking management system, and relates to the technical field of scientific research management, and the system comprises a data fusion collection module which is used for collecting data of all stages of a scientific and technological achievement full-life cycle, and carrying out the format conversion and protocol adaptation of the data; the safe and credible storage module is used for storing the collected data by adopting a mixed framework combining distributed storage and blockchain storage; the intelligent analysis and decision module is used for analyzing the stored data based on an artificial intelligence model, mining data values and generating decision suggestions; and the dynamic interaction application module provides visual achievement traceability query, real-time state monitoring, intelligent early warning and decision support functions for different user roles. A semantic mapping rule is dynamically learned by using a reinforcement learning algorithm through the adaptive protocol translation unit, a heterogeneous protocol is automatically adapted, and seamless joint of multi-source data is realized.

The invention provides a full-life-cycle water and fertilizer management method and system for blueberry planting, and relates to the field of crop fertilization, and the method comprises the steps: obtaining water and fertilizer management experiment data of a plurality of blueberry growth stages; based on the water and fertilizer management experimental data of the plurality of blueberry growth stages, establishing a growth-related weighted undirected graph of the plurality of blueberry growth stages; determining a key growth factor and a key water and fertilizer management factor of each blueberry growth stage based on the water and fertilizer management experimental data of the plurality of blueberry growth stages and the growth-related weighted undirected graph of the plurality of blueberry growth stages; based on the water and fertilizer management experiment data, the key growth factors and the key water and fertilizer management factors of the blueberry growth stage, determining candidate water and fertilizer management schemes of the blueberry growth stage; based on the candidate water and fertilizer management scheme of each blueberry growth stage, a water and fertilizer management scheme for the whole life cycle of blueberry planting is generated, and the method has the advantages that accurate supply and intelligent regulation of water and fertilizer are achieved, and the water and fertilizer utilization rate is increased.

The invention relates to a salt cavern integrated energy system full life cycle economic evaluation method, and the method comprises the steps: obtaining electric energy supply and demand data in a target region, and building a mathematical model of a salt cavern integrated energy system based on the electric energy supply and demand data and at least one salt cavern geological parameter; based on the mathematical model, an objective function is determined with the maximum comprehensive economic benefit in the whole life cycle, and a double-layer optimization model of the salt-cavern comprehensive energy system is established; and performing multi-scene evaluation by using a double-layer optimization model to perform modeling and optimization solution on each scene to obtain economy of the system in different scenes, and determining the optimal configuration of the salt cavern integrated energy system according to the economy of the system in different scenes. Therefore, the problems that the overall value is difficult to accurately evaluate and the scientificity of investment decision and the rationality of resource allocation are influenced due to the fact that a static analysis mode is adopted in related technologies and multiple factors such as related complex technologies, economy and environment are not fully considered are solved.