75results about How to "Realize accurate quantification" patented technology

The invention discloses an underground operation and maintenance method and system based on big data analysis, and relates to the technical field of coal mine underground operation and maintenance. The underground operation and maintenance method based on big data analysis comprises the following steps: evaluating the stability performance of a sensor; evaluating signal fluctuation; evaluating sensor interference; and operation and maintenance feedback. According to the method, the sensor stability performance evaluation value is obtained through underground sensor monitoring data, whether stability adjustment is carried out or not is judged, then the signal fluctuation evaluation value is obtained by combining the signal fluctuation data and the sensor stability performance evaluation value subjected to stability adjustment, and whether signal fluctuation adjustment is carried out or not is judged. A sensor interference evaluation value is obtained by combining the signal fluctuation evaluation value obtained after signal fluctuation adjustment and sensor interference data, whether sensor interference adjustment is carried out or not is judged, and finally operation and maintenance feedback is carried out, so that the effect of improving the underground operation and maintenance efficiency is achieved. The problem of low underground operation and maintenance efficiency based on underground data in the prior art is solved.

The invention relates to the technical field of air conditioner dispatching, in particular to a mass air conditioner multi-target day-ahead optimal dispatching method and system considering carbon emission flow. The method comprises the steps that a multi-target optimization model of air conditioner load day-ahead scheduling is constructed with the minimum temperature comfort deviation and the minimum comprehensive cost as targets; wherein the optimal solution of the multi-objective optimization model comprises the minimum temperature comfort deviation and the minimum comprehensive cost, and the comprehensive cost comprises the electricity cost and the carbon emission cost; determining a constraint condition in the multi-objective optimization model, and converting the constraint condition into a penalty function; the constraint conditions comprise a temperature constraint, a power constraint, a load balance constraint, a switch state constraint, a carbon emission constraint and an electric power cost constraint; solving an optimal solution of the multi-objective optimization model by adopting a parrot optimization algorithm, and controlling the operation of each air conditioner according to the optimal solution of the multi-objective optimization model; according to the method, dual-objective rapid optimization scheduling of the power consumption cost and the carbon emission cost of the mass air conditioners is realized.

The invention discloses an intelligent frequency modulation method and system for a photovoltaic inverter and a storage medium, and relates to the technical field of power systems. The intelligent frequency modulation method for the photovoltaic inverter comprises the following steps: voltage monitoring coincidence evaluation; performing frequency monitoring coincidence evaluation; and evaluating power monitoring coincidence. According to the invention, the voltage monitoring circuit is evaluated to obtain a voltage monitoring coincidence evaluation index and determine whether voltage frequency modulation is carried out, and then the frequency monitoring circuit is evaluated to obtain a frequency monitoring coincidence evaluation index and determine whether frequency modulation is carried out. And finally, evaluating the power detection circuit to obtain a power monitoring coincidence evaluation index, and judging whether to perform power frequency modulation, thereby achieving the effect of improving the response accuracy in the intelligent frequency modulation process of the photovoltaic inverter, and solving the problem of insufficient response in the intelligent frequency modulation process of the photovoltaic inverter in the prior art.

The invention discloses a drainage basin and lake endogenous phosphorus pollution quantitative calculation method, and relates to the technical field of drainage basin and lake nutritive salt traceability, and the method comprises the steps: determining a drainage basin boundary, and collecting endogenous phosphorus output and exogenous phosphorus input near a drainage basin, and samples in a lake water body and other media; obtaining the phosphate oxygen isotope value delta < 18 > O (PO4) of each pollution source and the source-tracing water body of the lake; and substituting a phosphate oxygen isotope value delta18O (PO4) into a Bayesian model and a mixed end member model, calculating to obtain a relative contribution rate of endogenous phosphorus output and exogenous phosphorus input, combining the relative contribution rate with different types of land area data, and determining an actual relative contribution rate of the endogenous phosphorus output and the exogenous phosphorus input to the phosphorus load of the river basin lake water body. According to the method, the actual relative contribution rate of endogenous phosphorus output to the phosphorus load of the lake water body is quantitatively calculated on the basis of traditional drainage basin lake exogenous phosphorus tracing, and a supporting basis is provided for accurate quantitative calculation of drainage basin lake phosphorus.

The invention discloses a method for in-situ determination of higher fatty acid in Baijiu, and belongs to the technical field of Baijiu detection. In order to realize in-situ simultaneous detection of a plurality of higher fatty acids in Baijiu, the invention provides a method for determining a plurality of higher fatty acids in Baijiu based on combination of ultra-high performance liquid chromatography and a quadrupole-electrostatic field orbitrap mass spectrometer, according to the method, liquid sample introduction is carried out directly or after membrane passing, and analysis is carried out through a high-resolution liquid mass spectrometer. And performing external standard method quantification after SIM mode scanning to obtain the content of the 14 higher fatty acids in the to-be-detected sample. According to the method, tedious pretreatment and derivatization processes are avoided, the 14 trace or trace higher fatty acids in the Baijiu can be accurately quantified at the same time, accurate verification can be achieved in Luzhou-flavor Baijiu, Fen-flavor Baijiu, Maotai-flavor Baijiu and rice-flavor Baijiu, the steps are simple, and operation is convenient.

The invention provides a power business anti-quantum cryptography migration progressive control method based on risk awareness, and belongs to the technical field of migration control, and the method comprises the steps: collecting key parameters of all to-be-migrated businesses in a power system, carrying out the standardization preprocessing of the collected key parameters, and constructing a business feature matrix; acquiring relevant parameters of quantum attacks in real time, and calculating a real-time quantum attack risk value of each service to be migrated; based on the service feature matrix and the real-time quantum attack risk value, constructing a dynamic migration rhythm adaptation model, and calculating a migration priority, a migration rate and a migration interval of each service to be migrated; and constructing a migration fault-tolerant and recovery model, monitoring the migration process in real time, calculating a fault influence range and recovery cost based on fault information, and executing corresponding adjustment operation. Accurate, dynamic and high-reliability control of anti-quantum password migration of the power business is realized, safe and stable operation of the power business in the migration process is guaranteed, and the migration efficiency and the anti-quantum attack capability are improved.

The invention belongs to the field of soil environment pollution simulation and prediction, and provides a prediction method based on a two-point chemical unbalanced model, which is used for quantitatively evaluating the effect of regulating and controlling the migration behavior of p-nitrophenol in acid red soil by straw stalk biochar. According to the method, a biochar-red soil composite medium system is constructed, a soil column leaching experiment is carried out to obtain penetration curve data, a two-point chemical unbalance model in CXTFIT software is utilized to carry out parameter inversion, and key parameters such as a retardation factor, a two-region distribution coefficient, a mass transfer coefficient and a liquid phase degradation coefficient are accurately identified. Model verification shows that the goodness of fit is high, and a parameter change rule reveals the inherent mechanism of enhancing the pollutant retention capacity, changing the proportion of fast and slow adsorption sites and influencing mass transfer kinetics by the biochar. Based on the verified model, the long-term migration behavior and pollution risk of p-nitrophenol under different environmental conditions can be simulated, and a scientific basis is provided for groundwater pollution control and remediation strategy optimization.

The invention relates to the field of pavement detection, in particular to an error detection method of pavement abrasion detection equipment, and the error detection method comprises the following steps: obtaining pavement form information; performing fitting processing on the pavement form information by using a preset data fitting processing algorithm to obtain at least one tectonic depth change curve; determining a first pavement abrasion value based on each structural depth change curve and preset configuration information; based on each structural depth change curve, generating test boards in one-to-one correspondence with each structural depth change curve; detecting the test plate by using pavement abrasion detection equipment to determine a second pavement abrasion value; and determining a detection error of the pavement abrasion detection equipment based on the first pavement abrasion value and the second pavement abrasion value. A large amount of original data can be fitted through a preset data fitting processing algorithm, so that the constructed depth change curve can meet different scene requirements. By generating the test board, the authenticity of the detection error is ensured, and the applicability and accuracy of the error detection method are improved.

The application belongs to the technical field of fluorescent probe detection, and discloses a bimetallic nanocluster fluorescent probe compound and application thereof in preparation of a product for rapidly detecting acetylene. The fluorescent probe compound is a gold-copper bimetallic nanocluster solution modified by adamantane thiol, which is used as a fluorescent probe for acetylene detection. The solution has a clear and controllable preparation process. Through specific proportioning and reaction conditions of cuprous chloride, adamantane thiol, tetrachloroauric acid, sodium borohydride and other reagents, a probe solution with stable fluorescence characteristics can be prepared. The bimetallic nanocluster fluorescent probe compound is applied in preparation of a product for rapidly detecting acetylene. Through specific combination of fluorescent molecules and acetylene gas to trigger the secondary effect of fluorescence quenching, detection is realized, and other substances cannot trigger quenching. At a wavelength of 700 nm, the linear relationship (R 2 >0.99) between the fluorescence intensity and the acetylene concentration realizes accurate quantification of the acetylene concentration, and the minimum detection limit is 0.026 muL / L.

A method and device for evaluating the influence effect of an academic recommendation algorithm based on a social robot, the method comprising: collecting academic papers, citation relationships and author information through a data interface or a web crawler, and screening target personnel data according to a research field; constructing a virtual social robot and establishing a research interest vector of the virtual social robot based on historical literature and research theme information of the target personnel; presetting multiple literature acquisition strategies for the social robot to form differentiated experimental conditions; controlling the social robot to perform operations such as paper retrieval, access and click recommendation on an academic platform, and recording literature access paths, recommendation results and browsing sequence data in real time; calculating the research theme distribution of the social robot according to a literature set contacted in the experiment, and comparing the research theme distribution with the initial research interest, so as to obtain the change degree of the research direction; and comparing the change results of the research direction of the social robot under different strategies, and quantitatively evaluating the influence degree of the recommendation system on the research direction evolution of the scientific researchers.

The invention relates to the technical field of cultural relic identification and visual large model fusion, in particular to a ceramic cultural relic full-dimensional identification system and method based on multi-modal visual large model and microscopic feature fusion. After high-quality applicable data are screened, other data are calibrated with the optimal mode as the reference, cross-mode deviation is eliminated, and a data foundation is built firmly; the core features are converted into topological nodes, association strength is calculated, feature association edges with guidance are established, a traceable and explainable identification guidance link is generated, and the strong ceramic identification logic requirement is met; the optimal link is screened to be combined with the pre-training model, and the identification precision and the careful property are balanced by depending on a confidence hierarchical judgment mechanism, so that the black box pain point of the traditional model is effectively solved, and the credibility of the identification result is improved.

The invention discloses a hydropower station peak capability evaluation method under an extreme scene. The method comprises the following steps: automatically classifying hydrological scenes of the hydropower station based on a ten-day runoff drought threshold value and a ten-day runoff flood threshold value of the hydropower station in a kurtosis period; based on the hydrological scene classification result of the hydropower station, simulating the operation process of the hydropower station by using a hydropower guarantee supply absorption operation model, and obtaining the peak duration of the hydropower station so as to construct a training set; and constructing a hydropower station peak capability evaluation model based on an extreme gradient lifting method, training the hydropower station peak capability evaluation model by using the training set to obtain a trained hydropower station peak capability evaluation model, and further obtaining a peak capability evaluation result and a corresponding visual analysis result of the hydropower station in a peak summering period. According to the method, the problem that the peak capacity of the hydropower station in extreme weather cannot be quantitatively evaluated by a traditional method is solved, the conversion from passive response to active pre-judgment is realized, and the scientificity and foresight of a power grid supply guarantee decision are improved.

The application discloses a kind of high-wet high-salt environment under transformer cellulose insulating paper aging evaluation method, comprising the following steps: constructing cellulose insulating paper multi-environment molecular dynamics simulation model;Simulation and extraction micro-characteristic parameters;Based on the improved entropy weight-TOPSIS method, the correlation model of micro-characteristic parameters and aging degree is established;Based on BP neural network, the mapping relationship model of field detection index and micro-characteristic parameters is constructed;Transformer cellulose insulating paper aging state evaluation and life prediction.The method of the application is aimed at the special service environment of high-wet high-salt offshore transformer, starting from micro-mechanism, the precise evaluation of insulating paper aging degree is realized through quantitative algorithm, and the evaluation result is fitted to the actual operating state of transformer under marine environment, to provide scientific basis for operation and maintenance and life evaluation of offshore wind power transformer.

This invention discloses an intelligent exercise assessment and early warning system based on multi-source biosignal fusion, belonging to the field of exercise risk early warning technology. It collects multi-source data, including electromyography (EMG), heart rate, blood oxygen, skin conductance, and acceleration, from various wearable sensors to assess muscle fatigue, cardiovascular load, exercise status, and environmental factors. After preprocessing, the system uses quantitative models such as the muscle fatigue correction index and the cardiovascular load dynamic coefficient to calculate a comprehensive exercise risk early warning index by integrating physiological state and environmental factors. This results in a normalized risk classification, corresponding to low, medium, and high risk levels and marked with different color icons. This system overcomes the limitations of single signals, considering the synergistic effects of multi-dimensional physiological indicators and the influence of environmental factors, improving the accuracy of assessment in complex scenarios. It can effectively intervene before risks develop and is suitable for scenarios such as mass fitness and professional training. It is integrated into lightweight wearable devices without interfering with the exercise process.

The invention belongs to the technical field of medical image data processing, and relates to a medical image data processing method and system, and the method comprises the steps: obtaining the medical image data of a marked lesion, inputting a lesion feature extraction model for training, judging whether the lesion feature extraction model meets a specified requirement or not, creating a three-dimensional lesion generation model, and training the three-dimensional lesion generation model. Acquiring medical image data of specified parts of a current patient at different angles in different periods, and sequentially inputting the medical image data into the trained focus feature extraction model for focus feature extraction; inputting the lesion characteristics of the current patient in different periods into the trained three-dimensional lesion generation model, respectively generating three-dimensional lesion model data in different periods, and transmitting the three-dimensional lesion model data to an illness state prediction model; the disease prediction model respectively calculates the focus development speed and the overall development speed of two adjacent periods; and the development speed of the lesion in each direction is determined, and the development of the lesion is predicted. Medical image data in different periods can be processed, and doctors can be assisted to diagnose the condition of a patient.

The application belongs to the technical field of tunnel engineering disaster prevention and reduction, and specifically discloses a tunnel / gallery structure toughness calculation method under disaster coupling, which comprises the following steps: determining the disaster source type borne by the target tunnel / gallery structure; constructing a lining index system based on key parameters; constructing a tunnel / gallery-rock-soil refined numerical model, determining a disaster intensity index, and carrying out vulnerability analysis; collecting multiple sets of measured data of the toughness index of the target tunnel / gallery structure at different times; combining weighting based on dynamic game equilibrium and decision test and evaluation laboratory method, and calculating the dynamic weight of each index; according to the multiple sets of measured data and the above toughness index system, multiple composite performance parameters are solved; through comprehensive evaluation of the multiple sets of composite performance parameters, the damage state and toughness strength of the target tunnel / gallery structure are determined, and the comprehensive toughness grade of the structure is determined. The application can realize accurate quantification of the toughness of the tunnel / gallery structure under disaster coupling.

The invention belongs to the technical field of food safety, and particularly relates to a short-chain free fatty acid determination method and application thereof. The determination method of the short-chain free fatty acid comprises the following steps: S1, extracting the short-chain free fatty acid; s2, derivatization reaction of short-chain free fatty acid: under the protection of argon, adding a dichloromethane solution containing 1.5 mL of a fluorescent derivatization reagent into a drying flask containing 6mg of an extract, stirring for 2 hours at room temperature under the condition of blue light, and performing HPLC (High Performance Liquid Chromatography) determination after the reaction is finished; and S3, determining the short-chain free fatty acid. The novel and efficient fluorescent derivatization reagent is used in the determination method, and the determination method can improve the sensitivity and the accuracy of the HPLC method for determining the short-chain free fatty acid.

This invention relates to the field of bid document management technology, and in particular to an automatic bid document review method and system based on natural language processing. The method includes: identifying target sections; determining historical performance and financial information; judging whether false performance exists; analyzing the complete sentences containing target keywords; calculating material scores; determining price deviation; and determining the review result. This invention achieves multi-dimensional and comprehensive review of bid documents by intelligently analyzing multiple sections such as qualifications, risks, materials, and pricing. This effectively improves the comprehensiveness and depth of the review. Utilizing natural language processing technology, it can accurately identify and extract key information from bid documents, overcoming the subjectivity and limitations of traditional manual review. Quantitative assessment based on risk level significantly improves the accuracy and relevance of risk identification. By comprehensively weighing material scores and price deviation, it significantly improves review efficiency.

The present application relates to carbon emission monitoring technical field, especially to a kind of integrated energy house's carbon emission monitoring method and system.The method includes the following steps: periodically monitoring real-time supply and return water temperature difference in integrated energy house heating system normal heating state, to assess the loop flow imbalance degree of each heating loop;According to loop flow imbalance degree, respectively account the overheating loss caused by user behavior and the heat source degradation energy consumption caused by system compensation operation, to convert the carbon emission increment data of heating system;Flow regulation processing is carried out to overcurrent loop and undercurrent loop, and integrated collector valve adjustment instruction data is generated.The present application realizes accurate quantification of additional carbon emission caused by uneven flow distribution by monitoring distributed heating pipe network flow in integrated energy house.

The application discloses a laboratory motion line safety early warning method and device, which realizes personnel space-time trajectory reconstruction and accurate identification of abnormal motion line segments by real-time collection of relevant personnel positioning and behavior monitoring data streams, formation of motion line basic data with space-time stamps and identity tags, and combination with standard templates, and provides reliable support for risk assessment. By combining personnel characteristics and basic risk coefficients corresponding to the identity, the personal risk contribution value is calculated and the personal risk field is generated, realizing accurate quantification and differentiated assessment of personnel risk. Superimposing the personal risk field generates a dynamic group risk heat map, which can intuitively present the global risk distribution; building a risk coupling network can predict the conduction effect of high-risk areas, dynamically determine the early warning threshold according to the regional personnel and activity types, effectively improve the early warning accuracy, and reduce false positives and false negatives. In summary, the application realizes accurate control and risk early disposal of the whole process of laboratory motion line safety, and significantly improves the fine and intelligent level of safety management.

This invention provides a multi-sensor fusion-based dual-modal health data acquisition system for equipment operation, comprising: a miniaturized data acquisition terminal, an edge computing module, and a low-power Bluetooth transmission module. The miniaturized data acquisition terminal is fixedly installed on the operating mechanism of the monitored equipment and internally includes an integrated sensor module. The integrated sensor module is used to collect operational behavior modal data and health modal data in real time during equipment operation. The low-power Bluetooth transmission module is used to transmit the operational behavior modal data and health modal data to the edge computing module. The edge computing module is used to perform time alignment and feature extraction on the operational behavior modal data and health modal data, and determine the dynamic correlation between equipment operational behavior parameters and equipment wear parameters based on the time alignment and feature extraction results. This effectively solves the problems of data isolation, delayed analysis, and disconnect between training and maintenance in traditional methods.

The invention discloses a freight return data processing method and system based on the Internet of Things, and relates to the technical field of electric digital data processing. The freight return data processing method based on the Internet of Things comprises the following steps: evaluating the influence of transportation quality; recording efficiency evaluation; and evaluating data transmission. According to the method, the unqualified quality influence coefficient and the qualified quality influence coefficient are obtained through the food return transportation related data, then the entry efficiency evaluation coefficient is obtained based on the unqualified quality influence coefficient and the food return entry related data, and whether data entry efficiency adjustment is performed or not is judged; and finally, combining the station transmission data and the qualification entry efficiency evaluation coefficient to obtain a data transmission evaluation coefficient, and judging whether the data transmission evaluation coefficient meets a transmission qualification condition or not, thereby achieving the effect of improving the accuracy of food return data acquisition, and solving the problem of low accuracy of food return data acquisition in the prior art.

This application relates to a method and application for determining the amount of conductive agent in each layer of a multilayer coated electrode. The method includes the following steps: determining the optimal amount of conductive agent in a single-layer coated electrode with m different areal densities, wherein the single-layer coated electrode includes a single active material layer; based on the optimal amount of conductive agent in the single-layer coated electrode with different areal densities, constructing a fitting curve between the optimal amount of conductive agent in the single-layer coated electrode and the areal density of the single-layer coated electrode, obtaining a functional relationship with areal density as the independent variable G and conductive agent amount as the dependent variable B; for each active material layer in the multilayer coated electrode, the sum of the areal densities of the active material layer and all active material layers located on the surface of the active material layer away from the current collector is used as the independent variable G in the functional relationship, and the obtained dependent variable B is used as the amount of conductive agent in the active material layer. This achieves precise quantification of the amount of conductive agent in each layer of the multilayer coated electrode.

The application discloses a mine area closed goaf pollution release early warning method based on water quality and water level monitoring, and belongs to the underground water monitoring field. The method comprises the following steps: collecting water level data and representative water samples of water-filled rock layers, performing water chemical and isotope analysis on the water samples, analyzing the direct contribution water volume of each water-filled rock layer, inputting a data set containing dynamic monitoring parameters and future expected parameters into a pre-trained water level prediction model to obtain a predicted water level, calculating the expected exchange flow between the closed goaf and the water-filled rock layer by using the Darcy seepage formula based on the predicted water level and the goaf shape, and calculating the expected pollutant discharge amount in the prediction period. If the expected pollutant discharge amount exceeds a preset safety threshold, a warning for the corresponding closed goaf is generated, so that the early warning of the pollutant discharge of the closed goaf is realized, and timely and scientific decision-making basis is provided for the active prevention and control and precise management and control of the mine area underground water pollution risk.

The invention relates to a table tennis practice equipment cleaning demand prediction system based on big data analysis, and belongs to the technical field of data analysis. According to the system, equipment use data and environment data are collected in real time through a data collection module, a pollution index is dynamically calculated through a pollution index calculation module, a future cleaning demand time point is predicted based on a long-short-term memory network, and a decision execution module outputs a cleaning signal according to a prediction result. The problem of resource waste caused by fixed-period cleaning is solved, precise quantification of the equipment pollution state and intelligent prediction of the cleaning requirement are achieved, the maintenance efficiency is remarkably improved, and resource consumption is reduced.

The application discloses a battery pack energy state estimation method, device, equipment, storage medium and product, which are used for accurately and reliably estimating the energy state of a battery pack. The method comprises the following steps: acquiring a first operation data sequence of a first battery pack, wherein the first operation data sequence comprises operation data of the first battery pack at multiple time points; inputting the first operation data sequence into a first model to output a first energy state of the first battery pack; wherein the first model is obtained by training based on a second operation data sequence and a second energy state of a second battery pack, the single cell parameter distribution of the second battery pack is similar to the single cell parameter distribution of the first battery pack, and the single cell parameter distribution of the battery pack is obtained by a second model based on an operation data sequence of the battery pack.

This invention relates to a multi-energy flow carbon footprint tracking method based on a carbon memory matrix and conservation constraints, belonging to the technical fields of energy internet, industrial carbon management, and intelligent manufacturing. It constructs a multi-source energy-carbon coupled data foundation to achieve unified data across multiple time scales; establishes an energy storage carbon memory matrix model with time and temperature hierarchies to characterize the dynamic evolution of carbon intensity in the energy storage system; based on this, it constructs a multi-energy flow directed carbon flow graph to achieve unified carbon tracking and allocation for electrical, thermal, and cooling energy flows; introduces carbon flow conservation constraints and a weighted least squares state estimation method to achieve physical consistency verification and self-repair of carbon data; finally, it constructs a mixed-integer linear programming model with explicit embedded carbon memory constraints to achieve optimal multi-energy coordinated scheduling of carbon emissions throughout the entire life cycle. This invention realizes the transformation of carbon footprint from static accounting to dynamic tracking, and from post-event statistics to proactive optimization, significantly improving the reliability of carbon data and the effectiveness of low-carbon scheduling.

The quantitative medicine outlet bottle comprises a bottle body, a bottle cap, a medicine dividing piece and a medicine outlet piece, a bottle opening is formed in one end of the bottle body, the bottle cap is correspondingly connected with the bottle opening in a threaded mode, an isolation piece is arranged in the bottle cap, a plurality of medicine outlet holes are formed in the isolation piece, the medicine outlet holes are matched with pills, and a rotating groove is formed in the side, correspondingly away from the bottle body, in the bottle cap. The medicine distributing part is rotationally arranged in the rotating groove and tightly attached to the medicine outlet holes, so that the number of the medicine outlet holes can be controlled by rotating the medicine distributing part, the medicine outlet part is rotationally arranged in the rotating groove and corresponds to the side, away from the medicine outlet holes, of the medicine distributing part, and a medicine containing groove is formed in the side, corresponding to the partition part, of the medicine outlet part; by means of the linkage design of the medicine distributing piece and the medicine discharging piece, accurate quantification, flexible adjustment and convenient taking of the pills are achieved, the problems that in the prior art, dosage is not accurate, operation is complex, and the pills are prone to being blocked are solved, and the medicine dispensing device has remarkable practical value and market prospects.

The invention discloses a novel energy system safety risk analysis method and device based on carbon-energy coupling, and the method comprises the steps: inputting energy data and a carbon emission factor, and building a carbon-energy coupling dynamic model; generating typical risk scenes such as energy price fluctuation and extreme weather, and reducing to obtain a simplified scene set; then an upper-lower layer coupled risk quantification model is constructed, the upper layer focuses on energy supply cost and carbon emission optimization, and the lower layer focuses on power system operation cost minimization; and finally, solving based on a Monte Carlo method, and outputting a multi-dimensional risk assessment index. According to the method, the problem that multi-field risk interaction is difficult to describe by a traditional method is solved, accurate quantification of the multi-scene risk of the provincial novel energy system is realized, and scientific support is provided for provincial energy safety decision.

This invention discloses a lactation-promoting composition containing a lime flavonoid extract, comprising, by weight, the following raw material components: 10-50 parts lime flavonoid extract, 5-30 parts synergistic active ingredients, and 20-85 parts excipients; wherein the lime flavonoid extract contains ≥80% total flavonoids (calculated as hesperidin), of which hesperidin ≥40%, neohesperidin ≥15%, naringin ≥10%, and hesperidin ≥5%. Using the lime flavonoid extract as the core, it achieves highly efficient lactation promotion through multi-target synergistic effects. In livestock applications, it can increase milk yield in dairy cows by 15%-28% and milk yield in sows by 20%-38%, while significantly improving milk fat, milk protein, and other milk nutritional indicators.