269 results about "Multivariable linear regression" patented technology

The invention discloses an artificial intelligence rice water and fertilizer real-time monitoring method and system, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: inputting a farmland feature data set into a soil thermodynamic diagram generation model, carrying out the high-resolution reconstruction of the farmland feature data set through a GAN adversarial network, and generating a whole-field high-precision soil thermodynamic diagram; based on the whole-field high-precision soil thermodynamic diagram, a collision relation between the fertilization amount and historical farming data is detected according to an FCL causal algorithm, a preliminary causal diagram is generated, and a causal diagram structure of the fertilization amount and the historical farming data is constructed by adopting a multiple linear regression method; based on a causal diagram structure, the multi-order causal effect of the fertilization amount, the soil parameters and the historical yield is analyzed through a dynamic allocation algorithm, and a water and fertilizer regulation and control strategy is formulated in combination with a multi-objective optimization algorithm. According to the method, the soil thermodynamic diagram generation model is constructed, so that the fuzzy problem of the edge of the field and the salinization area is solved, a high-fidelity soil space state substrate is provided for water and fertilizer regulation and control, and invalid irrigation is reduced.

The invention provides a farmland yield prediction method and system based on soil parameter inversion, and the method specifically comprises the steps: collecting the soil profile information and earth surface three-dimensional information of a target farmland through a ground penetrating radar and a laser radar, and carrying out the precise inversion of the water content and porosity of soil based on a water-porosity inversion model, therefore, a high-precision three-dimensional soil distribution model is constructed, crop growth monitoring data is combined, a farmland yield prediction model is established, the relationship among soil structure characteristics, crop growth indexes and meteorological factors is quantified, and high-precision farmland yield prediction is realized. According to the method, through accurate modeling of the double-pore structure and optimization of the multiple linear regression prediction model, yield prediction is more accurate and reliable, and the method is particularly suitable for farmland management under different soil types and meteorological conditions.

The invention discloses a rice polishing precision control method, device and equipment and a storage medium, and belongs to the technical field of rice processing. The method comprises the following steps: fitting a rice processing data prediction model through a multiple linear regression algorithm, inputting rice polishing precision into the rice processing data prediction model to obtain rice processing data, polishing rice to be polished according to the rice processing data, and adjusting working parameters of rice polishing equipment to obtain polished rice. Polishing processing data can be automatically adjusted according to the polishing precision, the conditions that rice is excessively polished and the polishing precision does not reach the standard are prevented, and energy waste is reduced.

The invention relates to the technical field of automatic medical treatment, provides an infusion process control scheme, and particularly relates to an infusion port risk prediction method and device and electronic equipment. According to the method, clinical-related medical data is constructed and screened through a multiple linear regression method to obtain clinical infusion risk-related data, and the clinical infusion risk-related data is coded and subjected to weight updating to obtain an input sequence; and performing risk prediction on the input sequence based on the prediction model to obtain a final risk prediction result.

The invention provides an in-situ curing and repairing method for heavy metals in river sediment. The in-situ curing and repairing method comprises the following steps: S1, detecting the content and physicochemical properties of the heavy metals in the river sediment; s2, designing a repair material formula; s3, preparing and putting a repairing material; s4, mixing and stirring the bottom mud and the repairing material; s5, monitoring and evaluating the repairing effect; and S6, repairing effect feedback and adjustment. According to the method, multiple linear regression, curved surface fitting, self-organizing mapping and mixed density network algorithms are fused, the sediment heavy metal content and physicochemical property data are fully utilized, a repair material formula can be accurately designed, and compared with a traditional single algorithm or empirical formula design, the repair material proportion can better fit the actual pollution condition, and the repair efficiency is improved. The utilization rate of repair materials is improved.

The invention relates to the technical field of welding, and particularly discloses an anti-fatigue welding process for a long-distance pipeline supporting steel structure. The process comprises the steps that a font groove structure is determined, and pre-welding polishing pretreatment and layered welding are conducted; fatigue samples of weld toe areas with different arc radiuses are collected, crack feature coordinates and stress concentration coefficients are extracted, failure modes are divided based on clustering, the arc radiuses are verified as main control factors of fatigue failure through multiple linear regression, and a core control range is determined by combining process capability indexes; carrying out fatigue verification on the basis of core parameter extension radius gradient, carrying out process compatibility matching in combination with a controllable interval and a thermal deformation interval of equipment, and outputting a radius candidate value through a response surface method; on-site construction constraints are collected, laboratory path space compatibility is analyzed, a formed track is optimized in a segmented mode, and constraint compensation factors are embedded. The anti-fatigue performance of the weld toe area of the supporting steel structure can be improved, and quality guarantee is provided for welding of the long-distance pipeline supporting steel structure.

The invention discloses a method for determining pesticides in water by liquid chromatography and tandem mass spectrometry, which comprises the following steps: acquiring retention time data of different pesticide compounds in a liquid chromatography system, recording retention behavior characteristics of each pesticide compound under optimized chromatographic conditions by adjusting the composition proportion of a mobile phase and a gradient elution program, and determining the retention behavior characteristics of each pesticide compound under optimized chromatographic conditions. Obtaining a standardized retention time spectrum library data set; establishing a quantitative relation model between molecular structure parameters and chromatographic retention time by adopting a multiple linear regression algorithm through key parameters such as molecular weight, polar surface area and lipid-water partition coefficient in the molecular structure parameter matrix, and obtaining a structure-retention correlation prediction model; and constructing a classification prediction model between molecular structure parameters and environmental durability by adopting a support vector machine algorithm through activity index data in the biological activity prediction result, and determining the environmental durability grade of the pesticide compound according to molecular stability parameters and degradation half-life characteristic values.

The invention relates to the technical field of traffic management, and discloses a road low-visibility monitoring method and system based on video analysis, and the method comprises the steps: carrying out the quantification of the degradation features of a reference object region obtained through dynamic recognition in a target road monitoring video, and generating a degradation value sequence of multiple features of the reference object region; and performing spatial-temporal feature extraction on the degradation value sequence to obtain a steady-state feature vector of the reference object region, performing multiple linear regression calculation, performing early warning grading on the road visibility in the target road monitoring video according to the obtained dynamic visibility index of the target road monitoring video, and generating an early warning scheme. Through multi-frame trajectory association and streaming verification, four-dimensional features of a reference object area are synchronously extracted, collinearity features are automatically removed, a high-precision dynamic visibility index is output, the problems of repeated signal calculation and prediction errors are avoided, and the accuracy and stability of road low-visibility monitoring are improved.

The invention provides a circulating water low-turbidity water quality online control process based on alkalinity control. On-line monitoring equipment including an alkalinity analyzer, a turbidity meter and the like is arranged at key positions such as a water inlet of a circulating water system. The alkalinity analyzer adopts an acid-base titration and potential detection technology, the measurement precision reaches + / -0.01 mmol / L, and data is transmitted at least once per minute. By simulating a multi-working-condition experiment and by means of multiple linear regression and a neural network, an alkalinity and turbidity correlation model is constructed, and the prediction accuracy of the model exceeds 95%. And the central control system performs fusion analysis on water quality parameters by applying algorithms such as fuzzy logic and the like and sends an instruction to the intelligent dosing system. The system is internally provided with an advanced algorithm and can adaptively adjust the dosing of the medicament. Practice shows that the process can stably control turbidity and reduce equipment scaling and medicament consumption cost, and has outstanding advantages in the aspect of circulating water quality control.

The invention discloses a saline-alkali soil improvement effect evaluation method based on digital twinning, and relates to the technical field of saline-alkali soil improvement effect evaluation, and the method comprises the following steps: collecting saline-alkali soil improvement effect monitoring data including multi-dimensional soil data, crop growth data and meteorological data, and carrying out the preprocessing of the collected data, calculating the salinization index, fertility index, porosity and permeability of the soil, analyzing the soil improvement effect of the saline-alkali soil to obtain the grade of the soil improvement effect of the saline-alkali soil, and obtaining an improvement effect feedback coefficient by utilizing the preprocessed crop growth data and meteorological data and combining with a multiple linear regression algorithm; the multi-dimensional data acquisition technology, the multiple linear regression algorithm, the digital twinborn model construction technology and the model optimization technology are closely combined with the modern information technology, so that the problems of incomplete analysis data, inaccurate evaluation result and low intelligent degree possibly existing in a traditional evaluation method are solved.

The invention discloses a coal-fired boiler combustion instability monitoring method and system based on multi-parameter fusion, and the method comprises the steps: constructing a hearth flame imaging system which comprises an image detector, a lens, a stainless steel cavity and a cooling air channel; the flame image intensity is converted into radiation intensity through blackbody furnace calibration, and the flame temperature and emissivity are calculated based on the Planck law; extracting flame characteristic parameters including average radiation intensity, temperature, emissivity and standard deviation thereof; constructing a load-flame characteristic parameter model, and predicting parameter reference values under different loads through multiple linear regression; and calculating a combustion instability index CII, carrying out weighted fusion on the normalized deviation of the real-time measured value and the predicted value, and judging that CII is equal to 1 when the flame area is 0 or the average temperature is lower than 900 DEG C. The flame radiation image and the unit operation parameters are fused, and the limitation of traditional single-parameter monitoring is solved; combustion instability dynamic quantitative evaluation is achieved through the CII index, and the method is suitable for early warning of the deep peak regulation working condition.

The invention relates to a wind noise modeling method and system based on machine learning and application, and belongs to the field of marine acoustics and environmental noise modeling, and the method comprises the steps of data preprocessing and feature extraction, hybrid network model building, hybrid network model training and verification. According to the method, a hybrid network model of multiple linear regression and a multi-layer perceptron is constructed based on a known physical mechanism of wind noise and two main noise generation mechanisms of surface turbulence and bubble oscillation, a linear relation and a non-linear relation are modeled respectively, outputs of the two models are integrated through a weighted fusion strategy, and a multi-layer perceptron model is constructed. Smooth transition modeling from a low-wind-speed linear relation to a high-wind-speed nonlinear relation is achieved.

The invention relates to the technical field of exploration and development of water flooded layers, in particular to a sandy conglomerate water flooded layer porosity determination method based on an XRD (X-Ray Diffraction) logging technology, which comprises the following steps of: performing correlation analysis on the content of each mineral in mineral content data and the actually measured porosity of the same group; screening out mineral types of which the determination coefficient with the actually measured porosity is greater than a determination coefficient set value; performing multiple linear regression fitting on the content of each mineral type obtained by screening and the corresponding actually measured porosity of the same group to obtain an optimal multiple linear regression model; and for the rock debris sample of the non-coring section of the reservoir stratum of the glutenite water flooded layer, obtaining the porosity calculated value of the rock debris sample through the optimal multiple linear regression model. The porosity data of the rock debris sample of the non-coring section of the reservoir stratum of the glutenite water-flooded layer can be obtained, and the porosity data of the oil reservoir water-flooded layer of the whole well can be obtained by combining the porosity data of the coring section of the rock debris sample, so that the porosity data of the whole oil reservoir water-flooded layer can be obtained.

The invention discloses an overhead transmission line X-ray intelligent flaw detection method based on a six-rotor unmanned aerial vehicle, and relates to the technical field of X-ray intelligent flaw detection, and the method comprises the following steps: collecting flaw detection data including flight attitude data, X-ray imaging data, overhead transmission line data and positioning data, carrying out preprocessing and feature extraction on the collected data; adjusting the hovering position of the six-rotor unmanned aerial vehicle based on the preprocessed positioning data; and based on an output result of the intelligent flaw detection model, analyzing the severity of internal defects of the overhead transmission line, and further generating an X-ray intelligent flaw detection report of the overhead transmission line. According to the method, a multi-sensor data acquisition technology, a data preprocessing technology, a feature extraction technology, a weight analysis technology, a flight attitude adjustment technology, a multiple linear regression modeling technology and a neural network modeling technology are closely combined with a modern information technology; accurate detection and high-precision positioning of internal defects of the overhead transmission line are achieved.

The invention relates to a mechanical evaluation system based on rotation and inclination of a bracket-free appliance, and belongs to the technical field of orthodontic equipment. The system comprises four core units: an integral dental arch constraint unit simulates oral temperature through a constant-temperature water bath assembly, adjusts model postures through a camera holder type adjustable dental arch base, and constructs a precise constraint environment; the clinical displacement driving unit is based on an anchorage center bearing lever structure, and positioning-driving-data synchronization is achieved through adjustable anchorage positioning, multi-direction servo driving and displacement closed-loop calibration. The single-tooth mechanical sensing unit collects dynamic mechanical data, and interference is removed through Kalman filtering; and the data processing unit corrects attenuation deviation by using multiple linear regression in combination with environmental parameters, generates a mechanical attenuation curve, calculates a time attenuation percentage, and outputs a mechanical evaluation report by comparing with a clinical suitable force application interval. The evaluation accuracy and clinical suitability of the method provide a scientific basis for the optimization of an orthodontic scheme.

The invention belongs to the technical field of battery management, and particularly discloses a parameter identification method of an equivalent circuit model and an SOC estimation method and system.The parameter identification method of the equivalent circuit model comprises the steps that the equivalent circuit model in the charging direction and the discharging direction is built; respectively carrying out mixed pulse tests on the equivalent circuit models in the charging direction and the discharging direction of each SOC point to be tested to obtain voltage and current data of the parameter identification test of each SOC point to be tested; and obtaining key parameters of the equivalent circuit model in the charging direction and the discharging direction by using a multiple linear regression method. The SOC estimation method comprises the following steps: determining a battery SOC estimation path according to an open-circuit voltage change rate; and according to the key parameters and the SOC estimation path of the battery, establishing simulation models in the charging direction and the discharging direction, and obtaining SOC estimation values in the charging direction and the discharging direction of the battery by using the simulation models. The accuracy of the equivalent circuit model can be improved, the SOC estimation precision is further improved, and the method is more suitable for actual working conditions.

The invention relates to a large underground cavern group crustal stress field inversion method under a complex geological condition, and provides a method for inversing a crustal stress field of a large underground cavern group by taking a river valley center line or a ridge line as an inversion numerical model boundary to solve the problems that underground cavern group crustal stress measured data is discrete and difficult to accurately predict under the background of deep river valley terrain and multi-fault development. And selecting actual measurement points which are far away from the fault and are in three-dimensional distribution. The method comprises the following steps: acquiring a displacement boundary and a gravitational acceleration magnitude of a three-dimensional numerical model through limited measured data by adopting stepwise multiple linear regression and evolutionary neural network joint inversion, positively loading the displacement boundary and the gravitational acceleration magnitude to a model boundary to obtain a regional crustal stress field, and extracting measured point data for preliminary verification; and simulating caving excavation, and analyzing whether the stress concentration area is consistent with the observed stress type damage position or not. The method integrates the advantages of the two methods, more optimal solutions can be selected, the problem that precision and calculated amount are difficult to consider in stress field prediction of the complex structure motion area is solved, and the method is suitable for crustal stress field prediction of the complex structure area.

The invention discloses a short-time voltage fluctuation suppression method, device and equipment based on partition fluctuation evaluation, and relates to the technical field of power system control. The method comprises the following steps: under a minute-level time scale, taking the comprehensive minimization of active power loss, voltage fluctuation in adjacent time periods and reactive power regulation cost as a target, constructing a multi-period reactive power voltage optimization control model, and obtaining a reference voltage and a reference reactive power control quantity; a power system is partitioned through a modularity-based reactive voltage control robust partitioning algorithm, and influence areas which are mainly influenced by new energy fluctuation are identified on the basis of partitioning. And constructing a partition fluctuation evaluation index and setting a partition optimization event triggering mechanism. And under a short-time scale, constructing a subarea reactive voltage optimization control model based on quadratic programming by utilizing a subarea space decoupling technology, and executing subarea coordination optimization. And correcting the partition reactive voltage optimization control model through a model correction method based on multiple linear regression.

The invention belongs to the technical field of advertisement putting, and discloses an accurate customer obtaining and advertisement putting system and method based on big data. The method comprises the following steps: S1, multi-source data acquisition: acquiring Internet public data, enterprise private domain data and third-party compliance data to form a comprehensive data pool; s2, data structured storage: the collected data are classified and stored, and an index is established. According to the method, through the collaborative application of multiple technologies such as integrated distributed crawlers, generative adversarial networks, Kalman filtering, edge calculation, multiple linear regression and Lasso regularization, full-link optimization from data acquisition to effect landing is realized, the core problems of inaccurate customer acquisition and poor advertisement effect in the prior art are solved, and the service life of the advertisement is prolonged. Manual intervention cost is reduced through an automatic mechanism, the comprehensive goals of improving customer obtaining precision, optimizing advertisement effect, reducing marketing cost and guaranteeing long-term effectiveness are finally achieved, and a more systematic and more efficient technical support is provided for enterprise digital marketing.

The invention relates to the technical field of leather wastewater treatment, and provides an ultraviolet-visible spectrum-based leather wastewater ozone dosage and COD (Chemical Oxygen Demand) prediction method. The method comprises the following steps: firstly, determining the types of organic matters in a tannery wastewater sample, and determining the distribution characteristics of the organic matters in different fluorescent regions through three-dimensional fluorescence spectrum analysis, so as to discriminate the types of organic pollutants; secondly, screening key wavelengths, and determining characteristic absorption wavelengths representing different organic matters through an ultraviolet-visible light spectrum; then carrying out an ozone oxidation experiment, and synchronously measuring a COD value and an ultraviolet absorption spectrum under different ozone addition amounts; based on experimental data, a multiple linear regression and exponential regression prediction model of ozone dosage and a COD prediction model based on partial least squares regression are established. According to the invention, accurate control of ozone dosage and real-time monitoring of COD are realized, the treatment efficiency is improved, the operation cost is reduced, the adaptability to water quality fluctuation is enhanced, and the method has a wide engineering application prospect.

The invention relates to the technical field of petroleum and natural gas drilling engineering, and particularly discloses a multi-field coupled complex fractured formation fracture width distribution inversion method, which is characterized in that a drilling fluid leakage model considering a fluid-solid coupling effect is constructed based on a porous elastic mechanics theory, and a roughness correction factor is introduced to correct a traditional cubic law; the application of the Darcy law in the matrix is perfected, a matrix and crack coupled drilling fluid leakage mathematical model is established, the influence rule of key parameters such as fluid viscosity, pressure difference and crack width on the drilling fluid leakage amount is obtained through numerical simulation, and on the basis, the drilling fluid leakage amount is calculated. According to the method, the crack width multiple linear regression inversion equation with the fluid viscosity, the pressure difference and the accumulated leakage as independent variables is established, the inversion equation has high prediction precision and practicability and can be used for rapidly and accurately inversing the crack width on site, and theoretical and technical supports are provided for multi-scale fractured formation leakage mechanism understanding and leakage prevention and control optimization.

The present invention discloses a method for regulating carbon accumulation in degraded karst forest soil by synergistic microbial functions, comprising: measuring and collecting bacterial and fungal diversity sequence data of degraded karst forest soil samples by high-throughput sequencing equipment, storing the original sequencing data including diversity, community structure, functional abundance values, etc. in a computer-readable storage medium, and constructing a structured microbial community database; based on the database, a computer system performs screening of core functional microorganisms that affect carbon storage; based on the screening results, the computer system performs the following processing: calculating the functional abundance index of core microorganisms; determining the microbial function weight by multivariate linear regression; establishing a carbon accumulation regulation coefficient calculation model; preparing a microbial agent and generating a control instruction including the microbial agent application amount and the vegetation coverage optimization plan according to the calculated value of the carbon accumulation regulation coefficient and the soil organic carbon saturation deficit value, and transmitting the control instruction to the field operation equipment.

The invention discloses an electric equipment line risk online monitoring method based on multi-source data fusion, and particularly relates to the field of electric line monitoring, and the method comprises the steps: collecting the electric, insulation, physical and environmental data of a line according to a preset frequency, and carrying out the standardization processing to obtain corresponding standardization parameters; dividing the standardized parameters into input and output dimensions, calculating output dimension parameter weights by using an entropy weight method, and constructing a theoretical value model of each output dimension by using multiple linear regression; respectively constructing electrical, insulating and physical risk functions based on the standardized parameters and theoretical values; constructing a collaborative coupling matrix in combination with each risk function value and historical data, and calculating a total risk value through a nonlinear fusion logic construction function; defining a dynamic threshold value based on each risk value, judging an abnormal point when the threshold value is exceeded, and triggering iterative optimization of the model; the fault probability is calculated by combining the total risk value and the abnormal point data, and a three-level early warning mechanism is set to judge the line risk; according to the scheme, multi-dimensional risk collaborative monitoring is realized, and the risk identification accuracy and real-time performance are improved.

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

The invention discloses a microbial function synergistic degraded karst forest soil carbon accumulation regulation and control method, which comprises the following steps: measuring and collecting bacterial and fungal diversity sequence data of a degraded karst forest soil sample through high-throughput sequencing equipment; original sequencing data including diversity, community structures, functional abundance values and the like are stored in a computer readable storage medium, and a structured microbial community database is constructed; based on the database, the computer system performs core function microorganism screening influencing carbon storage; based on the screening result, the computer system performs processes comprising: calculating a functional abundance indicator of the core microorganism; determining a microbial function weight through multiple linear regression; establishing a carbon accumulation regulation coefficient calculation model; and preparing a microbial agent, generating a control instruction containing the application amount of the microbial agent and a vegetation coverage optimization scheme according to the calculated value of the carbon accumulation regulation coefficient and the soil organic carbon saturation deficit value, and transmitting the control instruction to field operation equipment.

The invention relates to the technical field of photovoltaic energy utilization, and discloses an intelligent photovoltaic hot water system-oriented temperature control management and energy scheduling system, which comprises a photovoltaic cell panel electric energy collection unit, a heat storage material electric energy storage unit and a self-adaptive temperature control and energy scheduling unit, the photovoltaic cell panel electric energy collection unit is used for collecting solar energy and storing electric energy when illumination is sufficient; the heat storage material electric energy storage unit is used for storing redundant heat in a phase change material through the corrugated plate flat plate solar air collector; the self-adaptive temperature control and energy scheduling unit is used for preferentially utilizing the photovoltaic cell panel electric energy collection unit to heat and store electricity when the illumination is sufficient; when the illumination is insufficient. By introducing the heat demand prediction and decision tree energy scheduling model based on multiple linear regression, the use path of photovoltaic power generation, energy storage and power grid energy supply can be dynamically optimized according to the real-time environmental parameters, the photovoltaic electric energy utilization rate is maximized, and invalid heating and energy waste are reduced.

The invention relates to the technical field of combustion vibration prediction, in particular to an Agent-driven multiple linear regression prediction and residual error abnormal parameter alarm method, and provides the following scheme: real-time operation data is input into a pre-trained linear regression model through an intelligent agent, a combustion vibration prediction value is generated, and the combustion vibration prediction value is calculated; and the operation condition of the gas turbine is analyzed by calculating the residual error between the actual vibration value and the predicted vibration value. Related parameters are extracted through a data processing classification layer, nonlinear feature linearization is achieved by combining a physical model fine tuning kernel function, and the performance of a regression model is optimized. In addition, residual analysis and long-term change trend recognition abnormal modes are adopted, and accurate monitoring and abnormal alarm of combustion vibration are achieved. The method improves the prediction precision and calculation efficiency, and guarantees the stable operation of the gas turbine.

The application belongs to the field of flood control scheduling, and discloses a method for calculating the scheduling response relationship of an upstream reservoir to a midstream and downstream control station, comprising the following steps: generating the river channel hydrodynamic state under the reservoir regulation and storage process by using a Mike model, and adding a measured flood sequence to form an upstream reservoir scheduling-downstream river channel hydrodynamics state sample. The influence period of the upstream discharge flow is determined, the response relationship between the upstream reservoir discharge flow and the downstream control station flow is extracted by using an Elman neural network, and the performance of the model is evaluated. On the basis of the upstream reservoir discharge flow-downstream control station water level data, a flow-water level model for the reverse calculation of the upstream reservoir storage and discharge process is established by using multiple linear regression. The water level or flood discharge flow target of the downstream river channel is determined, the upstream reservoir discharge flow process and the storage process are inversely calculated, and the reverse control of the upstream reservoir is realized. The application can provide a stronger basis for the flood control scheduling of a basin, thereby reducing the scheduling risk.

The present application relates to the technical field of battery, especially a kind of performance prediction method of all-vanadium redox flow battery based on parameter compensation.The method first constructs the multi-physical field coupling simulation model including electrochemical reaction, ion transport, fluid dynamics and thermal management according to the structural parameters of all-vanadium redox flow battery in energy storage power station;In the preset running length, according to the operating parameters, the model is run, and the stack performance index data is obtained;The orthogonal test method is used for sensitivity analysis of structural parameters, and the regression coefficient is obtained by multiple linear regression;The regression coefficients are sorted and the target regression coefficients are selected, the parameter mapping relationship is established according to the performance index fluctuation value, and the compensation is carried out when the fluctuation value changes more than the threshold value;Finally, the weight factor is determined based on the analytic hierarchy process and entropy weight method, and the key structural parameters are screened out.The present application overcomes the limitations of fixed parameters and single environment in traditional methods, and realizes the accurate prediction of the performance of all-vanadium redox flow battery under different operating environments.

The present invention discloses a supply chain production scheduling optimization method and system based on multi-source data. The method extracts supplier procurement data through a data crawler, analyzes the commonality of raw materials in combination with a knowledge graph, and generates a raw material commonality list; based on delivery records and product attributes, a linear regression is used to predict the quality stability value, and the process similarity is calculated through a text similarity algorithm to construct a process similarity matrix; in combination with historical sales data, a multivariate linear regression and time series analysis are used to construct a market demand prediction model. The complex relationships among raw material commonality, process similarity, and market demand are characterized through a graph neural network to generate a product association network diagram, and the Pareto optimization is used to find the optimal balance point between delivery time and resource utilization rate to form a production scheduling plan; finally, an integer programming algorithm is used to comprehensively evaluate the performance score, quality stability value, raw material commonality list, and production scheduling plan to optimize the supplier portfolio. The present invention improves the production scheduling efficiency and accuracy and meets complex business requirements.