42 results about "Cost prediction" patented technology

The invention provides an iron-making production ore blending process method based on multi-target intelligent optimization and dynamic feedback regulation. The method comprises the steps that ore components, price and production data are collected, a quality and cost prediction model is built, a multi-target optimization model is built with molten iron quality, cost, blast furnace smooth operation and environmental protection as targets, and a Pareto optimal ore blending scheme is solved through an evolutionary algorithm. And real-time data is monitored on line, model predictive control is triggered to automatically adjust ore blending when setting is deviated, a sintering-pelletizing-blast furnace collaborative simulation model is built to optimize cross-process parameters, solid waste classified recovery and resource utilization are synchronously implemented, and intelligent low-carbon ironmaking is achieved. The method can improve the quality of molten iron, reduce the production cost, improve the operation efficiency of a blast furnace, reduce pollutant discharge, realize resource utilization of solid wastes, and improve the comprehensive benefits and environmental protection level of ironmaking production.

The invention relates to a financial cost prediction method and device based on machine learning, equipment and a medium. The method comprises the steps of performing dimension reduction processing according to enterprise financial data to obtain a dimension-reduced data set; performing invisible feature analysis to generate an invisible feature set containing periodic fluctuation features and project association features; generating an initial prediction model based on the set training; analyzing the importance of each invisible feature and screening reliable feature subsets; performing cross-scene verification on the initial model through the subset to generate a stability index, and further optimizing the model; and inputting real-time financial data into the optimized model, and outputting a cost prediction result. By adopting the method, the problem of insufficient prediction accuracy and stability caused by ineffective processing of high-dimensional data, excavation of invisible features and verification of model stability in the existing method can be solved, and the accuracy and reliability of financial cost prediction are improved.

This invention provides a method for predicting staple food costs and related hardware, comprising: performing the following steps on any target staple food: acquiring cost data corresponding to multiple cost data items of the target staple food in different statistical periods; for any statistical period other than the baseline statistical period, determining at least one change characterization data corresponding to any cost data item; the change characterization data includes at least one of year-on-year data and month-on-month data; for any statistical period other than the baseline statistical period, determining a comprehensive evaluation index corresponding to the statistical period based on each change characterization data of each cost data item and the influence weight corresponding to each change characterization data; and determining the predicted cost of the target staple food in future statistical periods based on the comprehensive evaluation index corresponding to each statistical period and the cost data corresponding to each cost data item in at least some statistical periods.

The invention relates to a dynamic project cost intelligent prediction method, system and equipment, and the method comprises the steps: anchoring a prediction dimension through a cost motivation map through a closed-loop process of cost motivation quantification, multi-source data structuring, feature engineering and hybrid model dynamic prediction, and carrying out the directional mapping from multi-source data to a cost motivation dimension, performing corresponding feature processing according to feature types on the basis of feature classification defined by the cost motivation quantization atlas to form a structured feature set for model training; project data of different stages are input into a trained mixed cost prediction model, dynamic project cost prediction is achieved, and the mixed cost prediction model comprises an XGBoost model used for extracting static features and an LSTM model based on extracted historical time series data features. Compared with the prior art, the method has the advantages of realizing more accurate and more adaptive project full-stage dynamic cost prediction and the like.

This invention proposes a product cost prediction method, system, and electronic device using a multi-mode interpolation strategy, relating to the field of data processing technology. The method includes acquiring product cost data to be predicted; determining whether missing values ​​exist in the product cost data; if missing values ​​exist, judging the significance of the trend and seasonality of the cost data based on set rules, and dynamically interpolating for each case to obtain the interpolation result corresponding to the missing value; and inserting the interpolation result into the corresponding position in the cost data to be predicted, thereby achieving product cost prediction. This invention performs qualitative analysis of the trend and seasonality of the cost data to be predicted, classifies cases according to the significance of the trend and seasonality, and automatically assigns different interpolation models to form interpolated data. This ensures that the exponential smoothing algorithm remains usable even when the time series is interrupted, and guarantees prediction accuracy.

This invention proposes a cost prediction method for 3D models of steel box girders based on reinforcement learning. It utilizes intelligent algorithms to deeply mine data from the 3D models of steel box girders, improving the accuracy and efficiency of cost prediction. The 3D model data of the steel box girder is preprocessed, converting the raw data into a standardized format and extracting multi-dimensional information such as geometric features, material properties, and structural complexity. A cost prediction model is constructed using a deep reinforcement learning algorithm. By optimizing the reward function and training strategy, the model can gradually approximate the actual manufacturing cost. The generalization ability and prediction accuracy are verified by comparing the model's prediction results with actual project data. Visualization tools are used to overlay the prediction results onto the 3D model, intuitively displaying high-cost areas and providing decision support for engineering design optimization and resource allocation. This method not only improves the accuracy of steel box girder manufacturing cost prediction but also provides a feasible technical solution for cost prediction of other complex structures.

The invention belongs to the technical field of consultation service management, and discloses a project cost consultation service management method and system, a terminal device and a storage medium, and the method guarantees data transparency and non-tampering through a block chain, assists intelligent decision making through AI, and finally achieves project cost consultation service in combination with an automatic process. Through AI intelligent auxiliary decision making and automatic process management, the service efficiency and decision making quality are remarkably improved, personal errors are reduced, factors such as material price fluctuation and construction difficulty are comprehensively considered through a cost prediction model, a high-precision budgeting scheme is generated, a manufacturing cost teacher is helped to quickly complete budgeting, and meanwhile, the cost prediction efficiency is improved. The automatic process module can automatically process tedious tasks such as contract approval, payment and settlement and the like, and the traditional work which needs several days or even several weeks is shortened to be completed within several hours, so that the labor cost is reduced, the project period is greatly shortened, and the overall business efficiency is improved.

This application relates to a method, system, and related equipment for engineering cost prediction based on MLP (Mechanical, Material, and Practical) estimation, belonging to the technical field of engineering cost estimation. The method includes: acquiring construction stage information of the target project, including the scheme study stage, preliminary design stage, and design expansion stage; acquiring construction variables for the corresponding construction stages based on the construction stage information; acquiring a preset MLP cost prediction model, which includes different quantities of engineering costs corresponding to different construction stages; and acquiring cost tables for different construction stages based on the construction variables and the MLP cost prediction model. This application has the effect of better displaying the cost changes that may occur during the project.

The invention is suitable for the technical field of road infrastructure maintenance management, and provides a cost prediction collaborative analysis method and system based on data analysis, and the method comprises the steps: obtaining a real-time collaborative cost element set of a specific road segment shielded by an upper structure, historical road segment data in a historical maintenance period, and corresponding maintenance records, screening historical maintenance samples consistent with the road section data in the preset maintenance period; extracting the historical deterioration degree of the specific road section in the historical maintenance sample, and taking the average value as the average deterioration degree of the specific road section in the preset maintenance period. Refined prediction of the maintenance cost of a specific road section shielded by an upper structure can be realized. The specific road section is divided into the air inlet section, the middle section and the air outlet section, the transverse air speed distribution and the evaporation rate difference are combined, the dry and wet state changes of all the sections are quantified, and the problem that in the prior art, local airflow disturbance and heterogeneous degradation are ignored, and consequently the cost prediction error is large is solved.

This invention is a method for scaling prediction of flow battery stack performance and cost based on single-cell data. First, performance data of laboratory single cells is acquired. Then, an area scaling factor is calculated based on target stack parameters to correct system auxiliary power consumption. Finally, a least-squares optimization inversion method is used to obtain the stack integration efficiency reduction factor. An extreme value statistical model of the full-lifecycle decay trajectory is used to determine the lifetime reduction coefficient, achieving accurate prediction of stack-level performance. Finally, the corrected performance parameters are input into a levelized energy storage cost model to achieve system-level economic prediction. This invention solves the problem of insufficient accuracy in predicting performance and cost from laboratory samples to engineering systems, establishing a complete cross-scale prediction system from materials to stack to system, providing reliable decision support for energy storage system planning.

This invention relates to the field of engineering cost prediction technology. Its purpose is to provide a method for constructing an onshore wind farm cost prediction model based on a Stacking ensemble machine learning model, addressing the problems of low accuracy or insufficient stability in existing wind farm cost prediction technologies. The technical solution can be summarized as follows: acquiring multi-dimensional historical data features of multiple prior construction projects, including at least the total investment per kilowatt; for any prior construction project, using the corresponding total investment per kilowatt as the target variable, and the remaining features forming a feature matrix as feature variables, integrating them into a single data sample to establish an original dataset containing multiple data samples; performing preprocessing; constructing a Stacking ensemble machine learning model, and training and testing it. The beneficial effect is that it improves the accuracy and versatility of the constructed model, making it suitable for onshore wind farm cost prediction.

The invention discloses a concealed conduit salt elimination project multi-objective optimization and cost prediction method and system, and the method comprises the steps: determining a soil hydraulic parameter, a water and salt migration parameter and a first prediction total cost, and carrying out the calculation of the first prediction total cost according to the soil hydraulic parameter, the water and salt migration parameter and the linear prediction cost; constructing a spatial topological feature, a water-salt dynamic feature and an economic cross feature; and inputting the spatial topological characteristics, the water-salt dynamic characteristics and the economic cross characteristics into a prediction model to obtain the underground pipe spacing, the pipeline diameter, the irrigation water amount and the prediction cost. The method solves the problems that an existing empirical model is difficult to accurately describe the nonlinear relation between parameters, actually measured data is scarce, area coverage is insufficient, and the generalization ability of the model is limited, the prediction precision is remarkably improved, multi-target collaborative optimization of the concealed conduit salt elimination project is achieved, and the design iteration period is shortened.

The invention discloses a cost prediction method and system applied to the smelting industry, and relates to the technical field of data analysis, and the method comprises the steps: setting a standard line of each aspect of content according to the characteristics of the various aspects of content; the standard line of each parameter of each aspect of content is comprehensively set by combining historical data, content characteristics of each aspect and smelting process simulation conditions, and a reliable basis is provided for subsequent stable conditions and unstable conditions and prediction. The difference between the standard line and the actual line of each aspect content is compared to distinguish a stable part and an unstable part on the actual line, and a prediction strategy is selected according to the unstable part, so that targeted prediction is performed, and the change condition of each aspect of the smelting process is better predicted. For abnormal conditions, additional cost is analyzed, comprehensive cost is calculated, the accuracy and adaptability of smelting cost prediction are improved, the management condition of the smelting cost is optimized, and efficient operation of smelting process tasks is guaranteed.

The present disclosure relates to a chemical flooding operation cost prediction method, comprising: determining the relevant factors of cost prediction; dividing the chemical flooding operation cost prediction stage according to the chemical flooding development stage; and establishing a staged principal component operation cost prediction model based on the relevant factors. The present method is a multi-factor classification, staged principal component operation cost prediction method based on motivation. Compared with the prior art, the present disclosure establishes a chemical flooding operation cost determination method based on the correlation analysis method between indexes based on the analysis of the factors affecting the operation cost and the mathematical statistics theory, and solves the subjectivity of human analysis of the main influencing factors. Meanwhile, the accuracy and reliability of the prediction results of the method have been verified by the actual oil field test, so that the cost trend prediction of different stages in the whole process of chemical flooding can be grasped.

The invention discloses a sub-item cost prediction method and device based on label driving and a storage medium, and relates to the technical field of project cost prediction, and the method comprises the steps: collecting target project feature information, matching corresponding option labels for the target project feature information according to a preset classification rule, processing the labels through feature coding, and generating labeled features. And then, disassembling the target project into each sub-item project, calling a target model corresponding to each sub-item, and predicting the unit price of each sub-item in combination with the tagging features to obtain a target unit price. And through a deterministic calculation rule, target unit price and project quantity data are coupled, and sub-item project cost summary data are output. And finally, based on error distribution information obtained by model training, adjusting the cost summary data, and calculating to obtain a prediction result of the total cost of the project. The problem that the efficiency of frequently modifying indexes of an early-stage scheme and the cost prediction precision are difficult to balance is solved through feature tagging, item splitting and unit price pre-processing, cost calculation and error tuning feedback.

The invention relates to the technical field of project management, in particular to a BIM-based project cost dynamic adjustment method and system, and the method comprises the following steps: carrying out the linkage budget adjustment based on the number of components and the stage progress change, and carrying out the statistics to generate project cost dynamic data. According to the method, time change analysis of the component number is realized through data mapping of component numbers and time nodes, the reference amount data of various components in the budget structure is combined, the actual influence of the component number change on the budget amount is quantified, a continuous conduction path of budget difference is constructed, and a time sequence response relationship of budget adjustment is defined; dynamic statistics and staged optimization control of project cost are realized by acquiring starting and ending time and change records of a construction stage, analyzing a coverage offset ratio of a plan and actual time, associating budget error response and constructing a linkage relationship between time change and budget adjustment; and the dynamic response capability of project cost management and the accuracy of stage cost prediction are enhanced.

The invention discloses an engineering change management method and device, a storage medium and a program product, and belongs to the technical field of building information models. The objective of the invention is to solve the problem of deviation between theoretical engineering quantity and actual settlement quantity in the prior art. The method comprises the following steps: acquiring a change instruction, and identifying an associated component based on a preset semantic dependency graph; calculating the theoretical engineering quantity of each component; feature vectors containing geometric complexity are extracted from BIM attributes of the components and input into a machine learning model to obtain an engineering quantity deviation coefficient, and the model is obtained through data training of theoretical engineering quantity and actual settlement engineering quantity of historical projects; and multiplying the theoretical engineering quantity by the deviation coefficient to obtain a predicted engineering quantity, and generating an analysis report. Through the machine learning model, the influence of factors such as construction complexity on the project amount is quantified in a data driving mode, the theoretical project amount is corrected into the predicted project amount closer to the reality, and the project amount and cost prediction accuracy in change management is improved.

The invention relates to the technical field of data processing, and discloses a data writing strategy optimization method and device, equipment and a medium, and the method comprises the steps: carrying out the structural processing of operation data, and obtaining a standard data set; performing feature extraction on the standard data set to obtain a cost feature set; respectively training a cost prediction model and a strategy optimization model based on the cost feature set, and analyzing a target writing strategy generated by the strategy optimization model through the cost prediction model to obtain a mapping relationship between the target writing strategy and the estimated cost; and generating a write-in strategy parameter set according to the mapping relationship, pushing the write-in strategy parameter set to a write-in client to execute a data write-in task, and feeding back operation feedback data generated by an execution result to the cost prediction model and the strategy optimization model to perform strategy optimization. The method and the device can be applied to financial science and technology or medical care service program systems, and can realize automatic optimization of the write-in strategy, reduce the cost and improve the query performance and the system stability.

The application relates to the technical field of architectural design, and provides a three-star green building cost optimization method, device, equipment and medium, which can construct a full-life cycle incremental cost prediction model of a green scheme based on the interaction between technical measures, so as to accurately perform building cost prediction; a plurality of to-be-optimized technical measures are selected from each technical measure included in the green scheme through cost sensitivity analysis, so as to lock the technical measures with high conversion rates for optimization; a target function is constructed according to annual total energy consumption, annual carbon emission and the full-life cycle incremental cost prediction model, and a constraint condition is constructed according to a three-star green building scoring rule, so that multiple optimization targets can be considered; a Pareto front solution set of the optimization model is solved based on an orthogonal table, so that the solving efficiency can be improved; and a target scheme is selected from a plurality of candidate schemes according to a value coefficient and a dynamic benefit balance point, so that an optimal three-star green building cost optimization scheme can be reasonably selected.

The application relates to the technical field of data processing, and discloses an engineering cost prediction method and system based on a large model and a storage medium. The method comprises the following steps: standardizing historical cost data to construct a dynamic cost feature knowledge base; inputting the knowledge base into a Transformer pre-training large model to obtain a prediction value; obtaining an adjustment coefficient matrix through project similarity clustering and error learning; matching design parameters with the knowledge base, and calculating a hierarchical cost table by using a heterogeneous graph network; and correcting the cost table based on the adjustment coefficient to obtain a prediction result. The application realizes accurate prediction and dynamic adjustment of engineering cost, solves the problem of insufficient expression of element correlation in traditional cost prediction, can be corrected according to time and project characteristics, and significantly improves prediction accuracy and adaptability.

This application relates to a method, system, equipment, and medium for predicting and simulating hospital operating costs, belonging to the field of intelligent medical management technology. The method includes: receiving and parsing external alarm signals to extract event characteristics; combining current event characteristics with similar historical data to predict the time distribution of patients arriving at the hospital. Based on injury classification and time distribution, each patient is mapped to a standard resource demand package, and then matched to the corresponding grid according to grid function labels to obtain the expected load of each grid in future time periods. Based on load identification of supply and demand grids, preset scheduling strategies are run in parallel, calculating the comprehensive performance index score of each strategy and the overall operation cost of the scheduling process, pushing relevant data to the command center for visualization, and generating an executable instruction set based on the optimal scheduling strategy. This application has the beneficial effect of achieving precise control of hospital operating costs and optimizing the efficiency of medical resource scheduling.

The present application relates to the technical field of resource management, and more particularly to a single asset cost allocation and collection analysis method, device and medium, which generates an initial predicted cost by combining asset state information with a pre-trained cost prediction model, and trains a distance optimization model based on clustering results and asset state information, so that the final target predicted cost can reflect the state characteristics of individual assets and the cost commonality of similar assets, solving the allocation distortion problem caused by ignoring asset heterogeneity in traditional methods, improving the accuracy of cost analysis, grouping assets according to the distance of asset state information through clustering processing, and associating cost information, initial predicted cost and asset state distance through training loss function, so that the cost allocation result and the asset state information difference between assets form a quantitative mapping, realizing dynamic adaptation of cost and asset state information, overcoming the static rule limitations of traditional methods, and improving the flexibility of cost analysis.

The invention discloses a project construction cost data management method, system and device and a medium, and belongs to the technical field of project management, and the method comprises the steps: obtaining project construction data, classifying the project construction data, and obtaining a construction cost data set; calculating a weight coefficient matrix among different project engineering data through the cost data set to form a dynamic cost calculation reference; combining the dynamic cost calculation reference with actual project progress data, and calculating project engineering data by using the weight coefficient matrix to obtain a project cost predicted value and a cost deviation index; according to the cost prediction value and the cost deviation index, the weight coefficient matrix is adjusted at the same time; and storing the updated cost data set, the weight coefficient matrix and the cost management and control suggestions in a database. The method has the beneficial effects that the established dynamic cost calculation reference updating mechanism is combined with the exponential smoothing method and the triggering condition setting, so that the change of cost elements can be responded in real time and the system stability can be kept.

The invention relates to the technical field of cost prediction, in particular to a new energy project decommissioning cost prediction method and system based on a machine model, and the method comprises the steps: obtaining full-life-cycle basic data and multi-scene decommissioning related historical data of a new energy project; based on the full-life-cycle basic data and multi-scene decommissioning related historical data, static features and dynamic features related to decommissioning cost are extracted, and a multi-dimensional feature set is constructed; and dividing the multi-dimensional feature set into a training data set and a verification data set, constructing an initial machine prediction model based on the training data set, and optimizing parameters of the initial machine prediction model through iterative training. According to the method, the training data set and the verification data set are separated, and iterative optimization is carried out, so that the performance of the initial machine prediction model can be continuously improved, the problem of over-fitting or under-fitting in the model can be eliminated, and the model has higher generalization ability.

The invention discloses a power transmission and transformation project cost prediction method and system based on data mining, and relates to the technical field of project cost prediction, and the method comprises the steps: collecting multi-dimensional data covering project attributes, environmental factors and historical cost, and building a project database; performing missing value filling and preprocessing on the data, and calculating a correlation coefficient matrix; determining the number of principal components based on principal component analysis, calculating a principal component load matrix, and extracting a candidate key field set; and carrying out partial correlation analysis, cross validation and regression significance test on the candidate fields and the project cost data, identifying key factors which have significant influence on the cost, constructing a project cost prediction model based on the key factors, and carrying out scientific prediction on the cost of a new project. According to the method, the problems of high dependence and low data utilization rate of traditional experience estimation are solved, systematic identification and cost prediction of key factors of the cost are realized, and support is provided for investment decision and cost control of power grid construction.

The application relates to an effluent BOD concentration prediction method based on a WSFA-AFE ILSTM neural network and relates to the field of artificial intelligence.The application proposes a WSFA-AFE method aiming at the problem that the input characteristic variable and input history step length are difficult to determine when a neural network is used to predict a multivariate time sequence of effluent BOD.The method can adaptively extract dynamic characteristic variables in the multivariate time sequence, so that the neural network can better predict the effluent BOD concentration.The application proposes an ILSTM neural network aiming at the problems that the standard LSTM neural network has a large number of structure parameters and the training process is time-consuming.The application simplifies the recursive term weight in the structure equation, reduces the number of required training parameters in the network, and accelerates the convergence speed through a parameter updating algorithm.The application realizes efficient, accurate and low-cost prediction of effluent BOD concentration at future time according to the data collected in the sewage treatment process.

The application provides a cost prediction method and a terminal device. The method automatically analyzes to-be-predicted cost information by using a large language model, extracts structured data, and reduces manual errors. A plurality of pre-training models are used for parallel prediction, a plurality of sets of cost prediction results are output, and abnormal values are identified and excluded, thereby enhancing prediction robustness. Meanwhile, a classifier model is trained based on historical data, a confidence level is configured for each prediction model, automatic selection of the model is realized, and prediction accuracy is improved. When weighted summation is performed on the cost prediction results, for a cost prediction result with an anomaly, the confidence level corresponding to the prediction model corresponding to the anomaly is deleted, and a reasonable predicted cost price is obtained. Finally, the predicted cost price is compared with a real-time networking price, a predicted cost price carrying suggestion information is output, deviations are corrected in a timely manner, the prediction result is closer to the actual market situation, and thus the problem of low prediction accuracy of a material cost price is solved.

The invention relates to a KNN-BP-based box-type substation equipment cost prediction method and system. The method comprises the steps of obtaining historical cost data of a historical box-type substation and key factor data corresponding to key factors, and performing preprocessing; calculating a feature weight matrix between the preprocessed data; key factor data of to-be-predicted box-type substation equipment are obtained, distances are calculated based on a KNN algorithm and the feature weight matrix, key factor data of historical box-type substations corresponding to the first K distances are screened out after ascending sorting to serve as features, historical cost data serve as prediction labels, and a training sample set is obtained; training the BP neural network into which the penalty term is introduced by using the training sample set; and inputting key factor data of to-be-predicted box-type substation equipment into the trained BP neural network for cost prediction to obtain a cost prediction result.

The application discloses a cost prediction method based on a solid waste heat treatment neural network model and belongs to the technical field of solid waste heat treatment.The application is used for solving the problem of accurate prediction of the cost of solid waste heat treatment.A first hidden layer of a solid waste heat treatment prediction model based on deep learning is constructed; a second hidden layer of the solid waste heat treatment prediction model based on deep learning is constructed; an output layer of the solid waste heat treatment prediction model based on deep learning is constructed; a loss function of the solid waste heat treatment prediction model based on deep learning is designed; the input layer, the first hidden layer, the second hidden layer and the output layer are sequentially connected to obtain the solid waste heat treatment prediction model based on deep learning; based on the prediction result after reverse normalization, the prediction result of the solid waste heat treatment prediction model based on deep learning with actual physical quantity is obtained, and the cost prediction method based on the solid waste heat treatment neural network model is constructed.

The present application belongs to the field of energy storage system frequency control, and discloses a safe reinforcement learning load frequency control method, system, device and medium. The safe reinforcement learning load frequency control method comprises the following steps: constructing a frequency control model; acquiring power system historical data to construct a labeled data set; constructing a cost prediction network according to the labeled data set, taking the state and action of the frequency control model as the input of the cost prediction network, and predicting the control cost at the current time; constructing a deep reinforcement learning controller, and converting the inequality constraint of the objective function in the deep reinforcement learning controller based on the Lagrange multiplier; according to the conversion result, optimizing the actor network of the deep reinforcement learning controller by using the predicted control cost at the current time, applying the optimized actor network to real-time load frequency control, and realizing the control of the load frequency. The present application takes into account the operation cost and frequency modulation performance of the power system, and guarantees the comprehensive benefit of the power system.