86 results about "Cost prediction" patented technology

The invention discloses an AI and BIM-based project cost control method and system, and the method comprises the steps: obtaining the three-dimensional model data of a target building, analyzing the file structure of the three-dimensional model data, recognizing the type, size parameters and material attributes of a component, building a mapping relation between a component coding system and an attribute tag if the three-dimensional model data passes the integrity inspection, and carrying out the construction cost control of the target building. Generating a standardized component data set; constructing a material price fluctuation prediction model according to the historical price record, intercepting time series data by adopting a sliding window mechanism, and if the price fluctuation amplitude in the material price fluctuation prediction model exceeds a preset threshold, triggering an early warning identifier, and generating cost prediction data with a risk level; and carrying out association mapping on the standardized component data set and a quota library by adopting a coding matching mechanism, and if component attributes are successfully matched with quota entries, converting and generating engineering quantity data based on geometric parameters, and calculating the cost of a single project. The cost prediction accuracy is improved.

The invention belongs to the technical field of intelligent supply chain management and optimization, discloses a product supply chain comprehensive optimization system and method based on big data, and aims to solve the problem that a traditional supply chain management system based on historical data and a static model is difficult to quickly adapt to changes. Forming a real-time evaluation vector; integrating the environment data, constructing an environment feature vector, and training to obtain a risk prediction model in combination with a real-time evaluation vector, thereby obtaining a risk probability of each node in the future, and obtaining a risk evaluation vector; based on the risk assessment vector, in combination with the inventory level and the transportation path, performing joint optimization on the inventory configuration and the distribution path to generate an optimization strategy; updating each node of the supply chain according to the optimization strategy, monitoring the state of the updated supply chain in real time, and generating a cost prediction vector when abnormity is monitored; and taking corrective measures on the supply chain according to the cost prediction vector to ensure stable operation of the supply chain.

The invention relates to the technical field of data processing, and discloses a project 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 Transform pre-training large model, and performing fine tuning to obtain a predicted value; obtaining an adjustment coefficient matrix through item similarity clustering and error learning; the design parameters are matched with a knowledge base, and a hierarchical cost table is calculated by using a heterogeneous graph network; and performing time correction on the cost table based on the adjustment coefficient to obtain a prediction result. According to the method, accurate prediction and dynamic adjustment of the project cost are realized, the problem of insufficient element association expression in traditional cost prediction is solved, targeted correction can be carried out according to time and project features, and the prediction accuracy and adaptability are remarkably improved.

The invention discloses a medical health cost prediction system and method based on multi-source data fusion, and relates to the technical field of medical data analysis, the system firstly fuses a plurality of heterogeneous medical information sources to generate structured data; then, multidimensional features related to the cost are extracted to construct a feature representation vector; and the cost prediction modeling module constructs a cost prediction model through graph structure modeling and semantic embedding, carries out joint optimization by combining a graph attention mechanism and semantic similarity, and outputs a prediction result by fusing a time sequence and static characteristics. A joint optimization algorithm of graph semantic comparison loss and prediction deviation loss is introduced into model training, and positive and negative sample pairs are constructed through a cost label distance. And the feedback optimization module triggers model updating when the prediction deviation exceeds a threshold value, and dynamically adjusts a model structure and parameters through a deviation index and a sample confidence factor. And the prediction interpretation module analyzes influence factors based on intermediate layer features or gradient propagation, outputs an interpretation report, and improves the transparency and credibility of the model.

The invention relates to the technical field of industrial equipment predictive maintenance and asset management, in particular to a machine vision equipment operation and maintenance cost analysis intelligent management method, which comprises the following steps of: acquiring equipment operation state, external environment and historical operation and maintenance work order data through a sensor group and an equipment log interface, and fusing and removing redundancy to form a multi-source data stream; static and dynamic features are extracted by using a pre-trained health state evaluation model and fused by means of an attention mechanism, and a real-time health state index in a 0-1 interval is output; constructing a dynamic cost prediction model, taking health related parameters, spare parts, manpower and depreciation cost as input, and predicting expected operation and maintenance cost of a specific time window in the future; establishing an optimization decision model by taking minimization of the total operation and maintenance cost and maximization of the equipment availability rate as double targets, and generating an optimal maintenance, spare part purchasing and scheduling scheme; and executing the scheme and acquiring actual data, comparing the actual data with a predicted value for feedback, and iteratively optimizing the core model. The operation and maintenance management accuracy and economy are improved, and the method is suitable for intelligent operation and maintenance of the machine vision equipment.

The invention relates to the technical field of project cost review auxiliary methods, in particular to a power grid project cost review auxiliary method and system based on multi-modal feature fusion, and the method comprises the steps: obtaining project quantity list information, drawing information and historical cost data of a power grid project construction project; constructing an engineering quantity list knowledge graph based on the engineering quantity list information and the drawing information; constructing a historical project cost data graph based on the historical cost data; according to the project quantity list knowledge graph and the historical project cost data graph, generating a fusion feature vector through a multi-modal feature fusion network; predicting the future project cost based on the fused feature vector; and outputting the future project cost prediction result and the cost review suggestion, and through multi-modal feature fusion, the project quantity list, the drawing information and the historical cost data are comprehensively utilized, so that the data utilization rate and the comprehensiveness of information extraction are greatly improved.

A system uses both a present cost model and a future cost model trained on logged order data to compute a prediction of costs for delivering orders either without further delay, or with delay to allow time to potentially batch orders for delivery with other orders (and thereby reduce delivery cost). A comparison of the outputs of the present and future cost models is used to determine whether to delay assigning the order in expectation of batching order with other orders. Calculations may additionally be performed for the constituent orders of an order batch to apportion the delivery cost saving resulting from batching among the different orders. The system can analyze previously-logged data associated with prior orders to obtain features that characterize the prior orders. Using these features, and the known actual delivery costs from the prior completed deliveries, the system can train the present and future cost models.

The invention relates to the technical field of cost prediction, in particular to an operation cost prediction method and system based on artificial intelligence, and the method comprises the following steps: collecting and sorting human resource cost, material cost and energy use cost, creating an independent data set for each cost category, representing nodes through different cost categories, and predicting the cost of each cost category; edges are represented by interaction among cost categories, node weights are determined through cost influence analysis, and a cost factor relation graph is constructed. According to the method, the adaptability and accuracy of cost prediction are effectively improved by constructing the cost factor relation graph and the dependency relation graph and introducing dynamic data processing and time sequence analysis, node data are allowed to be updated in real time to reflect changes of market and operation conditions, and the accuracy of cost prediction is improved through quantitative cost deviation analysis. The difference between prediction and reality can be accurately determined, cost control is finer through screening and trend analysis of key nodes, and resource allocation optimization and budget making in a complex market environment are helped.

The invention provides a project cost intelligent management and control method and system based on dynamic multi-dimensional modeling in the technical field of project management and intelligent decision technology crossing. The method comprises the following steps: S1, creating a project cost prediction model; s2, acquiring a large amount of historical project data to construct a data set; s3, dividing the data set into a training set, a verification set and a test set, training the project cost prediction model through the training set, and verifying and testing the trained project cost prediction model through the verification set and the test set; s4, acquiring real-time project data, preprocessing the real-time project data through a streaming computing engine, and inputting the preprocessed real-time project data into the project cost prediction model to obtain a project cost prediction report; and S5, executing project cost management and control based on the project cost prediction report. The method has the advantages that the reliability, timeliness, accuracy and flexibility of project cost management and control are greatly improved.

The invention discloses a project cost data visualization monitoring method and system, and the method comprises the steps: building a causal conduction path of a project digital twinborn dynamic simulation risk event, quantifying the increment influence on a cost chain, and combining the prediction capability of a machine learning model for the overall cost trend, thereby achieving the visual monitoring of the project cost data. And carrying out fusion calculation on the local cost increment caused by the sudden risk and the global predicted value to generate a final total completion cost predicted value which reflects the macroscopic trend and contains the impact of the sudden event, and finally dynamically rendering a fusion prediction result in a three-dimensional model space through a unified visualization engine. Thus, the defect that static BIM association lacks risk conduction analysis is overcome, the problem that a traditional prediction model and construction logic are unhooked is solved, a decision view with the overall trend control and emergency traceability is provided for engineering managers, engineering cost management is fundamentally promoted to be converted from post-event accounting to pre-control, and the engineering cost management efficiency is improved. And the cost anti-risk capability of the complex engineering project is obviously improved.

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 discloses a project cost prediction method fusing MDS-LOF and GBRT. The method comprises the steps of S1, selection and primary processing of project cost data; s2, performing project feature analysis and reserving core data; s3, performing dimension reduction processing by using a multi-dimensional scaling analysis (MDS) method; s4, using a local outlier factor (LOF) algorithm to identify abnormal values and removing the abnormal values; s5, training is carried out by using the GBRT prediction model and the processed data, and a trained model is obtained; and S6, outputting and verifying the trained prediction model. According to the invention, by constructing the adaptive prediction model, the influence caused by sudden policy regulation and control is weakened, so that the project cost prediction result is more in line with the market law of the building construction cost, and scientific basis and reference value are provided for project early-stage decision making and cost prediction of investment subjects such as enterprises and governments in a complex policy environment period.

The invention discloses an automatic project cost review method based on big data, a medium and equipment, and the method comprises the steps: generating a dynamically adjusted data collection strategy through project information, and enabling the data collection strategy to trigger an updating mechanism according to a preset condition; collecting heterogeneous cost data based on a data collection strategy and performing normalization processing to form a first data set; the first data set is input into a cost prediction model, a risk prediction model and an expected income model, the cost prediction model is constructed through fusion of ARIMA and LightGBM, and the risk prediction model adopts an XGBoost model with auditing entries as adjustment parameters; and finally generating a review report by integrating the output cost prediction coefficient, the risk level, the avoidance suggestion and the expected income range. According to the method, dynamic optimization of data acquisition and collaborative decision of a multi-dimensional analysis model are realized, and the automation level and the result reliability of project cost review are improved.

The invention provides a traffic hydrogen cost calculation and prediction method and system, and the method comprises the steps: obtaining original cost data of a plurality of links in a hydrogen energy industry chain through a manual input interface and an API interface, carrying out the normalization processing, and generating standard cost data; and processing the standard cost data of the link through a cost calculation prediction model corresponding to the link and matched with the station end type, outputting cost prediction results of multiple links of the target year, calculating a comprehensive cost prediction result of the target year, generating and visually displaying a first traffic hydrogen cost prediction report, in the calculation and prediction process, four links of hydrogen production, transportation, hydrogen storage and terminal application covered by a hydrogen industry chain are comprehensively considered, and different calculation and prediction methods are adopted in the difference of the transportation, hydrogen storage and terminal application links in the two modes of hydrogenation and hydrogen exchange, so that a user can conveniently carry out calculation and prediction according to the actual situation of himself / herself. And the traffic hydrogen cost under different situations can be comprehensively and accurately calculated and predicted.

The invention relates to the technical field of project cost, and discloses a method, a system, equipment and a medium for precisely checking a cost range based on geographic region characteristics, and the method comprises the steps: collecting project engineering data, and carrying out the data preprocessing; performing feature extraction based on the project engineering data to obtain project geographic features and engineering cost features, and performing feature analysis; constructing a project construction cost model, and calculating to obtain a project construction cost prediction range; and comparing the construction cost with a preset construction cost range to check the construction cost, judging whether the construction cost exceeds the preset construction cost range or not, and adjusting and optimizing the project construction cost if the construction cost exceeds the preset construction cost range. According to the method, the cost range of various engineering projects in different geographic areas can be predicted, the prediction precision is improved, through comparison with actual cost data, the prediction accuracy is evaluated by adopting a statistical method, the high accuracy of the final cost range is ensured, and a visual and comprehensive decision basis can be provided for a decision maker according to a cost check result.

The invention belongs to the technical field of constructional engineering, and particularly relates to a self-adaptive constructional engineering cost control method and system. A load sensor installed on a tower crane is used for monitoring the weight of a hoisted material, the number of hoisting times is combined, the real-time using amount of the material is calculated through a formula, and a big data analysis technology and a machine learning algorithm are used for constructing a cost prediction model. Factors such as manual efficiency change coefficients at different construction stages, shutdown time length caused by abnormal weather and additional cost coefficients caused by the shutdown time length are considered, and the cost change trend can be accurately reflected; the problems that a traditional construction engineering cost control method cannot adapt to various uncertain factors in engineering, so that cost prediction is not accurate, and coping strategies are lacked are solved, cost hyper-branched and resource waste are prevented, and the problems of accurate control and efficient management of cost are solved.

The invention relates to a project cost dynamic monitoring method and device based on big data, equipment and a medium. The method comprises the steps of obtaining multi-source data of engineering cost, and performing standardization processing on the multi-source data to obtain a standardized engineering matrix; preprocessing the standardized engineering matrix, the external dynamic factors and the ERP contract terms to obtain historical information vectors; inputting the historical information vector into an LSTM neural network to obtain short-term cost prediction; inputting the historical information vector into an XGBoost model to obtain long-term cost prediction; and integrating the short-term cost prediction and the long-term cost prediction to generate a cost prediction result. By adopting the method, the engineering cost can be dynamically monitored, and the engineering cost can be effectively controlled.

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 the technical field of oil and gas field production cost prediction methods, in particular to an oil and gas operation cost prediction method and device.The oil and gas operation cost prediction method comprises the steps that a block similar to a block to be predicted is searched in historical data, and the single well cost and the single well liquid amount of the similar block in the Nth year and the (N-1) th year are obtained; based on the single well cost of the Nth year and the N-1 year, the single well fixed cost and the ton liquid variable cost are calculated; obtaining the well opening number and the liquid production capacity in the to-be-predicted block; and calculating the operation cost of the to-be-predicted block based on the single well fixed cost, the well opening number, the ton liquid variable cost, the liquid production capacity and the oil production capacity. The single well fixed cost and the ton liquid variable cost of the to-be-predicted block are rapidly and accurately calculated, the problem that classification of the fixed cost and the variable cost of a related factor method is inaccurate is solved, the problem that an overall assignment method is influenced by human factors is solved, and the problem that classification of the fixed cost and the variable cost is difficult is solved; the purpose of preventing prediction from being influenced by human factors is achieved.

The embodiment of the invention provides a cost prediction method and device, and the method comprises the steps: inputting a target image carrying a target process into an image recognition model, obtaining target process data outputted by the image recognition model, and enabling the target process data to comprise a process color register number and a process size; determining a first prediction cost from cost storage data according to the target process data, and determining a first cost prediction curve according to the first prediction cost and a first piece number range; performing combined calculation on the target process data and production parameter data corresponding to the target process to obtain a second cost prediction curve, the production parameter data including the second piece number range; determining a target cost prediction curve according to the first cost prediction curve and the second cost prediction curve, and determining a target cost corresponding to a target number from the target cost prediction curve; and based on a unified standard prediction mode, a prediction result is stably and efficiently output.

The invention relates to the technical field of project cost management, in particular to a construction engineering cost management system which comprises a stage boundary module, a stage characteristic fitting module, a global parameter optimization module, a double-layer dynamic inference module and a real-time adaptation adjustment module. According to the method, the accuracy of stage division is improved by extracting the single-stage construction period consumption and the stage cost ratio and combining calculation of the slope and the inflection point and accurately recognizing the stage demarcation point, the definition of segmented data is enhanced through cost change rate difference calculation and stage boundary point marking, and the accuracy of stage division is improved. On the basis of calculation of the ratio of resource investment to stage cost increment, the fitness of a cost trend model is optimized, smoothness and accuracy of the overall cost trend are achieved through global error extraction and demarcation point adjustment, dynamic prediction is combined with segmented cost volatility and newly-added data, the accuracy and timeliness of the cost prediction range are optimized, and the cost prediction efficiency is improved. And calculating the construction period consumption and the cost increment deviation value in real time, and dynamically adjusting the trend parameters.

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.

The invention relates to the technical field of building construction management, and discloses a building outer wall construction cost prediction and control system based on big data analysis, and the system comprises a data collection module, an analysis engine, a decision support module and a visualization module. The core method comprises the following steps: calculating a construction behavior entropy based on high-frequency dynamic data collected from a construction site through an analysis engine so as to quantify the disorder degree of an operation process; a cost potential energy model is established in combination with the construction behavior entropy and the external environment constraint and is used for quantifying the risk of actual cost hyper-branched in the future; and constructing a conduction network based on a working decomposition structure (WBS) to predict an evolution path of the risk, and diagnosing a systematic source by identifying resonance between behavior entropies of different dimensions. According to the method, dynamic quantification, predictive identification and root diagnosis of cost risks can be realized, passive control is changed into active intervention, and the perspectiveness and the refinement level of project cost management are remarkably improved.

The invention provides a fabricated building cost prediction method based on an XGBoost algorithm. The fabricated building cost prediction method comprises the following steps: selecting a plurality of key feature indexes influencing the fabricated building cost by using a Pareto technology; collecting index data corresponding to each key feature index of the plurality of prefabricated construction projects, and preprocessing each index data; and for each fabricated building project, fitting the preprocessed index data of the fabricated building project by using an XGBoost algorithm to obtain the predicted building cost of the fabricated building project, and optimizing the parameters of the XGBoost algorithm based on the real building cost of the fabricated building project. A fabricated building cost prediction model is obtained; and obtaining index data corresponding to each key feature index of a to-be-predicted fabricated building project, and performing building cost prediction by using the fabricated building cost prediction model. According to the method, the precision and efficiency of prefabricated building cost prediction are improved.

The invention relates to the technical field of data analysis, and discloses a smelting cost prediction method and system based on data analysis, and the method comprises the following steps: collecting multi-source data of the same metal material and the metal smelted by the same production line in the smelting process, the multi-source data comprising material data, energy consumption data, production operation data and actual marginal cost increase rate; preprocessing the multi-source data, wherein the preprocessing comprises data cleaning, missing value filling, abnormal value detection and data normalization; static features and dynamic features related to the smelting cost are extracted based on the preprocessed data. According to the invention, by fusing the multi-source data and respectively extracting the static and dynamic characteristics by using the convolutional neural network and the recurrent neural network, the stable and changing cost influence factors in the smelting process can be comprehensively captured, so that the comprehensiveness and accuracy of cost prediction are remarkably improved; and the defect of insufficient consideration of time sequence dynamic factors in a traditional method is overcome.

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.

The present application relates to a kind of hierarchical cost intelligence accounting method, belong to cost intelligence accounting field.The method includes five main steps.Cost data is obtained, cost data is preprocessed, and cost data is divided into training set and test set;Weighted time series additive decomposition model is built, and cost data in training set is extracted by data after model, and trend item, seasonal item and residual error item are obtained;Trend item, seasonal item and residual error item are processed, and new training sample set is obtained;Cost prediction model is built, and the cost prediction model includes CNN residual module and bidirectional LSTM module, and new training sample set is input into cost prediction model to obtain predicted cost total amount;Error evaluation is carried out using cost total amount in test set and the predicted cost total amount, to evaluate the prediction performance of model.The present application improves the accuracy of cost intelligence accounting and the ability of capturing complex cost change characteristics.

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.