189 results about "Cost Controls" patented technology

The invention discloses a multi-dimensional intelligent management method and system for whole-process cost, and the method comprises the steps: generating a time-space associated structured cost data cube according to heterogeneous cost data of the stages of project planning, design, construction and completion; outputting a dynamic cost prediction curve and deviation sensitive nodes based on the structured cost data cube; according to the dynamic cost prediction curve, performing multi-party task allocation optimization by using a block chain enabled BIM / CIM collaboration platform, and generating a collaboration instruction set of smart contract coding; outputting a risk probability matrix and an advanced early warning signal based on the collaborative instruction set and the real-time engineering data flow; and according to the risk probability matrix, adopting a multi-objective optimization algorithm to generate an anti-interference decision scheme set, and outputting an optimal cost control strategy after digital twinborn simulation verification. By using the embodiment of the invention, the cost data of each stage of the project can be efficiently integrated, dynamic cost prediction is realized, and collaborative decision and effective risk management are optimized.

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 discloses a corrugated carton production management system and method, and relates to the technical field of corrugated carton production, the system comprises a production scheduling module, a material tracing module, an equipment cooperation module, a multi-mode detection module and a cost control module, and solves the problem that supply and demand relationships, production efficiency and quality management and control are not coordinated; according to the technical key points, an order ID is coded into an integer chromosome, sequential crossover and reverse mutation operation are applied, a fuzzy comprehensive evaluation model is constructed by combining Hash chain table and local hill climbing method optimization, and factors are quantified through a trapezoidal membership function, so that the order delivery punctuality rate and the equipment utilization rate are improved, and the mold replacement cost is reduced; the real-time production cost is collected and calculated through Kafka and Flink, an operation cost method model is constructed to maintain a standard cost database, abnormity is detected through a CEP engine, the cost hyper-branched risk is predicted through an LSTM-Transform hybrid neural network, a thermodynamic diagram is generated through a cache result to assist decision making, the cost accounting delay time is shortened, and the hyper-branched processing efficiency is improved.

The invention discloses a sheet metal flexible production line real-time scheduling method and system based on digital twinning, and relates to the technical field of production scheduling and optimizing.The method comprises the steps that according to production data, the deviation between a predicted value and an actual value of a digital twinning body is calculated; based on the deviation, dynamically correcting the mapping relation between the equipment processing efficiency and the output quality in the digital twin; scheduling deduction is carried out based on the corrected digital twinborn body, a scheduling scheme is generated, and the scheduling scheme fuses the corrected equipment processing efficiency and output quality constraint. According to the method, scheduling deduction is carried out by adopting a multi-objective optimization algorithm taking the punctual delivery rate, the equipment comprehensive efficiency, the energy consumption, the quality risk and the raw material overconsumption as optimization objectives based on the corrected digital twinborn body, so that the generated scheduling scheme can meet the multi-dimensional requirements of production efficiency, resource utilization, cost control, quality guarantee and the like; and the overall production benefit of the metal plate flexible production line is improved.

The invention relates to the technical field of engineering cost control, and discloses an engineering cost control method and system based on big data. The method comprises the steps of collecting engineering project full-cycle cost data streams, and generating a standardized cost data set through data cleaning; constructing a dynamic cost feature library, and extracting multi-dimensional features such as time sequence fluctuation, resource allocation discretization and supplier association; inputting the feature library into a cost anomaly detection model constructed by a pre-training deep learning network, outputting a cost deviation index, and triggering a correction instruction if the cost deviation index exceeds a threshold value; matching historical cases to generate an optimization strategy set containing material replacement, construction period adjustment and supplier replacement; and virtual deduction is carried out on the optimization strategy through an engineering digital twin system, and the strategy with the predicted cost curve closest to the target value is screened as a final execution scheme. The method depends on big data and a deep learning technology, full-cycle dynamic management and control of the engineering cost are achieved, and the precision and feasibility of cost control are improved.

The invention relates to the technical field of cost control, in particular to a project cost progress management control method and system, and the method comprises the following steps: collecting attendance equipment material data, combining and calculating resource expenditure density, deducing cost pressure in combination with processes and positions to generate a pressure value sequence, and superposing payment records to calculate daily payment intensity; inputting an abnormal factor algorithm to detect deviation to generate a response fault, analyzing process change to screen a risk section to generate a structural fault, extracting a progress and payment intensity ratio to screen an asynchronous period, constructing a control time axis to analyze a relationship between expenditure density and payment intensity slope, and generating a progress management control result set. Attendance checking, equipment operation and material consumption data are collected to derive cost pressure, abnormal deviation is recognized, risks are early warned, the process and resource change trend is analyzed, the relationship between progress and payment intensity is coordinated, the precision, real-time performance and response efficiency of cost control are improved, and it is guaranteed that a project investment target is smoothly achieved.

The invention discloses a power plant fire coal cost management and control and dispatching system which comprises a data acquisition module, a management and control dispatching module and a data display window. The data acquisition module is used for acquiring power plant real-time load data, power plant fire coal consumption speed, historical fire coal price, inventory period, international energy price index and policy document; and the management and control dispatching module is used for inputting related data integration through a mathematical model, analyzing the market trend and price fluctuation, and predicting and controlling the fire coal cost. Limitations of a traditional fire coal management system can be broken through, complex decision-making elements are quantified through mathematical modeling, dynamic balance of cost and risks is achieved, the response speed and accuracy of a fire coal cost prediction model to market changes can be effectively improved through the method of combining a dynamic threshold value and online learning, and the market development prospect is wide. The enterprise can realize dynamic optimization and risk controllability of cost prediction in uncertainty of coal price fluctuation, transportation delay and the like.

The invention discloses a cold rolling process-oriented steel coil cost fine accounting method, which comprises the following steps of: acquiring steel coil production related data in a cold rolling process, and performing data processing on the steel coil production related data; and calculating the steel coil production related data according to set cost elements and cost element calculation rules to obtain steel coil cost and a cost accounting report. According to the method, comprehensive and fine accounting of the steel coil cost is realized, and a basis is provided for cost control of steel coil manufacturing.

The invention discloses an intelligent bidding method and device based on an Apple advertisement marketing interface, and relates to the technical field of advertisement putting, and the method comprises the steps: constructing a double-branch machine learning model comprising a long and short-term memory time sequence branch and a gradient boosting tree feature branch; training the model by using historical delivery data to obtain a target prediction model; collecting real-time delivery data through an Apple advertisement marketing interface, and generating a plurality of candidate bidding strategies according to a preset business rule; inputting the real-time data and the candidate strategy into a target prediction model to obtain a conversion probability prediction value and a cost distribution prediction value of the candidate bidding strategy; based on the predicted value, calculating a strategy score value through a score function, and screening out a high-score strategy combination; and integrating keywords, audiences, time periods and region adjustment parameters of the strategy combination, and generating an optimal bidding strategy package. According to the application, the Apple Ads advertisement bidding strategy can be efficiently and automatically optimized so as to improve the putting efficiency and the cost control.

The invention relates to the field of engineering project management, and discloses an engineering project full-process management system construction method, which comprises the following steps that project optimization objectives and constraint conditions are defined, the optimization objectives comprise project progress, cost and resource utilization rate, and the constraint conditions comprise task dependence, resource availability, construction period and budget limitation; based on the targets and the constraint conditions, a multi-target optimization model is constructed, and the multi-target optimization model adopts a weighted sum method to merge a plurality of target functions; and solving the optimization model by using a Lagrange multiplier method to obtain an optimal resource allocation and task scheduling scheme. By combining the dynamic control theory, the Internet of Things technology and the fluid mechanics model, real-time dynamic adjustment of project resource scheduling is achieved, resource allocation is optimized, project progress and cost control efficiency is improved, and it is ensured that the project efficiently and flexibly responds to changes in the execution process.

The invention is applied to the technical field of engineering cost, and discloses a BIM parameterized model-based cost fine control and pre-settlement auditing system, which comprises a data acquisition module, a calculation module, a calculation module and a calculation module, the data comprises building component geometric parameters, building material market real-time price data, cost quota standard data and historical project cost auditing data, the building component geometric parameters cover the size, the material and the number, and the building material market real-time price data comprises supplier quotation, freight and tax; the cost quota standard data comprises national and local quota sub-items and rate standards. According to the construction cost fine control and pre-settlement auditing system based on the BIM parameterized model, the data acquisition module is automatically connected with a multi-source database and performs standardized processing on unstructured data, and the data verification unit is combined to perform logic verification on data such as geometric parameters of building components, building material prices and the like, so that complexity and errors of traditional manual calculation are avoided.

The invention discloses an intelligent engineering cost investment control system and method fusing multi-source data, and relates to the technical field of engineering cost management. The system comprises a data acquisition module, a data preprocessing module, a multi-source data fusion module, an intelligent decision module and a cost control execution module. The data acquisition module is used for extracting geometric, material, quantity, construction process and time parameters of the components from the BIM model; the data preprocessing module eliminates noise and unifies formats; the multi-source data fusion module constructs a fusion feature matrix; the intelligent decision-making module performs deep feature mining through an improved convolutional neural network and outputs an accurate cost prediction result; and the control execution module executes multi-stage regulation and control according to the deviation between the predicted cost and the target cost. Accurate prediction and dynamic control of the project cost are realized, and scientificity and effectiveness of project cost management are improved.

The invention discloses an engineering cost control method and device based on deep learning. The method comprises the steps of obtaining historical cost data, current construction progress data and engineering budget data of an engineering project; based on the historical cost data, the current construction progress data and the project budget data, constructing a multi-dimensional state representation containing an accumulated cost deviation, a progress deviation and a budget constraint, and obtaining a current project cost control state vector; inputting the current engineering cost control state vector into a trained deep learning network, and outputting engineering regulation and control parameters; according to the engineering regulation and control parameters, the resource input amount and the construction rhythm of the engineering project are jointly adjusted, and a construction adjustment plan is generated; and determining a current cost control execution scheme of the engineering project based on the construction adjustment plan. According to the invention, the accuracy of engineering cost regulation and control can be effectively improved.

The invention provides a BIM model-based engineering cost evaluation method and system, and the method achieves the engineering cost evaluation through dividing BIM model data into a plurality of construction regions, and comprises the steps: firstly, dividing a BIM model into a plurality of construction region data; determining material, labor and equipment demand data of each area; obtaining current market price data; respectively calculating material cost, labor cost and equipment cost of each area; combining the three types of costs to generate a regional estimated comprehensive cost; and finally, integrating the costs of all regions to form project initial cost data. According to the method, regional cost calculation is realized, the accuracy of engineering cost estimation is improved, the cost is more transparent, and cost control and optimization of each region are facilitated. Meanwhile, the method supports the real-time supervision of the construction cost of each region in the construction process, and provides a more scientific and efficient decision basis for project management.

The invention relates to a digital and intelligent supply chain collaborative management system and method, and belongs to the technical field of supply chain management. The digital and intelligent supply chain collaborative management system comprises a basic management module, a supply chain collaborative module, a data analysis module and an integration module; a multi-level role system is constructed, and authority management is performed on the supply chain collaboration module, the data analysis module and the integration module after authority configuration is performed on multi-level roles; according to the production plan, supplier selection, order management and cost control are optimized through a digital means, a deep learning model is adopted to predict the obtained data of the supply chain cooperation module, and a prediction result is fed back to the supply chain cooperation module to adjust the production plan. The data of the supply chain collaboration module is displayed through a visual interface; data channels of ERP, WMS, TMS and MES are established to support intelligent decision and resource scheduling of a supply chain cooperation module, and the overall operation efficiency of the supply chain is improved.

The invention provides a construction method of a 5D building model, and relates to the technical field of construction progress management. The method comprises the following steps: constructing a 4D building model of a building project; for the sub-model of any component, binding the sub-model with cost data; wherein the cost data comprises the project amount and the unit price, corresponding to each task node in the construction project, of the component; driving each sub-model in each 4D building model based on the planned arrangement of the building project, and distributing the cost data to a task node in a time axis corresponding to each sub-model to obtain a 5D building model; wherein the planned arrangement represents an execution sequence of the construction project. According to the method, the building model can be jointly driven by time and cost, and the construction progress of the building project is dynamically reflected in the time dimension and the cost dimension, so that the accuracy of whole-process cost control of the building project is improved.

The invention discloses a dynamic environment multi-target adaptive decision-making system and method based on Monte Carlo tree search, and relates to the technical field of agent decision-making and control, and the system comprises the steps: collecting a community livelihood material scheduling state through an information collection module, and updating a task library according to the community livelihood material scheduling state; through an MCTS core module, based on a current community livelihood material scheduling state and a current task library, a multi-target comprehensive reward model and a Monte Carlo tree search method are utilized to generate an optimal decision scheme; and displaying a decision path searched by the Monte Carlo tree corresponding to the optimal decision scheme through a decision process transparency module, and marking the access times of each node on the decision path and an accumulated multi-target comprehensive reward value. According to the invention, real-time response of dynamic scheduling of community livelihood materials can be realized, and cost control, efficiency improvement and resident satisfaction can be considered at the same time.

The invention discloses an electric power remote cost control method, system and device based on edge calculation, particularly relates to the technical field of power grid edge calculation control, and is used for solving the problem of instruction failure or contradiction caused by concurrent triggering of heterogeneous rules of existing edge nodes. The method is realized by constructing a dynamic conflict arbitration mechanism, and specifically comprises the following steps: collecting power grid node load data and user time sequence load data in real time through an edge node; if a closed-loop conflict is formed based on a rule triggering causal chain and a user load synchronous action proportion exceeds a set threshold value, judging a business rule conflict and generating a conflict rule set; extracting user power consumption behavior characteristics and power grid load sensitive characteristics according to the conflict rule set; evaluating a linkage rule activation probability triggered after execution of any rule based on the feature data; the actual execution weight of each rule is dynamically generated in combination with the activation probability component proportion; and a control instruction is generated and executed according to the highest weight rule, so that the accuracy of execution of instructions such as arrearage tripping and demand response power protection is improved.

This invention discloses a cost control method for comparing and selecting mechanized construction route schemes for overhead power lines. The method involves planning the route based on actual project conditions, determining the initial route length L1, the terrain proportions of the areas traversed by the route, the overall short-distance transport distance, the construction scale, the selection of conductors and ground wires, and the selection of tower modules. It also calculates the cost per kilometer of the project structure under different terrain conditions. The route is then adjusted to be closer to existing rural roads, village roads, and farm roads, reducing the overall short-distance transport distance. The cost per kilometer of the project structure under different terrain conditions is calculated accordingly. Based on the difference in cost per kilometer before and after adjustment, combined with the project route length and the proportions of different terrains, the increased length ΔL of the economical route is calculated. The relationship between the difference between the adjusted route length L2 and the original route length and ΔL is determined. If L2 - L1 ≤ ΔL, the adjusted route is more economical. This method effectively plans more economical and reasonable route schemes that are convenient for mechanized construction.

This invention discloses a collaborative management method and system for the entire warehousing and logistics process, belonging to the field of warehousing and logistics information technology. Addressing the technical shortcomings of existing warehousing and logistics management, such as low inventory accuracy, poor scheduling efficiency, lagging information collaboration, and insufficient cost control, this invention integrates the entire process of inbound, storage, picking, outbound, transportation, and traceability, constructing a closed-loop management system of "data perception - algorithm decision-making - process collaboration - dynamic optimization." Through multi-dimensional data collection, optimized algorithm scheduling, cross-system data collaboration, and full-process traceability technology, this invention achieves precise inventory control, optimized logistics route planning, and efficient resource allocation, significantly improving warehousing and logistics operational efficiency, reducing overall costs, and enhancing management transparency. It is applicable to the digital management needs of warehousing and logistics in various industries, including manufacturing, retail, and e-commerce.

The present invention provides a method and system for dynamically annotating engineering cost data based on BIM technology. By combining a standardized deep learning network and a customized deep learning network, it efficiently extracts cost component elements from target BIM engineering cost dynamic data and generates a confidence sequence associated with a preset cost mapping knowledge set, thereby accurately identifying the target cost component elements. Further, the standardized deep learning network and the annotation network are used to carefully annotate the target cost component elements, generating a confidence sequence associated with another preset cost mapping knowledge set to accurately obtain the target cost description label of the target cost component elements. This method not only significantly improves the accuracy and efficiency of dynamically annotating engineering cost data, but also realizes in-depth mining and refined management of BIM engineering cost data, contributing to accurate estimation of engineering costs, cost control, and project management.

The invention relates to the technical field of automobile dies, solves the problem that the manufacturing difficulty is influenced by mostly neglected physical characteristics such as die structural strength and process complexity in the prior art, and particularly relates to a die C value calculation method based on multi-dimensional data. According to the method, a calculation strategy set comprising a general strategy, a high-order strategy and a cost control strategy is constructed, a current calculation strategy is determined, basic data of a target mold is obtained and preprocessed, a standard feature vector is constructed, and corresponding operation is executed in a standard database based on the preprocessed basic data so as to obtain a corresponding basic correction coefficient. According to the method, nonlinear fitting of the influence of the physical specification and the structural difficulty of the mold on the manufacturing cost is realized, the accurate correction coefficient can be automatically matched according to the specific attribute of the mold, and the subjective randomness of artificial experience estimation is eliminated, so that the final C value can reflect the actual consumption of the automobile mold in the manufacturing process more truly and objectively.

The invention discloses a power transmission and transformation project data index verification deepening method and system, and relates to the field of project cost management and cost control. The method comprises the following steps: determining extraction rules of technical and economic indexes and index data of different transmission and transformation project types; establishing a database, and dividing the database into a plurality of tables according to power transmission and transformation project types; according to an extraction rule, extracting and averaging historical engineering data of each technical-economic index, collecting engineering information at the same time, and storing the engineering information in a table of a power transmission and transformation engineering type to which the engineering information belongs, so as to obtain a historical library; determining examination nodes and to-be-examined project information of the power transmission and transformation project; matching the to-be-reviewed project information with project information in a historical library, and comparing index data; and when the deviation of the technical and economic indexes meets the dynamic early warning rule, early warning is triggered. According to the invention, engineering index verification deepening can be carried out based on the historical data of the power transmission and transformation project, and the accuracy and normalization of project review are ensured.

The application provides a reinforcement learning-based engineering cost project cost control strategy optimization method and system, and relates to the technical field of engineering cost, which comprises the following steps: in step 2, according to an initial cost control strategy, resource allocation and cost control are performed at a current construction stage to obtain actual cost, progress and quality data; in step 3, the actual cost, progress and quality data are integrated to construct a multidimensional data distribution model, form an abstract decision space, fit discrete data into a continuous decision surface, calculate the curvature of a local area, and analyze the structural change characteristics of the decision surface; based on the structural change characteristics, the abstract decision space is divided into regions, the original data is mapped to the corresponding partitions, the intrinsic characteristics of each partition are extracted, and strategy correction parameters are generated. The application realizes dynamic self-adaptive optimization of the cost control strategy of the engineering cost project, and improves the accuracy of the cost control of the engineering project.

The application provides a kind of invariant set control and guaranteed cost control method and system of multiple equilibrium point switching generalized system, belongs to the field of hybrid system control. In order to solve the problem that traditional switching generalized system is difficult to directly apply to different equilibrium points of actual engineering application, and the traditional guaranteed cost control method is invalid due to the switching characteristics of multiple equilibrium points. The application considers the more general case of multiple equilibrium points, and studies the invariant set control strategy method of multiple equilibrium point switching generalized system, so as to realize more accurate control of the behavior of complex switching system. On the other hand, for the guaranteed cost control problem in practical application, the application designs a multiple equilibrium point switching generalized guaranteed cost controller to ensure that the cost of the system can have an upper bound. This innovation provides strong technical support for the practical application of switching generalized system theory.

The invention relates to the technical field of blast furnace ironmaking, and particularly discloses an energy efficiency improvement and cost control method for a hydrogen-rich carbon circulating oxygen blast furnace system. Aiming at the problems of high decarburization energy consumption, insufficient equipment operation efficiency and high maintenance cost of the existing HyCROF system, five core measures of low-grade waste heat gradient utilization, regeneration gas cooling system transformation, decarburization compressor efficiency optimization, amine liquid system intelligent management and control and cost closed-loop early warning are cooperatively implemented; the system energy consumption is obviously reduced, and the operation cost is accurately controlled. According to the method, the cost of molten iron can be reduced by 40-50 yuan / ton, the cost difference between the method and a traditional blast furnace is greatly reduced, meanwhile, the system operation stability and the resource recycling rate are improved, key support is provided for commercialized popularization of the HyCROF technology, and the method is suitable for hydrogen-rich carbon circulating oxygen blast furnace systems of various scales.

The present invention belongs to the technical field of supply chain management and discloses an intelligent procurement system and method for a supply chain; the system comprises: collecting demand forecast data and supplier performance data; conducting in-depth mining of demand forecast data to predict future procurement demand; generating capability portraits based on supplier performance data, and conducting quantitative evaluation of the performance scores of different suppliers according to a pre-built evaluation system; obtaining historical order data, and combining the capability portraits to construct a supplier relationship network topology and identify synergy effects between suppliers; integrating performance scores, future procurement demand and synergy effects to recommend an optimal supplier combination; obtaining procurement management data, and combining the supplier performance data of the optimal supplier combination to intelligently formulate procurement strategies; the present invention can not only improve procurement efficiency, but also enhance the flexibility and resilience of the supply chain, thereby effectively improving the overall response speed, cost control capabilities and operational quality of the supply chain.

The application discloses a building engineering cost prediction method based on deep learning and belongs to the technical field of building engineering cost prediction. The method comprises the following steps: constructing a cost time sequence knowledge graph of a target building engineering, wherein the graph comprises historical cost data of multiple engineering nodes, material price fluctuation sequences and construction progress mapping relationships; performing spatiotemporal feature aggregation processing on the cost time sequence knowledge graph through a deep learning encoder to generate a cost dynamic feature tensor; calling a pre-trained engineering cost prediction network to perform multitask decoding processing on the cost dynamic feature tensor to generate a basic cost prediction interval and a key cost-sensitive node distribution; performing market fluctuation elasticity correction processing on the basic cost prediction interval to generate a corrected cost prediction interval; and generating a cost control strategy scheme according to the key cost-sensitive node distribution, wherein the scheme comprises material procurement time sequence optimization parameters and fund allocation rhythm adjustment parameters.

The invention relates to an industrial and commercial enterprise management method and system based on the Internet of Things, and the method comprises the steps: obtaining production operation data and equipment asset data through Internet of Things equipment, and generating a structured data set according to the classification of preset service labels; based on the production operation data set, optimizing a production plan and productivity matching through a constrained programming algorithm, and generating a production scheduling scheme in combination with an equipment state; based on the equipment asset data set, utilizing a machine learning model to predict an equipment fault and calculate a maintenance period, and combining a maintenance standard to generate an equipment asset maintenance plan; and performing time conflict detection and resource allocation optimization on the production scheduling scheme and the equipment asset maintenance plan, and generating a comprehensive management scheme through a multi-target priority ranking algorithm. According to the method, by dynamically coordinating production resources and equipment maintenance tasks, the productivity utilization rate and the equipment reliability are improved, the non-planned shutdown loss is reduced, and collaborative optimization of enterprise operation efficiency and cost control is achieved.

The present invention discloses a parking lot revenue prediction method, which relates to the field of digital parking operation technology. The method refines and analyzes the historical parking data of the parking lot to obtain different operating day types such as weekdays, weekends, holidays, and store anniversary events, and uses the average charging vehicle volume and average charging time of different operating day types as revenue parameters for future revenue calculation; based on a deep learning algorithm, a parking lot revenue prediction model for predicting future set time periods can be constructed; the prediction method can achieve accurate and specific time predictions for future set time periods for different parking lots, allowing parking lot managers to better plan parking resources. The prediction results can help the finance department formulate budgets more accurately, conduct cost control and financial planning, and reduce operating risks. Through revenue prediction, market trends and consumer behavior can be analyzed, and marketing strategies and pricing strategies can be adjusted in a timely manner to attract more users.