228 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 belongs to the technical field of supply chain management, and discloses an intelligent purchasing system and method of a supply chain. Comprising the steps of collecting demand prediction data and supplier performance data; carrying out deep mining on the demand prediction data, and predicting future purchase demands; generating a capability portrait based on the performance data of the suppliers, and performing quantitative evaluation on performance scores of different suppliers according to a pre-constructed evaluation system; historical order data is acquired, a supplier relation network topology is constructed in combination with the capability portraits, and a synergistic effect between suppliers is identified; fusing the performance score, the future purchase demand and the synergistic effect, and recommending an optimal supplier combination; acquiring purchase management data, and intelligently formulating a purchase strategy in combination with the supplier performance data of the optimal supplier combination; according to the invention, the purchase efficiency can be improved, and the flexibility and toughness of the supply chain are enhanced, so that the overall response speed, cost control capability and operation quality of the supply chain are effectively improved.

The invention provides a BIM-based engineering progress and cost dynamic management and control optimization method and system, and relates to the technical field of engineering management and control optimization, and the method comprises the steps: building a digital twin basic platform with a real-time sensing capability based on a BIM model and multi-source sensor data; a progress plan and cost reference data are imported, a progress-cost correlation model is established, a comprehensive deviation value is calculated, and multiple groups of optimization schemes including resource configuration, process organization and cost control parameters are automatically generated; parallel simulation is carried out on the digital twin platform, progress and cost prediction results are obtained, multi-target evaluation is carried out, and an optimal scheme is selected and issued to a site for execution; and continuously collecting actual execution data, comparing the actual execution data with a simulation result, and when a deviation exceeds a threshold value, updating a progress-cost correlation model in the digital twin platform by using the actual data, thereby realizing adaptive evolution and forming dynamic collaborative management and control of progress and cost. The accuracy and efficiency of progress and cost control can be effectively improved, and the project risk is reduced.

The invention provides a project cost management method based on artificial intelligence, and relates to the technical field of project cost, and the method comprises the following steps: 1, data mining and analysis: extracting valuable information from a large amount of historical project cost data through building a data mining model, and analyzing the mined data through a data analysis tool; step 2, automatic measurement and pricing; step 3, constructing a cost prediction model; step 4, risk assessment and early warning; step 5, project progress and cost collaborative management; step 6, intelligent cost control; and 7, intelligent supplier management. By constructing the data mining model and using the neural network algorithm to cooperate with the association rule mining algorithm, deep mining can be effectively performed on the engineering cost data, and meanwhile, the data analysis tool is used to analyze the data, so that the workload of data collection is reduced, and the efficiency of engineering cost management 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 an intelligent IT asset management system, and the system comprises a configuration asset management module which is used for automatically collecting IT asset information in a network, building a complete configuration item relation model, and supporting the dependency relation mapping and impact analysis between configuration items; the service desk management module is used for establishing a service catalog, defining a processing flow, response time and a service level protocol, automatically carrying out classification and priority evaluation on the received service requests through an intelligent classification engine, and distributing the service requests to proper processing personnel according to a preset rule; the contract supplier management module is used for full-life-cycle management of suppliers and contracts; the IT service financial module is used for multi-dimensional budget management and fine management of IT cost; and the multi-tenant management module is used for realizing entity separation and authority control. The problems of a traditional IT asset management system in the aspects of asset tracking, service management, cost control and the like are solved, and a set of complete IT service management solution is provided for enterprises.

The invention relates to the technical field of engineering cost based on AI, in particular to an engineering cost database platform based on AI intelligent identification and a use method, and by arranging the engineering cost database platform based on AI intelligent identification comprising a measurement and calculation and AI module and an interactive display module, precipitation and index analysis can be carried out on measurement and calculation data, and the calculation and calculation efficiency is improved. And through data matching, the data in the list can be automatically compared, and the tedious work of manual comparison is avoided. By setting a database, an index library and a material price library, project cost data, including a standard list, project amount data and market price data, of already-completed construction projects of an enterprise can be collected and sorted, and then various indexes influencing the construction project cost are abstracted from the project cost data; and the method is used in the cost consultation service to realize rapid measurement and calculation of the cost of the project to be constructed, and assists the whole process cost management and control of the construction project at the same time; and finally, continuous improvement of the cost consultation service value is realized.

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 provides a BIM technology-based engineering cost dynamic data labeling method and system, and the method comprises the steps: carrying out the efficient construction cost component element extraction of target BIM engineering cost dynamic data through combining a standardized deep learning network and a customized deep learning network, generating a confidence coefficient sequence related to a preset construction cost mapping knowledge set, and carrying out the construction cost component element extraction of the target BIM engineering cost dynamic data; therefore, the target cost component elements can be accurately identified. Furthermore, a standardized deep learning network and an annotation network are utilized to carry out detailed annotation on the target cost component element, and a confidence coefficient sequence associated with another preset cost mapping knowledge set is generated, so that a target cost description label of the target cost component element is accurately obtained. According to the method, the marking accuracy and efficiency of the dynamic data of the engineering cost are remarkably improved, deep mining and fine management of the BIM engineering cost data are also realized, and accurate estimation, cost control and project management of the engineering cost are facilitated.

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 present invention discloses a method for analyzing the aging of assembled anti-collision components based on BIM5D technology, which relates to the field of municipal infrastructure management. The construction project is three-dimensionally modeled using modeling software, the bill of quantities is input into the corresponding component type parameters, the construction project schedule information is compiled using relevant software, and then the cost information and progress information are integrated using BIM5D technology, and the aging model of each task is analyzed based on the WBS work breakdown structure and earned value method. The real-time progress of the project and the change of funds are provided to project managers and construction technicians from different angles such as time dimension and project dimension, the degree of deviation is evaluated, the cause of the deviation is analyzed and corrective measures are proposed. The present invention can effectively predict the progress-cost control problems that occur in the actual production of the project, and provide a reliable aging analysis method for key node control and early warning.

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 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 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 belongs to the field of slope engineering construction, and provides a slope blasting lumpiness prediction method based on a Buble Sort-GWO-ELM model to solve the problem that the construction quality and cost control are affected due to the fact that the slope blasting lumpiness cannot be accurately predicted in the slope construction process, and the slope blasting lumpiness prediction method comprises the steps that blasting control parameters are monitored; performing data preprocessing; constructing a blasting control parameter data set; the blasting control parameters are screened by using bubble sorting in combination with a square difference algorithm, and characteristic parameters which have the greatest influence on a prediction result are selected; training an extreme learning machine (ELM) model, and performing optimization and verification by using an improved grey wolf optimization algorithm model parameter; determining optimal parameters of an extreme learning machine (ELM) model, and establishing a blasting effect prediction model; and predicting by using the blasting effect prediction model. According to the method, the blasting parameters can be scientifically predicted and reasonably controlled, the accuracy of blasting construction can be improved, and the economical efficiency and safety of the whole project can be 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 present invention discloses a printing production information collection and monitoring system and method, which are used for production quality and cost control blocks, and include a data collection module, a central control module, a data storage module, a visualization display module, and a communication module; wherein a plurality of data collection modules are used to respectively collect the total energy consumption of the plant area, the energy consumption of each workshop, the energy consumption of each printing machine, the amount of printing paper used, and the amount of ink used; the central control module is used to process and analyze the collected data, including calculating energy consumption, material utilization rate, production efficiency, and generating an analysis report; the data storage module is used to store the data collected by the data collection module, and the data processed and analyzed by the central control module; the visualization display module is used to visualize the collected data and the processed and analyzed data through a user interface; the communication module is used to connect the modules, and the present invention can monitor and analyze various types of data in real time, and provide comprehensive production status and quality analysis.

The invention discloses a resource use charging management method and device of a cloud product, a server, a computer readable storage medium and a computer program product. The method comprises the following steps: after monitoring that the operation of a cloud product is completed, collecting resource use information of the cloud product, calling a pre-trained machine learning model to predict the demand resource quantity of a user according to the resource use information and historical data, predicting the resource use trend of the user by using the machine learning model, and dynamically adjusting resource allocation according to a prediction result. Accurate prediction of resource use trend and automatic adjustment of resource allocation are realized, and a more flexible and transparent cost control means is provided for users on the basis of improving charging accuracy. Besides, according to the method, a real-time monitoring and prediction algorithm is adopted, after each operation is completed, a preset charging rule is utilized to carry out cost calculation according to the resource use information, a real-time bill is generated, and the user can check the resource use condition and the cost expenditure at any time.

The invention discloses a BIM forward design-based building engineering cost management and control method and system, and relates to the field of data processing methods specially suitable for business. In the method, design element information of a horizontal construction area and a vertical construction area is collected; generating a regional cross node matrix based on the design element information, and calculating a density coefficient; determining a high-risk cost node; recording an initial value and a change value of a design parameter of each high-risk cost node; calculating a cost fluctuation coefficient; determining a parameter adjustment range based on the cost fluctuation coefficient, and screening out selectable parameter combinations; calculating the cost value of the high-risk cost node; when the cost value is not in the preset cost value interval, adjusting the parameter adjustment range according to a preset adjustment scheme; and when the cost value is in the preset cost value interval, outputting a final design scheme of the corresponding parameter. The method is used for improving timeliness and accuracy of building engineering cost management and control.

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.