132 results about "Cost Controls" patented technology

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 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 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 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.

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 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 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 the technical field of computers, in particular to a project cost information service platform which comprises a multi-source data acquisition subsystem, a big data storage and cleaning platform, an AI dynamic prediction engine, a BIM analysis module, a cost calculation and analysis module and a user service interface. Through the dual-mode design of the crawler unit and the API interface unit of the multi-source data acquisition subsystem, the data acquisition frequency is improved to once acquisition every 15 minutes, and the time difference between the cost data and the actual price of the market is ensured not to exceed 30 minutes. Meanwhile, the big data storage and cleaning platform integrates multi-source data such as real-time market offers, government guidance prices, historical contracts and macroeconomic indexes through standardized processing and association mapping, data islands are broken, a cost data set covering full dimensions is constructed, and the precision of cost management and control is remarkably improved.

The utility model relates to a fireproof door leaf which comprises a door leaf body, a first panel covering and connected to the rear side of the door leaf body and a second panel covering and connected to the front side of the door leaf body, and the door leaf body comprises a door leaf frame and a door leaf core plate embedded in the door leaf frame. The door leaf frame comprises a rectangular frame formed by connecting four door leaf frames arranged along the four edges of the fireproof door leaf. Each of the four door leaf frames comprises a first wooden frame and two first inorganic fireproof plates with the length extending in the length direction of the first wooden frame, and the two first inorganic fireproof plates are connected to the front side and the rear side of the first wooden frame respectively. According to the fireproof door leaf, the door leaf frame and the window frame are reasonably arranged, the structural stability of the fireproof door leaf is improved, and meanwhile it can be guaranteed that the fireproof door leaf has the good fireproof performance, the light weight level and the cost control level.

The application relates to a cost control method and device of recommendation information, a computer device and a storage medium. The method comprises the following steps: obtaining a target conversion cost value of recommendation information in a push cycle; the push cycle comprises a historical push sub-cycle in which information push has been performed and a future push sub-cycle in which information push has not been performed; reading historical push data of each push crowd in the historical push sub-cycle; determining a value interval of a pricing coefficient corresponding to each push crowd; determining a real-time pricing coefficient corresponding to each push crowd based on the target conversion cost value, the historical push data and the value interval; and determining a conversion cost value when each push crowd is pushed in the future push sub-cycle based on the real-time pricing coefficient and the target conversion cost value. The method can improve the accuracy of push cost control of recommendation information.

The invention relates to the technical field of financial science and technology, in particular to an intelligent transaction optimization system based on source network load storage. According to the technical scheme, the intelligent transaction optimization system based on source network load storage comprises an electric power balance optimization module, a system security and stability module, a cost control optimization module, an intelligent scheduling control module, an intelligent operation and maintenance management module and a market transaction optimization module; according to the invention, through cooperative work of the matching engine and the electronic contract module, a direct transaction channel of a source network load storage whole link is established. Through a standardized electronic contract and an intelligent matching algorithm, real-time and efficient matching between green electricity producers and consumers is realized, information barriers and institutional cost in traditional transactions are eliminated, mobility of green electricity transactions is improved, transaction cost is reduced through a direct transaction mode, and participation enthusiasm of market subjects is enhanced.

The utility model belongs to the technical field of information identification, particularly relates to a limiting device for a shielding door of RFID (Radio Frequency Identification) scanning equipment, and provides the following scheme aiming at the problems that the existing limiting device is complicated in structure and high in cost, and the burden of purchasing and using the equipment is increased for some small enterprises or users with strict cost control. A rolling frame is rotatably connected in the support, electromagnetic shielding cloth is fixedly wound on the rolling frame, and a horizontal rotating shaft is fixedly installed in the rolling frame. When an RFID scanning device sends out a door opening / closing instruction, the driving motor rotates through the horizontal rotating shaft and the unwinding shaft to drive the electromagnetic shielding cloth of the shielding door to ascend and descend; when the electromagnetic shielding cloth of the shielding door arrives at a designated position and the positioning triggering device on the indexing adjusting gear triggers the microswitch, the driving motor can be controlled to stop, so that the electromagnetic shielding cloth can be stably limited, and good practicability is achieved.

The utility model relates to the technical field of metering boxes, and discloses a cost control meter box modularization rapid assembly structure comprising a fixed plate, a fixed cylinder is fixedly connected in the fixed plate, a moving column is slidably connected in the fixed cylinder, the outer wall of the moving column is fixedly connected with a first limiting sheet, and the outer wall of the moving column is fixedly connected with a second limiting sheet. The outer wall of the first limiting piece is slidably connected with a moving block, the outer wall of the moving block is fixedly connected with a spring, the outer wall of the moving block is fixedly connected with a second limiting piece, and a metering box assembly is arranged on the outer wall of the fixing plate and used for protecting internal equipment from being affected by the external environment. According to the utility model, the moving column is pressed to drive the first limiting piece to move, the first limiting piece drives the moving block to drive the second limiting piece, and the second limiting piece is used for limiting and fixing the limiting block, so that the effects of flexible configuration according to actual requirements and convenience in replacement or maintenance when a fault occurs are achieved.

The application discloses a power transmission engineering cost data management method and system, and belongs to the technical field of power engineering digital management. In view of the problems of isolated storage, high tampering risk and insufficient dynamic prediction ability of existing cost data, the application comprises the following steps: firstly, through integration of Internet of Things sensors, manual input terminals and external databases, real-time collection of equipment prices, construction energy consumption, environmental parameters and other data is carried out to integrate multi-modal data; secondly, a multi-node alliance chain is constructed, data blocks are divided according to engineering stages, and the whole life cycle of data is ensured to be traceable, then, a hybrid model combining LSTM time series prediction and graph neural network is deployed for real-time analysis; finally, a multi-dimensional dashboard containing geographic information superposition, risk heat map and resource flow topology is generated according to the data; and the application provides a full-chain digital solution for power transmission engineering cost control.

The invention discloses a project cost and material price list calculation method based on artificial intelligence, and the method comprises the steps: obtaining resource conditions and project type information matched with project demands from historical data through querying a preset database, and obtaining an initial data set; according to the initial data set, grouping resource conditions and engineering types by adopting a clustering analysis method, and determining a grouped feature set; if the dynamic change in the grouped feature set exceeds a preset threshold value, training a use rule model through a random forest algorithm to obtain a trained use rule model; for the trained use rule model, inputting current market supply and demand data, judging the influence degree of market supply and demand on price prediction, and obtaining an influence weight; and aiming at the predicted price value, combining the use rule model output, generating a final use amount and price combination of the material list, judging whether the combination meets a cost control requirement, and obtaining optimized list data.

The invention provides a maintenance project cost management and control method and system based on dynamic generation and a decision tree model, and belongs to the technical field of project management and informationization, and the method comprises the steps: obtaining project quota data, and obtaining a comprehensive unit price through employing a comprehensive unit price dynamic generation algorithm; carrying out dynamic budgeting on the current maintenance item by adopting a cosine similarity algorithm to obtain a unit price corresponding to historical data with the highest similarity, and judging whether the unit price is an abnormal unit price or not; if no abnormity exists, the unit price obtained through calculation is adopted to calculate the maintenance project cost; and if the unit price is abnormal, analyzing the abnormal unit price data by adopting a gradient boosting decision tree model, and accounting the maintenance project cost according to a unit price analysis result. According to the invention, through collection and analysis of historical maintenance project data, dynamic generation and an intelligent matching algorithm are combined, a maintenance plan and budget are optimized, resource waste is reduced, it is ensured that the cost of the maintenance project is controlled within a reasonable range, and unnecessary expenditure is reduced.

This invention relates to the field of power engineering cost control technology, specifically to an intelligent cost deviation control method for power plant renovation projects oriented towards a cloud platform. The method includes: establishing a cost monitoring process on an industrial cloud platform encompassing deviation detection, impact tracing, and control decision-making; identifying the initiation point and propagation path of cost deviations during the deviation detection stage; integrating the transmission relationship of project sub-items deviations with external market data flow disturbance signals during the impact tracing stage to map the multi-level impact chain of cost deviations; and analyzing the contribution weight and control sensitivity of deviations at each level during the control decision-making stage to generate a differentiated pre-control instruction set, collecting the cost flow response trajectory after instruction execution, iteratively correcting instructions based on their matching degree with the expected control trajectory, and updating the control strategy online. This method can fully present the deviation attribution logic, adapt control instructions to the characteristics of sub-items, achieve closed-loop intelligent control of cost deviations, and enhance the dynamic adaptive optimization capability of the control process.

The invention discloses an equipment maintenance cost management and control system and method, and relates to the technical field of industrial equipment management. The system comprises an Internet of Things data sensing layer, a maintenance digital twinborn construction module, a multi-dimensional dynamic preventive maintenance cost prediction engine, a cost control and execution layer based on an intelligent contract and a self-adaptive cost allocation and optimization feedback loop. The method comprises the following steps: collecting data in real time and constructing an equipment maintenance digital twinborn body; the prediction engine generates a virtual maintenance unit containing predicted cost based on the twinborn body data; when the risk exceeds a threshold value, a block chain intelligent contract is automatically triggered, and automatic and traceable management and control are carried out on budget, process and payment; after maintenance is completed, cost is automatically shared, and data is fed back to optimize the model. According to the method, the change of the maintenance cost from passive response to active prediction and from post-event recording to whole-process automatic management and control is realized, transparency is ensured through an intelligent contract, a self-optimization closed loop is formed, and the refinement level and cost effectiveness of cost management and control are improved.

The application discloses a kind of multi-channel advertisement intelligent distribution control system and method based on deep learning, comprising the following steps: collecting multi-source advertisement delivery data, structured input dataset including user behavior sequence, advertisement content sequence and channel attribute sequence are constructed;Structured input dataset is encoded to feature vector, and three kinds of feature vectors are generated;Three kinds of feature vectors are input into ternary gate fusion structure, and fusion feature representation is generated;Fusion feature representation is input into improved PLE model, and click rate prediction value, conversion rate prediction value and budget cost prediction value are output;Strategy scoring function is constructed based on the above three prediction results, and the distribution score of candidate advertisement and channel combination is generated;According to distribution score ranking, the highest score combination is obtained, distribution instruction is generated and output to multi-channel advertisement system.The application improves the precision and cost control ability of advertisement distribution strategy under multi-target delivery, and is suitable for large-scale online advertisement delivery scene.

The invention discloses an efficient vertical machining center BOM building method, and belongs to the technical field of machine manufacturing. The method comprises the following steps that S1, a BOM frame is built based on the modular design concept; s2, carrying out association design by utilizing three-dimensional design software; s3, establishing a BOM change management mechanism; and S4, introducing a related research and development product management informatization system. According to the method, the building efficiency is improved, a large amount of manual input work is avoided, and the BOM building time is greatly shortened; the accuracy is improved, human errors are reduced, and the accuracy and integrity of BOM information are ensured; the method adapts to design changes, can quickly respond to the design changes, timely updates BOM information, and effectively reduces production risks and cost increase caused by the design changes. Data management is facilitated, efficient management and utilization of BOM information are achieved, and data query and analysis of links such as production plan making, purchase management and cost control are facilitated.

The invention relates to the field of engineering technology construction, and discloses an automatic quantity increasing and calculating and manufacturing cost control device for a water supply pipeline. The water supply pipeline automatic quantity extraction and calculation and cost control device comprises a digital twin data middle station used for aggregating, storing and managing full life cycle data of a water supply pipeline project, an AI cost prediction and optimization engine used for training a machine learning model based on historical data, the device comprises a design model layer used for responding to changes of a design model and instantaneously calculating engineering quantity variation and cost variation, and an intelligent interaction application layer used for visually presenting cost information, early warning signals and optimization suggestions to a user, and has the advantages of efficient data integration, accurate prediction and optimization, real-time response, intelligent interaction, high data credibility and the like. The problems that in the prior art, data decentralized management is low in efficiency, cost prediction is static, amount calculation and pricing are lagged, interaction experience is insufficient, and decision support is weak are solved.

The application relates to a construction cost control method, system, device and medium, and relates to the technical field of engineering cost, which comprises the following steps: analyzing a plurality of waste images collected from a waste stacking area to identify the types of waste and count the quantity of each type of waste; according to the real-time situation of a construction site, a three-dimensional model of a current construction project is constructed, the three-dimensional model is compared with a predetermined construction plan model, the expected consumption of each type of material in the current construction stage is obtained, and the scrap rate of each type of material is calculated; it is judged whether each type of material exceeds a preset scrap rate threshold, if yes, the source of the waste corresponding to the scrap rate of the material is found, the loss cost is calculated, and a cost abnormality warning and a construction abnormality warning are issued. The application can control the cost by controlling the scrap rate of engineering materials, and reduce the increase of the cost caused by the increase of waste.