78 results about "Cost assessment" patented technology

The invention provides a military clothing production scheduling optimization method and system based on an intelligent algorithm, and the method achieves the intelligent management of a production process through the construction of a dynamic state model and a disturbance cost evaluation system. Firstly, a reference production plan is generated by using a global optimization algorithm, and an event sensing module is deployed to monitor the production process in real time. When a task insertion event is captured, the system automatically collects related production parameters, triggers a disturbance cost evaluation mechanism, and dynamically predicts and quantifies the comprehensive influence of different interruption schemes. And based on the quantification result and a preset decision strategy, the system determines an optimal interrupt execution scheme, calls a rapid local rearrangement algorithm coupled with a disturbance cost index, adaptively adjusts the affected plan segments, and finally generates and issues an optimized production instruction sequence. According to the method, the whole process from disturbance identification to scheme execution is intelligentized, and the problem of dynamic scheduling for emergency tasks in military clothing production is effectively solved.

The invention relates to the technical field of building surveying and mapping, in particular to a high-precision building surveying and mapping method and system, and the method comprises the steps: collecting a point cloud model and an image of a to-be-surveyed building, segmenting the point cloud model into point cloud regions, obtaining the difference coefficient of each point cloud region, recognizing each building region, and removing the point cloud data outside the building region. Performing hole filling on the removed point cloud model; obtaining each structural surface of each building area, obtaining discrete coefficients of each boundary point and each non-boundary point of each structural surface, and screening each noise suspected point from all the non-boundary points of each structural surface; and obtaining a reliability coefficient and a point cloud distortion coefficient of each building area, performing denoising processing on the point cloud data of each building area, and obtaining a three-dimensional model of the to-be-mapped building. According to the method, the denoising precision of the point cloud data is improved, the precision of the three-dimensional model is improved, and therefore more accurate and more visual data support is provided for subsequent building surveying and mapping and project cost evaluation.

The invention relates to the technical field of cost assessment, in particular to a civil construction project cost assessment optimization system, which comprises a feature analysis module, a cost estimation module, a real-time monitoring module, a model training and tuning module, a risk analysis module, a decision support module, a financial simulation module and a reinforcement learning cost adjustment module. According to the invention, a convolutional neural network algorithm and a principal component analysis method in the feature analysis module excavate building information model data and screen key features, and the cost estimation module combines a multi-layer perceptron neural network and a linear regression model to enhance the cost mode analysis capability and calculate the project cost. The digital twinborn technology and the dynamic programming algorithm realize tracking of project progress and real-time monitoring of cost change in the real-time monitoring module, and the genetic algorithm and the machine learning algorithm realize continuous optimization and self-learning of model parameters in the model training and tuning module. And a Monte Carlo simulation method and a sensitivity analysis method are used for identifying and evaluating project risks in the risk analysis module.

An equipment system optimization method driven by deep reinforcement learning comprises the following steps: sequentially constructing an equipment system architecture model and a combat scene simulation model based on a DoDAF framework, then carrying out parameter space sampling in the equipment system architecture model by using a uniform design method, and according to capability items and equipment elements defined by the equipment system architecture model, carrying out parameter space sampling on the equipment system architecture model; constructing a combat effectiveness evaluation model and a system cost evaluation model for evaluating the system; constructing a parameter-modulated deep reinforcement learning (PM-DRL) model to explicitly embed system parameters into an agent state space, and performing data collection and evaluation through the trained PM-DRL model to obtain lt; system parameter-evaluation result gt; a data set; and finally, constructing an optimization model by taking the agent model as a target function, and determining an optimal solution, namely system parameter configuration, by combining the solved Pareto frontier with the preference of a decision maker. According to the method, under the same combat effectiveness requirement, system parameter configuration with lower construction cost can be obtained through optimization.

The invention provides a software development cost evaluation method and device, a medium, equipment and a product. The method comprises the steps of obtaining a demand document corresponding to a target software project; performing paragraph splitting on the demand document to obtain at least one functional process; splitting each functional process into a plurality of sub-processes; identifying keywords in each sub-process, and calculating a CFP score of each sub-process according to a corresponding relationship between the keywords and a preset CFP basic score; and outputting a cost evaluation report about the target software project according to all the sub-processes and the CFP score of each sub-process. According to the method, manual item writing and workload calculation are not needed, the function process and the sub-process thereof can be automatically split and written according to the content in the demand document, the automation degree of the COSMIC method is improved, the complexity of original work is reduced, the time cost is reduced, and meanwhile the accuracy and the normalization of the CFP score of the calculation project are improved.

The invention relates to a multi-angle comprehensive evaluation system and method for a domestic sewage treatment process, and the system comprises a technical evaluation module, an economic evaluation module, an environment evaluation module, a comprehensive evaluation module and an analysis module, and constructs a technical efficiency evaluation index TEA according to an effluent stability index and the stability of technical operation; taking the weighted sum of the macroscopic cost evaluation index, the cost evaluation indexes of different types and the cost evaluation indexes of different stages as an economic evaluation index EA; the comprehensive environmental evaluation index CEA is equal to CEAS + CEAG-delta CEAW; the results of the technical evaluation module, the economic evaluation module and the environment evaluation module are obtained, and a weighted summation result is used as a comprehensive evaluation result CE; relative advantages and disadvantages of the domestic sewage treatment process are integrally evaluated through CE, and technical, economic or environmental improvement measures can be taken according to relative sizes of output results of the technical evaluation module, the economic evaluation module and the environmental evaluation module. According to the method, the actual operation state of the technology can be presented more accurately, and the evaluation accuracy is improved.

The invention provides a mechanical part intelligent cost evaluation system and method based on a knowledge graph, and relates to the field of mechanical manufacturing, and the method comprises the steps: converting discrete process parameters, equipment capability and other process elements into multi-dimensional knowledge nodes through semantic modeling, and constructing a hypergraph structure based on a process constraint relation and a cost conduction rule, a full-link knowledge network covering'feature-process-resource-cost 'is formed; performing semantic representation on a plurality of process paths by adopting a hybrid embedding strategy, dynamically capturing implicit coupling effects among different process paths through an attention mechanism, and strengthening a framework optimization path selection strategy; and finally, the cost-driven weight of each path is quantified through a path probability evaluation model based on a classifier, adaptive recommendation of a process chain global optimal solution is realized, and the problem of dimension disasters in dynamic process combination optimization of a traditional method is solved.

The invention discloses a construction cost evaluation system and method based on engineering list intelligent matching. The system comprises a block chain material account book module, a material supply and demand intelligent scheduling module and an abnormity self-healing processing module. The block chain material account book module is used for constructing a material full-life-cycle evidence storage network, the evidence storage network comprises purchasing, transportation, warehousing, receiving and final verification, data of each step of operation is uploaded to a block chain in real time, an NFT chip containing a unique two-dimensional code and RFID is implanted into each material package, and unique material identification and data non-tampering evidence storage are achieved; according to the system, the blockchain material account book module is used for carrying out chaining and evidence storage on data of the whole process of material purchasing, transportation, warehousing, receiving and verification, and each material is identified by a unique electronic chip. The whole life cycle information of the material can be completely and accurately recorded, and due to the non-tampering characteristic of the block chain, the authenticity and reliability of the data are guaranteed.

The invention provides an excitation system and method for participation of an electric vehicle user in demand response based on the Internet of Vehicles. The system comprises a user response cost evaluation module, a user grouping module, an incentive mechanism design module, an incentive mechanism optimization module and a trust guarantee module, and the user response cost evaluation module calculates response cost of each user based on Internet of Vehicles data and user behavior data. The user grouping module divides each user into different groups based on the Internet of Vehicles data and the user behavior data, the incentive mechanism design module designs a multi-level incentive mechanism based on the response cost of the user and the grouping result, and the incentive mechanism optimization module optimizes the incentive parameters of each level of incentive mechanism based on a reinforcement learning method. And the trust guarantee module records the user response data and the distribution result of the incentive mechanism based on the block chain technology. The system can accurately quantify dominant and implicit costs, realizes differential excitation, and can also realize dynamic adjustment of an excitation strategy.

The present application belongs to the technical field of energy storage economic benefit evaluation, and aims to provide support for new energy storage investment strategies for the future form of power grids. Specifically, a whole life cycle cost evaluation method of energy storage technology suitable for multi-dimensional scenarios is provided, which comprehensively evaluates the economy of different energy storage technologies. The method considers multiple life cycle cost factors such as capital expenditure, operating cost, end-of-life cost and charging cost, and combines energy power ratio (E / P), terrain adaptability and different power application scenarios to simulate and quantify the uncertainty of cost parameters using a Monte Carlo prediction model, and predict the future trend of energy storage technology cost. Finally, intuitive evaluation is performed through detailed cost analysis and the constructed superimposed visualization method. The present application provides scientific decision support for power system planning and energy storage technology investment, and has significant practical value and theoretical significance.

This invention discloses a rapid cost assessment method for engineering changes based on a knowledge graph, comprising: S1, synchronously collecting multi-source heterogeneous data for entity extraction and relation alignment to construct an engineering knowledge graph; S2, inputting change instructions, using a bidirectional graph neural network to identify initial nodes and generate a change impact subgraph; S3, introducing an improved dynamic hierarchical graph attention network with a spatiotemporal decay dependency mechanism, constructing a dynamic decay factor to weight and encode features at each level, and outputting a change feature vector; S4, calling quota standards for cost mapping, performing difference calculation and automated splitting, and outputting a basic cost dataset; S5, combining historical change database similarity matching, and outputting corrected estimated values ​​after deviation calibration and nonlinear correction; S6, encapsulating and generating an assessment report and writing it back to the knowledge graph archive. This invention achieves deep analysis of change propagation paths and refined dynamic cost estimation, improving assessment efficiency and accuracy.

The present invention relates to the field related to engineering cost assessment, and discloses a pipeline layout management method and system based on data analysis. By collecting and evaluating environmental geological data and the environmental requirements for pipeline layout, the degree of transformation required for the corresponding environmental geology when pipelines are laid out in various locations in the assessment area is judged, and then the cost of pipeline layout can be estimated according to the unit cost of different environmental geological transformations, so as to determine the cost-optimal path for pipeline layout between points AB, and realize full-process cost planning and solution output.

The invention provides an unmanned aerial vehicle routing inspection method and system based on semantic guidance and an asset value cost graph, and the method comprises the steps: reasoning a possible distribution region of a target when the target is not directly observed through analyzing the structure law of semantic information, such as an equipment number, a function partition identifier and the like, in a transformer substation; generating a confidence map to guide the unmanned aerial vehicle to efficiently inspect; constructing a semantic cost graph fusing the static asset value and the dynamic operation risk, and quantifying potential loss of different regions when collision or falling occurs; according to the real-time health state of the unmanned aerial vehicle, semantic guidance is emphasized during normal inspection through a dual-guidance function fusion and switching mechanism, and the unmanned aerial vehicle is automatically switched to asset value cost minimization guidance in an abnormal or emergency state to realize safe disposal; the system adopts a framework of semantic reasoning, cost evaluation and flight control module decoupling, and guidance intelligence and control reliability are ensured. According to the invention, the inspection blindness of the unmanned aerial vehicle is obviously reduced, and the abnormality handling risk is reduced.

The present invention relates to the field of energy storage assessment technology, and in particular to a method for assessing the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios, including the following steps: determining the full life cycle of a new power system that needs to be calculated based on energy storage technology; determining the optimal power sales curve based on the predicted value and actual value of the power generation of wind turbines; evaluating the full life cycle benefits of the wind-solar hybrid energy storage system to be tested according to the optimal power sales curve; determining the system boundary for carbon footprint accounting of the new power system based on the full life cycle; collecting activity level data and emission factor data within the full life cycle based on the system boundary; calculating the carbon footprint of the new power system within the life cycle based on the activity level data and emission factor data, and increasing the proportion of renewable energy based on the carbon footprint. The present invention can effectively reduce the number of switching times of energy storage charging and discharging, thereby extending the service life of the hybrid energy storage system.

This invention belongs to the field of demolition cost assessment technology, specifically disclosing a method for assessing railway route selection and land acquisition costs based on a big data model. The method includes the following steps: constructing a graphical database and a core land acquisition database; acquiring policy-related land acquisition documents and dynamic data for the target area; creating an external database; identifying key factors affecting land acquisition costs; constructing a big data prediction model for land acquisition costs; training the model; and integrating it into a railway route selection design system. When inputting or modifying route plans in the route selection design system, the system calls the big data prediction model for land acquisition costs in real time. Based on the geometric parameters and spatial location of the route, it dynamically calculates and feeds back the corresponding land acquisition cost prediction results, adjusting the route plan until cost control requirements are met. This technical solution achieves dynamic and accurate assessment of land acquisition costs through the deep integration of multi-source heterogeneous databases and a big data prediction model.

The invention belongs to the technical field of rail transit cost assessment, and particularly relates to a rail transit project cost intelligent assessment method based on big data, which comprises the steps of inputting a cost file of a project to be assessed, including sub-items, a list, a quota and labor, material and machine information; performing index physical examination early warning, matching historical sample data, rejecting abnormal values through quartile, calculating a confidence interval, and detecting cost deviation; checking the features, comparing with a standard feature configuration library, and verifying the integrity and normalization of the engineering features; through cooperation of index physical examination early warning and feature examination, the problem of engineering type recognition errors can be quickly responded, so that the defects of a static model are overcome, construction cost files with engineering type labeling errors can be quickly responded, the false alarm frequency can be reduced, and the defects of the construction cost files can be quickly analyzed based on a multi-stage examination mechanism. Compared with manual checking, the method and the device have the advantages that the speed and the efficiency are higher, and the checking is more comprehensive.

The invention discloses a multi-sensor fused intelligent waste steel sorting system, and relates to the field of industrial automation. A central processing module; and a sorting execution module. According to the method, the component proportion and the three-dimensional space distribution characteristics of the whole scrap steel are synchronously collected through a multi-sensor fusion framework, accurate quantification of the economic value of the scrap steel is achieved in combination with a dynamic value scoring formula, and a three-way sorting mechanism is driven based on a scoring threshold value; the problems of cost evaluation distortion and sorting decision disjunction caused by local sampling in the prior art are effectively solved, the reprocessing energy consumption is reduced while the high-value metal recovery rate is increased, the reliability and traceability of the sorting process are guaranteed through a redetection buffer area and a block chain evidence storage mechanism, and finally the maximum utilization of waste steel resources is achieved.

The present invention provides a method, device and electronic device for evaluating the production cost of a product, the method comprising: acquiring and storing data of all production nodes of a product, receiving and storing production factor data of the product, acquiring first data matching the production factor data from the data of the production node, determining the number of workstations for producing the product according to the first data and the production factor data, and calculating the working hours of each workstation, calculating the production cost of the product according to preset cost accounting elements, the first data, the working hours of each workstation and the production factor data, and generating an evaluation report according to the production cost of the product and the data of all production nodes. The present invention can improve the accuracy and efficiency of product production cost evaluation, and effectively reduce the manpower cost of product production cost evaluation.

This invention relates to the field of vehicle insurance claims management technology, and discloses a method, system, electronic device, and storage medium for constructing a life table for vehicle insurance claims. This invention integrates internal claims data with external industry data to construct a vehicle damage cost model and a personal injury cost model, and collects data and operational trajectories from the entire claims process. It combines cost deviation calculation and multi-dimensional personnel information to construct a claims personnel profile model, applies the evaluation results to personnel management, and iteratively optimizes based on feedback data. Simultaneously, it utilizes cost assessment and personnel profile correction to generate a graded evaluation of the institution's claims capabilities. This solves the problems in traditional vehicle insurance claims such as the lack of a unified quantitative cost standard leading to significant differences in loss estimation, the single dimension of personnel assessment making it difficult to accurately identify high-risk personnel, and the reliance on macro indicators in institutional evaluation failing to pinpoint micro-level shortcomings. It achieves improvements in the quantitative assessment of case cost, accurate identification of personnel risks, and scientific grading of institutional capabilities in vehicle insurance claims life table pricing.

This application provides an automatic cost assessment method, system, medium, and product for petrochemical software projects, relating to the field of artificial intelligence technology. In this method, the cost assessment system utilizes a large language model to perform preliminary function point identification on project documents and retrieves the names of the preliminarily identified function points using a petrochemical industry knowledge base, supplementing it with professional information such as scale and complexity from historical projects. Then, the cost assessment system iteratively corrects the function point information through interaction between the retrieved information and the preliminary function point list. Finally, the cost assessment system determines the complexity level of each function point, calculates the adjusted number of function points based on the project complexity, and then calculates various cost information and generates an assessment report based on the adjusted number of function points and a project parameter library. This method combines the preliminary identification using a large language model with professional information from an industry knowledge base, improving the accuracy of function point identification through iterative correction, thereby enhancing the reliability of cost assessment.

The invention discloses an AI intelligent agent-based test verification evaluation method and a related device, and relates to the technical field of computers, the method comprises the following steps: carrying out structured segmentation on a test evaluation document based on a father-child segmentation mode to obtain a double-layer segmentation structure document; and vectorizing the contents of the sub-blocks to generate a vectorized knowledge base. And calling a data extraction agent based on a data extraction rule, taking a test data management software database as a data source, and extracting test plan, implementation and result information from the document by using a large language model. And based on a test verification evaluation task planning agent, vectorizing user evaluation requirements, and calculating semantic similarity in combination with a vectorization knowledge base to obtain related evaluation algorithm description and test cost accounting standards. And according to the description and the standard, evaluating test verification process sufficiency, progress and cost based on the test verification sufficiency, progress and cost evaluation agent to obtain a test verification evaluation result. The method can quickly obtain the evaluation result, and supports the leader decision.

The invention relates to a vehicle (1) that is at least partially automated, comprising a computing unit (3) for determining an optimal route between a starting node and a destination node using an A* search algorithm for cost determination based on a plurality of distance-based costs.In a vehicle that significantly reduces the computational effort required to plan an optimal route for at least partially automated driving, the computing unit (3) for planning a trajectory of the vehicle (1) using a modified A* search algorithm comprises an analysis unit (5) which evaluates characteristic parameters of the vehicle (1) at predecessor nodes from a trajectory history, which characterize dynamic influences, and a trajectory determination unit (7) which, together with the distance-based costs, evaluates the costs caused by the dynamic influences of the vehicle (1). The computing unit (3) is designed to control actuators (9) for longitudinal and lateral control of the vehicle (1) such that the vehicle (1) follows the trajectory determined by the modified A* search algorithm.

The invention provides a model-based intelligent sewage treatment method and system, and the method comprises the steps: obtaining a plurality of pieces of historical state information, each piece of information comprising first observation information, second observation information, an operation instruction, a reward value and a cost value; inputting the observation information into a feature extraction network to obtain a feature value; inputting the characteristic values into a decision maker network, a benefit evaluation network and a cost evaluation network to respectively obtain a probability value, a benefit value and a cost value; inputting the evaluation values into an objective function optimization decision maker network, a benefit evaluation network and a cost evaluation network; repeating the above steps until a preset number of times is reached, and obtaining a trained decision maker network; and obtaining current observation information and inputting the current observation information into the trained network to obtain an operation instruction to control the sewage treatment process. According to the invention, the sewage treatment process can be accurately controlled, the dual goals of water quality optimization and energy consumption reduction are realized, and the sewage treatment efficiency and economy are improved.

This application relates to the field of advertising placement control technology and discloses an advertising precision placement method based on recommendation algorithms. The present invention constructs a time-segmented placement state quantity around the current placement cycle, and on this basis, combines the state sequence of similar time periods in history to recursively represent subsequent placement opportunities. Then, it incorporates the unit budget immediate revenue index of the current candidate placement action, the subsequent unit budget revenue index, and the current audience reach saturation level into the placement opportunity cost assessment. Finally, under the constraint of the upper limit of the allocable budget for the current time period, it generates an advertising placement strategy. This unifies the previously fragmented conversion ranking, budget advancement, and audience reach control into the same processing chain. It can not only avoid the excessively rapid consumption of the initial budget and the loss of revenue opportunities in the subsequent time period caused by placing ads based solely on the current conversion probability estimate, but also reduce the risk of revenue decline caused by continuing to place ads after the audience reach saturation.

A multi-variety processing group line opportunity maintenance system considering quality reliability coupling comprises a data acquisition and degradation parameter estimation module, a comprehensive cost evaluation module, a baseline preventive maintenance plan generation module and a fault detection and opportunity maintenance decision module. According to the method, the comprehensive cost in multiple aspects of the production process including the product quality is quantified, a preliminary preventive maintenance (PM) plan is firstly generated on the basis of the production schedule, and then the optimal opportunity maintenance strategy is obtained by taking the total cost in an opportunity window as a target function under the sudden fault situation. And analyzing a system performance evolution rule before and after maintenance, thereby remarkably improving the accuracy and economy of a maintenance decision.

The invention discloses a project cost information management method based on industrial big data, and relates to the field of project information management.The method comprises the steps that to-be-evaluated project information is matched with an internal database and an external database, and historical internal reference data and historical external reference data are obtained; comparing the historical internal reference data with the historical external reference data in the same time period to obtain an overall difference and an individual difference; if the total is not different from the individual, performing project cost assessment on the to-be-assessed project information based on a big data technology to obtain a to-be-invested amount; if the total is not different but the individuals are different, determining a mutual relation between individual difference items, and performing project cost evaluation on the to-be-evaluated project information according to the mutual relation to obtain a to-be-invested amount; and if so, performing project cost assessment on the to-be-assessed project information according to the credibility of the database to obtain a to-be-invested amount. The method has the effect of improving the accuracy of the evaluation result.

The invention discloses a main network construction cost rapid auxiliary review method and system, and the method comprises the steps: firstly obtaining construction cost data information and construction cost budget information of a main network construction cost, and recognizing each construction cost demand type and construction cost data distribution information based on the construction cost data information; then, based on the cost budget information, identifying a cost list, and based on the cost data distribution information and the cost list, identifying cost evaluation index values and cost evaluation reasons; then, based on the cost evaluation index value and the cost evaluation reason, identifying a cost evaluation result, and based on the cost evaluation result, generating main network auxiliary evaluation information of the main network cost; according to the method, the cost evaluation result of each cost demand type is comprehensively analyzed, workers are assisted to comprehensively analyze each cost demand type, and each cost demand type is comprehensively analyzed through the evaluation index, so that the timeliness, the accuracy and the comprehensiveness of main network cost evaluation analysis are improved, and the efficiency of main network cost evaluation analysis is improved. And meanwhile, the review efficiency is further improved.

The embodiment of the invention provides a data cost evaluation method, electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining metadata information and a blood relationship graph of each data node in to-be-evaluated data; determining physical cost information of each data node according to the metadata information of each data node; determining target score information of each data node according to the physical cost information of each data node and the blood relationship graph, wherein the target score information of each data node is used for representing the importance degree of each data node; and according to the multiple pieces of target scoring information, determining cost evaluation information of the to-be-evaluated data. The method is used for achieving the technical effect of improving the data cost evaluation accuracy.

The invention discloses a system for engineering cost big data analysis and intelligent decision support, and relates to the technical field of construction engineering cost assessment, and the system comprises an Internet of Things collection terminal which is used for collecting geographic information data of each building area; the sensor is used for collecting structural data of each structural member of the building in each building area; the server is used for collecting material supply chain data; the processor is used for obtaining geographic risk data of the building area through nonlinear calculation according to the geographic information data; and according to the structure data and the geographic risk data of the building area where the building is located, outputting a structure data predicted value at the beginning moment of the project through a neural network. According to the scheme, the supply coefficient and the dynamic priority calculation mechanism are introduced, objective engineering loss evaluation is combined with a subjective strategy, so that the material scheduling efficiency is optimized, and the problem of resource waste caused by single supply demand judgment in the prior art is solved.

The present invention relates to the technical field of fishery cost management, specifically a real-time data collection method and system for intelligent cost accounting of marine fisheries, which obtains ship operation positioning data to calculate the speed change rate, screens stable periods to reconstruct operation sections, matches fuel records to analyze flow and level to calculate fuel consumption, combines wind speed, ocean current, and tide level to analyze disturbance direction to divide interference levels, and integrates time, location, behavior type, fuel consumption, and interference level to construct index splitting entries. The present invention identifies continuous operation intervals through speed change rate, combines fuel level and flow data to cross-validate and screen effective fuel consumption, and reduces statistical deviations. Through the dynamic mapping of equipment operating parameters and energy consumption output, a refined splitting of operation behavior and energy loss is achieved, and the ability to identify abnormal working conditions is improved. Environmental parameters such as ocean current and wind speed are introduced to calculate the combined force of disturbances, quantify the actual impact of natural conditions on energy consumption, and correct cost assessment errors.