29 results about "Cost effectiveness" patented technology

The invention provides an operation system and an operation control method of an intelligent agent product. According to the system, through a knowledge base templated import and dynamic maintenance mechanism, batch rapid import of multi-format product documents can be supported, and manual operation is greatly reduced. According to the system, a fine-grained multi-role authority management system is constructed, authority boundaries of a super administrator, an operation administrator, customer service personnel and equipment developers are clearly divided, and the safety and manageability of the system are enhanced. The system can comprehensively collect and analyze operation data including question and answer logs, control logs, model calling times, cost and the like; a multi-dimensional operation result is generated through hotspot problem analysis, control frequency statistics and the like, meanwhile, the cost-benefit ratio is calculated, and a data basis is provided for identifying user requirements and optimizing an intelligent agent response strategy. According to the system, through standardization and configuration design of an equipment adaptation process, a novel equipment access template is provided, a data demand and a test process are defined, a new product access period is shortened, and the market response speed is improved.

The invention discloses a comprehensive evaluation method and system for a pollutant control line in the iron and steel industry, and solves the problem that the evaluation result of the pollutant control line is inaccurate due to the fact that the evaluation dimension of the pollutant control line is single and full-life-cycle decision cannot be supported in the prior art. Performing cost evaluation and benefit evaluation on the cost index and the benefit index based on the exponential decay model, constructing a fusion evaluation matrix in combination with the cost evaluation and the benefit evaluation, and determining a cost evaluation grade and a benefit evaluation grade based on the fusion evaluation matrix in combination with a close degree method; according to the method, cost evaluation and benefit evaluation are carried out on the cost index and the benefit index based on the exponential decay model, and the change rule of the technical performance along with time can be dynamically reflected, so that the long-term cost-benefit prediction error is remarkably reduced; the cost reference value and the benefit reference value are combined to construct the fusion evaluation matrix, so that the economy and effectiveness of the full life cycle of the technology can be simulated more truly.

The present invention relates to computer implemented adaptive and iterative methods to improve and accelerate a quality control decision or a performance test in a specific technical field while reducing the overall cost of the quality control or performance test process, respectively; the method comprising: constructing a sequential test; iterating the following steps using the sequential test until a decision is made, obtaining a set of samples, checking the decision of the criteria for the set of samples, determining in an adaptive way the optimal number of additional data samples required for the subsequent iterative step if no decision has been made in the current iteration.

The present application relates to the technical field of logistics distribution, in particular to a logistics distribution method and system based on the Internet of Things, comprising: calculating the predicted travel time cost of each road section in the road network in multiple future time windows through a space-time prediction model; generating a discrete signing success probability distribution for each order within the multiple time windows using a Bayesian inference model; applying a multi-objective optimization algorithm to obtain a set of Pareto optimal distribution scheme sets; selecting a final distribution route from the Pareto optimal distribution scheme set according to a preset risk preference or cost benefit strategy, and issuing it to the vehicle terminal for execution. The present application can provide decision makers with a scientific basis for selection based on different business strategies, making the final distribution decision more flexible and strategic.

The present application relates to the technical field of industrial process control and intelligent manufacturing, in particular to a wood shaving mixture quality control and efficiency optimization management system, comprising a performance factor deconstruction unit for collecting real-time agent attribute data to generate an agent attribute vector; converting the agent attribute vector into a standardized performance factor vector; a total factor exposure evaluation unit for calculating a real-time total factor vector; solving the factor deviation degree of the real-time total factor vector and an ideal factor vector; and discriminating the factor deviation degree to generate a qualified signal, a first-level deviation signal or a second-level deviation signal; a formula dynamic optimization unit for constructing a cost-benefit optimization function, solving a new mixing ratio and issuing an execution; a model closed-loop correction unit for constructing a performance prediction model; and updating a factor mapping matrix; the present application solves the technical problem of difficult unified management and evaluation caused by high heterogeneity of raw materials, effectively ensuring the stability of the final product quality.

The application discloses a line intelligent planning and fault early warning method suitable for power engineering design and relates to the technical field of power engineering. The method comprises the following steps: through spatial semantic segmentation and a cost surface model, quantifying geographic information, geological conditions and construction cost into a continuous decision surface, combining a dynamic clustering algorithm to identify a large-area low-cost area as a primary feasible region; based on spatial features, geological stability and facility correlation data, constructing multi-dimensional decision indicators, and through real-time correlation coefficients, identifying "fault regions" such as terrain mutations or facility conflicts, and triggering an abnormal processing mechanism; selecting a seed region with the optimal construction condition as an anchor point, constructing a joint probability model of spatial features and cost, combining an expectation maximization algorithm and graph optimization technology, and generating an optimal path that takes into account the feasibility probability and cost benefit. The application improves the scientificity and economy of cable planning under complex geological conditions and provides key technical support for the intelligentization of power engineering.

The invention relates to the technical field of industrial process control and intelligent manufacturing, in particular to a wood shaving mixing quality control and efficiency optimization management system which comprises a performance factor deconstruction unit used for collecting real-time agent attribute data to generate agent attribute vectors; converting the proxy attribute vector into a standardized performance factor vector; the total factor exposure evaluation unit is used for calculating a real-time total factor vector; calculating the factor deviation degree of the real-time total factor vector and the ideal factor vector; the factor deviation degree is judged, and a qualified signal, a first-level deviation signal or a second-level deviation signal is generated; the formula dynamic optimization unit is used for constructing a cost-benefit optimization function, solving a new mixing proportion and issuing and executing the new mixing proportion; the model closed-loop correction unit is used for constructing a performance prediction model; updating the factor mapping matrix; the technical problem that unified management and evaluation are difficult due to high heterogeneity of raw materials is solved, and the quality stability of final products is effectively guaranteed.

The application discloses a kind of financial data analysis early warning systems based on artificial intelligence, it is related to financial analysis technical field, system includes: data acquisition module, for obtaining the consumption record data of staff;Behavior pattern analysis module, for the behavior pattern analysis of the consumption record data, identifies historical consumption behavior;Consumption mode library construction module, for according to the historical consumption behavior, constructs consumption mode library;Consumption intention identification module, for identifying current consumption intention, the current consumption intention includes consumption type, budget range and time requirement;Consumption optimization suggestion generation module, for according to the current consumption intention and the consumption mode library, generates consumption optimization suggestion.The application can combine historical consumption behavior and current consumption intention to generate consumption optimization suggestion, to realize financial data analysis early warning, improve cost efficiency, reduce operating cost.

The invention discloses an active power distribution network hierarchical autonomous balancing method based on scenarized multiple targets, and belongs to the technical field of power system power distribution network operation control. Comprising the following steps: defining a balance range according to a unit, grid and partition three-level architecture, and dividing a receiving type group, a balance type group and a sending type group by double indexes; a typical scene is described in multiple dimensions, an extreme load day is selected through a convex hull algorithm, a conventional day is selected in normal distribution, and new energy output is fitted in combination with Beta distribution to generate a credible curve; a differential active balance strategy is adopted, candidate measures are matched based on scene features, an optimal scheme is selected by adopting cost-benefit scores, and hierarchical power flow optimization cross-voltage hierarchy collaboration is realized; and full-dimensionally evaluating adequacy and safety, and dynamically iteratively adjusting the full-dimensionally evaluated adequacy and safety. The method improves the power grid balance capability and the new energy consumption rate, reduces the operation cost, guarantees the system stability, and is suitable for the intelligent operation of the active power distribution network.

The invention relates to the technical field of cloud-side cooperation processing, and particularly discloses a scenic spot data analysis method and system based on cloud-side cooperation, a terminal and a medium, and the method comprises the steps that an edge node generates a preliminary event processing result through local processing logic, determines a data reporting strategy according to an event grade division result, and carries out data reporting; collaborative semantics representing a processing purpose are carried during data reporting. The cloud end portraits the to-be-processed tasks according to the task parameters and the current overall operation situation of the scenic area to obtain portraying features of the to-be-processed tasks; screening out a processing unit for executing the current to-be-processed task according to the portrait features in combination with the task priority, the data affinity and the cost effectiveness; the processing unit performs execution according to a pre-constructed scenic region knowledge graph to generate a processing result; and performing multi-level early warning according to a processing result, and generating and issuing a system control instruction. According to the invention, unnecessary data uploading is reduced, and end-to-end response time is shortened; and the accuracy and efficiency of model processing are improved.

The invention discloses a multi-objective planning method, system and equipment for an integrated energy system and a storage medium, and the method comprises the steps: carrying out the mathematical modeling of key equipment in the system, building a multi-energy flow balance constraint, and further building a multi-objective optimization function comprising cost, carbon emission and system toughness. On the basis, a marginal cost curve reflecting toughness, environmental protection and comprehensive improvement economic cost is generated through iterative optimization; and further converting the comprehensive marginal cost value into natural logarithmic coordinates and carrying out second-order difference calculation, and taking an inflection point which is generated for the first time from negative to zero or positive as a planning scheme with the optimal cost benefit. According to the method, the toughness is improved and the marginal cost of carbon emission is reduced through quantitative evaluation, and an innovative inflection point identification method is utilized, so that effective balance of economy, toughness and low-carbon property is realized, and a decision-making basis giving consideration to benefits and robustness is provided for planning of a comprehensive energy system.

The invention discloses a curve track-bridge coupling system support stiffness identification method based on a vehicle scanning method, which belongs to the field of engineering structure monitoring, and comprises the following steps: establishing a curve double-beam system mechanical model with moving elastic mass, deducing a control equation, and solving by adopting a modal superposition method and Laplacian transformation to obtain the support stiffness of a curve track-bridge coupling system. Coupled system frequencies are extracted from contact points or vehicle responses, in particular track moduli are efficiently identified using fourth frequency components of first order modalities. The method has the advantages of being high in operation maneuverability, high in cost-benefit ratio and outstanding in implementation efficiency, and is suitable for health monitoring of the curve track-bridge coupling system.

The invention discloses a multi-channel intelligent customer acquisition and cost optimization method and system, and the method comprises the steps: integrating a plurality of customer acquisition channels, such as a social media platform, a search engine, an offline store, a cooperative merchant, and the like through building a standardized data interface, and collecting customer flow data, marketing activity data and channel putting cost data; the system carries out cleaning, duplicate removal and normalization preprocessing on collected data, and a prediction model is established by using big data technologies such as time sequence analysis, geographic information system analysis and customer portrait analysis in combination with machine learning algorithms such as a decision tree and a random forest. Based on cost-benefit ratio calculation and a Markov decision process, the system dynamically adjusts marketing resource distribution of each channel, including advertisement putting budget and popularization personnel configuration. According to the method, the customer obtaining cost can be remarkably reduced, the customer obtaining efficiency is improved, the market competitiveness is enhanced, scientific data support is provided for management decision, and intelligent customer obtaining and refined cost management of a consumer financial institution are realized.

The invention provides a multi-modal data set construction method and management system, and relates to the technical field of data management and maintenance, and the method comprises the following steps: collecting the related feature information of data in a data set; systematized preprocessing is carried out on the related feature information; generating a storage quality index according to the related feature information score, and constructing a storage value evaluation system; when the storage space is lower than a space threshold value, collecting correction dynamic information of the data, and generating a storage correction coefficient through the correction dynamic information; and generating a storage optimization index based on the storage quality index and the storage correction coefficient, and re-evaluating the data. According to the method, a storage quality index and storage optimization index quantitative index system is introduced, a fine management closed loop which takes the data value as the center and actively adapts to business and resource changes is formed, and the utilization rate of storage resources and the cost effectiveness are remarkably improved while the data quality and the application efficiency are guaranteed.

Systems and methods for generating and implementing real-time decarbonization roadmaps for at least one industrial facility are disclosed. In one embodiment, a method comprises obtaining GHG emissions data from at least one sensor associated with the facility's equipment, storing and categorizing the emissions data according to direct and indirect (Scope 1 and Scope 2) emissions, and forecasting long-term emissions under a business-as-usual scenario. The method further involves receiving and evaluating decarbonization initiatives—such as energy efficiency measures, carbon capture and storage (CCS) projects, or emissions offsetting strategies—based on their applicability and cost-effectiveness. By generating marginal abatement cost curves (MACCs) that consider capital and operational expenses, implementation feasibility, and project timelines, the method identifies prioritized emissions reduction measures. The output includes implementation directives provided to a controlled facility emissions system, which can adjust equipment parameters, integrate renewable energy solutions, or otherwise modify operating conditions to achieve targeted GHG reductions.

The invention discloses a new energy output prediction method and system based on a Mama model, and the method comprises the steps: collecting historical new energy output data and natural environment data in the same period, and carrying out the multi-dimensional data fusion according to a time sequence, and forming a historical data set; performing normalization processing on the data in the historical data set; based on the normalized historical data set, adopting a progressive training strategy to train a pre-constructed Mamba model; and inputting the current natural environment data into the trained Mamba model, and outputting a new energy output prediction result. The method has the advantages of being high in prediction accuracy, high in prediction speed, high in adaptability, remarkable in cost effectiveness, efficient in data utilization and good in expandability.

The present application relates to a kind of intervention effect analysis method, device and equipment based on illegal accident, and it is related to the field of intelligent transportation.The intervention effect analysis method based on illegal accident provided by the present application not only pays attention to the change rate of illegal accident data, but also emphasizes the role of two key factors, intervention cost and intervention efficiency.In particular, this method calculates the cost-benefit ratio of each intervention measure by dynamically monitoring the illegal and accident data of a certain section of a road within a certain period of time, combined with the changes before and after the implementation of specific intervention measures, thereby achieving a more comprehensive and in-depth evaluation of the effectiveness of intervention measures.In this case, it can be revealed which types of intervention measures are most likely to bring significant safety improvements, and it can also help identify strategies that may consume excessive resources but have little effect, providing strong data support for policymakers.

The invention discloses a reservoir safety monitoring method based on edge calculation, and particularly relates to the technical field of hydraulic engineering safety monitoring. According to the method, the reservoir geographic grid model is constructed and the static environment parameters are pre-calculated, so that an objective and uniform data base is established for evaluation, a quantifiable index system covering three dimensions of monitoring perception, data timeliness and cost effectiveness is established, and accurate calculation logic is defined for each dimension; different layout schemes are converted into a group of calculable coefficients, and finally a comprehensive and comparable comprehensive efficiency index is generated through weighted fusion, so that quantitative sorting of advantages and disadvantages of the schemes is realized, subjectivity and randomness of decision making are greatly reduced, and scheme comparison and selection are based on basis, scientific and transparent.

The invention discloses an enterprise planning and site selection method based on probability statistics, which relates to a data processing technology and comprises the following steps: acquiring enterprise site selection characteristics from sample enterprises; the method comprises the following steps: converting enterprise site selection characteristics into a quantifiable multi-level index system, dividing a research area into a plurality of grid units, placing sample enterprise data of a sample enterprise in the grid units, and respectively detecting whether each index is related to enterprise site selection through chi-square test; if the indexes are irrelevant to enterprise site selection, the indexes are removed from the multi-level index system; counting probability distribution of each index according to index grading; and calculating a final site selection probability from the multistage index system according to the probability distribution, and endowing the final site selection probability to the grid unit to obtain a grid site selection probability graph. According to the method, probability statistics and geographic information are combined, simplicity, interpretability and cost effectiveness are kept, practicability and fusion with traditional planning are more emphasized, and implementation obstacles caused by excessive dependence on complex technologies are avoided.

The invention relates to an intelligent electric energy meter design priority decision processing method and device, equipment, a storage medium and a program product, and relates to the technical field of intelligent electric energy meters. According to the invention, the design priority information of the intelligent electric energy meter can be accurately and reliably provided. The method comprises the steps that a five-dimensional intelligent electric energy meter design measurement model is established, a target layer indicates a design target of an intelligent electric energy meter, a criterion layer indicates a plurality of design dimensions under the design target, and an index layer indicates a plurality of factor indexes under each design dimension; design dimensions include core performance, cost effectiveness, security reliability, user and deployment convenience and sustainability. Determining a first relative weight according to an importance comparison result among the plurality of design dimensions; determining a second relative weight according to an importance comparison result among a plurality of factor indexes under each design dimension; obtaining a global weight of each factor index according to the first and second relative weights; and obtaining design priority decision information of the intelligent electric energy meter based on the global weight.

The “Good Customer Score (GCS)” consists of the components necessary using data and analytics to effectively identify a “Good Customer.” GCS is to be used in predictive modeling projects designed to help improve the cost effectiveness and the new development of Acquisition, Retention, Recovery, Cross-sell, Reward, and Loss Prevention efforts. Adding the GCS to the Enterprise Data Warehouse would allow the enterprise to begin using GCS to develop specific targeted customer strategies.

The application discloses a vertical AI application evaluation data distribution semi-automatic fitting method and system. The method comprises the following steps: obtaining a business specification document, and constructing a double-layer directory structure according to the business specification document; obtaining historical data; based on the double-layer directory structure and the historical data, high-frequency or core attribute combinations are counted to obtain test points; the types of test questions in the historical data are analyzed to determine the proportion of the types of test questions; according to the test points and the proportion of the types of test questions, a corresponding number of knowledge-based questions and logic-based questions are extracted to form a preliminary evaluation set; the preliminary evaluation set is audited, and the preliminary evaluation set is optimized and adjusted according to the audit result. By implementing the method of the application, the utility problem of the existing evaluation data set in the application of the vertical field can be solved, and the model performance evaluation can accurately reflect the actual business performance and has cost effectiveness.

The invention relates to the technical field of energy conservation and emission reduction, and discloses an energy conservation and emission reduction measure determination method based on carbon emission prediction, which comprises the following steps: dividing an emission data set to obtain a training set, a verification set and a test set, constructing a prediction model by using the training set and the test set to obtain a trained prediction model, and performing prediction based on the trained prediction model to obtain a prediction result. The method breaks through the limitation of a traditional static model, captures the causal relationship of multiple variables along with time evolution through the emission coefficient, and provides support for prediction; meanwhile, prediction and intervention form closed-loop cooperation, medium-and-long-term emission trend prediction is achieved based on a prediction model of emission coefficient weighting, intervention measures are designed in combination with a generation model embedded with energy conservation constraints, and it is ensured that the measure combination strictly follows the physical law. And the technology maturity, the cost benefit, the social acceptance and the policy compliance are evaluated through the multi-dimensional scoring matrix, and the whole-process scientific guidance from prediction to measure landing is formed.

The invention relates to an intelligent building aided design method, system and device and a medium. The aided design method comprises the steps that a three-dimensional building information model is constructed based on building basic parameters; analyzing a preset technical contradiction matrix to generate a monitoring strategy, and outputting an enhanced building information model and a monitoring strategy file; dynamically adjusting sensor deployment parameters; performing data preprocessing in combination with external environment data to generate a space-time fusion database; performing risk trend prediction and dynamic calculation of an early warning threshold through a machine learning model, and outputting a risk early warning instruction set and a structure damage report; simulating mechanical distribution of different reinforcement schemes, calculating a cost-benefit ratio, and generating an interactive decision panel and a recommendation scheme report; and receiving a feedback score of the user on the recommendation scheme report and an early warning accuracy statistical result through the interactive decision panel, and updating a deployment rule in the monitoring strategy file and a parameter weight of the machine learning model. According to the invention, the safety monitoring precision and the early warning response speed of the building are improved.

To enable advantages and disadvantages to be grasped in introducing a system having an information acquisition function of a knowledge system or the like.SOLUTION: A cost-effectiveness calculation device for calculating cost-effectiveness in introducing a system having an information acquisition function for acquiring information represented by a knowledge system includes a cost acquisition part for acquiring cost about introduction of the system, a first modeling part for calculating a first model showing relations of the cost, an information amount showing amount of the information and information quality showing the quality of the information, a second modeling part for calculating a second model showing relations of the information amount, the information quality and information acquisition performance showing the performance of the information acquisition function by using the first model, and a cost-effectiveness calculation part for calculating cost-effectiveness in introduction the system including the cost and the information acquisition performance being an advantage / disadvantage index in the introduction of the system by using the first model and the second model.SELECTED DRAWING: Figure 1

The application discloses a curve track-bridge coupling system support stiffness identification method based on a vehicle scanning method, belongs to the field of engineering structure monitoring, and establishes a curve double-beam system mechanical model with a mobile elastic mass, deduces a control equation, and solves the control equation by using a modal superposition method and Laplace transformation, extracts a coupling system frequency from a contact point or a vehicle response, and especially efficiently identifies track modulus by using a fourth frequency component of a first-order modal. The application has the advantages of strong operation maneuverability, high cost benefit ratio and outstanding implementation efficiency, and is suitable for health monitoring of a curve track-bridge coupling system.

To provide a design support system that assists even inexperienced design engineers in appropriately selecting new materials and components suitable for the purpose of product design, and that can also present the annual cost-effectiveness of design changes using new materials and components. [Solution] The design support system 1 is equipped with a database 120, a machine learning means 110, an input means 310, and a display means 320. The database stores design information for multiple materials and parts that make up a product in advance. The machine learning means generates a trained model 130 that extracts design information with a high degree of fit to the design target information, using the multiple design information as training data 112. The trained model then calculates the functional fit and cost fit for existing materials and parts and new design candidates individually, based on the design target values ​​input by the design engineer. Furthermore, the display means displays the respective fit scores side by side so that the design engineer can easily understand the comparison results.

The invention discloses a second-hand car price prediction method based on a hybrid model. The second-hand car price prediction method comprises the steps of calculating a brand reputation index, a car rare index and a configuration complexity score through enhanced feature engineering; carrying out price interval classification on the to-be-predicted vehicle by adopting an improved K-nearest neighbor algorithm; according to the price interval, calling a constructed layered random forest model to carry out accurate price prediction; and setting an initial weight based on the price interval, carrying out weighted fusion on prediction results of the K nearest neighbor algorithm and the random forest through a dynamic weight fusion algorithm, and carrying out calculation to obtain a final price prediction value and a confidence interval. According to the method, the calculation efficiency is improved, the prediction precision is remarkably improved, rare vehicle model prediction is highlighted, the robustness is enhanced, and the cost effectiveness is remarkable.

The invention discloses a financial data analysis early warning system based on artificial intelligence, and relates to the technical field of financial analysis, and the system comprises a data obtaining module which is used for obtaining consumption record data of employees; the behavior pattern analysis module is used for performing behavior pattern analysis on the consumption record data and identifying historical consumption behaviors; the consumption mode library construction module is used for constructing a consumption mode library according to the historical consumption behaviors; the consumption intention recognition module is used for recognizing a current consumption intention, and the current consumption intention comprises a consumption type, a budget range and a time requirement; and the consumption optimization suggestion generation module is used for generating a consumption optimization suggestion according to the current consumption intention and the consumption mode library. According to the method, the consumption optimization suggestions can be generated in combination with the historical consumption behaviors and the current consumption intention, so that financial data analysis and early warning are realized, the cost effectiveness is improved, and the operation cost is reduced.