26 results about "Cost efficiency" patented technology

The embodiment of the invention provides an elastic fragment routing and cold and hot data optimization method based on multi-dimensional feature prediction and related equipment, and belongs to the technical field of distributed database storage optimization. The method comprises the following steps: receiving a data stream from Internet of Things equipment; performing analysis and feature extraction on the data stream to obtain multi-dimensional feature data; according to the multi-dimensional feature data and the time sequence prediction model, performing prediction analysis on a fragmentation strategy to obtain an optimal fragmentation strategy; analyzing the routing decision according to the data priority of the data stream and a dual-mode routing engine to obtain routing decision result data; generating a target index name and creating a target index according to the optimal fragmentation strategy and the routing decision result data; according to the target index name, the data stream is routed to the target index according to the fragmentation key for batch writing; the fragmentation keys are from multi-dimensional feature data. According to the embodiment of the invention, intelligent prediction of the fragment number and automatic hierarchical storage of data can be realized, and the problems of resource elastic scaling and cost efficiency optimization are fundamentally solved.

The invention discloses a cloud service cost optimization and prediction analysis system based on artificial intelligence, and relates to the technical field of cloud service cost optimization, and the system comprises a cost feature deep analysis module which accesses a cloud service full life cycle standardized cost data set, and generates a cost efficiency evaluation matrix and an abnormal attribution report; the multi-modal prediction engine module is used for constructing a multi-scene cost prediction model; the dynamic optimization decision module is used for generating a resource dynamic scheduling strategy, a service type selection optimization scheme and a cost budget dynamic allocation plan, and calculating an expected cost saving rate and a risk coefficient of each scheme; and the closed-loop iteration upgrading module tracks the implementation effect of the optimization scheme in real time and updates the three-dimensional characteristic spectrum and prediction model parameters. According to the invention, through a three-dimensional characteristic spectrum, multi-algorithm fusion prediction, SLA constraint verification and a closed loop iteration mechanism, and by matching with an intelligent interaction visualization and early warning module, intelligent transformation of the cloud service cost from passive accounting to active prediction and from experience optimization to scientific decision is realized.

An artificial intelligence-based business management system for real-time decision optimization, consisting of: a data acquisition unit configured to ingest structured, semi-structured and unstructured data streams from enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, Internet of Things (IoT) devices, external market feeds and financial transaction systems, encrypting, timestamping and verifying the data prior to further processing; a graph processing unit that is communicatively connected to the said acquisition layer, wherein the unit is configured to encode heterogeneous data into dynamic graph structures comprising nodes representing business entities and edges representing transaction or relationship dependencies, wherein the unit is further configured to perform deduplication, metadata tagging and real-time data synchronization; a decision optimization unit operationally linked to the graph processing unit, wherein the decision optimization unit includes modules for reinforcement learning, modules for Bayesian optimization and multi-objective solvers configured to simulate multiple alternative decision paths and select an optimal path based on performance indicators such as cost efficiency, resource utilization, customer satisfaction and risk minimization; a real-time inference control unit with specialized hardware cores, including at least one graphics processing unit (GPU), a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC), wherein the accelerator performs inference tasks of the decision optimization unit with a latency in the millisecond range; a diagnostic processing unit configured to generate causal diagrams, feature mapping maps, and interpretable result summaries according to the optimization outputs; and a control interface unit configured to transmit optimized decisions to process controls within the enterprise, robot actuators, planning systems or interactive dashboards, with the interface supporting bidirectional communication for higher-level interventions, error feedback and triggers for re-optimization.

The invention discloses a warehousing network model and algorithm based on two-stage model collaborative optimization, and belongs to the technical field of warehousing network models. The invention discloses a warehousing network model and algorithm based on two-stage model collaborative optimization. The warehousing network model comprises an analysis and prediction module, an inventory collaborative management module and an optimization model. According to the invention, the problem of lack of overall planning of the full-link cost of the storage network in the prior art is solved. Through collaborative optimization of the first stage model and the second stage model, balance of long-term cost and short-term cost is realized, full-link cost optimization is achieved, the storage space utilization rate is improved, maximum utilization of manpower, equipment and space resources is realized, and through integration of regional economic data, population density, a traffic network, competitor layout and other multi-dimensional data, the real-time performance of the system is improved. Costs, efficiency and risks of different site selection schemes are calculated, a clear quantitative basis is provided for long-term decisions such as storage node layout and facility scale determination, and large deviation of demand probability fitting is avoided.

A business management system with integrated business intelligence and IT-supported optimization, consisting of: a computer device comprising at least one processor and a memory arrangement for storing executable instructions; a data acquisition and integration unit that is operationally connected to the at least one processor and is configured to receive heterogeneous enterprise data from a variety of source systems, including financial transaction systems, operational control systems, human resource information systems, supply chain systems, customer interaction systems, and IT infrastructure monitoring systems, wherein the data acquisition and integration unit is further configured to normalize syntactic formats, resolve temporal inconsistencies, and map source-specific attributes into a unified enterprise data representation stored in the storage system; a business intelligence processing unit that is operationally coupled with the data acquisition and integration unit and is configured to calculate business performance indicators through multidimensional aggregation, correlation analysis, and trend extraction over the unified business data representation, with the calculated business performance indicators being time-stamped and permanently stored; an optimization unit that is operationally coupled with the business intelligence processing unit and is configured to compare the calculated business performance indicators with stored business objectives and constraints and to determine optimized business control parameters that correspond to resource allocation, operational planning, cost efficiency or performance improvement; a control and orchestration unit that is operationally linked to the data acquisition and integration unit, the business intelligence processing unit, and the optimization unit, wherein the control and orchestration unit is configured to coordinate the execution sequence, forward the results of the business intelligence analysis to the optimization unit, and manage feedback by incorporating the effects of applied optimized business control parameters into subsequent data acquisition cycles; and a communication interface that is connected to at least one processor and configured to exchange data and control signals with external enterprise systems and user terminals.

The invention provides a household appliance control method and system, and relates to the technical field of intelligent household appliances. According to the method, a user instruction is subjected to localization analysis by introducing an intention complexity classifier, and a processing path is dynamically allocated according to an analysis result: a simple instruction is directly executed by a user terminal and feedback is generated, so that low-delay response and offline availability are realized, and the interaction fluency is improved; complex instructions are uploaded to the cloud service platform to be processed, multi-step task analysis and equipment cooperative control are completed through the powerful computing power of the cloud service platform, and the intelligent level in a complex scene is guaranteed. Through cooperative processing of local real-time response and cloud deep planning, dependence on cloud resources and sensitive data transmission are remarkably reduced, efficient, safe and consistent user interaction experience is supported while network loads and computing overhead are reduced, and performance, privacy and cost benefits are considered.

Embodiments of the invention provide a data processing method, a computing device, a computer readable storage medium and a program product. The data processing method comprises the steps of obtaining a to-be-processed short message and a prompt text corresponding to the to-be-processed short message; the to-be-processed short message and the prompt text are input into a short message evaluation model, an evaluation report output by the short message evaluation model is obtained, and the evaluation report comprises risk grades for grading the to-be-processed short message according to an auditing rule in the prompt text; according to the risk level in the evaluation report, determining an audit result corresponding to the to-be-processed short message; according to the technical scheme, the labor cost of overall auditing is remarkably reduced, the auditing efficiency is improved, the risk of missed auditing and false auditing caused by artificial fatigue can be reduced, and optimal balance of cost, efficiency and accuracy is achieved.

This invention relates to a method, system, and storage medium for overselling computing power compatible with multi-architecture GPUs. The method includes: acquiring GPU computing power resources of different architectures from different suppliers; classifying each GPU computing power resource according to its manufacturer to form different resource pools; generating a logical computing power inventory available for listing and trading based on resource parameters, historical load data, and real-time pressure monitoring values; obtaining the willingness to trade between the demander and the supplier; calculating whether the logical computing power inventory of the supplier's GPU computing power resources matches the demander's task requirements based on the willingness to trade; if they match, matching the transaction; generating a time-sensitive identity authentication token and sending it to the demander, which is used to jump to and access the corresponding GPU computing power resources for computing operations. Compared with the prior art, this invention has advantages such as improved resource utilization, flexibility in computing power allocation, operational cost efficiency, and system stability.

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 present application relates to a kind of carbon footprint intelligent matching and fast accounting system and method based on BOM table, the system includes BOM table extension data correlation unit, intelligent matching unit, missing data intelligent replacement unit, whole-process fast calculation unit and carbon footprint and cost comprehensive output unit, by automatically parsing BOM table and calling enterprise interface to obtain supply chain, cost and energy consumption data, realize data automatic acquisition and extension;Adopt natural language processing technology to standardize and intelligently match material name, combine multi-dimensional pedigree matrix to filter optimal emission factor;For unmatched data, intelligent replacement is carried out based on rule base and quantification uncertainty;Adopt parallel computing engine to realize whole life cycle carbon emission fast accounting, and the result with confidence interval is output through Monte Carlo simulation, finally, carbon cost efficiency index, stage contribution degree and supplier comprehensive evaluation report are generated by fusing cost data, significantly improve the degree of automation, calculation efficiency and result reliability of accounting.

The application relates to a task scheduling method and system based on a cross-region Storm flow processing framework, and the task scheduling method comprises the following steps: constructing a cross-region Storm flow processing framework based on a geographic distribution cloud environment, and performing task scheduling between nodes in all clouds; calculating the total cost of task scheduling of each node according to the resource use cost and energy consumption cost of all nodes performing tasks on each cloud; calculating the request delay time of task scheduling to each cloud according to the average response time of all nodes performing tasks on each cloud; calculating the target value of all nodes performing task scheduling on each cloud according to the total cost and the request delay time; constructing a priority list according to the target value, and preferentially selecting nodes with higher priority in the priority list to place tasks. The application fully considers cost efficiency and request delay constraints, preferentially schedules tasks to nodes with higher priority to perform, and obviously reduces the task scheduling cost.

The invention relates to the technical field of strategy optimization, in particular to a reinforcement learning-fused battery purchase strategy adaptive optimization method, which comprises the steps of generating an original battery purchase strategy by utilizing a pre-trained reinforcement learning model based on a current market state; inputting the original battery purchasing strategy into a strategy simulator for constraint conformity simulation, and obtaining a simulation execution result; wherein the strategy simulator is internally provided with a plurality of business constraint rules which can be dynamically configured; when the simulation execution result triggers any business constraint rule, parameter correction is carried out on the original battery purchasing strategy according to the triggered business constraint rule, and the corrected battery purchasing strategy is output and executed; and otherwise, directly executing the original battery purchasing strategy. According to the invention, by combining the comparison of the simulation execution track and the built-in business constraint rule, the battery purchasing strategy is effectively ensured to maximize the cost benefit and strictly comply with the business constraint.

The application discloses a bridge full-life-cycle carbon emission reduction cost efficiency optimization method and device based on reinforcement learning, computer equipment and a readable storage medium, comprising: acquiring carbon emission data, cost data and optional scheme parameters of each link of the full-life-cycle, and constructing a dynamic database; constructing an optimization model based on the database, and defining unit economic carbon reduction efficiency as an objective function; constructing a reinforcement learning environment, initializing a strategy network, and iteratively training through a reinforcement learning algorithm to solve optimal strategies of each link; and running the optimal strategy in a verification set, and outputting total full-life-cycle carbon emission, economic cost and link carbon reduction efficiency analysis results. The application realizes multi-link collaborative optimization by combining dynamic database real-time data updating and reinforcement learning, and provides intelligent and low-carbon full-life-cycle decision support for bridge engineering.

The invention discloses a server resource processing method and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: obtaining request data corresponding to a preset time interval before a current time by taking the current time as a reference, and carrying out the extraction according to a historical time sequence and a time sequence characteristic corresponding to a current time sequence, the method comprises the following steps of: obtaining corresponding characteristic parameters capable of representing request resource association, processing based on actual characteristic parameters to obtain prediction parameters for measuring a capacity expansion and contraction mechanism to realize a determination process of dynamic prediction parameters, comparing the prediction parameters with a threshold value to determine the corresponding capacity expansion and contraction mechanism, and realizing dynamic response through threshold value judgment. The resource demand can be pre-judged in advance, passive response is changed into active pre-distribution, and the delay of a capacity expansion and contraction mechanism is reduced through a prediction mode. Service interruption caused by insufficient resources and waste caused by excessive resources are effectively avoided, the resource utilization rate and the cost effectiveness are remarkably improved, and the reliability of the system is also improved.

A system enables clients to utilize resources that have been committed from one or more third-party resource providers, allowing them to execute various operations on a data platform. The system tracks the aggregate historical use of these resources over a specific period and monitors any unused resources within a first term commitment over an initial time period. The system also tracks requests for additional resources representing times when demand exceeded the baseline commitment and required supplemental, short-term resources. This collected data is then fed into a predictive model. The model analyzes these inputs to generate a forecast of future resource needs, providing an informed view of anticipated demand for upcoming periods. Based on this forecast, the system executes a first term commitment for resources, securing the necessary capacity over a second time period in alignment with the forecasted usage, thus optimizing resource allocation and cost efficiency.

This invention discloses a method for logistics demand forecasting and flexible capacity allocation based on big data, relating to the field of big data processing. The method includes: establishing a high-quality data foundation through multi-source data collection and standardized processing; extracting core features using a hybrid model to achieve accurate demand forecasting; simultaneously integrating real-time capacity data to construct a dynamic evaluation system; generating an initial matching scheme based on the forecast results using a multi-objective optimization algorithm; and dynamically adjusting the scheme through real-time feedback; finally, evaluating the execution effect from four dimensions: timeliness, cost, efficiency, and service; and reverse-optimizing the forecasting model and allocation algorithm to form a closed-loop system of "data-driven - intelligent decision-making - dynamic control - feedback iteration". The advantages of this invention are: it uses full-link data-driven processing as its core, achieving accurate demand forecasting through multi-source data standardized processing and hybrid modeling; relying on IoT dynamic capacity management and multi-objective optimization algorithms to achieve flexible allocation; and efficiently balancing the multi-dimensional objectives of logistics operations.

The invention discloses a reinforcement learning-based bridge full-life-cycle carbon emission reduction cost efficiency optimization method and device, computer equipment and a readable storage medium, and the method comprises the steps: obtaining carbon emission data, cost data and optional scheme parameters of each link of a full life cycle, and constructing a dynamic database; constructing an optimization model based on the database, and defining unit economic carbon reduction efficiency as a target function; constructing a reinforcement learning environment, initializing a strategy network, carrying out iterative training through a reinforcement learning algorithm, and solving an optimal strategy of each link; and verifying the optimal strategy for centralized operation, and outputting a full-life-cycle total carbon emission amount, economic cost and link carbon reduction efficiency analysis result. According to the method, data is updated in real time through the dynamic database, multi-link collaborative optimization is realized in combination with reinforcement learning, and full-life-cycle decision support with intellectualization and low carbon is provided for bridge engineering.

The invention provides a multi-server federated learning data unloading and server association joint optimization method, which comprises the following steps: S1, constructing a system mathematical model, and defining the total cost of a system as a target function, the total cost of the system comprising a data unloading cost, a data training cost, a model synchronization cost and a model updating cost; defining a link capacity constraint, a computing resource constraint and a system stability constraint; s2, determining whether a current scene is a feasible scene or not, wherein the feasible scene refers to training that system resources can bear all arriving data; and S3, if the scene is a feasible scene, executing a feasible scene optimization sub-process. According to the method, the system throughput can be improved, the cost efficiency is improved, the model precision is guaranteed, the scene adaptability is enhanced, and the convergence speed is increased.

This invention provides an energy-saving optimization control method and system for chemical equipment. The method collects historical operating data and performs operating condition clustering to construct an initial feasible domain for control variables. It constructs a multivariate heat transfer evaluation factor including energy cost, efficiency, temperature margin, pressure drop loss, and control stability, and uses a neural network to train and generate heat transfer evaluation channels, integrating multiple conflicting energy-saving indicators into a unified comprehensive evaluation value. Based on this, it employs multi-objective optimization and a genetic algorithm for progressive optimization, selecting the control parameter vector with the highest comprehensive energy efficiency from a global perspective, ensuring the rational allocation of energy throughout the system. Finally, it verifies the scaling status of equipment online, eliminating control strategies that may fail due to equipment deterioration before execution, thus solving the problem of energy efficiency decline caused by limiting control of chemical equipment to local equipment and ignoring equipment deterioration.

Methods and apparatus disclosed herein introduce a comprehensive framework for distributing generative-AI workloads across diverse enterprise hardware. Multiple routing strategies disclosed herein include user-controlled, algorithm-controlled, hybrid, and dual-path routing with feedback to accommodate varying user expertise and resource availability. The routing logic is detailed for Question Answering (QA) tasks, Retrieval-Augmented Generation (RAG)-based tasks (e.g., document parsing and retrieval), and agent tasks, including evaluation of resource availability, model complexity, and content characteristics. User feedback can be obtained to continuously refine routing decisions through Large Language Model (LLM) based and traditional machine learning models. Methods and apparatus disclosed herein initiate routing decisions to maintain cost-efficiency, performance optimization, and accuracy across heterogeneous computing environments.

The invention discloses a hardware development integration method and device, and relates to the technical field of hardware development, and the method comprises the following steps: an upper computer scans to obtain an I2C address corresponding to slave equipment; based on the I2C address of the slave equipment, judging that the corresponding slave equipment is in a working state; and setting a reference voltage set value and an output voltage set value corresponding to the slave equipment in the working state, and obtaining the resistance value of a feedback resistor corresponding to the slave equipment. The device is scanned, the address of the device is displayed, the resistance value of the feedback resistor is automatically calculated based on the input reference voltage and the output voltage, and the device has the technical advantages of being low in cost and high in efficiency.

The invention relates to the field of aerospace craft overall design and the like, and discloses a solid rocket power cost performance prediction method based on coordinate descent depth integration, which comprises the following steps: constructing a multi-source fusion database of a solid rocket engine, and training an integrated sparse neural network based on coordinate descent optimization; the integrated sparse neural network comprises an additive model, and the additive model is constructed by a base learner and a corresponding weight coefficient; constructing a target function including a mean square error term and a norm regularization term in the training of each base learner of the integrated sparse neural network; the norm regularization item is used for applying row sparse constraint to a weight matrix from an input layer to a hidden layer of the base learner; for a new design scheme of the solid rocket engine, characteristic parameters of the solid rocket engine are obtained, input characteristics are constructed, the input characteristics are input into the trained integrated sparse neural network, and a predicted output response is obtained and used for carrying out cost estimation or efficiency estimation on a power system of the solid rocket engine; the method is high in convergence speed and high in precision.

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.

The application relates to a public cloud multi-tenant performance monitoring method, device, equipment, storage medium and program product. The method comprises the following steps: collecting a plurality of low-level indexes of a cloud application runtime on a host of a target virtual machine node and a target virtual machine; the low-level indexes comprise at least one of CPU utilization, a hardware counter, a host resource state, resource usage characteristics, a hardware event vector and interference intensity; inputting the plurality of low-level indexes into a proxy model for performance evaluation to obtain a first evaluation result; the first evaluation result comprises a target performance degradation estimation value, a performance degradation reason of the cloud application and a contribution degree of the degradation reason; the target performance degradation estimation value is used for representing a degradation degree of the cloud application; and performing degradation evaluation according to the first evaluation result to obtain a second evaluation result. The above method improves the resource utilization rate and cost efficiency of the public cloud infrastructure, and further enhances the foresight and accuracy of cloud application performance management.

The invention discloses a modular man-machine collaborative express sorting system and method based on an iterative algorithm. The system comprises an annular main conveying line, a plurality of standardized sorting modules arranged along the annular main conveying line and a system control center. And each sorting module comprises a cache region, a man-machine cooperation station and a rotatable turntable with a plurality of sorting grooves. The core of the method is that a system control center operates a set of iterative sorting optimization algorithm. According to the algorithm, containers to be processed are dynamically distributed to all stations through multi-round iterative calculation according to global scanning express data, the current station of the system and the number of sorting grooves, and target delivery areas are dynamically distributed or combined for the sorting grooves of all the stations. Through the iterative process of'distribution-sorting-transfer-redistribution ', the problem of complex sorting when the number of delivery areas is far greater than the number of physical sorting grooves is efficiently solved. According to the method, the scale of the sorting system is flexible and adjustable, the sorting strategy is intelligently and dynamically optimized, and excellent balance is achieved among cost, efficiency and flexibility.