61 results about "Business value" patented technology

A computerized method for comprehensive ESG performance management quantifies multi-dimensional business value through integrated processes. A universal sustainability intelligence module receives ESG data from multiple sources, encompassing environmental, social, and governance information. An AI-driven performance intelligence engine generates sustainability insights using a universal framework that includes topic-agnostic insight generation, cross-topic opportunity optimization, universal project evaluation, and integrated pathway development. A multi-dimensional value quantification engine calculates business value metrics across cost reduction, revenue enhancement, risk mitigation, capital structure optimization, workforce value creation, supply chain sustainability, and intangible value creation. A causal linkage analysis engine establishes relationships between ESG improvements and business outcomes using attribution algorithms. A blockchain trust foundation stores immutable records using cryptographic verification. The system generates comprehensive ESG performance reports including sustainability insights, business value metrics, and verified attribution of business value to specific ESG improvements.

The invention discloses an enterprise project database intelligent management method and system, and relates to the technical field of data management and resource optimization. The method comprises the following steps: verifying the integrity of business unit project data in real time through an intelligent cleaning engine, and structurally storing the business unit project data to a central project database; integrating the progress, the cost, the risk field, the commercial value and the strategic weight to dynamically calculate a comprehensive value index; based on the value index and the real-time resource inventory, a resource allocation scheme is generated through a hierarchical adaptation strategy, and a priority list is output; tracking progress deviation during execution, and triggering data isolation and index optimization when risk offset exceeds a threshold value; risk root causes are analyzed, value weights are dynamically adjusted, a rule base is managed in a back-feeding mode, and system self-optimization is achieved. According to the method, the problems of resource allocation rigidness, risk response lag and lack of dynamic value guidance in traditional project management are solved, the resource utilization rate is improved, and the project delay risk is reduced.

The invention relates to the field of manufacturing industry, in particular to a pomegranate peeling production line-oriented multi-process collaborative scheduling optimization method, which comprises the following steps of: acquiring and processing a first business data stream from an internet of things interface, and inputting historical sensor data into a business prediction model to generate a second business data stream; generating a third service data stream by using the data of the multi-mode sensor array; constructing a multi-target business cost function through a business decision optimization engine; starting an iterative optimization business simulation process to obtain an optimized scheduling scheme; and generating a corresponding digital production work order according to the optimized scheduling scheme. According to the method, equipment state, equipment health degree cost and quality indexes are analyzed through fusion of three business data streams and a multi-objective cost function construction method, comprehensive commercial value evaluation is generated, iterative optimization simulation and closed-loop optimization are started when conditions are met, scheduling optimization of timeliness and value fluctuation in perishable material scheduling is achieved, and the scheduling efficiency of perishable materials is improved. And the commercial value creation and self-adaptive scheduling capability of production is improved.

The invention discloses a bank information system product management method, which comprises links of product planning and design, detailed function design and development, multi-dimensional test and product release, product exit and resource management and full life cycle data management, and defines business value and technical feasibility evaluation standards. The test, approval and resource management requirements are standardized, product full-life-cycle standardized digital and intelligent management is achieved, a computer program of the storage medium comprises a product catalog and function management module, an information retrieval display module and a transaction cockpit module, and SLA index retrieval, user-defined analysis and information change notification are supported. According to the method, the problems of lack of product management whole-process specifications, dispersion of directory platforms, insufficient decision data support and lack of service quality management and control are solved, product whole-process standardized management is realized, cross-department cooperation efficiency is improved, data support is provided for decision, product service quality is guaranteed, and the method has remarkable application value.

An embodiment extracts, from vulnerability data describing a vulnerability applicable to a connected asset within a network of connected assets, a set of technical impacts of the vulnerability. An embodiment generates, using a business value context of the connected asset and the set of technical impacts of the vulnerability, a network layer impact score corresponding to the connected asset and the vulnerability. An embodiment generates, using the business value context of the connected asset and the set of technical impacts of the vulnerability, an enterprise layer impact score corresponding to the connected asset and the vulnerability. An embodiment generates, using the network layer impact score and the enterprise layer impact score, a remediation plan for the connected asset, the remediation plan comprising a planned adjustment of the connected asset to ameliorate the vulnerability.

The invention discloses an AI knowledge service system and method based on a financial intelligent context protocol, and relates to the cross technical field of artificial intelligence and financial science and technology, and the system comprises a financial query understanding and routing layer, a financial document relationship modeling layer, a financial intelligent context protocol intelligent context regulation and control layer, and a financial compliance guarantee layer. Through financial field innovation of a financial intelligent context protocol (F-MCP) protocol, the application effect of AI knowledge service in a financial scene is remarkably improved; in the technical level, compared with a traditional retrieval enhancement generation (RAG) system, the financial illusion rate is reduced by 75%, and the retrieval precision is improved by 45%; in the business level, the key pain points of poor timeliness, high compliance risk and the like of financial information are effectively solved; in the engineering level, the integration complexity is reduced by 40% through modular design, and financial-level security deployment is supported; and on the application level, a technical basis is provided for large-scale deployment of financial AI application, financial science and technology innovation and industry digital transformation are promoted, and the method has remarkable technical value, commercial value and social value.

The invention discloses a data annotation method and device based on a graph structure and community discovery and a storage medium, and belongs to the field of data processing. Customer transactions, commodity attributes, behavior tracks and environment data are obtained, and a heterogeneous graph network including a basic physical layer (transaction association and commodity attribute mapping), a behavior semantic layer (behavior pattern and semantic association) and an environment association layer (environment dynamic influence) is constructed. A community discovery algorithm is adopted to mine customer groups with cross-department business values, and a three-level label system for quantifying basic value attributes and fluctuation coefficients, behavior patterns and commodity preferences and dynamic track characteristics is constructed. Therefore, unified understanding of customer behaviors, deep mining of multi-dimensional customer relationships, dynamic evolution capture of customer values and hierarchical customer cognition construction are realized, and the problems that data islands are caused by task guidance, dynamic value mining is restricted by single data dimensions, and hierarchical cognition construction is restricted by flattening of a label system in a traditional method are solved.

The invention discloses a digital asset value evaluation method and system for power dispatching. The method comprises the steps of firstly obtaining a digital asset set in a target scheduling scene, then determining internal attributes of the digital asset set based on a power system physical topology and an operation rule, and determining external value characteristics of the digital asset set based on a power market environment and a scheduling decision demand. Thirdly, value calculation is carried out on various digital assets by utilizing the model in combination with real-time and historical scheduling operation data, and a basic value score is output; and generating a comprehensive value evaluation result and a value label according to the score in combination with a preset rule and an adjustment factor. Finally, the evaluation result and the label are applied to a power dispatching auxiliary decision support system, and a quantitative basis is provided for dispatching plan optimization, data asset internal pricing and external transaction. According to the method, accurate, dynamic and business value evaluation of the power dispatching digital assets is realized, and value release and efficient utilization of data elements are effectively supported.

The invention provides an enterprise value chain carbon emission data processing method, system and device and a medium, and the method comprises the steps: integrating an external carbon emission parameter and operation activity data of an internal business system, building an incidence relation of different data source fields, generating a data set with a unified identifier, and carrying out the processing of the data set; the method comprises the steps that supplier data are acquired, whether the supplier data contain a carbon emission value or not is identified, if the supplier data contain the carbon emission value, the carbon emission value is directly used as an actual carbon emission value, otherwise, a theoretical carbon emission value is calculated through a material structure of a target commodity and a dynamic coefficient library, and finally a visual report of carbon emission distribution is generated based on the actual or theoretical carbon emission value. According to the method, automatic treatment and accurate checking of enterprise total value chain carbon emission data are realized, the data accuracy and management efficiency are remarkably improved, and the method is suitable for enterprise carbon footprint accounting and supply chain emission reduction strategy making under the carbon neutralization target.

The invention relates to the technical field of equipment asset management and operation and maintenance decision, and discloses a fluid measurement and control equipment full-life-cycle operation and maintenance management system based on multi-source data, and the system comprises the steps: distributing an operation and maintenance depreciation right initial limit for equipment; the method comprises the following steps of: acquiring the multi-source data reflecting the physical loss, the business value, the real-time operation efficiency and the operation stability of the equipment, determining the product of a plurality of factors based on the multi-source data, dynamically calculating the consumption rate of the ODR, and further driving the operation and maintenance decision according to the residual limit. The consumption cost of the value budget management process is adjusted by the business value, the operation efficiency and the stability risk in real time, and a quantitative bridge for connecting the physical world and the business decision is established, so that the operation and maintenance decision can be carried out based on a clear economic basis instead of a single physical threshold value; therefore, the asset management level and the decision-making quality are improved.

The invention relates to the technical field of smart community service and real estate data collaboration, in particular to an intelligent and digital driven real estate and community integrated service system. According to the technical scheme, the system comprises an AI intelligent data middle platform, a real estate service intelligent module, a community service digital module, a cross-scene intelligent matching module, a full-process digital operation module, a commercial value increment module, a multi-terminal collaborative interaction module and a safety and compliance management module, all the modules operate collaboratively, and a real estate and community full-service chain is endowed. The business ecology is constructed, the house property and community full-business chain is deeply endowed through intelligent and digital technologies, the problems of insufficient intelligence, data collaboration deficiency, low operation efficiency, weak business value and the like in the prior art are thoroughly solved, and a virtuous circle of technology endowment, business optimization and business value increment is constructed. The method accords with the core appeal of the current capital market for intelligentization, digitization and high growth potential, and has remarkable technical innovation, commercial feasibility and capital attraction.

The invention belongs to the technical field of self-media operation and business decision, and particularly relates to a self-media account business intelligent quotation method and system, and the method comprises the following specific steps: S1, collecting self-media account basic data, user portrait data, commercial value data and market dynamic data; s2, encrypting and storing the sensitive data through AES-256; s3, adopting a random forest algorithm to construct a basic quotation model, and combining account operation fluctuation and market nodes to realize dynamic adjustment; s4, displaying quotation composition details to the account party and supporting parameter adjustment preview; and S5, generating a multi-format personalized quotation list, and negotiating and recording full-link data based on the quotation and the cooperation effect. According to the method and the system, through integrating the self-media account basic data, the user portrait data, the commercial value data and the market dynamic data, the key information influencing the quotation is covered in multiple dimensions, the quotation association factors are comprehensively considered, the deviation caused by single data limitation is reduced, and the quotation accuracy is improved.

This invention provides a semi-automatic method for constructing and iterating an ontology in the financial and tax domain, comprising the following steps: S1, system initialization and configuration; S2, data acquisition and preprocessing; S3, based on a few-shot learning model and an expert rule base, automatically extracting core concepts from structured financial and tax data and inferring relationships between concepts, performing hierarchical modeling according to financial and tax business logic, and generating a draft ontology model; S4, multi-dimensional automatic verification and manual optimization of the draft; S5, ontology output and version management; S6, dynamic iterative update of the ontology; S7, iterative automatic verification of the ontology in multiple dimensions. This invention achieves four core advantages: improved efficiency, quality assurance, scenario adaptation, and dynamic updates, solving key technical challenges in the construction and application of ontology in the financial and tax domain, providing core support for the efficient operation of financial and tax intelligent agents, and possessing broad application prospects and commercial value.

The invention relates to the technical field of big data, provides a metadata management method and device based on semantic feedback, equipment and a medium, is applied to financial and medical health care service scenes, and can reinforce a learning mechanism to initialize a state space, an action space and a reward function based on semantic feedback. The limitation that traditional reinforcement learning only depends on technical indexes is broken through, and alignment of technical optimization and business value is achieved; a metadata autonomous scheduling engine is utilized to generate a current state, a current action is selected from an action space by utilizing a current strategy, and a strategy executor is called to execute the current action, so that a global optimal scheme can be generated in combination with a running state and business semantics, and a local optimization trap is avoided; and in a preset time period, calling a metadata autonomous scheduling engine to perform strategy optimization according to the experience playback buffer area, and executing the next round of metadata management according to a new strategy, so that continuous optimization of the strategy is realized, and long-term change of a service mode can be self-adapted.

The application relates to artificial intelligence and provides a business notification method, a system, equipment and a storage medium. The method comprises the following steps: acquiring a business amount, a risk coefficient, a risk probability and historical feedback information; performing product calculation processing on the business amount, the risk probability and the risk coefficient to obtain a business value; calculating a preset expected handling date based on the historical feedback information to determine a handling time period; judging whether the risk probability meets a preset handling mark probability range to determine a handling probability result; obtaining a single decision according to the business value, the handling time period and the handling probability result; and determining a mode of sending a business notification to a user according to the single decision. The business notification method provided by the application can make a single decision according to a business condition, reduce the occurrence of repeated notification, improve business notification efficiency and save operation cost.

The invention provides a service processing method based on multi-dimensional business semantics and related equipment. The method comprises the following steps: acquiring service data; the service data comprises at least two of the following data: middle station service and interface directory document data, an SG-CIM data model and service support business scene data; performing business value evaluation on the service data based on a first type of indexes to obtain a business value degree; and performing service health assessment on the service data based on a second type of indexes to obtain a service health degree, weighting processing is carried out based on the business value degree and the service health degree, and a corresponding health score is determined; in response to detecting that the health score meets a preset condition, determining a service governance type based on the business value degree and the service health degree; calling a multi-mode knowledge base based on the service governance type, and generating a service governance scheme; and processing the service based on the service governance scheme.

The invention relates to the technical field of data management, data capitalization and digital ecological construction, and discloses a data element-oriented dual-coding system and an association mapping method and system thereof, and the system comprises a data scene code DSC which is used for uniquely identifying a business scene; and the data part code DPC is used for uniquely identifying the data part, and accurate tracing and measurement between a data solution and a business value are realized by establishing an association mapping relationship between the DSC and the DPC and distributing a value contribution weight. According to the method, the core pain points that businesses and technical semantics are disjointed in a data element process and the value is difficult to quantify and trace are effectively solved, and basic technical support is provided for development, management, pricing and transaction of data assets.

The invention provides a website business value evaluation method, system and device and a medium, and the method comprises the steps: obtaining the amount, income and expenditure data of a specified channel, and generating a user-level list wide table according to the amount, income and expenditure data; obtaining a business evaluation demand, determining a target dimension and a target index according to the business evaluation demand, performing data summarization statistics on the user-level list wide table according to the target dimension and the target index, and generating a multi-dimensional data model; obtaining a plurality of driving elements of a target website for the key business value, and quantifying the target indexes related to the driving elements based on the multi-dimensional data model to obtain quantitative index values; and determining an overall business value score of the target website according to the quantitative index value and a preset target value. The business value evaluation efficiency and accuracy are improved through the network business value evaluation system.

The invention discloses a medical image SaaS processing method and system based on AI driving. The method comprises the following steps: acquiring behavior data of a user on a medical image SaaS platform; based on the behavior data, predicting the consumption demand of resources in a specific period in the future through a prediction model; generating an SaaS service combination matched with the corresponding user according to the usage demand; monitoring usage data of a user on the SaaS service combination, and performing SaaS service value attribution analysis based on the usage data to quantify the service value of the SaaS service; and integrating the quantitative results of the business value to generate a value attribution report, and making a customer contract extension management decision based on the value attribution report. According to the method, the consumption demand of a specific period in the future is predicted based on customer behavior data, an elastic mixed service combination containing basic capacity and an excess call package can be recommended to a customer, the cost saving value and the efficiency improvement value are quantified through value attribution analysis, a visual value attribution report is generated, and the user experience is improved. And a data-driven customer contract extension management decision is realized.

The invention relates to the technical field of data processing, in particular to a data asset total cost accounting method based on staged cost collection, and the method comprises the steps: firstly defining a full-service life cycle stage of data assets from collection, storage, processing to application; constructing an exclusive cost motivation library for each stage and establishing a mapping rule; collecting fine-grained resource consumption data through an automatic interface, and classifying and converting the fine-grained resource consumption data to corresponding stages according to rules; and finally, calculating the direct cost of each stage based on the motivation and the internal unit price, scientifically sharing the indirect cost, summarizing to obtain the total cost, and generating a staged cost view and report. According to the invention, the method achieves the spanning from the conventional accounting according to time or financial subjects to the precise cost accounting according to the data business value flow stage, can clearly reveal the specific distribution of the cost in the core links of data collection, processing, application and the like, and effectively solves a problem that the cost input of each business stage cannot be precisely accounting.

The invention discloses an online training student management system and method, and the system comprises a data collection module, a data processing module and an authority verification module, and relates to the technical field of teaching and training management. According to the online training student management system and method, learning behavior details of students can be more completely captured by collecting text interaction data, operation track data and audio and video data, and the accuracy of collected data is improved by preprocessing behavior data, so that the learning efficiency is improved. Reliable data input is provided for generation of a structured intention report and calculation of course priorities, then the credibility of decision basis is improved, dynamic adjustment of a learning path is performed through a timing path dynamic adjustment mechanism, the learning efficiency and learning quality of students are improved, and in cooperation with calculation of a multi-dimensional evaluation report and a business value contribution value, the learning efficiency and learning quality of the students are improved. An enterprise can clearly judge the contribution of training to a business target, a data basis is provided for scientific distribution of training budget, and the situation of resource waste can be effectively reduced.

The invention discloses an intelligent data service system and method for enterprise management, and the method comprises the steps: carrying out the multi-dimensional heat classification, scene recognition, competition scoring and intelligent scheduling management of enterprise data, firstly fusing the module demand weight, historical contribution degree and real-time access frequency, generating a data competition score capable of reflecting the business value, and carrying out the calculation of the data competition score. Then constructing a service scene label system containing multiple trigger rules such as time, behavior, outside and competition, identifying a current service situation and outputting scene weight; scenarized activeness indexes SAI are calculated based on access features, scene weights and competition scores, correction is carried out through the competition scores, resource allocation in a three-layer enterprise-level storage structure is guided, data migration is achieved through a double-hop migration path and a competition priority queue, elastic capacity expansion with zero interruption of a system is guaranteed, hot spot recognition misjudgment can be effectively avoided, and the hot spot recognition efficiency is improved. The utilization efficiency of high-value low-frequency data is improved, and the enterprise data management performance is remarkably optimized.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a motorcade business value processing strategy analysis method, device, equipment and medium, and the method comprises the steps: obtaining multi-dimensional feature data of a target motorcade, and recognizing the incidence relation between features in the multi-dimensional feature data; performing cross modeling analysis on the association relationship to obtain a value association factor of the target motorcade; analyzing a value control index of the target motorcade according to the value correlation factor; clustering and layering the target motorcade according to the value control index to obtain motorcades of different levels, and identifying initial value levels of the motorcades of different levels; correcting the initial value level by using a preset business rule to obtain target value levels of motorcades of different levels; and generating business value processing strategies of the motorcades of different levels according to the target value level. According to the invention, the accuracy of motorcade business value processing strategy generation can be improved.

The present application relates to the field of manufacturing, in particular to a multi-process collaborative scheduling optimization method for pomegranate peeling production line, comprising collecting and processing first business data stream from Internet of Things interface, inputting historical sensor data into business prediction model to generate second business data stream; generating third business data stream from data of multi-modal sensor array; constructing multi-objective business cost function through business decision optimization engine; starting iterative optimization business simulation process to obtain optimized scheduling scheme; generating corresponding digital production work order from the optimized scheduling scheme. Through the three business data stream fusion and multi-objective cost function construction method, the device state, device health degree cost and quality index are analyzed, and the comprehensive business value evaluation is generated. When the conditions are met, the iterative optimization simulation and closed-loop optimization are started, the scheduling optimization of time effectiveness and value fluctuation in perishable material scheduling is realized, and the business value creation and adaptive scheduling ability of production are improved.

The invention discloses a data application scene evaluation method and device and a storage medium, relates to the technical field of data application, is used for improving the accuracy of data application scene evaluation, and comprises the following steps: determining the business value of a to-be-evaluated data application scene according to first data; determining data utilization information of the to-be-evaluated data application scene according to the second data; and based on the business value of the to-be-evaluated data application scene and the data utilization information of the to-be-evaluated data application scene, determining an evaluation result of the market application value of the to-be-evaluated data application scene. The method and the device are applied to a data application scene evaluation process.

The invention discloses a full-dimension coverage index design and development method, which performs index design by constructing a process model framework of index design, index development and index directory and data application, constructs an all-directional coverage index dimension system, remarkably improves the suitability and comprehensiveness of data analysis, and improves the data analysis efficiency. And meanwhile, the reusability of indexes is enhanced, and the development and maintenance costs of enterprises are reduced. According to the flexible multi-dimensional design scheme, the index system can quickly respond to dynamically changing business requirements, and it is ensured that an enterprise keeps high efficiency and flexibility of data analysis in a complex and changeable environment. Besides, the user not only can autonomously adjust the dimension configuration and deeply mine the business value according to the demand, but also can trace the source and definition of the index through the index directory to ensure the transparency and credibility of the data, thereby providing stronger decision support and business insight ability for enterprises.

The invention relates to the technical field of natural language processing, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a bidding document generation method, device and equipment and a medium, and the method comprises the steps: obtaining a bidding demand document, and extracting the core element features of the bidding demand document; screening historical matching cases according to the core element features, and extracting a structured qualification configuration unit, a business value configuration unit and a parameter logic configuration unit from the historical matching cases; generating an initial qualification text by using the structured qualification configuration unit, and calibrating the business value parameters in the business value configuration unit to obtain business value configuration parameters; bidding parameter logic is generated according to the parameter logic configuration unit; logically fusing the initial qualification text, the business value configuration parameter and the bidding parameter into bidding file content; and filling the to-be-generated bidding file according to the bidding file content to obtain a target bidding file. The bidding file generation accuracy can be improved.

The present application discloses a data annotation method, device and storage medium based on graph structure and community discovery, which belongs to the field of data processing. The present application obtains customer transactions, product attributes, behavior trajectories and environmental data, and constructs a heterogeneous graph network including a basic physical layer (transaction association and product attribute mapping), a behavioral semantic layer (behavioral pattern and semantic association), and an environmental association layer (dynamic impact of the environment). A community discovery algorithm is used to mine customer groups with cross-departmental business value, and a three-level labeling system is constructed to quantify basic value attributes and fluctuation coefficients, behavioral patterns and product preferences, and dynamic trajectory characteristics. This achieves a unified understanding of customer behavior, in-depth mining of multi-dimensional customer relationships, dynamic evolution capture of customer value, and hierarchical customer cognition construction, solving the problems of data silos caused by task orientation in traditional methods, the constraints of dynamic value mining caused by a single data dimension, and the restrictions of hierarchical cognition construction caused by the flattening of the labeling system.

The application discloses an enterprise project database intelligent management method and system, and relates to the technical field of data management and resource optimization. The method comprises the following steps: verifying the integrity of business unit project data in real time through an intelligent cleaning engine and storing the data in a structured manner to a central project database; fusing progress, cost, risk fields and dynamically calculating a comprehensive value index of business value and strategic weight; based on the value index and real-time resource inventory, generating a resource allocation scheme through a hierarchical adaptation strategy and outputting a priority list; tracking progress deviation during execution, triggering data isolation and index optimization when the risk offset exceeds a threshold; dynamically adjusting the value weight by analyzing the risk root cause and feeding back the management rule library to realize system self-optimization. The application solves the problems of rigid resource allocation, lagging risk response and lack of dynamic value orientation in traditional project management, improves resource utilization and reduces project delay risk.

The application discloses a kind of human resource data management methods based on big data, it is related to data management technical field, the method is by introducing behavior density characteristic vector and behavior density score V1, accurate quantitative determination mechanism based on multidimensional behavior characteristics and behavior timeliness is realized, personnel interactive behavior activity and behavior value can be carried out more actual business law dynamic evaluation by human resource system, by pre-processing, standardization and multidimensional Euclidean combination to operation frequency parameter, operation confirmation event parameter and inspection coverage range parameter, and using natural logarithm attenuation function models the time value of behavior, the application can effectively reflect the objective phenomenon that personnel behavior intensity attenuates with time.Compared with the behavior judging mode of existing fixed condition, static rule, the application can automatically identify high behavior value data when facing a large number of historical behavior data, avoid deleting the record that still has business value.