38 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 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.

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 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 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 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 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.

The embodiment of the invention provides an information retrieval method and device, equipment and a computer storage medium. According to the information retrieval method provided by the embodiment of the invention, customer requirements and user feedback are quantified into optical signals through a photogrowth algorithm, retrieval parameters and resource allocation are dynamically optimized, and deep structured analysis is performed on the retrieval result in combination with a knowledge graph and a scenarized Prompt template, so that the retrieval efficiency is improved. Based on a multi-modal decision tree, accurate information reaching is achieved, the linkage action of a business system is automatically executed, finally, self-adaptive adjustment of retrieval parameters and resources, deep business analysis of information and automatic conversion from information to business value are achieved, the retrieval accuracy, the analysis depth and the business response efficiency are remarkably improved, and the method is suitable for large-scale popularization and application. The problems of parameter static state, resource waste, analysis shallow layer and business disjunction are solved.

The invention provides a media platform determination method and device based on multi-dimensional features and a medium, and relates to the technical field of media platform determination, and the method comprises the steps: extracting the multi-dimensional feature information of each media platform to be evaluated; based on the multi-dimensional feature information, platform feature factors, user difference factors, content complementation factors and business value factors of all media platforms are calculated respectively; carrying out fusion calculation on the platform feature factors, the user difference factors, the content complementation factors and the business value factors of all the media platforms to obtain comprehensive weights of all the media platforms; performing normalization processing on the comprehensive weight of each media platform, and determining a target media platform in model training and decision making of the cross-media collaborative system according to a normalized weight result; the purpose of determining the optimal recommendation platform can be achieved.

The invention discloses a pork selling customer comprehensive management method and system, and relates to the technical field of customer management, customer information of each time of accepted pig activity is obtained from each pork selling customer system, the customer information is copied and transmitted in a secrecy manner and sent to a central screening library, and a self-adaptive screening network is used for carrying out first-round screening on all customer information of each time of activity; feature customer information capable of reflecting basic activity conditions is reserved, then second-round screening is carried out on first-round reserved information based on a multi-dimensional business value evaluation model, low-value information is screened out through evaluation and dynamic threshold comparison, and core customer information is reserved; and generating an activity effect portrait based on the core customer information reserved in each round of activity, and carrying out integration and trend modeling on the activity portraits arranged according to a time sequence to generate a customer comprehensive feedback time sequence report of the accept pig activity. By constructing a two-stage adaptive screening and time sequence analysis framework, the comprehensive management efficiency of pork selling customers is remarkably improved.

The invention discloses a test information intelligent decision-making system based on machine learning, and relates to the technical field of software test management, and the system comprises a test management center which is used for calling data and sending the data to a global data sensing unit; the global data sensing unit is used for outputting a static asset dependency tree and a dynamic business value map; the map construction mapping unit is used for deducing a potential commercial risk propagation path set; the risk propagation calculation unit is used for calculating a quantified expected commercial risk value and sending the quantified expected commercial risk value to the decision execution unit; and the decision execution unit is used for generating a release blocking instruction or an intelligent test instruction. According to the method, the association graph of the code assets and the business value is constructed, the change risk is quantitatively calculated by using historical data and real-time traffic, and the test strategy and the release decision are dynamically adjusted according to the business risk value, so that test resources are inclined to high-risk business, and the test efficiency is improved. And the software delivery efficiency and security are improved while the service continuity is guaranteed.

According to the progressive software quality verification method and system, code submission is used as a trigger point, seven layers of progressive check from grammar format, content integrity, technical accuracy, consistency, implementation feasibility, business value to final integrated verification are implemented, and each layer is provided with a quantitative standard and a verification algorithm. And upper layer verification can be started after the lower layer reaches the standard, so that systematic control from technical details to business values is realized. Furthermore, automatic correction, content complementation or optimization suggestions are provided based on rules, knowledge bases or large model capabilities, so that intelligent processing from problem discovery to auxiliary repair is realized, and the efficiency is greatly improved.

The application provides a business data evaluation method, device and equipment and a computer readable storage medium. The method comprises: obtaining a metadata set generated in a business process; the metadata set represents description information corresponding to technical data and process definition in the business process; performing feature extraction on the metadata set from a business value dimension to obtain a metadata feature set; the business value dimension is a business management and operation state represented by technical metadata and business metadata; performing importance evaluation classification on the metadata feature set using a target multi-classification model to obtain an importance classification result corresponding to the metadata set; wherein the target multi-classification model is obtained by performing importance evaluation classification training on an initial multi-classification model using a feature sample set with labeled classification results; and performing data analysis on the business based on the importance classification result. Through the application, the efficiency and accuracy of business data evaluation can be improved through artificial intelligence technology.

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.

This invention discloses an enterprise multi-business interconnection system for data processing, relating to the field of enterprise business process optimization technology. It includes a value stream definition module, a process analysis module, a requirement backpropagation module, a business interconnection module, a value stream processing module, and a value verification module. Based on enterprise business objectives, this invention determines the end-to-end business value stream and decomposes processes to identify business nodes, states, and collaborative relationships. Then, based on optimization objectives, it deduces the minimum necessary data interconnection scope. Subsequently, it implements data interaction between heterogeneous businesses according to this scope and integrates and processes multi-source data to construct a business interaction view, generating operational performance indicators. Finally, the value verification module quantifies and displays the optimization effect. This invention achieves value-driven interconnection and optimization, effectively improving the targeting and measurability of cross-system business operations.

The invention relates to an intelligent procurement risk prediction and abnormal behavior detection method. The method comprises the following steps: constructing a multi-dimensional procurement risk index system; the indexes are classified, and a quantitative scoring model and a Boolean judgment model are included; screening the indexes based on a feature importance algorithm; and based on a dynamic weighted aggregation model, fusing objective data and expert analysis suggestions, carrying out weight distribution and risk value calculation on the simplified index set, and outputting a comprehensive risk value of the purchase project. The bid invitation risk dynamic prediction method has the beneficial effects that the bid invitation risk dynamic prediction is realized by constructing a comprehensive algorithm, the actual benefits are reflected in multiple dimensions of risk prevention and control, efficiency improvement, cost saving, management optimization and the like, and a remarkable business value closed loop is formed.

This invention relates to the field of enterprise management technology and discloses an enterprise management system based on cloud-based intelligent collaborative processing. The system includes: periodically extracting business data through a data acquisition interface; constructing a node state vector containing load, risk, and response dimensions using a state quantification module; subsequently, establishing a system state transition and task input matrix using a network evolution analysis module; and calculating the cumulative impact within a prediction window and the disturbance amplification of a single task on the overall network stability using this matrix. Finally, a smart scheduling module weighted and fused this disturbance amplification with the importance of the task itself to generate an optimized task execution sequence, achieving intelligent scheduling that balances business value and system stability.

The embodiments of the present specification disclose a method and device for training a reinforcement learning system for automatic bidding. The reinforcement learning system comprises an environment model and an agent, the environment model is used to predict the state distribution of a business environment, the business environment state comprises the budget consumption and the obtained business value of a business party, and the agent is used to determine the current bidding parameter of the business party according to the current environment state. The method comprises: firstly training the environment model by using a plurality of historical samples, wherein any first historical sample comprises a first state of a business environment, a first bidding parameter, and a second state of the business environment after the business environment is changed by using the first bidding parameter under the first state; and secondly training the agent by using a plurality of state-bidding parameter pairs constructed based on the sampling of the environment state space and the bidding parameter space, and the predicted state distribution corresponding to each state-bidding parameter pair obtained by inputting the state-bidding parameter pair into the trained environment model.

This invention provides a closed-loop data quality management method and system driven by a fourth-order intelligent engine. Based on the acquired business data governance requirements, natural language processing technology is used to intelligently cluster the requirements, and a governance priority decision matrix is ​​generated by combining the full-link impact scope and economic impact assessment. Quality verification rules are constructed according to the business data governance requirements through a dual-engine driven mode combining artificial intelligence generation and low-code orchestration. Based on the governance priority decision matrix and data lineage, the quality verification rules are aggregated and packaged into rule verification task groups. Based on dynamic baseline thresholds, a distributed task scheduling framework is used to execute verification tasks on incremental data in each rule verification task group. Based on the full-link impact scope, multi-dimensional root cause diagnosis and automated governance are performed on the verified abnormal data. Based on the governed business data, a mapping relationship between governance effectiveness and business value is established to generate a value assessment report.

The application discloses a kind of management method and system for micro portal platform, belong to building business management technical field, specifically include: based on the multi-source basic data of enterprise in building in the construction industry chain ecological map;Industry chain ecological map is extracted with upstream and downstream dependence relationship restoration to industry chain link, constructs industry chain structure model and identifies missing link;Filtering candidate enterprise set matched with missing link, obtain recruitment dynamic data and calculate space stress score;The number of already-registered enterprises that exist upstream and downstream dependence relationship with candidate enterprise is counted, calculate industry matching degree, and calculate comprehensive business value based on industry matching degree and space stress score;According to comprehensive business value descending order and carry out business reach task distribution.The application will enterprise dynamic site selection intention and building industry ecological demand joint quantitative evaluation, realize from passive fill in to active build ecology change, improve the precision of digital building business and industry ecological health degree.

The embodiment of the invention relates to the technical field of operation and maintenance monitoring, in particular to a system health degree evaluation method and device and electronic equipment. The method comprises the following steps: acquiring infrastructure performance data, application performance data and business index data of a target system as original data; performing standardization processing on the original data, and performing data arrangement according to the dimension of technical index-business index to generate standardized data; calling a business influence quantification model to calculate an influence coefficient of each technical index on each business index in the standardized data, and carrying out statistics to generate an influence matrix; according to the influence matrix, assigning a score weight of each technical index; and calculating single-index scores based on real-time technical index data in the standardized data, and performing weighted calculation on the single-index scores according to the score weights to generate a system health degree score. According to the method, the actual influence degree of the technical abnormality on the core business can be accurately quantified, and intelligent operation and maintenance transformation from pure technical monitoring to business value guarantee is realized.