100 results about "Business intelligence" patented technology

The invention discloses a dialogue type business intelligent analysis method based on a natural language, and relates to the technical field of business management systems, and the method mainly comprises the steps: recognizing the intention type of a user intention contained in a natural language request through a general large language model; according to the identified specific intention type, calling a corresponding special analysis module to perform semantic analysis and parameter extraction on the natural language request so as to generate an operable analysis instruction; calling a business intelligent system data interface to execute the analysis instruction, and obtaining corresponding structured data from a business intelligent system data warehouse; and generating a comprehensive analysis report which is matched with the identified intention type and the corresponding structured data and comprises visual elements and analysis explanations based on the identified intention type and the corresponding structured data. According to the method, the intention type of the natural language request of the user is understood through the large language model, so that the user with a non-professional background can easily perform complex data exploration, and the use threshold is reduced.

Implementations described herein relate to systems and methods to generate a response to a business intelligence question received from a user. In some implementations, a computer-implemented method may include receiving the question as a natural language string, determining one or more fragments based on the natural language string, identifying one or more query operators based on the one or more fragments, constructing a structured query tree based on the one or more query operators, executing at least a portion of the structured query tree on a data source, receiving, from the data source, an output result based on the execution, generating the response based on the output result, and providing the response to the user.

The invention aims to provide an intelligent data analysis multi-round reasoning method. The method comprises the following steps: receiving natural language query request data; performing entity recognition and intention recognition on the natural language query request data; performing complexity quantitative analysis on the natural language query request data by adopting complexity measurement evaluation to obtain a complexity evaluation result; constructing five types of Agents to carry out collaborative reasoning; a standardized cooperation interface and a data transmission protocol among the Agents are established; the accuracy and the reliability of a preliminary reasoning result are ensured through a cooperative conflict resolution mechanism; and sorting any preliminary retrieval result according to the evaluation data, and outputting a final retrieval result. According to the invention, intelligent processing of complex BI data analysis tasks is realized, and an efficient, reliable and intelligent technical solution is provided for enterprise data analysis and business intelligent application.

The invention provides an automatic process management method and system applied to foreign trade business intellectualization, and relates to the technical field of foreign trade business management.The method comprises the steps that firstly, initial order data, preset document template data, logistics resource configuration data and order change data of foreign trade business are obtained and integrated to form a foreign trade business total data set; then, carrying out change influence definition processing on the order change data, and determining influenced foreign trade business links and associated data; performing linkage adaptation processing on the associated data based on a defining result, and adjusting preset document template data and logistics resource configuration data; according to a linkage adaptation processing result and a foreign trade business process knowledge base, generating operation execution instructions of all links; finally, the instructions are distributed to the corresponding execution main bodies according to the execution sequence, operation execution results are received and integrated to form feedback information, automatic and intelligent management of the foreign trade business process is achieved, and the management efficiency and accuracy are improved.

The invention provides an intelligent engine collaboration method and system oriented to multi-modal query, and relates to the technical field of data collaboration process.The method comprises the steps that query dimension analysis is conducted according to a received multi-modal query request, a query analysis result is generated, tasks are dynamically distributed to matched engines based on the analysis result, and the tasks are distributed to the matched engines. All the engines execute processing operation in parallel or in a serialization mode, multi-source data association fusion is conducted on heterogeneous engine results, and fusion results are packaged into visual data to be fed back to a user; the system comprises a query dimension analysis module, an engine task distribution module, an engine execution module, a multi-source data association fusion module and a result visualization packaging module. According to the method, the multi-modal query fingerprint database and the graph structure collaborative network are constructed, and the pre-training graph convolutional network is combined to optimize the control parameters, so that the query performance is remarkably improved, the query delay is reduced, the accuracy is improved, and the method is suitable for complex scenes such as smart cities and business intelligence.

The application belongs to the technical field of business intelligence evaluation, and specifically provides a service provider SLA evaluation dynamic business model implementation method and system. The method mainly comprises: obtaining SLA evaluation source data of a service provider, wherein the SLA evaluation source data comprises operator SLA data, enterprise end-to-end APM data, third-party network quality data and dependent link probe data between service providers; taking the service provider as a node, setting a credibility weight for each node according to a preset rule, determining a directed edge between nodes according to a preset dependent relationship between service providers, and determining a connection weight of the directed edge according to the dependent link probe data. The application realizes dynamic and accurate evaluation under multi-dimensional data fusion, improves the objectivity and credibility of SLA achievement rate evaluation results, and provides reliable decision support for enterprise screening of cloud service providers and optimization of service deployment.

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.

Comprehensive systems and methods manage software lifecycle processes in an enterprise environment, and ensures consistent, secure, and efficient handling of software applications, packages, and distribution methods. The system features a web application interface that validates user input, enforces multi-level authentication, and manages user roles and permissions. A directory system dynamically selects stored procedures based on input data and current database structure, executing operations entirely within the database. Stored procedures enforce business logic, manage complex data relationships, support data encryption, and anonymize sensitive information. Hierarchical data structures organize records with multi-level granularity, temporal tracking, and partitioning. The database management system optimizes operations, implements automatic failover, and supports real-time data synchronization. The system also includes advanced error handling, real-time monitoring with predictive alerts, a secure audit trail, and a reporting module integrating with external business intelligence tools. Automated maintenance and disaster recovery ensure scalable, secure software lifecycle management.

Embodiments described herein are generally related to computer data analytics, and computer-based methods of providing business intelligence data, and are particularly related to systems and methods for use with enterprise data for profile matching and generating gap scores and upskilling recommendations. In accordance with an embodiment, the system can operate to match a set of position requirements with candidate attributes or skillsets, ranking them on the basis of match scores. The system can be used, for example, to determine a skill gap between the position requirements and candidate attributes, and recommend which skills might be augmented to better address the position requirements.

The invention discloses an enterprise business intelligent aid decision-making method, device and equipment based on a multi-source large language model and a storage medium, relates to the field of artificial intelligence, is applied to terminal equipment carrying a target intelligent aid decision-making system, and comprises the following steps: obtaining a to-be-decided task related to a target enterprise business to determine target label data; obtaining corresponding target portrait data from a target business portrait database of the corresponding dimension based on the target label data by using the target business knowledge database to generate a corresponding structured parameter packet; based on a task type corresponding to the task to be decided and a pre-acquired target mapping table, determining a target large language model corresponding to the task type; and utilizing the target large language model to generate an intelligent auxiliary decision related to the target enterprise business based on the structured parameter packet and the target cue word template, so as to complete the target enterprise business based on the intelligent auxiliary decision. According to the invention, different large models can be called according to business requirements.

The present application belongs to the technical field of artificial intelligence, in order to solve the problems of random error and result adaptability defect existing in ChatBI process, the present application provides a dialogue business intelligence platform process optimization method, system and equipment, the output results of each link in the dialogue business intelligence platform process are obtained, the output results of each link are sequentially checked in multidimension according to the process execution sequence, the process link that may have problems can be found in time, and the error is corrected and optimized in time before it spreads to the next process link; moreover, the output results are checked from three aspects of adaptation to the original demand of user input problem, compliance with the business logic of the process link itself and adaptation to the use demand of the downstream process link, the adaptability of the result is guaranteed, and the problem that the output of each process link is disconnected with the downstream process link without obvious error is avoided.

The invention belongs to the technical field of service intelligent evaluation, and particularly provides a service provider SLA evaluation dynamic service model implementation method and system, and the method mainly comprises the steps: obtaining the SLA evaluation source data of a service provider, the SLA evaluation source data comprises operator SLA data, enterprise end-to-end APM data, third-party network quality data, and dependency link probe data between service providers; the method comprises the following steps: by taking service providers as nodes, setting a credibility weight for each node according to a preset rule, determining directed edges between the nodes according to a preset dependency relationship between the service providers, and determining a connection weight of the directed edges according to dependency link probe data; according to the method, dynamic accurate evaluation under multi-dimensional data fusion is realized, the objectivity and credibility of an SLA achievement rate evaluation result are improved, and reliable decision support is provided for enterprises to screen cloud service providers and optimize service deployment.

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 relates to the technical field of power grid project cost management and business intelligence, and discloses a project cost multi-stage intelligent analysis and examination method, system, equipment and medium, and the method comprises the steps: building a power grid project cost data warehouse, and achieving the integration and standardization of multi-source cost data; establishing an OLAP multi-dimensional analysis model, and supporting general cost comparison and key index analysis in a budget calculation stage, feasibility research estimation, preliminary design budget calculation, completion settlement three-calculation comparison, first supply material check and review list consistency check in a settlement stage; designing an intelligent decision engine based on data mining, and generating review opinions through association rules, a classification algorithm and an anomaly detection technology; and a BI visual instrument panel is developed, and multi-dimensional display, real-time monitoring and interactive analysis of the power grid cost data are realized. Through systematic application of the BI technology, deep mining, intelligent decision and visual display of engineering cost analysis are realized, and the efficiency and precision of cost review are remarkably improved.

The present application relates to the technical field of data processing and business intelligence, and more particularly to a dynamic multi-mode SQL generation method and system based on a four-quadrant strategy, the method comprising the following steps: obtaining and quantifying the complexity and flexibility of user queries; dynamically switching SQL generation modes based on a four-quadrant strategy and a decision gateway; under the selected SQL generation mode, performing data processing and analysis, and returning query results. Through an intelligent routing engine, the present application performs accurate qualitative and quantitative analysis on user queries before SQL generation, automatically shunts to the optimal solution according to the analysis results, and takes into account the reliability and flexibility of queries.

The invention relates to a natural language to SQL query method and system based on a large model, and relates to the technical field of artificial intelligence and business intelligence. The method comprises the following steps: constructing a vectorization knowledge base containing a business context; based on the vectorization knowledge base, receiving and analyzing a natural language problem of the user, generating and executing an SQL query, and returning a query result to the user; based on the query result, generating a parameterized index SQL template from the query result, and storing the parameterized index SQL template in an index template library; and providing a configuration interface for a user to create a report template, binding at least one index SQL template from the index template library in a paragraph of the report template, and configuring a copywriting generation rule for each paragraph. The method has the effect of solving the problem that a large model is understood insufficiently in a complex service scene as much as possible.

The invention discloses an intelligent advertisement putting optimization method based on multi-source data aggregation, which relates to the technical field of advertisement putting, and comprises the following steps: on the basis of a user interest state vector and an advertisement unified representation vector set, utilizing a multi-task prediction model to parallelly calculate a pre-estimated target value of a candidate advertisement, and according to the pre-estimated target value of the candidate advertisement, calculating the pre-estimated target value of the candidate advertisement; constructing a comprehensive value function, solving a Pareto front of the comprehensive value function by using a multi-objective optimization algorithm, screening an optimal dynamic weight combination from the Pareto front, and calculating the real-time comprehensive value of each candidate advertisement by using the optimal dynamic weight combination. And calculating a final sorting score of each candidate advertisement according to the real-time comprehensive value of each candidate advertisement, and executing advertisement putting according to the final sorting score. According to the invention, the timeliness and correlation of advertisement putting are enhanced, and the advertisement matching precision and the business intelligence level are improved.

This invention discloses a method and system for constructing a data model for a business intelligence agent based on deep learning, relating to the field of deep learning technology. Through continuous processing from steps S1 to S4, the business intelligence agent no longer trains its model solely based on the final adopted decision results. Instead, it incorporates candidate information explicitly rejected during historical decision-making processes into the modeling scope. It retains excluded candidate decisions by constructing a set of rejected information (Una), and transforms the original rejected information into computable multi-dimensional structured data by generating a set of rejected features (Ufc). Then, based on the density of rejected information (Ind), it characterizes the concentration of rejection behavior at different decision stages, forming a rejected boundary representation (Nbd) which is applied to the business intelligence agent. When generating new candidate decisions, it can proactively identify and avoid historically high rejection density regions. Compared to existing methods that rely solely on input and output results for training, this approach overcomes the problem of insufficient learning of decision boundaries.

The invention discloses an enterprise strategic decision-making method and system based on affair graph enhancement, and belongs to the technical field of enterprise intelligent decision-making. The method comprises the following steps: constructing a affair graph according to multi-source business intelligence, wherein the affair graph comprises event nodes, entity nodes and relation edges; extracting a decision chain of the target subject based on the confidence coefficients of the event nodes and the relation edges in the event atlas; and mapping each decision chain to a predefined decision mode to obtain an enterprise strategic decision corresponding to the multi-source commercial information. According to the invention, an accurate and explainable enterprise decision chain and mode analysis result can be generated based on multi-source business intelligence.

The invention relates to the technical field of business intelligence and predictive analysis, in particular to a cigarette operator confidence index analysis and decision generation method. The method comprises the following steps: acquiring a user index, and calculating a standardized influence degree according to the user index; the user indexes are preprocessed based on the standardized influence degrees, the second-level indexes are dynamically aggregated based on a dynamic dispersion entropy weight method, confidence first-level indexes, market first-level indexes, confidence first-level index weights and market first-level index weights are obtained, and then confidence state comprehensive indexes and market state comprehensive indexes are calculated; calculating a demand state index according to the sales data index; generating a three-dimensional decision matrix based on the confidence state comprehensive index, the market state comprehensive index and the demand state index; and obtaining a cigarette operator decision according to the classification indexes and the three-dimensional decision matrix. In this way, real-time monitoring, accurate evaluation, trend prediction and intelligent decision support can be carried out on the operation conditions of the retail clients.

A system and methods for optimizing labor scheduling in retail environments by incorporating shrink factors alongside traditional considerations are provided. A machine learning model (MLM) receives traffic data, hourly labor data, overall shrink data, and proven shrink incidents as inputs. The MLM learns the labor-shrink causality across stores, connecting staffing levels to shrink impact. The MLM outputs a recommended labor scheduling plan for both assisted and self-checkout lanes, optimized for overall store margins rather than just labor costs. This approach balances labor efficiency with shrink prevention, potentially improving store profitability. In an embodiment, an application programming interface (API) is provided for consuming recommendations from the MLM as a service to integrate insights into business intelligence dashboards, providing a comprehensive solution for retail labor management.

A system and method for efficiently extracting and categorizing business information from online sources is disclosed. The system comprises a web crawler that obtains company domains from a database and collects depth-1 URLs from company homepages. A classification model, utilizing a fine-tuned BERT architecture, predicts which URLs contain relevant information for generating tags. A content extractor then extracts content from these predicted URLs using one or more modules. Finally, a large language model (LLM) processes the extracted content and generates tags using custom prompts designed for each tag category. These prompts are tailored to the nature of the extracted content, enhancing the context provided to the LLM. This multi-stage approach addresses challenges in processing large-scale, unstructured business data from diverse web sources, potentially offering improved efficiency, scalability, and accuracy in automated business intelligence gathering.

A retail prescriptive recommendations machine learning model applies a generic multi-layer architecture to specific sales key performance indicators (KPIs). The model uses a novel hierarchical logical layer of metrics and features related to sales KPIs, training on historical sales data to identify improvement opportunities, set achievable targets, and generate recommendations for each store. The implementation includes adjustments for different scales and distributions of features, a layered predictive model for hierarchical calculations, and optimization methods considering improvement directions for each feature. This solution enables store operations to make data-driven decisions to improve sales performance without requiring extensive technical or business intelligence (BI) expertise, addressing a significant challenge in retail operations management.

The invention discloses a business site selection decision system based on multi-objective optimization, which belongs to the field of business intelligence and artificial intelligence, and comprises a data acquisition module for extracting relatively accurate data from reports of annual regional economy, society, environment and the like; the data collecting and processing module is used for carrying out data cleaning, data standardization and data complementation on the collected data; the model building module is used for selecting a linear weighting method model, an analytic hierarchy process model, a fuzzy comprehensive evaluation method model or a multi-objective optimization algorithm model according to problem characteristics; the model solving module is used for designing an algorithm and solving a model by using a programming language to obtain an optimal or suboptimal site selection scheme; and the model optimization module is used for continuously optimizing the model and the algorithm and improving the scientificity and the effectiveness of the decision-making system. According to the invention, the user participates in the site selection process; a dynamic weight adjustment mechanism is set, and the target weight can be dynamically adjusted according to the market condition; a multi-objective optimization algorithm is introduced, and an optimal solution is searched from a global perspective.

To provide a system for enabling anyone to easily execute digital marketing without expert knowledge.SOLUTION: Means for receiving a merchandise concept from a user, means for automatically generating a delivery scenario in a AI based on the received merchandise concept, means for automatically generating advertisement content in the AI based on the automatically generated delivery scenario, means for adjusting the generated content and delivery scenario by a designer, means for detecting legal risks and conformity to brand guidelines of the advertisement content using the AI and presenting the detected legal risks and conformity to the brand guidelines to a legal department, means for delivering the generated advertisement content to influential target users and performing an AB test, means for evaluating delivery results with a BI tool and measuring CTR, CVR, and ROI, and means for analyzing evaluation results with the AI and improving the delivery scenario and the content.SELECTED DRAWING: Figure 1

A system and method for efficiently extracting and categorizing business information from online sources is disclosed. The system comprises a web crawler that obtains company domains from a database and collects depth-1 URLs from company homepages. A classification model, utilizing a fine-tuned BERT architecture, predicts which URLs contain relevant information for generating tags. A content extractor then extracts content from these predicted URLs using one or more modules. Finally, a large language model (LLM) processes the extracted content and generates tags using custom prompts designed for each tag category. These prompts are tailored to the nature of the extracted content, enhancing the context provided to the LLM. This multi-stage approach addresses challenges in processing large-scale, unstructured business data from diverse web sources, potentially offering improved efficiency, scalability, and accuracy in automated business intelligence gathering.

The invention discloses a low-profit order cause tracing method and system based on multi-source data, and belongs to the technical field of data processing and business intelligence, and the method comprises the steps: obtaining an order syndrome shadow file; detecting microscopic association by using a constraint and score mixed causal discovery algorithm, constructing a primary causal probe network, and mapping to generate a multi-layer nested causal forest; completing forest recombination evaluation based on the periodic trigger signal; and when a low-profit order is identified, a cooperative cause combination is locked through multi-path backtracking, a controllable variable is adjusted and optimized, node state evolution reasoning is carried out, and finally a strategy simulation result with both profit prediction and risk labeling is output. According to the technical scheme, the dynamic order shadow file is constructed, the multi-layer nested causal forest is generated, and collaborative backtracking and strategy simulation are performed, so that low-profit compound causes can be deeply revealed, and quantitative optimization decision support is provided.

The invention relates to the technical field of business intelligent analysis, in particular to a business data multi-modal acquisition and intelligent analysis system, which comprises the following modules: a multi-modal data acquisition and preprocessing module, which is used for acquiring text features, image features and sales replenishment time sequence data of a target commodity and an associated commodity, the method comprises the following steps: carrying out lexical meta-processing and semantic coding on a text, carrying out commodity target area identification and visual feature extraction on an image, and carrying out time alignment, missing value filling and normalization on time sequence data to obtain multi-modal feature data in a unified format. According to the method, the multi-modal features are extracted, the commodity relation graph subjected to time sequence screening and credible pruning is constructed, and then the graph structure and multi-modal information prediction output are fused, so that the problem that the prediction deviation is large due to the fact that a traditional method only depends on single sales volume data and cross-commodity association modeling is lacked is solved.

The application provides an automatic process management method and system applied to foreign trade business intelligence, relates to the technical field of foreign trade business management, and first acquires initial order data, preset document template data, logistics resource configuration data and order change data of foreign trade business, integrates to form a full data set of foreign trade business; then, change influence definition processing is performed on the order change data, and affected foreign trade business links and associated data are determined; then, based on the definition result, linkage adaptation processing is performed on the associated data, and the preset document template data and the logistics resource configuration data are adjusted; then, according to the linkage adaptation processing result and a foreign trade business process knowledge base, operation execution instructions of each link are generated; finally, the instructions are distributed to corresponding execution subjects according to the execution order, operation execution results are received and feedback information is integrated to form, and the automatic and intelligent management of the foreign trade business process is realized, and the management efficiency and accuracy are improved.

Configuring and automatically populating a columnar, multi-domain business intelligence data structure can support the continuation of a business workflow. Configuring includes ingesting data from a plurality of disparate sources, formatting it according to a standardized schema, and aligning it with workflow-derived data dimensions. This process includes parsing the source data, harmonizing data formats, normalizing strings based on locale-specific language or industry-specific terminology, identifying relevant dimensions, mapping each dimension to a respective column, and populating the data structure accordingly. The resulting alignment ensures that data is organized by dimension in a highly efficient and automated manner to facilitate multi-domain business intelligence operations.