58 results about "Enterprise data management" patented technology

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

Data loss prevention systems and methods in an enterprise data management and monitoring system may intercept a request to a network service, e.g., a service using an artificial intelligence and / or machine learning model. The systems and methods may represent contents of the request via one or more vector embeddings, which may be compared to vector embeddings corresponding to respective ones of a plurality of sensitive data elements in the enterprise. The data loss prevention system and methods may apply various data sensitivity policies based on determinations of whether sensitive data of the enterprise is included in the request to the network service, e.g., by blocking or redacting the request to prevent exposure of the sensitive data to the network service.

The invention provides an audit early warning management method and system for an enterprise expense bill, and relates to the technical field of enterprise data management, and the method comprises the steps: receiving a target bill, executing workflow arrangement and audit path matching under bill classification, and determining an audit sequence; triggering a prior node of the auditing platform, scanning bill information, disassembling to determine non-key content and key elements, executing mixed-sequence coding encryption, determining a reconstructed target bill and performing auditing circulation, executing authority authentication at an auditing sequence node, if the authentication is successful, performing reduction display and auditing processing on the reconstructed target bill, and determining an auditing result. And performing early warning management on the target bill. The technical problems of low auditing efficiency and poor data security caused by lack of automatic arrangement of the auditing process and insufficient bill sensitive information protection mechanism in the prior art are solved, and the technical effects of improving the intelligent level of the auditing process, enhancing the data security and realizing accurate early warning management are achieved.

The invention provides an enterprise data management system and method based on artificial intelligence. The system comprises a data acquisition module which acquires enterprise energy consumption data and equipment operation parameters in a cross-level manner through an interface; the data preprocessing module is used for performing exception repair on the energy consumption data by adopting an exception detection model, and performing deletion repair on equipment operation parameters by combining a knowledge graph and a filtering algorithm so as to generate an effective data set with a space-time label; the knowledge graph generation module constructs an ontology model containing equipment entities, energy media, metering points and process links on the basis of the effective data set, and mines an association relationship between equipment states and energy consumption data through a graph neural network to generate an energy association knowledge graph; and the governance strategy generation module takes energy consumption optimization and data integrity as targets, generates governance rules in combination with the carbon emission data, and performs root cause analysis based on the energy association knowledge graph, so as to generate an enterprise data governance strategy and govern enterprise energy consumption data and equipment operation parameters according to the enterprise data governance strategy.

The embodiment of the invention provides an enterprise material master data management method and device and a storage medium, relates to the technical field of enterprise data management and information, is applied to processing equipment deployed with a material data management model enhancement package, obtains a target material model suitable for a target enterprise, analyzes a material data table file based on the model, and stores the analyzed material data table file. The method comprises the steps of obtaining first material data, verifying the material data based on a quality verification rule base in an enhanced packet, cleaning the material data by adopting a material data cleaning tool in the enhanced packet according to a quality verification result to obtain second material data, and issuing a list and an external business system list according to the material data. And sending the second material data to the target business system through the material data release interface in the enhancement packet, thereby realizing definition and control for the material characteristic value, promoting the target enterprise to carry out comprehensive business process management conforming to the large enterprise, and further improving the material data sharing and cooperation efficiency of the multi-business system.

The invention belongs to the technical field of enterprise data management, and particularly relates to an enterprise data visual interaction management system fusing digital twinning and a BI board. Comprising an enterprise multi-source heterogeneous data adaptation access module, a data preprocessing output module, an enterprise entity twin dynamic construction module, a BI visual dimension data aggregation module, an enterprise operation anomaly multi-feature fusion recognition module and a fusion visual interaction presentation module. Influences of data chaos and deviation on analysis results are avoided from the source through an enterprise multi-source heterogeneous data adaptation access module, real-time synchronization of enterprise operation states and early warning of equipment faults are realized based on dynamic digital twinners, and a BI visual dimension data aggregation module meets requirements of users of different levels and reduces docking cost. And by distinguishing positive and negative features and calculating a directional anomaly deviation rate and a comprehensive anomaly degree, misjudgment caused by traditional single features or non-distinguishing directions is avoided, and enterprises are assisted to respond to risks in time and carry out targeted rectification.

The invention relates to the technical field of enterprise informatization and data processing, in particular to a digital enterprise data self-adaptive exchange and intelligent integration method, which comprises the following steps of: firstly, ensuring unification and accuracy of data structures of multiple departments through a preprocessing and field alignment technology; performing anomaly detection and correction, and outputting compliance correction data; then analyzing and dynamically identifying the data priority and the sensitivity level by utilizing multi-dimensional characteristics, and distributing different data by adopting an encryption or batch synchronization mode through a self-adaptive routing decision; and finally, multi-department synchronous data is archived to an enterprise data lake, and safe external access is realized in combination with an authority grading and auditing mechanism. According to the method, data redundancy is effectively avoided in cross-department data sharing, the data quality and security are ensured, and the complexity of enterprise data management and collaboration is remarkably reduced.

The invention discloses a process data retrieval optimization method, system and device based on a large model and a medium, belongs to the technical field of artificial intelligence and enterprise data management crossing, and aims at solving the technical problem of how to overcome the defects of low semantic matching precision and rigid slicing strategy in enterprise process data retrieval in the prior art. According to the technical scheme, the method comprises the steps of data preprocessing and multi-granularity semantic feature construction, specifically, enterprise process data are obtained, multi-granularity slicing preprocessing operation is conducted on the process data, and multi-granularity semantic features are constructed; mixed retrieval: adopting a vector retrieval and keyword retrieval combined double-layer recall mechanism to obtain an initial retrieval result; performing large model enhanced reordering: performing context sensing reordering on the initial result through the large model, and combining position attention and diversity filtering to generate a final retrieval set; and multi-mode query interaction: constructing a query interaction system, and supporting convenient calling of process data.

The invention provides a method for realizing a budget dynamic prediction technology based on a flexible formula and memory calculation, and belongs to the technical field of enterprise data management. The method comprises the following steps: acquiring enterprise historical data, and analyzing industry characteristics and business modes of an enterprise based on the enterprise historical data; determining a budget prediction model based on the industry characteristics and the business mode, and adjusting parameters of the budget prediction model through a cross validation method; configuring a dynamic budget formula in the budget prediction model based on the business demand and the data structure, wherein the dynamic budget formula comprises a formula based on dimensions, a table sample, an Exce l function, a business preset function and an external interface; and performing time sequence analysis, regression analysis and scene analysis by using a dynamic budget formula to generate a plurality of sets of budget prediction results. By integrating enterprise historical data, flexibly configuring a budget formula and applying a memory computing technology, efficient, accurate and dynamic budget prediction adapting to different business scenes is realized, and the accuracy of budget compilation is remarkably improved.

The invention relates to the field of classification and grading of enterprise data management, in particular to a metadata tag identification method based on multi-Embedding model integration, which comprises the following steps: selecting a plurality of pre-training Embedding models; pre-calculating all label name vector representations in the label library, and constructing a label vector library; receiving database table field metadata to be classified and graded; respectively calculating the cosine similarity of the input metadata and the tag library vector; dynamically adjusting the weight of each model based on a feedback learning mechanism; the weight of the model is updated, after T times of feedback, the weight increment of each model is accumulated, and it is ensured that the sum of the weights of all the models is 1 through a normalization function. By integrating a plurality of pre-training Embedding models, the problem of limitation of a single model in the aspect of field generalization is solved. The weight of the model is dynamically adjusted through a feedback learning mechanism, so that simple judgment of'non-black, namely white 'in a traditional method is avoided, and the accuracy of classification and grading is improved. Meanwhile, the model with higher performance can be replaced at any time, and the expansibility is high.

The invention provides an enterprise internal decision-making method, system and platform architecture based on multi-source data information aggregation, relates to the technical field of enterprise data management, and aims to obtain multi-source data information including enterprise internal data information and enterprise external data information, avoid the problem of single and isolated data sources and improve the enterprise internal decision-making efficiency. Deep learning, block chains, natural language processing technologies and big data analysis are integrated, multi-source data information is aggregated and analyzed, prediction topic creation, topic recommendation, prediction topic and reward distribution are realized, enterprise internal employee participation and external multi-source data are integrated, enterprise internal decision making is assisted, large-scale and high-frequency decision making requirements are supported, and the enterprise internal decision making efficiency is improved. And moreover, the wide market participation degree is improved, the accuracy and efficiency of enterprise internal decision making are improved, the participation enthusiasm of employees is aroused, and the comprehensiveness of enterprise internal decision making is improved. In the system and platform architecture provided by the invention, flexible replacement and expansion of components can be realized in combination with enterprise internal and external strategies and modular system design, in the subsequent system development and maintenance process, new functions or modules are easy to add, and in cooperation with a layered platform architecture, enterprise development or market environment change is flexibly adapted; and the fast-paced business requirement is met.

The invention relates to the technical field of data management, in particular to an ERP enterprise data management method and device based on big data and a medium, and the method comprises the following steps: generating an ERP enterprise data overview map and a sub-data display map of a target enterprise, and forming an initial data display map; responding to a management end request, receiving business data and classified collection information, determining a data collection dimension, positioning the business data, updating the sub-data display graph, and generating a real-time data display graph; receiving a decision request, calling a decision interface, and determining a target data type and a decision attribute according to input decision demand information; and calling a target decision strategy based on the decision attribute, traversing the real-time data display graph to obtain a target sub-data display graph, a business data layer and a data position, generating a decision support graph, and sending the decision support graph to a management end. According to the method, data integration and real-time updating of the multi-service system are realized, and the data positioning accuracy and the decision support efficiency are improved.

The invention is suitable for the technical field of data-in-channel management, and provides a data-in-channel anomaly detection method and system based on deep learning, and the method comprises the steps: setting a data collection frequency and range, collecting equipment data generated by equipment operation, an external environment and Internet of Things equipment, and carrying out the preprocessing; establishing an equipment behavior model, comparing with historical data, and identifying abnormal activities to generate an equipment abnormity table; monitoring equipment data generation frequency and quality in real time, dynamically evaluating value and risk level, setting an evaluation content change interval and triggering standard revision; and regularly evaluating actual use and potential value of equipment data, and correcting in combination with an external environment. According to the method, the comprehensiveness and accuracy of anomaly detection are improved through multi-dimensional data analysis and a deep learning model, and support is provided for enterprise data management.

The invention relates to an enterprise employee welfare issuing method and system, and belongs to the technical field of enterprise data management. According to the invention, the employees are divided into different subsidy personnel group lists according to the identity attributes of the employees, and the subsidy forms, subsidy quotas, subsidy use places and subsidy service lives enjoyed by the subsidy personnel group lists are determined, so that flexible and accurate configuration of subsidy crowds, subsidy standards, subsidy use places and subsidy service lives is effectively distinguished; and enterprise subsidy expenditure is effectively reduced, auditing risks are reduced, and transparent compliance management is realized. Meanwhile, for the virtual amount welfare fee, the electronic account is divided into an electronic wallet and an electronic coupon, and management and issuing of the virtual amount welfare fee are realized through association of the electronic wallet and welfare items and attribute setting of the electronic coupon, so that directional use and effective control of the virtual amount welfare fee are realized.

The invention discloses an enterprise online interaction data management system, and relates to the field of enterprise data management, the enterprise online interaction data management system comprises an enterprise management center, the enterprise management center is in communication connection with an employee management module, a business process management module, a database module, a business analysis module, an employee operation module and a data monitoring module; the enterprise management center is used for setting a corresponding management area for an enterprise; the employee management module is used for managing corresponding employees and managing comprehensive points of the employees; the business process management module is used for acquiring business data and distributing the business data to corresponding management areas; the business analysis module is used for analyzing and obtaining comprehensive business data corresponding to the business data; the employee operation module distributes the business data to the corresponding employees according to the comprehensive points and the comprehensive business data of the employees; the data monitoring module is used for monitoring the business completion progress of the employees; according to the invention, business data distribution in an enterprise is more reasonable to a certain extent, and the working efficiency of employees is improved.

The invention provides an intelligent report management method, relates to the field of enterprise data management, and is characterized in that a whole-process closed loop of recognition, merging, distribution, filling, reminding and integration is adopted. A template containing the field definition and the submission period is stored through a fixed report library; scanning report fields and calculating a coincidence rate to judge whether the report fields are merged; according to the coincidence rate, combining the repeated fields to generate tasks and reserving a unique data source; distributing tasks according to historical records and department responsibilities; receiving filling data, verifying and integrating to generate a summary report; and alarming in stages according to a preset deadline. The method solves the problems of repeated report filling, unsmooth information, forgetting report sending and the like of a traditional report, improves report management normalization and efficiency, promotes intelligent upgrading of data management, and is suitable for various enterprise report management scenes.

The invention relates to the technical field of enterprise data management, in particular to an employee behavior evaluation method based on a function engine and a CRM system.The method comprises the steps that after employee behavior configuration of a business management end is responded, an employee behavior rule is defined, then a process engine is called, behavior data and context information of an employee are packaged into fact data, and the fact data are stored in a database; and the fact data is transmitted to a rule engine through a standardized interface of the CRM system, and finally the rule engine is called to carry out matching analysis operation on the fact data and employee behavior rules to obtain employee behavior scores. Compared with the prior art, the rule engine designed by the invention can carry out matching analysis calculation on the fact data and the employee behavior rule, can adapt to instant modification of various rule definitions and carry out analysis matching, so that various complex employee behaviors are scored, and the problem of poor flexibility in the prior art is solved.

The invention provides a building enterprise data management and decision-making method based on a lake-warehouse integrated architecture, and relates to the technical field of building enterprise data management, and the method comprises the steps: collecting data information of a project construction stage in which a building enterprise is responsible, storing the data information in a data lake, and structuring all data in a data warehouse; construction stage labels are calculated according to the spatial feature information and the business feature information, the construction stage of the current building enterprise responsible project is judged according to the construction stage labels, and a space-time demand field is constructed according to the construction stage of the current building enterprise responsible project. S2, predicting resource demand quantity of all spatial positions of a construction site in the construction process, partitioning the construction site by taking a peak point of a space-time demand field as a seed point, specifically including physical partitioning and digital twinborn partitioning, and obtaining the resource demand quantity predicted in S2 and data of a data lake, and calculating an abnormal field in the construction stage according to the obtained data.

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.

This invention provides a data governance and decision-making method for construction enterprises based on a lake-warehouse integrated architecture, relating to the field of construction enterprise data management technology. The method includes collecting data information from the construction phases of projects managed by the construction enterprise, storing it in a data lake, structuring all data in the data warehouse, calculating construction phase labels based on spatial and business characteristics, determining the current construction phase of the project managed by the construction enterprise based on the construction phase labels, constructing a spatiotemporal demand field based on the current construction phase, predicting the resource demand for all spatial locations on the construction site during construction, partitioning the construction site based on the peak point of the spatiotemporal demand field as seed points (including physical partitioning and digital twin partitioning), obtaining the S2-predicted resource demand and data from the data lake, and calculating anomaly fields for the construction phase based on the obtained data.

The invention provides an enterprise data management method and device based on artificial intelligence, equipment and a medium, and belongs to the technical field of enterprise data management. According to the method, an enterprise data distribution diagram is obtained through an enterprise big data center, core data indexes and data textures and environmental influence features of all data distribution areas are collected, and a static risk response array is obtained through risk response feature learning. And obtaining an executable path of the enterprise resource management vehicle, and controlling a resource allocation execution track according to the static risk response array. During resource allocation, environment parameters of a current data distribution area are collected to determine a dynamic risk response value, and the allocation direction is corrected according to the dynamic and static risk response values. The method can accurately collect and analyze data, scientifically evaluate risks, reasonably allocate resources and dynamically adjust the resources in real time, effectively improve enterprise data management efficiency and quality, realize resource optimization configuration, and enhance the capacity of enterprises to cope with complex data environments and business changes.

System and methods use a network service registry to differentiate risks associated with use of respective network services, e.g., artificial intelligence (AI) and / or machine learning (ML) network services that may pose risks to use in an enterprise context based for example upon training, data retention, and / or content moderation policies of the respective network services. Upon a request (e.g., outbound network traffic) to an intended network service, systems and methods may intercept the request, reference the request to the service registry to identify risk identifiers of the intended network service, and select an approved network service(s) for the request (e.g., the same intended network service, and / or a different service). Thus, the systems and methods may mitigate or prevent potentially hazardous use of AI and / or ML network services in an enterprise.

This application proposes a big data-based enterprise data management method, belonging to the field of data management technology. When correcting data, it combines the relationships between multiple data points in the data to be corrected and uses these relationships to match data values ​​in a cloud server. Data obtained from the cloud data is then used to correct the local data, thereby improving the accuracy of data correction and further ensuring the consistency between the target data and the cloud data environment. This application also proposes a big data-based enterprise data management system and an electronic device.

This invention provides a system for enterprise data asset inventory and data relationship mining, including a data ontology module, a data relationship module, a data triple module, and a data visualization module. The data ontology module is used to establish an ontology database. The data relationship module establishes a database of data relationship modules, generated by an attribute relationship matrix between ontologies, with different ontology attribute relationship definitions established between every two different ontology domains. The data triple module automatically constructs association relationships between the ontology module and the data relationship module according to subject-verb-object rules. The data visualization module implements automatic image rendering, graph-based data display, and a data graph-to-natural language converter. This invention can protect data security and accessibility, and improve the efficiency of enterprise data management.

A system may include a network communication interface configured to receive network traffic, a processing unit, and a memory unit storing instructions for various engines that are executable by the processing unit. The various engines include a data policy enforcement engine that includes an intent detection engine configured to receive context data and content data relating to the network traffic and generate an intent indexer for the network traffic based on the context data and the content data. The intent indexer documents an operation request and a subject of the operation request that are connected to the network traffic. The various engines also include an intent policy control engine configured to administer aspects of the network traffic based on the intent indexer and the context data.

The invention belongs to the technical field of enterprise data management, and provides an artificial intelligence-based enterprise data management method and system, and the method comprises the steps: carrying out the comparative analysis of incremental data and total data in a data fusion processing process of the incremental data and the total data after the collection of enterprise big data, and obtaining a data fusion result; whether a Cartesian product appears or not is recognized, when the Cartesian product appears in the data fusion processing process, the impact amplitude of the Cartesian product on data fusion processing is evaluated according to the fusion data expansion degree and the fusion data coincidence degree, and it is guaranteed that key information is not missed in the data fusion process by evaluating the impact amplitude; when the impact amplitude is within a controllable range, effective integration of total data and incremental data can be ensured, an enterprise can have a complete data view, the pressure of data fusion on system resources is relieved, system crash or performance reduction caused by too large data size is avoided, and rapid data query and processing capacity is provided for the enterprise.

The invention relates to an enterprise data management method, platform and device and a medium, and belongs to the technical field of data management. The method comprises the following steps: acquiring enterprise data; performing format classification on the enterprise data; the structured data is stored in a storage area network form, the semi-structured data is stored in a key value storage form, and the non-structured data is stored in a distributed storage form or a network additional storage form; performing cold and hot data type division to obtain hot data and cold data; the hot data and the cold data are compressed by adopting a Snappy algorithm and Gzip respectively; and carrying out security level division. By means of the scheme, the enterprise data access efficiency of the user and the system performance are effectively improved, and the enterprise data are safer.

The invention relates to the technical field of enterprise data management, and discloses a data analysis and management method. The method comprises the following steps: acquiring data from an enterprise multi-source database, and preprocessing through a rule engine to generate a standardized business data set; constructing a business characteristic spectrum with a weight, and fusing multi-source data to form a global data model; training a prediction model based on the time series data to carry out anomaly detection; generating a multi-target resource scheduling scheme in combination with the service rule and the optimization strategy; outputting a multi-dimensional decision report through a visualization engine and monitoring a service state; and finally, dynamically optimizing the data model according to an evaluation result. According to the method, intelligent integration and dynamic optimization of enterprise multi-source data are realized, the accuracy of business decision and the resource utilization efficiency are remarkably improved through feature map construction and a multi-target scheduling strategy, and meanwhile, the method has real-time abnormity early warning and model self-optimization capabilities and provides comprehensive data support for enterprise operation.

The invention discloses a data governance and large model optimization method and system, and relates to the technical field of data processing and information. Comprising the following steps: step 1, carrying out data acquisition and preprocessing: acquiring enterprise full-type data, and carrying out cleaning and standardized preprocessing operation; 2, performing large model semantic analysis: performing semantic analysis on the preprocessed data by using a large model, extracting data entities, attributes and relationships, mining data association relationships, and analyzing the effect and value of the data in a business process in combination with a business knowledge base; step 3, demand mining and priority ranking are carried out: on the basis of a semantic analysis result of the large model, enterprise data management demand pain points are automatically identified, and priority ranking is carried out; 4, creating a data model: automatically generating the data model by utilizing the large model; and step 5, labeling the data assets: performing labeling and quality evaluation optimization on the data assets by using the large model.

The invention discloses a method based on CVAamp; the invention discloses a UTC double-region division and six-color coding data quality evaluation framework and a management method. A data quality problem is divided into a CVA warm color system single record problem and a UTC cold color system cross-system problem, six fixed color codes of white, orange, red, blue, grey and green are combined, and one-to-one mapping with six dimensions of integrity, effectiveness, accuracy, uniqueness, timeliness and consistency is performed, so that a globally initiated visual, standardized and easy-to-land data quality treatment system is formed. The method can be widely applied to enterprise data governance, CDMP and other examination teaching, data compliance and AI data governance and supervision scenes, and has extremely high novelty, creativity and practicability.