100 results about "Enterprise data management" patented technology

The invention relates to the technical field of product test data management, and discloses a product test data storage and management system based on an MES system. The system comprises a dynamic configuration module which can use a self-adaptive parameter adjustment algorithm to configure data acquisition parameters according to experiment types; the closed-loop feedback module is used for binding experimental data with production batches and process parameters based on a real-time association algorithm; the two-stage verification module is used for detecting data exception through a double-layer cascade verification algorithm; and the intelligent storage module is used for generating hierarchical storage labels by using a multi-dimensional structured coding algorithm. In addition, the system integration module realizes data interaction with ERP and SCADA systems based on an interface adaptation algorithm. The system solves the problems that in a traditional data management mode, collection parameters are fixed, data relevance is poor, a verification mechanism is imperfect, storage and retrieval are inconvenient and the like, data can be efficiently collected, accurately associated, strictly verified and intelligently stored, and the enterprise data management level and production competitiveness are improved.

The invention relates to the technical field of enterprise data management, in particular to a distributed collaborative office data processing method and system, and the method comprises the following steps: based on employee task completion records, evaluating various capability processing capabilities of employees through extracting and analyzing the project completion data, feedback frequency and interaction data of the employees, and calculating the task completion data of the employees; the task process of the current project is analyzed, the requirements of the target project for various skills are identified, and a capability and requirement data set is formed; according to the invention, through real-time monitoring and analysis of interaction data and feedback frequency of employees, capability of the employees is accurately assessed and project requirements are matched, reasonable human resource configuration is realized and flexibility of project management is improved, and through dynamic adjustment of data access authority, information security is ensured and a data leakage risk is reduced. Real-time data synchronization and consistency verification guarantee that data is accurate among multiple nodes, the team is helped to identify the reason of low efficiency in combination with analysis of cooperation efficiency, and the cooperation efficiency among team members is enhanced.

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.

In some implementations, a data lineage management device may identify that a source code has been uploaded to a software development hosting system. The data lineage management device may retrieve the source code from the software development hosting system. The data lineage management device may determine, using a data lineage analysis model, data lineage information associated with a dataset related to the source code, wherein the data lineage analysis model includes a machine learning model that is trained based on respective data lineage information of datasets associated with a plurality of source codes. The data lineage management device may post the data lineage information to an enterprise data management system.

The present invention discloses an online custom report management method and system combined with an ERP system, and relates to the technical field of enterprise data management, including: determining at least one report management task; custom editing at least one report data processing function corresponding to one report management task based on each report management task; determining the data update status of the ERP system based on the data history update log of the ERP system, wherein the data update status at least includes the data object type and the data update amount; setting an execution node for each report management task based on the data update status of the ERP system; obtaining the data update of the ERP system in real time, and automatically executing at least one report management task to automatically update the report; and using distributed database technology to store the report. The advantages of the present invention are: realizing direct docking with the ERP system, automatically obtaining and updating data from the ERP, and automatically adjusting the update tasks of the distributed report, thereby realizing efficient report management.

The invention discloses an enterprise internal control digital management method based on an AI large model, relates to the technical field of enterprise data management, and is used for solving the problems that in the prior art, enterprise data is difficult to supervise when being abnormal, and the data interaction security between enterprises is poor. According to the method, an internal control data warehouse is created for each department in an enterprise, enterprise operation data of each department is obtained and subjected to digital processing, and digital data to be controlled of each enterprise is obtained and stored in each internal control data warehouse; the method comprises the following steps: constructing an AI large model for supervising enterprise operation data corresponding to all departments of an enterprise, analyzing to obtain abnormal data in the enterprise corresponding to each department based on the AI large model, carrying out internal management and control, and when data interaction is carried out between different enterprises, judging whether the two enterprises have direct handover authority or not; and selecting internal associated data interaction or external transfer data interaction based on a judgment result, and managing and controlling the interactive transfer data of the two enterprises of data interaction in the whole process.

The invention discloses a data service method and system based on a domestic autonomous controllable environment, and relates to the technical field of enterprise data management. The method comprises the following steps: converging data from different service systems based on plug-in architecture design; through analysis and processing of the gathered source data, standardized management of the data is realized; performing index processing based on the gathered source data in combination with service requirements to obtain an index library; and packaging and publishing the treated standard data as standardized API interface data, and providing a data sharing service in combination with the index library. According to the invention, the distance from data to business can be shortened, the diversity requirements of business are met, a user is helped to conveniently and fully use the data, and the data generates business value, so that the problem of enterprise data islands in a domestic autonomous controllable environment is solved.

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 invention relates to the technical field of enterprise data management, and discloses an enterprise data medium station for multi-source heterogeneous data, which comprises an acquisition module, an analysis module, a reconstruction module and a management module. According to the application, efficient processing and management of enterprise multi-source heterogeneous data are realized by utilizing cooperative work of the acquisition module, the analysis module, the reconstruction module and the management module; the acquisition module comprehensively acquires data and generates metadata, the analysis module formulates a reconstruction strategy based on the metadata, the reconstruction module dynamically reconstructs the data and generates description, and the management module properly stores the data; on the basis of close cooperation of all the modules, a data island is effectively broken, the problems of incomplete data acquisition, inflexible reconstruction, nonstandard description, incomplete management and the like in the traditional technology are solved, the availability and decision-making value of enterprise data are improved, powerful support is provided for operation of enterprises in a complex data environment, and the system is suitable for popularization and application. And enterprises can better adapt to business change and development requirements.

The invention relates to the technical field of enterprise management, in particular to an enterprise data management method and system based on deep learning, and the method comprises the steps: collecting enterprise data, constructing an enterprise database, and carrying out the classified storage to form sub-databases of various types of enterprise data; obtaining a search text of a visitor in response to the data access request; the search text of the visitor is input into an enterprise data matching model, the enterprise data matching model is associated with an enterprise database, the model calculates the matching degree of the search text and each piece of enterprise data within the data range, meeting the visitor permission level, of the enterprise database, and the enterprise data with the matching degree meeting a preset threshold value is output; the model comprises a plurality of hidden layers, the sub-database of each type of enterprise data corresponds to one or more hidden layers of the model, and the number of the hidden layers of the selected enterprise data type is adjusted based on the selection of the visitor for the enterprise data. According to the invention, the real-time access requirement of enterprise employees to enterprise data can be flexibly and efficiently met.

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 relates to the technical field of enterprise data management, in particular to an enterprise data management method and system based on big data, and the method comprises the following steps: based on internal and external multi-source heterogeneous data of an enterprise, sorting the internal and external multi-source heterogeneous data of the enterprise according to timestamps, and recording data source identifiers, volumes and key fields; and matching the time information with the source identifier, and extracting a corresponding time span and a continuous interval. According to the method and the device, by accurately sorting the data timestamps and matching the data timestamps with the source identifiers, data changes are effectively tracked, the version control precision is improved, the data recovery capability is enhanced, data field attributes and association degrees are deeply analyzed, data structured management is enhanced, and the processing efficiency and accuracy are improved; data integrity is enhanced through cross check and similarity analysis, error accumulation is prevented, stable support is provided for decision making, a data storage path and node distribution are optimized, access efficiency is improved, load balance is ensured, and management cost is reduced.

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 discloses an enterprise data security management method and system suitable for big data analysis, particularly relates to the technical field of data processing related to computing systems or devices, and comprises a block chain technology, hash operation, authority control, an adaptive segmentation algorithm and the like. The source, the flow direction and the transmission time of each data transmission are recorded through the private block chain, a non-tampering record is generated, even if the data frequently flows among multiple departments, the transmission path of the data can be tracked, and the security and the integrity of the data are ensured; in addition, the number and granularity of data segments are dynamically adjusted according to the data transmission condition and the evaluation result, the safety management efficiency in a complex data flow environment is further improved, and the complexity of enterprise data management is reduced.

The invention provides an enterprise management data treatment method based on big data analysis, and relates to the technical field of big data, and the method comprises the steps: obtaining a target enterprise, and collecting the initial multivariate heterogeneous data of the target enterprise; receiving the initial multivariate heterogeneous data, and preprocessing the initial multivariate heterogeneous data to generate target multi-source heterogeneous data; acquiring a behavior pattern of the target multi-source heterogeneous data, constructing a pattern recognition model, and performing corresponding processing on the behavior pattern based on the pattern recognition model to generate a behavior pattern analysis result; and based on the behavior pattern analysis result and in combination with the behavior pattern frequency, generating a dynamic data governance strategy corresponding to the target enterprise, so that the requirements of modern enterprises can be met, dynamic behaviors of data are deeply analyzed and monitored in real time, enterprise management data are comprehensively and efficiently analyzed, the dynamic data governance strategy is implemented, and the enterprise management efficiency is improved. And meanwhile, the security of enterprise management data can be guaranteed, various complex data scenes can be effectively handled, and automation and intelligence of enterprise data management are realized.

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.

Methods and systems are presented for collectively storing, managing, and analyzing data associated with different data sources. A data management system defines an enterprise data model schema based on different data model schemas associated with the different data sources. The data management system generates, for each data source, an enterprise data model instance based on the enterprise data model schema. Data is ingested from the different data sources, and then transformed and stored in a corresponding enterprise data model instance based on a mapping between a corresponding data model schema and the enterprise data model schema. Upon ingesting the data from the data sources, one or more consolidated data views are generated that combine at least portions of data from different enterprise data model instances. The data arranged according to the one or more consolidated data views is presented on a device and / or further analyzed to produce an analysis outcome.

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.