197 results about "Enterprise system" patented technology

Techniques for operating a chatbot system for enterprise-level conversational agents are disclosed. These techniques are performed by an application or cloud service executing on one or more computing devices. An enterprise system can deploy conversational agents onto user devices to run as chat interfaces for logging analytics question-answering. One example application or cloud service may be a multi-model chat mechanism configured to support these chat interfaces with backend functionality. In response to an incoming question, the chat mechanism first consolidates the question with any conversation history and then, classifies the user's question as either a question regarding unstructured document data, a question regarding structured log data, or a hybrid question. Based on the classification, the chat mechanism can generate a proper large language model (LLM) response.

The present disclosure relates to cybersecurity architectures and systems for assessing and quantifying security threats and risks associated with machine-readable codes, such as quick response codes, barcodes, data matrix codes, and other types of codes. A security application comprises a multi-context threat assessment system configured to analyze a broad spectrum of risk assessment attributes across multiple contexts. These contexts relate to the machine-readable code itself, target network resources identified by the code, entities affiliated with the code, end-users interacting with the code, and enterprise systems policies. The system can evaluate various risk assessment attributes for each of these contexts to more accurately quantify potential security risks associated with the machine-readable codes. The security application further includes an API for extending its threat assessment capabilities to various digital ecosystems and an AI-powered learning network comprising language models and computer vision systems to enhance threat detection and risk quantification capabilities.

Techniques that enable users to generate an artificial intelligence (AI) integration configured to perform a task utilizing one or more machine learning models are described herein. An AI studio provides a user with options for creating a configuration object associated with an AI integration via a user interface. The AI studio may receive a request, from a user, to generate a prompt, the prompt defining an instruction to one or more machine learning model(s) (e.g., a large language model) to perform a task or generate an output based in part on input received from a user. A user may be presented with an option to select a type of transformer and deployment configuration to associate with the AI integration, the deployment configuration indicating an enterprise system and a deployment location within the enterprise system.

A system (100) for dynamic data pipeline configuration based on real-time enterprise integration context, comprising: (a) a context monitoring and event detection module configured to detect changes in enterprise systems in real time, including changes in data sources, schemas, system availability and regulatory conditions; b) an integration context analyzer configured to map and analyze the enterprise integration landscape, including data dependencies, formats, API endpoints, and metadata relationships; c) a pipeline orchestration and configuration engine configured to dynamically create, update, and deploy data pipelines in response to contextual triggers using the outputs of the integration context analyzer; (d) a rule-based decision and policy management module configured to apply user-defined and system-generated rules for data transformation, routing logic, security enforcement, and compliance controls during pipeline configuration; e) an AI and ML-driven optimization module configured to analyze historical data flow performance, predict bottlenecks, recommend schema transformations, and optimize pipeline efficiency based on learned patterns; f) a real-time data flow and transformation engine configured to perform adaptive ETL operations, handle schema evolution, support conditional transformations, and manage error recovery in live data streams; g) and an audit, logging and compliance module configured to maintain detailed records of pipeline actions, configuration changes and integration results for regulatory compliance, troubleshooting and traceability.

Systems and methods for automated temporary device connectivity include receiving data requesting that a first device be associated with a second device and generating a request for an identifier of the second device to be associated with the first device. This request may be sent to an enterprise system and the second identifier may be received from the enterprise system. Data authorizing the first identifier to be associated with the second identifier for pairing purposes may be generated and stored. An indication may be received that the first device has moved within a threshold range of the second device and commands may be sent to cause the first device and the second device to enter a pairing mode.

A computer-implemented method includes: receiving, by a computer device, a request for a new online account for accessing data stored in an enterprise system; determining, by the computer device, an identity associated with the request; determining, by the computer device, a risk score for the request; determining, by the computer device, validation steps based on the risk score; and executing, by the computer device, the validation steps.

An enterprise system may include one or more devices with sensors that measure operational parameters of the devices. The enterprise system may also include a sustainability platform system that obtains a sustainability model representative of a state of operations of the enterprise based on the measured operational parameters and receives sustainability target data that includes one or more threshold limits, one or more ranges, or both for one or more sustainability parameters. The sustainability platform system may also obtain one or more action plans for adjusting respective operations of the devices based on the sustainability model and the sustainability target data, simulate a performance of the action plans over a period of time relative to the sustainability parameters, and determine whether the simulated performance of the action plans is effective.

The invention discloses an intelligent agent platform and information system integration compatible method and equipment, belongs to the technical field of digital transformation, and aims to overcome the defects of high cost, long development period, difficulty in maintenance and the like of an existing intelligent agent platform and information system integration scheme. And dynamic upgrading of an enterprise system and access of a new system are difficult to adapt. The method comprises the following steps: constructing dynamic protocol conversion middleware supporting multiple protocols, and converting heterogeneous protocol data through the dynamic protocol conversion middleware to obtain standardized data; performing format conversion on the standardized data through a hierarchical data converter to obtain readable data; respectively transmitting the high-frequency calling data and the low-frequency calling data to an intelligent agent platform through a hybrid communication bus; wherein enterprise sensitive data in the readable data is transmitted to the intelligent agent platform in a token form after being subjected to desensitization operation and / or encryption operation.

Techniques for a service provider network to manage a meeting between individuals located in a conference room with one or more other individuals remote from the conference room are discussed herein. An Enterprise system can implement one or more machine learned models to generate tiles that represent different individuals associated with a meeting. A same or different machine learned model can automatically arrange a series of tiles for display that, when presented collectively, promotes inclusiveness and attention for both in-room and remote participants of the meeting. The meeting management techniques can include determining which content to include on a display device based on evaluating changes in behavior of the meeting participants over time.

The invention relates to the technical field of information security, in particular to a cluster self-discovery method suitable for a cloud server cipher machine, which comprises the following steps: when physical equipment of the cloud server cipher machine leaves a factory, a certificate management module generates an equipment certificate and a root CA certificate in combination with an enterprise CA system, and stores the equipment certificate and the root CA certificate; after the virtual cipher machine is created and started, the key management service module combines with the certificate management module to generate a virtual machine certificate, and stores the virtual machine certificate, the storage device certificate and the root CA certificate together; the cluster management service module enables the master node of the virtual cipher machine to discover the slave node of the virtual cipher machine in the same network domain through UDP broadcast, and executes three-level verification on the slave node of the virtual cipher machine from three aspects of an equipment certificate, a virtual machine certificate and an authentication signature; and the virtual cipher machine master node synchronizes the cluster master key to the virtual cipher machine slave node passing verification. According to the invention, hierarchical identity isolation and verification of the cloud server cipher machine host and the virtual cipher machine can be realized, and the security of cluster self-discovery and authentication stages is ensured.

A system and method for generating a guidance path for automated navigation of large agricultural equipment. Vehicle path data from leading vehicles can be recorded, stored or inputted into the system for generation of guidance paths offset from the swaths traversed by the leading vehicle. Combines, planters sprayers and other vehicles utilizing guidance system can be used to record leading vehicle data for use by the following vehicle in real time or in subsequent cycles. Enterprise systems can manage multiple grain carts and / or combines or other vehicles to maximize efficiency.

At least one non-transitory computer readable media storing instructions that, when executed by one or more hardware processors, causes performance of operations. The operations include receiving a natural language query defining a policy for a workflow. The operations also include retrieving supplementary information relevant to at least one of the workflow and the natural language query, wherein the supplementary information includes at least one code fragment. The operations further include building an input prompt for a large language model. The input prompt includes the natural language query and the supplementary information, and the input prompt directs the large language model to generate, based at least in part on the at least one code fragment, code that is executable to implement the policy for the workflow.

The invention relates to the technical field of ERP financial risk assessment, and discloses an ERP financial data risk assessment method based on machine learning. The method comprises the steps of obtaining multi-dimensional historical financial data in a target enterprise ERP system, and performing preprocessing to generate a standardized financial data set associated with a timestamp and a financial index type label; constructing a dynamic feature extraction network, and extracting time sequence correlation features and cross-dimension coupling features from the dynamic feature extraction network according to tags; initializing a risk assessment model containing a parallel anomaly detection branch and a trend prediction branch, wherein the two branches respectively receive corresponding features; iteratively optimizing the model by adopting a hierarchical adversarial training strategy until two branch outputs meet an error convergence condition; and inputting real-time ERP financial data, and generating an evaluation report containing a risk level mark and a confidence score. The method adapts to ERP financial data characteristics through cooperation of multi-dimensional feature extraction and a double-branch model, and can be effectively used for enterprise financial risk management and control.

The invention discloses an intelligent accounting and analysis system for enterprise operation cost and expense, and relates to the technical field of intelligent accounting and analysis of enterprise operation, comprising a data preprocessing module used for obtaining original cost data from an enterprise ERP system, a financial system and a production management system, performing cleaning, field mapping and time sequence alignment operations on the original cost data, and outputting a structured unified data set; the deviation decomposition module is used for dividing a total cost deviation into four types of components, namely a usage difference, a price difference, an efficiency deviation and a structure migration deviation, based on a budget value and an actual value in the structured unified data set, performing attribution analysis according to a department, a product line or a project dimension, and outputting deviation detail data; and the weight calculation module is used for generating a dynamic weight vector according to the historical fluctuation characteristics of each cost item in the deviation detail data and the business association strength of the cost item in the enterprise operation in combination with an external market change signal.

The invention relates to the technical field of artificial intelligence, in particular to a hierarchical tree and auto-reflection-based retrieval enhancement generation method, device and medium, the method comprises: constructing a multi-layer tree structure knowledge base, the knowledge base comprising a plurality of tree index structures constructed based on text similarity, enterprise organization architecture and level architecture; receiving user query and user identity information; generating a plurality of sub-queries corresponding to different retrieval intentions based on the user query and the user identity information; performing multi-path retrieval on each sub-query in the corresponding tree index structure to obtain a related knowledge fragment set; and based on an auto-reflection mechanism, a final answer is generated, and knowledge traceability is realized at the same time. According to the method, the problems of low knowledge base quality, poor retrieval result correlation and single retrieval path in the traditional RAG technology are solved, and the accuracy, correlation and interpretability of enterprise system questions and answers are remarkably improved.

The invention relates to the technical field of system compliance management, and discloses a system compliance early warning method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting system file data, and carrying out the standardization processing and structural analysis; data preparation is carried out based on the system metadatabase and the supervision fine tuning corpus set, an initial model is constructed and trained, a trained system large model is formed, and version management is carried out; determining early warning types, establishing a system revision early warning model for different early warning types, and triggering early warning reminding according to an output result of the early warning model; performing difference comparative analysis on the same-theme systems based on the system large model, and generating a compliance inspection report; receiving a natural language question, generating an answer and providing system traceability information; a system outline is generated according to system revision requirements, a chapter summary and clause content are generated based on the system outline, and real-time compliance check and clause recommendation are supported. According to the invention, the intelligent demand of enterprise system full-life-cycle management can be met.

The invention discloses a management method and system for outsourcing purchased parts, and relates to the technical field related to data management, and the method comprises the steps: connecting an enterprise ERP system, synchronously screening BOM information and supplier information of purchased parts in real time, and inputting repair data, receiving data and return data, the input mode comprises code scanning input and pc popup window selection input; carrying out full-process tracking analysis, and determining the order state of the purchased parts; and configuring an authority management strategy, and performing control management of accessible data and executable operation on the order state. The technical problems that in the prior art, information transmission is lagged and inaccurate, data entry efficiency is low, errors are prone to occurring, the purchasing process cannot be tracked in the whole process, and the data security risk is high due to the fact that authority management is missing are solved. The technical effects of improving the information processing efficiency and accuracy, realizing the transparency and traceability of the purchasing process and improving the enterprise supply chain collaboration efficiency are achieved.

Disclosed are systems and methods for detecting a vulnerability across programs of an enterprise system and automatically mitigating the vulnerability. The systems and methods utilize artificial intelligence (“AI”) systems to process data received from a particular network, such as systems data, software data, and software configuration data. The AI systems processes the software data and software configuration data and compares the data to known vulnerabilities stored to a database. The system maps the vulnerabilities to attack signatures. When a vulnerability is identified within the network, the AI systems run classification analysis and categorization analysis to determine the probability a vulnerability is a known vulnerability and the category of software it relates to. The system self-executes a rule that enables the attack signatures to protect against the identified vulnerability by either removing or patching.

The invention relates to an agent generation method and system for enterprise process automation. The method comprises the following steps: S1, obtaining a standard specification document of an enterprise; s2, knowledge extraction and knowledge graph construction; s3, semantic analysis and intention discrimination; and S4, process reasoning and automatic execution. According to the method, full-link intellectualization from enterprise system document automatic analysis to business process closed-loop execution is realized, context sensing and dynamic adaptation capabilities are realized, multiple types of tasks are supported, the analysis fine granularity and cross-system cooperation efficiency of business process rules are effectively improved, and high universality and expandability are realized.

The invention provides a product supply method and system and electronic equipment, and the method comprises the steps: collecting the supply information of multiple links in real time through Internet of Things equipment and an enterprise system, and the supply information comprises at least one of inventory information, order information, logistics information, a supply chain topological structure and supply environment parameters; performing anomaly detection and demand prediction on the supply information by adopting a streaming processing engine to obtain a response priority and a prediction demand; dynamically generating at least one initial supply strategy according to an improved multi-target genetic algorithm, the response priority and the prediction demand, and determining a target supply strategy based on simulation execution results corresponding to the initial supply strategies; and executing product supply based on the target supply strategy, and taking feedback data generated in the execution process as new supply information. Multi-chain real-time collaboration and dynamic evolution are supported, and the supply chain response speed and the resource configuration efficiency are improved through a closed-loop feedback mechanism.

The invention discloses a digital transformation maturity evaluation system and method based on a multi-dimensional quantitative index in the technical field of building industry digitization. The method comprises the following steps: acquiring various system data of an enterprise; the method comprises the following steps of: normalizing original data of sub-indexes of each dimension in data of each system of an enterprise through an evaluation engine deployed in a cloud server, weighting and aggregating, and calculating a comprehensive maturity index; calculating a dynamic evolution rate by using an evaluation engine in combination with historical comprehensive maturity index data, an enterprise resource investment ledger and an industry reference library; calculating bottleneck factors by using an evaluation engine in combination with enterprise self-evaluation data and an industry benchmark database; the enterprise bottleneck dimension is evaluated based on the bottleneck factor and then an improved scheme is fed back, the enterprise efficiency level is evaluated based on the dynamic evolution rate and then resource investment is adjusted, and the enterprise maturity is evaluated based on the comprehensive maturity index and then a stage route map is generated. According to the invention, the digital level of the building enterprise can be scientifically diagnosed, and transformation path planning is guided.

Disclosed are systems and methods for detecting a vulnerability across programs of an enterprise system and notifying remediation agent. The systems and methods utilize artificial intelligence (“AI”) systems to process data received from a particular network, such as systems, software, and software configuration data. The AI systems processes the software and software configuration data and compares the data to known vulnerabilities stored to a database. The system maps the vulnerabilities to attack signatures. When a vulnerability is identified within the network, the AI systems run classification analysis and categorization analysis to determine the probability a vulnerability is a known vulnerability and the category of software it relates to. The AI systems then runs a remediation agent analysis to determine the proper remediation agent to mitigate the vulnerability. Once a remediation agent is determined, a remediation agent is notified of the vulnerability and mitigates the vulnerability by removing or patching.

The invention discloses a dynamic self-verification method based on service flow-test flow homologous heterogeneous execution. The method comprises the following steps: uniformly describing service logic and a corresponding interface test contract through a strong type of domain specific language (DSL); the method comprises the following steps: simultaneously compiling homologous DSL descriptions into stateful business workflows executed by a process engine and stateless and combinable atomic test contract sets called by a test executor by utilizing a double-path compiling engine; during operation, loose coupling synchronous triggering and execution of a service flow and a test flow are realized based on a high-throughput event bus, and real-time and high-fidelity contract verification, performance monitoring and chaos probe testing are performed on cross-system interface calling in a production environment. According to the invention, the traditional post-event and off-line integration test is converted into the in-event, on-line and continuous system endogenous capability, and the industrial problems that the complex enterprise system group integration test is difficult and the operation and maintenance monitoring is passive are effectively solved.

The invention discloses a data integration method, system and device, a storage medium and a program product, and relates to the technical field of enterprise application integration, and the method comprises the steps: receiving a data access interface and a debugging command defined by a user based on an SQL template through an integration platform of an SaaS end, and the debugging command is issued to a front service client of an enterprise local system through a secure communication channel. And the front service client constructs an actual SQL statement according to the debugging command in combination with the input and output parameters in the SQL template, and executes and returns debugging result information to the integrated platform for display when accessing the database. And after the user confirms that the SQL interface information is correct, the platform issues the SQL interface information to the front service client for analysis and storage. According to the scheme, efficient and safe data integration between the old enterprise system and the SaaS application is achieved through an integrated architecture based on cooperation of a remote debugging mechanism of an SQL template and a safe data channel.

The invention belongs to the technical field of file hierarchical secrecy, and particularly discloses an equipment system file hierarchical secrecy method and system based on a general large language model, and the method comprises the steps: obtaining file query request data of a querier and a query permission of the querier; generating structural body data according to the obtained data and a system file data calculating keyword matching relevancy, theme relevancy and logic relevancy between the file query request data and a system file corresponding to each node in the system file database, and performing weighted summation on the three relevancy to obtain node relevancy; dividing node correlation into three levels of strong correlation, secondary correlation and weak correlation; according to the query permission and the node correlation, generating structural body data; and inputting the file query request data, the structural body data and the query permission of the querier into the general large language model to obtain a query result. And confidentiality of enterprise system file information can be realized.

The invention relates to the technical field of ISO authentication, and provides an ISO system service authentication process method. Comprising the following steps: constructing an enterprise system association analysis model based on a graph neural network, establishing an authentication strategy optimization mechanism based on reinforcement learning, designing a cross-modal fusion intelligent auditing system, constructing an industry authentication knowledge sharing platform based on federated learning, and implementing a continuous compliance monitoring system based on digital twinning. The method is supported by various frontier artificial intelligence technologies, systematically solves the core problems of low efficiency, inaccurate risk identification, insufficient privacy protection, limited knowledge sharing and the like in the traditional ISO system authentication, and greatly improves the intelligent level and management effect of the authentication process.

The invention discloses a hub machining multi-station cooperative control system based on the industrial internet, and relates to the field of hub machining. The multi-source data acquisition module accurately acquires multiple parameters and performs time-space alignment, the digital twin modeling module simulates the machining process and optimizes the parameters, the multi-station cooperative scheduling module uses a quantum heuristic algorithm to improve the equipment utilization rate, and the quality closed-loop control module detects defects and performs automatic repair. The industrial internet interface module is in butt joint with an enterprise system to achieve full-process digital management. According to the method, the hub machining cooperation efficiency is improved, and the order delay rate is reduced; quality control is enhanced, and the yield is improved; equipment health is optimized, and shutdown and unit energy consumption are reduced; operation and maintenance are enhanced, cost is reduced, low-carbon authentication is supported, and intelligent transformation is promoted.

The invention relates to an OA cloud service platform based on data sharing, and the platform comprises a data time sequence analysis module which is used for carrying out the seasonal decomposition and trend prediction of standardized historical sales data through employing a Prophet algorithm, and generating a sales prediction data set containing periodic features and a trend curve; the fusion module is used for receiving the structured product database and the sales prediction data set, performing feature alignment and dimension association with business data of an enterprise ERP system, and outputting a multi-source fusion data cube with a unified space-time label; and the spatio-temporal data warehouse architecture module is used for constructing a distributed data lake based on a Hadoop ecological system, implementing column type storage optimization on the multi-source fusion data cube, and integrating an Elasticsearch full-text retrieval engine to form a data warehouse supporting spatio-temporal dimension online analysis processing. According to the invention, the enterprise business process management efficiency, the data security and the decision intelligent level are improved.

The present disclosure relates to cybersecurity architectures and systems for assessing and quantifying security threats and risks associated with machine-readable codes, such as quick response codes, barcodes, data matrix codes, and other types of codes. A security application comprises a multi-context threat assessment system configured to analyze a broad spectrum of risk assessment attributes across multiple contexts. These contexts relate to the machine-readable code itself, target network resources identified by the code, entities affiliated with the code, end-users interacting with the code, and enterprise systems policies. The system can evaluate various risk assessment attributes for each of these contexts to more accurately quantify potential security risks associated with the machine-readable codes. The security application further includes an API for extending its threat assessment capabilities to various digital ecosystems and an AI-powered learning network comprising language models and computer vision systems to enhance threat detection and risk quantification capabilities.

A system and method are provided for authenticating client devices communicating with an enterprise system. The method includes providing a policy enforcement interceptor to intercept API calls and enabling the policy enforcement interceptor to communicate with a policy information point to query the at least one endpoint for entitlements associated with an account. The method also includes intercepting an API call to the application API, communicating with the policy information point to determine entitlements associated with the account by having the policy information point query an entitlements database and, when the entitlements returned to the policy enforcement interceptor are valid, invoking a policy decision point to validate the client device. The method also includes, when the client device is validated, permitting invocation of the API. The method also includes providing an API response to the client device to permit access to the application via the API.