26 results about "Enterprise process" patented technology

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention provides an enterprise business-oriented flow node intelligent optimization and scheduling method and system, and relates to the technical field of business processing, and the method comprises the steps: obtaining a business target description and an executed flow step sequence, retrieving historical flow nodes based on depth vector embedding, and generating business intention features through a bidirectional long-short term memory network; and constructing context state representation to perform structure matching, and finally constructing a dynamic heterogeneous graph structure to generate a weight recommendation sequence. According to the invention, accurate recommendation and intelligent scheduling of the enterprise process are realized, and the process execution efficiency and the business objective achievement rate are improved.

The invention relates to the technical field of enterprise management, in particular to an enterprise process custom configuration method and system based on a code-free technology, and the method comprises the steps: constructing a multi-layer directed hypergraph containing data, control and condition dependence, calculating an edge weight, and converting the dependence into a first-order logic constraint set; performing incremental satisfiability detection, and outputting a conflict core set containing conflict types and severity in combination with minimum unsatisfiability subset mining and strong connected component analysis; mapping the conflicts into modifiable elements, and generating parameterized candidate repair schemes based on a repair operation library and historical cases; converting the candidate scheme into a weighted constraint optimization problem, and solving an optimal scheme by adopting a branch and bound algorithm; influence propagation verification is completed in the copy, a differential patch and a rollback identifier are generated, transactional submission is carried out after passing, and the weight and the case library are updated. According to the method, automatic diagnosis and low-risk repair of implicit dependency conflicts are realized.

The invention relates to the field of artificial intelligence, and particularly discloses a cooperative office intelligent agent using method and device and a medium, the method comprises the following steps: receiving an input instruction from a user, and analyzing the input instruction to obtain an analysis result; generating a tool calling request based on the analysis result; based on the tool calling request, calling the target MCP Server to generate a tool execution result of the input instruction; and converting the tool execution result into a return result, and sending the return result to the user. And for an enterprise administrator, the intelligent scene access of the service can be completed only by additionally configuring the information of the MCP Server when the light application is originally added. Not only can the scene requirements of enterprise process automation, intelligent work order processing, cross-system data integration and the like be effectively met for enterprises, but also the MCP Server can be automatically expanded for enterprise users, and all the requirements of user office are met.

An artificial intelligence-based business management system for real-time decision optimization, consisting of: a data acquisition unit configured to ingest structured, semi-structured and unstructured data streams from enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, Internet of Things (IoT) devices, external market feeds and financial transaction systems, encrypting, timestamping and verifying the data prior to further processing; a graph processing unit that is communicatively connected to the said acquisition layer, wherein the unit is configured to encode heterogeneous data into dynamic graph structures comprising nodes representing business entities and edges representing transaction or relationship dependencies, wherein the unit is further configured to perform deduplication, metadata tagging and real-time data synchronization; a decision optimization unit operationally linked to the graph processing unit, wherein the decision optimization unit includes modules for reinforcement learning, modules for Bayesian optimization and multi-objective solvers configured to simulate multiple alternative decision paths and select an optimal path based on performance indicators such as cost efficiency, resource utilization, customer satisfaction and risk minimization; a real-time inference control unit with specialized hardware cores, including at least one graphics processing unit (GPU), a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC), wherein the accelerator performs inference tasks of the decision optimization unit with a latency in the millisecond range; a diagnostic processing unit configured to generate causal diagrams, feature mapping maps, and interpretable result summaries according to the optimization outputs; and a control interface unit configured to transmit optimized decisions to process controls within the enterprise, robot actuators, planning systems or interactive dashboards, with the interface supporting bidirectional communication for higher-level interventions, error feedback and triggers for re-optimization.

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 discloses a volatile organic compound real-time tracing method and system for an industrial park, and the method comprises the following steps: obtaining a VOCs component spectrum and three-dimensional meteorological field data in real time through a proton transfer time-of-flight mass spectrum and a Doppler laser radar, and carrying out the alignment and fusion with enterprise process data through a space-time alignment algorithm; constructing a time convolutional network and embedding a chemical mechanism module for constraint to obtain a TCN-MCM hybrid model, and jointly training data loss and mechanism loss through the TCN-MCM hybrid model; the method comprises the following steps: deploying an INT8 quantitative model on a Jetson AGX Xavier platform, and carrying out efficient reasoning and uncertainty quantification through dynamic scheduling computing power and a Monte Carlo method; establishing a transfer learning parameter library, aligning feature distribution of a source domain and a target domain based on maximum mean value difference loss, and establishing a parameter sharing mechanism to realize cross-park rapid deployment; according to the scheme, the traceability precision and the real-time performance can be remarkably improved, the cross-scene deployment cost is reduced, and the method is suitable for precise pollution management and control in a complex industrial environment.

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 an enterprise data analysis method, a computer readable storage medium and terminal equipment, and relates to the technical field of electric digital data processing, and the method specifically comprises the steps: obtaining enterprise economic production abnormal data and related financial statement data; marking abnormal events related to process change and equipment maintenance cost to form a first screening set, and extracting additional cost data and economic loss indexes; comprehensively analyzing the cost to generate an initial loss coefficient, and collecting a benefit evaluation index to generate an initial profit coefficient; and comparing and analyzing the two, calculating a loss adjustment index, and comparing the loss adjustment index with a preset threshold value to execute a corresponding strategy. Through the three-layer system of'initial loss coefficient-initial profit coefficient-loss adjustment index ', the economic loss and profit increase degree of the abnormal event is quantified, full-dimension profit and loss comprehensive balance is realized, a datamation basis is provided for decision making of enterprise process change, equipment maintenance and the like, and fine management of economic activities is facilitated.

The invention relates to the field of enterprise group portrait design, in particular to a design method for constructing a high-tech enterprise group portrait based on enterprise data, and realizes real-time information tracing and accurate information confirmation of high-tech enterprise production data information through analysis and design of the enterprise data. The design method comprises the following steps: step 1, capturing basic information and production industry data of each enterprise from an enterprise database; 2, inputting the production industry data into a preset production-creation evaluation model for analysis, and obtaining an innovation ability score; step 3, layering each enterprise according to the innovation ability score, taking a layering result as a label, performing feature clustering in combination with enterprise process parameters and productivity utilization rate, and establishing an enterprise group portrait; and 4, performing performance evaluation on the constructed enterprise group portrait, and optimizing and adjusting a parameter interval of the enterprise group portrait according to a performance evaluation result.

The invention provides an enterprise management process intelligent optimization system and method based on a knowledge graph, and the method comprises the steps: mapping an entity dependency relationship between graph structure nodes in an enterprise management process into the knowledge graph, and generating a process knowledge graph of enterprise management; determining time sequence influence characteristics of content update in the process knowledge graph through the dynamic attenuation factor of each management process sub-graph; calculating the data variation amplitude and the information entropy weight of each graph structure node, and further performing amplitude weighting on each data variation amplitude according to each information entropy weight to obtain the structure saliency of each management process sub-graph; and determining an aging evaluation index of each graph structure node according to the time sequence influence characteristics and each structure saliency, and carrying out batch updating on the enterprise management process based on each aging evaluation index. Based on the scheme, fusion updating of data value and data timeliness in enterprise process management can be realized, so that the execution efficiency of an enterprise business process can be improved.

This invention provides a method and system for intelligent optimization and scheduling of process nodes for enterprise business, relating to the field of business processing technology. The method includes obtaining a description of business objectives and a sequence of executed process steps, retrieving historical process nodes based on deep vector embedding, generating business intent features using a bidirectional long short-term memory network, constructing a context state representation for structural matching, and finally constructing a dynamic heterogeneous graph structure to generate a weighted recommendation sequence. This invention achieves accurate recommendation and intelligent scheduling of enterprise processes, improving process execution efficiency and the achievement rate of business objectives.

The application discloses a volatile organic compound real-time tracing method and system for an industrial park, which comprises the following steps: acquiring VOCs component spectrum and three-dimensional meteorological field data in real time by using proton transfer time-of-flight mass spectrometry and Doppler laser radar, and aligning and fusing the VOCs component spectrum and three-dimensional meteorological field data with enterprise process data through a space-time alignment algorithm; constructing a time convolution network and embedding a chemical mechanism module for constraint to obtain a TCN-MCM hybrid model, and training data loss and mechanism loss jointly through the TCN-MCM hybrid model; deploying an INT8 quantization model on a Jetson AGX Xavier platform, and performing efficient inference and uncertainty quantization through dynamic scheduling of computing power and a Monte Carlo method; establishing a transfer learning parameter library, aligning source domain and target domain feature distributions based on a maximum mean difference loss, and establishing a parameter sharing mechanism to realize rapid deployment across parks; and the scheme can significantly improve tracing accuracy and real-time performance, reduce cross-scene deployment costs, and is suitable for precise pollution control in complex industrial environments.

The invention discloses an automatic flow initiating method and system based on natural language processing, and belongs to the technical field of natural language processing. The method comprises the following steps: acquiring natural language input information of a user; performing intention recognition on the input information, and determining a matched target process template; extracting parameter information corresponding to the template from the input information; generating a flow work order based on the template and the extracted parameter information; and carrying out circulation processing on the process work order. By using the pre-trained natural language processing model and the named entity recognition model, the technical problems of low efficiency, poor normalization and dependence on manual intervention of a traditional process initiation mode are solved. According to the method, historical data are collected and analyzed, a closed-loop optimization mechanism is established, and continuous iteration is performed on the intention recognition model, the parameter extraction model and the process template so as to adapt to business changes, improve the accuracy of the system and the user satisfaction, and realize intelligentization and high efficiency of enterprise process management.

The invention provides an automatic flow identification and conversion method based on an unstructured document, and relates to the technical field of enterprise flow digital transformation, and the method comprises the steps: collecting the unstructured document, carrying out the automatic identification, and generating an image document and a text document; based on a hierarchical AI model strategy, extracting image features of the image document by adopting a convolutional neural network model, and extracting text features of the text document by adopting a natural language processing model; the image features and the text features are mapped and fused into element semantic features through a multi-modal information fusion algorithm, verification and complementation are carried out in combination with an element recognition method, and fusion feature data are generated; and on the basis of the conversion rule, converting the fused feature data into the structured process data, and executing dual verification and error repair to generate the standardized flow chart, so that efficient, accurate and automatic conversion from the unstructured document to the standardized flow chart can be realized, the enterprise process digitization efficiency is remarkably improved, and the labor cost is reduced.

This invention discloses a multi-agent dynamic scheduling and skill sharing system and method, comprising a user interaction layer, a main scheduling layer, an execution layer, and a skill registration layer. The main scheduling layer uses a lightweight large language model to achieve task semantic understanding, subtask decomposition, and dependency analysis, constructing a directed acyclic graph (DAG) of task dependencies and automatically determining serial and parallel execution strategies. The execution layer consists of multi-domain-specific execution agents, supporting differentiated configuration of the underlying model according to task complexity. The skill registration layer decouples skills from execution agents through a global skill registration center, enabling cross-agent sharing, on-demand mounting, and hot-swappable expansion in a skill-as-a-service model. This invention significantly improves task matching accuracy, execution efficiency, and resource utilization, with auditable operation links and lower costs, making it suitable for complex task automation scenarios such as autonomous driving software development, enterprise process automation, and content creation.

The present application relates to the technical field of enterprise process management and message control, and provides a process task urging method based on urging interval verification and double-channel state write-back, comprising: after receiving an urging request, querying the latest urging record according to the process instance identifier in the urging request, and judging whether to allow this urging based on the interval between the current time and the latest urging time; when the urging is allowed, obtaining the process task information according to the task identifier in the urging request, generating a process title and urging content according to the business type of the process task, and creating an urging record; respectively calling a first message channel and a second message channel to send the urging message, and writing back the sending states of the first message channel and the second message channel to the urging record respectively. The present application can reduce message noise, improve business identification efficiency, improve touch reliability and full-process traceability.

The invention relates to the technical field of environment supervision, and particularly provides an enterprise process pollutant prediction method and system based on heterogeneous graph dynamic reasoning, and the method comprises the steps: obtaining a feature set of pollution discharge nodes, and determining the feature vectors of the pollution discharge nodes through a feature set encoder based on the feature set; performing similarity calculation on the feature vectors by using a pollutant vector library, and determining associated pollutants of the pollution discharge nodes and confidence scores of the associated pollutants; sorting the confidence score of each associated pollutant according to the similarity calculation result, and selecting a preset number of associated pollutants as key pollutants from high to low based on the sorting result; performing time sequence probability distribution prediction on the real-time monitoring data of the key pollutants by using a dynamic Bayesian network model, and judging whether an abnormal emission behavior occurs or not based on a prediction result; and generating disposal suggestions based on the key pollutants and the abnormal emission behaviors. According to the method provided by the invention, the environment supervision efficiency can be improved.

The application discloses a process automation method and system based on a multi-modal intelligent agent, and belongs to the field of artificial intelligence and process automation. The method comprises collecting multi-modal process data and preprocessing; constructing a knowledge graph and a vector index, obtaining an evidence set through mixed retrieval; using a RAG-ReAct dual-engine collaborative reasoning to process the evidence set to generate an executable process plan, and calling an external tool to execute the plan; performing consistency detection and recursive reflection on the reasoning and execution process to generate reflection data and scheduling parameters; training the reasoning engine based on the reflection data, and performing parameter directional injection on the reasoning engine; dynamically scheduling and scaling the computing resources based on the scheduling parameters; constructing a reward signal, and continuously optimizing the process automation method through reinforcement learning. The application solves the problems that the traditional government and enterprise process automation method cannot effectively cope with cross-modal semantic fusion, evidence traceability, complex reasoning consistency and high concurrency stability.

The invention provides an environmental protection technology service consultation management system based on big data, and relates to the technical field of environmental protection informatization and big data intelligent services. The coverage integrity, the time validity and the multi-source consistency of the consultation data are comprehensively evaluated, and the consultation deviation risk caused by insufficient data quality is reduced; further combining enterprise process characteristics, emission characteristics and regulation application conditions, analyzing the process matching, the expected treatment effect and the implementation risk of the environmental protection technical scheme, and outputting optimization suggestions aiming at the technology type selection and the implementation path; finally, the compliance implementation effect and the operation stability of the technical scheme are comprehensively evaluated, a risk prompt, constraint suggestion and data feedback mechanism is formed, and scientific decision and continuous optimization of the environmental protection technology consultation scheme are realized.

The invention relates to the technical field of enterprise process management digitization, in particular to a process asset management system and method based on a BPM management system. Comprising a process framework construction unit; a multi-dimensional authority management unit; a flow chart increment analysis unit, wherein the flow chart increment analysis unit adopts a collaborative strategy of initialized total analysis and timing increment analysis; a role classification permission unit; an approval responsibility closed loop unit; and a demand scheme merging unit. According to the method, targeted optimization of existing process management defects is realized through a multi-dimensional technical design: in the aspect of digital management of process assets, standardized generation of a tree-shaped business classification framework is realized through hierarchical construction and a uniqueness verification mechanism of a process framework construction unit; the problem of disordered process framework organization in a traditional scheme is solved, and the hierarchical management efficiency of process assets is improved; the problem of low flow change analysis efficiency in the prior art is effectively solved.

The application discloses a cross-system real-time synchronization method and device for process task data. The method comprises the following steps: defining a standard data transmission object comprising a task identifier, a process instance identifier, an approval state and an approval opinion; implanting a synchronization trigger at four key nodes of process starting, task completion, rejection and transfer, collecting process engine data in real time and pushing the process engine data to a business system; and performing an update operation after the business system receives the data, so as to ensure the consistency of data in the two systems. The application replaces a traditional polling mode with an event-driven mechanism, solves technical problems such as data islands, state asynchronization and low query efficiency, realizes real-time, complete and accurate synchronization of process task data, reduces the coupling degree of the system, supports approval track tracing and department dimension statistical analysis, and improves the digital level of enterprise process management.

The invention discloses a multi-prediction model coupled enterprise safety index prediction and evaluation method. The method comprises the following steps: data acquisition and preprocessing; converting the processed data into a corresponding time sequence, and checking the stability of the time sequence; adopting an exponential smoothing method, an autoregressive moving average method and an SVR prediction method to establish a plurality of time sequence prediction models; the STL decomposition algorithm is adopted to decompose the time sequence index data into trend, seasonality and residual errors, and the trend of the index data is analyzed; substituting the stationary time sequence, selecting an optimal model, and predicting the indexes by adopting the optimal model; and importing the predicted data into a performance evaluation system, carrying out evaluation scoring and grading, and carrying out early warning analysis. The invention provides a multi-prediction model coupled index trend prediction method, which can effectively improve the accuracy of index prediction data.

The present invention relates to an artificial intelligence driven enterprise automation system and an associated operational approach that enables adaptive, validated, and continuous automation of enterprise workflows. The system integrates cognitive processing with generative artificial intelligence to analyze enterprise operational data, characterize workflow structures, and infer execution behavior in real time. By generating multi-dimensional workflow representations and persistent workflow fingerprints, the invention enables accurate identification of legitimate enterprise processes and dynamic detection of execution deviations. Validation mechanisms compare real-time workflow behavior against adaptive reference profiles to determine execution conformity and to govern enterprise task execution through graduated control responses. The invention further supports continuous learning through selective reinforcement based on validated executions, ensuring that workflow characterizations evolve with enterprise operations while preventing propagation of anomalous behavior.

The invention discloses an enterprise process-level carbon emission accounting method and device based on electric power data, and can be applied to the technical field of carbon emission accounting. The method comprises the following steps: taking power consumption data of a target enterprise in a target time period as input, and predicting the main product yield of the target enterprise in the target time period by using a prediction model of the main product yield of the target enterprise; based on the main product yield of the target enterprise in the target time period and the energy consumption intensity and the material consumption intensity of the different processes of the target enterprise, calculating first carbon emission of the different processes of the target enterprise in the target time period; and correcting the first carbon emissions of the different processes of the target enterprise in the target time period to obtain the carbon emissions of the different processes of the target enterprise in the target time period.

The invention relates to an intelligent integration method and device for an enterprise report and a business process and a medium, the method is applied to an enterprise report server, and the method comprises the following steps: the enterprise report server receives a to-do task request from a user; an enterprise report server requests a task ID and context information of a current task from a business process engine, wherein the context information comprises a process definition Key, a task definition Key and a process variable; the enterprise report server dynamically renders a personalized interactive interface according to the context information received from the business process engine; and after receiving the data submitted by the user through the interactive interface, the enterprise report server updates the data back to the process variable of the business process engine, and calls the business process engine API to drive the process to the next node. Compared with the prior art, the method has the advantages of realizing deep fusion of the enterprise report and the business process, improving intelligent management of the enterprise process and the like.