34 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 relates to the technical field of information management, and discloses a project cloud platform data management method based on a process market, and the method comprises the steps: obtaining process demand information inputted by an enterprise user, and obtaining an enterprise name of the enterprise user; crawling enterprise process information through a web crawler tool, and performing multi-level classification to construct a process market platform; obtaining the business scope and the enterprise scale of the enterprise user according to the enterprise name, and determining the enterprise type of the enterprise user according to the business scope; performing keyword extraction on the process demand information input by the enterprise user to determine a target keyword of the process demand information; and matching a demand flow list in a flow market platform according to the enterprise type, the enterprise scale and the target keyword, and pushing the demand flow list to the enterprise user. According to the method, an intelligent process market platform is constructed, keyword extraction and analysis are carried out on demands of enterprise users, automatic aggregation and accurate matching of process resources are realized, and the process adaptation accuracy is remarkably improved.

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 belongs to the crossing field of artificial intelligence and laser cladding technologies, and particularly discloses a laser cladding knowledge base construction method based on a large model. According to the method, laser cladding knowledge dispersed in patent, literature and enterprise process parameter tables is collected by means of a multi-modal data processing technology, and unstructured data is converted into structured JSON format data by means of technologies such as HTML conversion, OCR recognition and YOLO defect detection. A constructed training set comprises mapping relations among application objects, effect labels and technical features, and a large model has a certain causal reasoning ability through QLoRA adapter injection and a three-stage fine tuning strategy. The system continuously updates the knowledge base through a dynamic optimization mechanism, and realizes the functions of intelligent recommendation of process parameters, defect image recognition, technical trend analysis and the like. Compared with a traditional method, a multi-modal knowledge base is constructed, and deep association and intelligent reasoning of domain knowledge are achieved.

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.

In some embodiments, an artificial intelligence vision system may be integrated with multiple enterprise software applications and the artificial intelligence vision system and method may transform the human-machine interface (HMI). The artificial intelligence vision system and method may use augmented reality, virtual reality, and / or mixed reality devices to create a new level of data interaction and collaboration for enterprise processes. Synchronization may be enabled between virtual environments presented via multiple computing devices and / or between virtual environments presented via different applications executing on the same user device. The artificial intelligence vision system and method may provide a seamless immersive and interactive experience for users by providing accurate multi-dimensional virtual environments mapped to physical environments. Enterprise insights may be generated and provided to users to enable the users to perform real-time or near real-time action regarding assets of an enterprise.

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.

In some embodiments, an artificial intelligence vision system may be integrated with multiple enterprise software applications and the artificial intelligence vision system and method may transform the human-machine interface (HMI). The artificial intelligence vision system and method may use augmented reality, virtual reality, and / or mixed reality devices to create a new level of data interaction and collaboration for enterprise processes. Synchronization may be enabled between virtual environments presented via multiple computing devices and / or between virtual environments presented via different applications executing on the same user device. The artificial intelligence vision system and method may provide a seamless immersive and interactive experience for users by providing accurate multi-dimensional virtual environments mapped to physical environments. Enterprise insights may be generated and provided to users to enable the users to perform real-time or near real-time action regarding assets of an enterprise.

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 application discloses an operation method and system for ensuring process safety and quality control of petrochemical enterprises, and relates to the technical field of process safety and quality control of petrochemical enterprise process changes, and the method comprises the following steps: the overall architecture comprises dividing reference nodes, optimizing operation time, and reconstructing operation variable increments. The method comprehensively considers the influence mechanism of the numerical value of the intermediate transition point and the operation time on the production process, solves the difficult problems of simultaneously realizing the safety, economy and optimality of the production process during the large-scale working condition change process of the petrochemical enterprise, and realizes the target of guiding the operation personnel to select the intermediate transition point through scientific means, thereby providing technical support for solving the operation problems in the working condition switching process of the production device of the petrochemical enterprise.

The invention relates to the technical field of enterprise process approval automation, in particular to a process approval system and method based on big data and a computer program product, and the system comprises a data layer, a rule engine layer and an algorithm layer; wherein the algorithm layer is configured to process target process information received from the data layer on the basis of a rule factor library constructed by the rule engine layer to generate a structured abstract; and according to the target process information, calculating a first risk index and a second risk index of a target process based on the rule factor library and machine learning, and dynamically calculating a comprehensive risk index of the target process. According to the method, the structured approval abstract is automatically generated based on the rule engine, the information retrieval time of an approver is remarkably shortened, and information overload is avoided. In addition, the comprehensive risk index of the target process is calculated based on a machine learning model and an analytic hierarchy process, and potential risks can be marked in real time.

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 invention discloses an enterprise process system full life cycle data architecture establishment method and device, a medium and equipment, and relates to the technical field of information. Through modeling design from a top layer to a bottom layer, the process system of an enterprise is managed in a digital form from demand to optimization and improvement of the full life cycle; element and digitization of the whole life cycle of the process system are realized, a whole life cycle data architecture of the enterprise process system is established, one-part definition and multiple-part reference of the element data of the process system can be realized, and complete information data management of the whole life cycle process of the complex enterprise process system is supported. Enterprise process system elements such as external files, compliance management, business rules, business processes, IT development and the like of enterprises are subjected to full-life-cycle management and operation in a digital form, and re-management of the enterprises based on model management is achieved.

The invention discloses an enterprise process safety performance evaluation method and system based on a multi-system collaboration degree measurement model, and the method comprises the steps: carrying out the scoring of all indexes of process safety through a Delphi method, screening the indexes, and constructing a process safety performance index system; each index is collected, acquired and calculated; calculating the system order degree of sequence parameters of each subsystem of the process safety performance index system, weighting the sequence parameters of each subsystem by using an entropy weight method, and integrating by using a linear weighted summation method, so as to construct an order degree measurement model of each subsystem; and finally, establishing a process safety performance evaluation system collaboration degree measurement model by using the order degree of each subsystem, obtaining a collaboration degree value, and obtaining an evaluation result according to an evaluation rule. According to the method, a more scientific process safety performance evaluation index system is constructed, a multi-system collaborative degree measurement process safety performance evaluation model is established, and quantitative tracking and traceability analysis of a refinery enterprise process safety performance evaluation result are realized.

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.