537 results about "Enterprise management" patented technology

The invention provides a service data security protection method and system for digital enterprise management, and the method comprises the steps: obtaining an access request sequence initiated by a target user at a service operation terminal, generating a dynamic access control strategy vector according to a historical behavior track associated with an access operation identifier, and carrying out the dynamic access control strategy vector; analyzing the dynamic access control strategy vector through a strategy decoder, and generating a real-time access permission instruction; based on the real-time access permission instruction, performing homomorphic encryption mapping on a sensitive data segment in the request context information to generate a ciphertext transmission channel, and performing security parameter synchronization on the ciphertext transmission channel and the cross-department conjoint analysis model; and calling a federated learning framework to carry out multi-modal feature fusion on the real-time operation flow of the service operation terminal, generating an abnormal operation confidence score, triggering a cross-level interception protocol when the abnormal operation confidence score exceeds a dynamic threshold, and rolling back the current session state to a security baseline version. According to the invention, the accuracy and response timeliness of anomaly detection can be improved.

The invention discloses a causal atlas-driven enterprise intelligent decision-making method and system, and belongs to the technical field of enterprise management. The method comprises the following steps: constructing an enterprise-level causal atlas by obtaining enterprise business processes, operating indexes and unstructured text data; executing path reasoning based on the to-be-decided data, and determining a multi-hop causal path chain; determining a current association rule and a decision suggestion in combination with an expert rule base; causal maps and rule tags of different industries are introduced, and a migratable causal path and an association rule are identified based on structural similarity and a semantic mapping rule; and finally, current and migration knowledge is fused to generate an enterprise decision execution scheme and a causal reasoning result. According to the scheme, based on comprehensive application of current and migrated knowledge, scientificity, accuracy and interpretability of decision making are effectively enhanced, meanwhile, dynamic updating and inter-industry knowledge sharing are supported, enterprise decision making efficiency and response speed are remarkably improved, and enterprises are assisted to achieve intelligent and fine management.

The invention discloses an intelligent interactive enterprise management simulation system and a method thereof. The method comprises the following steps: constructing a multi-level state causal graph model with a multi-dimensional feature tag; fusing interaction data of the user, constructing a behavior intention tensor, and identifying a situation transition intention through a multi-modal fusion reasoning algorithm; a personalized interaction strategy is dynamically generated by a context-driven interaction optimization generation algorithm; a context semantic tensor is constructed based on a semantic context dynamic matching algorithm, a semantic compression response is realized in combination with a current intention and a historical information path, and a future strategy plan is actively generated through a causal relationship backstepping inference device; and establishing a situation feedback learning and weight updating mechanism, and dynamically adjusting a state causal graph, an intention tensor and an interaction strategy parameter to realize self-evolution closed-loop optimization of the model. Context changes can be perceived in real time, and the decision intention of the user can be deduced deeply.

The invention provides a user behavior data mining method and system applied to digital enterprise management, and the method comprises the steps: collecting the multi-dimensional behavior data of a target user in a business operation interface, carrying out the multi-modal data analysis of the multi-dimensional behavior data, generating a behavior track feature set with time sequence relevance, and carrying out the mining of the behavior track feature set; training an adaptive time sequence analysis model based on the behavior trajectory feature set, capturing a long and short term dependency relationship in a user behavior mode by the time sequence analysis model through a dynamic window division strategy, generating a potential loss risk prediction index, and constructing an interaction process parameter matrix according to the potential loss risk prediction index; and calling the optimized interaction process parameter matrix to drive a service operation interface to reconstruct, generating an interaction interface adaptive to the current user behavior mode, and iteratively updating the time sequence analysis model through an incremental feedback mechanism in a preset verification period. According to the invention, the comprehensiveness and accuracy of user behavior pattern mining can be improved.

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

The invention discloses an enterprise management optimization method and system of big data enabling ERP, and relates to the technical field of enterprise management, the method comprises the following steps: creating an enterprise research and development task, obtaining research and development resource information, and constructing a task-resource-technology associated dynamic knowledge graph; the task priority is adjusted based on a multi-task game, and a task-resource-technology task line is reconstructed; carrying out adaptive granularity disassembly on the reconstructed task line, generating task key points and establishing parallel sub-task lines; a task line is further optimized, a cross-department cooperation scheme is formulated, and enterprise management optimization is promoted. Therefore, real-time perception, dynamic priority response and efficient resource allocation are achieved, and the cross-department cooperation level is improved.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention relates to the technical field of enterprise portraits, in particular to a real-time dynamic enterprise portrait construction method and system based on high-dimensional financial data, and the method comprises the steps: (1) multi-source data integration and feature extraction; (2) constructing a knowledge graph ontology model; (3) dynamic event driving and real-time analysis; (4) updating the event triggering risk assessment model; and (5) generating and visualizing an enterprise portrait. According to the scheme, the defects of a traditional financial evaluation model can be overcome, more comprehensive, real-time and accurate risk evaluation and strategic guidance can be provided for investors, supervision organizations and enterprise managers, and financial data analysis is promoted to be converted from a single static normal form to a multi-dimensional dynamic normal form.

The invention relates to the field of data processing, and particularly provides a big data mining method and system based on digital enterprise management, and the method comprises the steps: obtaining multi-modal session data generated in the process of carrying out digital service interaction between a target enterprise and a user, extracting a user demand feature set from the multi-modal session data, and based on a preset multi-task learning framework, carrying out joint training on the user demand feature set, generating a demand analysis model, calling the demand analysis model to analyze the user demand feature set, generating an optimization strategy set associated with the target enterprise service process, and sending the optimization strategy set to the server. And updating the digital service execution logic of the target enterprise according to the optimization strategy set, and performing incremental training on the demand analysis model based on user feedback data. According to the invention, the response efficiency and the function adaptability of the digital service process can be improved.

The invention relates to the technical field of workflow optimization, in particular to an enterprise management process optimization method and system, and the method comprises the following steps: obtaining the length of an approval task queue, the average approval time and the number of pending tasks, calculating a task backlog rate, calling task flow fluctuation to set a node load state coefficient, and generating a load distribution state of approval nodes. According to the invention, the task circulation priority is calculated based on the time constraint and the service weight, so that the emergency task is circulated preferentially, the approval efficiency is improved, the approval path is sorted through the node processing capability and is adjusted and optimized, the task circulation adapts to the load change, the flow blocking risk is reduced, and the task state change record is screened based on the circulation path. The data screening accuracy is improved, the effectiveness of task execution data is ensured, the abnormal detection is combined with the time sequence trend and abnormal node analysis, abnormal task tracking is more efficient, problem nodes are traced accurately, and abnormal task processing is accelerated.

The invention relates to the technical field of enterprise management optimization, in particular to an enterprise management optimization method and system based on big data matching, and the method comprises the following steps: S1, collecting enterprise operation data, industry reference data and supply chain dynamic data, and outputting standardized enterprise data; s2, matching the standardized enterprise data with a preset industry feature library to generate a multi-dimensional service label set; s3, constructing a dynamic priority evaluation model, and generating a priority ranking matrix; s4, establishing a resource characteristic spectrum database, and generating a strategy matching degree matrix; s5, generating a resource configuration scheme according to the strategy matching degree matrix; and S6, when the monitoring threshold changes, triggering a weight calibration mechanism. According to the invention, through big data matching and dynamic priority evaluation, enterprise resource configuration decisions are optimized, operation efficiency and market adaptability are improved, and risks and cost are reduced at the same time.

An information technology system generally including a cloud-based management platform with a micro-services architecture having a unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods with respect to the value chain network entities of the platform.

The invention discloses a dialogue record question and answer method and system based on a thinking chain and an intelligent agent reflection mechanism. The method comprises the following steps: transcribing a voice call into a text through an automatic voice recognition (ASR) technology, and extracting a retrieval condition and a question to be answered from user input based on a thinking chain cue word; and then, generating an SQL statement by using a natural language, retrieving a target dialogue record from a local database, and optimizing a retrieval result through an agent reflection mechanism so as to ensure the query accuracy. Finally, answers are generated in combination with a retrieval enhancement generation (RAG) technology, and high-quality intelligent question and answer services are provided for the user. According to the invention, the voice technology and the language model are deeply coordinated, so that efficient and accurate intelligent question and answer service can be provided for users in scenes such as enterprise management, customer service and intelligent cabins.

The invention discloses a safety risk partition management method and system for chemical enterprise safety supervision, and relates to the technical field of chemical enterprise management, and the method comprises the following steps: carrying out the feature extraction of safety monitoring data, and obtaining the safety performance feature data of each region; key risk factors influencing the safety performance of each area are obtained; establishing a risk prediction model based on the key risk factors, and predicting the safety performance trend of each region at the future moment by using the risk prediction model; and based on the safety performance trend, evaluating the safety performance state of each region, and taking safety management measures according to the evaluation result. According to the method, the safety performance characteristic data is analyzed by using a risk analysis algorithm, key risk factors influencing the safety performance of each area can be identified, an enterprise can be helped to quickly start emergency response, loss after an accident is reduced, the accident rate is effectively reduced, and the safe production environment of the enterprise is guaranteed.

The invention relates to the technical field of enterprise management, in particular to an enterprise digital performance quantitative management platform based on machine learning, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, an evaluation model construction module, a performance evaluation module, a decision support module and a visual display module. The data acquisition module acquires multi-dimensional data in real time, the data preprocessing module preprocesses the multi-dimensional data, the feature extraction module extracts features of the multi-dimensional data, the evaluation model construction module constructs an enterprise performance evaluation model based on the extracted features, and the performance evaluation module predicts and evaluates the performance of employees or departments in real time. The decision support module provides scientific decision support based on the evaluation result, and the visual display module displays the multi-dimensional data, the performance evaluation result and the management decision through a visual interface; according to the invention, efficiency and precision of enterprise performance management can be greatly improved, resource configuration is optimized, and strategic goal realization of enterprises is promoted.

The invention provides an enterprise project and personnel management method and system based on a knowledge base and an AI model and a medium, and belongs to the technical field of enterprise management.The method comprises the steps that implementation experience of historical projects of an enterprise is collected and analyzed based on a knowledge graph, and a project knowledge base is constructed; enterprise employees are evaluated, classification is carried out in combination with evaluation dimensions, and labels are set; and collecting real-time operation data of the project, and recommending and adjusting personnel allocation for the project in combination with employee labels and project attributes. According to the invention, full-life-cycle digital management of the project is realized, and a traditional management mode is broken; the project management efficiency and the resource utilization rate are improved through AI-driven dynamic decision and resource allocation; a knowledge base capable of being self-optimized is constructed, and technology and talent support is provided for enterprise development.

The invention provides a low-code enterprise-level intelligent assistant development platform, and relates to the field of intelligent assistant development. The low-code enterprise-level intelligent assistant development platform comprises an application layer, a platform layer, a capability layer and an underlying service. According to the low-code enterprise-level intelligent assistant development platform, a low-code enterprise-level intelligent assistant development platform is realized, and by means of the platform, a user can develop enterprise-level intelligent assistants suitable for various industries, thereby helping enterprises to realize cost reduction and benefit increase; a user can complete the creation of a knowledge base, a workflow and an intelligent agent only through simple configuration or arrangement, a low use threshold is achieved, enterprise internal services or third-party MCP services can be accessed by means of a plug-in tool and a platform, uploading and publishing of user-defined tools are supported at the same time, high expandability is achieved, and the method can be widely applied to the field of enterprise management. The platform supports high-order functions such as multi-person collaborative development, multi-modal knowledge base construction, multi-modal data interpretation and processing and construction of multiple types of agents, and can meet complex enterprise-level application scenes.

The invention discloses a decision scheme generation method and device based on complex multi-modal management data, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: receiving enterprise management data of texts, numerical values and images, extracting a unified feature vector, carrying out the clustering analysis through the dynamic updating of torque clustering and spectral clustering, and obtaining a clustering result; and a causal knowledge graph is constructed according to the result, and a decision scheme is generated by using a fourth-order inference engine, so that efficient processing and real-time response of an enterprise to dynamic multi-modal management data are realized, the cross-modal data analysis capability is enhanced, and the decision accuracy is improved.

The invention provides a digital enterprise management data storage method and system based on cloud computing, and the method comprises the steps: obtaining a real-time management data set in an enterprise data flow, generating a virtualized storage node, determining a target encrypted fragment index matched with the virtualized storage node according to a preset storage path optimization model, and storing the target encrypted fragment index into a database; based on a dynamic storage path corresponding to the target encrypted fragment index, segmenting the structured data block and the unstructured data block into encrypted data units distributed across regions, and mapping the encrypted data units to corresponding cloud storage partitions; dynamically distributing storage resources of the cloud storage partitions through a load balancing algorithm, and adjusting priority weights of dynamic storage paths according to real-time flow characteristics; and when an access request of the encrypted data unit is detected, redistributing the concurrent connection number of the cloud storage partitions. According to the method, the collaborative optimization problem of resource allocation and safety efficiency can be solved.

Aspects of the disclosure relate to providing a secure large language model data platform. The secure large language model uses a machine-learning large language model and gateway to prevent attacks and unauthorized access to enterprise-managed information and resources. The secure large language model may utilize pre-enrollment at a secure gateway providing a unique identification to each client. A private / public key pair may be generated and stored in the secure gateway database and large language model respectively. In some embodiments, a unique anonymization rule set may be generated and used for each client. Threat actors cannot query the large language model directly based on the pre-enrollment process. Unauthorized requests cannot be decrypted by the large language model due to missing paired keys.

The invention belongs to the technical field of enterprise management, and particularly relates to an enterprise accurate matching system and method based on worker skills, and the method comprises the steps: building a skill data collection business chain through systematically analyzing a skill chain in an industrial worker skill improvement process; key management services for data security and the like are provided, and key security capabilities of data privacy security, accurate access control and the like of the data center are realized; a mixed skill evaluation model theory oriented to complex data ecology such as industrial worker skill data is researched, an enterprise employee skill ability skill evaluation model is constructed, and fine-grained multi-dimensional description of employee ability is completed; a knowledge graph construction technology for exploring the capability of industrial workers is adopted, a multi-field and multi-dimensional comprehensive talent management framework is designed, accurate matching of personalized demands of the industrial workers and enterprises is realized, and accurate management and control and accurate decision of related functional departments are met.

The invention relates to the technical field of operation supervision, in particular to an enterprise management-oriented enterprise operation analysis supervision method and system, and the method comprises the steps: obtaining food propaganda data and quality inspection data uploaded by a manufacturer in food sales software; performing feature extraction on the propaganda data and the quality inspection data, and obtaining propaganda feature data and quality inspection feature data containing feature attraction levels according to attraction degrees of features to users; analyzing the matching degree of the quality inspection feature data and the propaganda feature data to obtain a feature matching result; and determining rectification measures of the manufacturer according to a matching result. According to the method, food supervision measures in multiple links are adopted for food data of manufacturers in the food sales software, so that the manufacturers possibly causing food safety events can be supervised, and the manufacturers can rectify and construct a good food sales environment; and the problem that a supervision department lacks supervision means in the aspect of food safety is solved.

The invention relates to the technical field of enterprise management and information processing, in particular to an intelligent contract-driven enterprise tax risk management system, which adopts a rule management layer and contract execution layer separation architecture: the rule management layer is responsible for configuring and testing a tax rule and generating a rule version identifier; and the contract execution layer is composed of intelligent contracts deployed in the block chain, and comprises a rule state contract for anchoring a rule version and a business risk contract for executing business logic. And coupling the off-chain rule with the on-chain contract through the rule version identifier. And the business risk contract monitors an on-chain evidence storage event, obtains a current effective rule version, delegates under-chain service calculation, verifies a returned result by signature, version, time and the like, and executes corresponding treatment after the verification is passed. For high-risk disposal, the system provides an on-chain arbitration mechanism by governing a contract. According to the method, dynamic and safe updating of the tax risk management rule is realized on the premise of ensuring that the process is automatic, credible and non-tampering.

The invention relates to the technical field of information management systems, in particular to a full-process intelligent office system which comprises a data layer, an AI engine layer, a service engine layer, an application layer, an interface layer, a central data bus, a unified identity authentication center, a safety protection center, a system monitoring center, an intelligent workflow engine (1000), a real-time notification pushing mechanism and a multi-terminal adaptation framework. Data barriers of all departments of an enterprise are broken through through the AI technology, full-process data intercommunication is achieved, enterprise management forms a complete closed loop, and the management complexity and the information island problem are reduced; all functional modules are coordinated through a central AI agent, and data consistency and business process continuity are ensured. By realizing the full-process digital management of the enterprise, the system obviously improves the office efficiency of the enterprise, reduces the management cost, enhances the competitiveness of the enterprise, and is particularly suitable for the intelligent operation and management requirements of small and medium-sized enterprises and entrepreneurship teams.

Various embodiments are directed to apparatuses, methods, computer program products, and systems related to multi-factor digital transmission screening. In some embodiments, an outbound digital transmission originating from a monitored enterprise management system may be detected. One or more data elements associated with the outbound digital transmission may be applied to one or more anomalous transmission prediction models to generate a prediction associated with the outbound digital transmission. The one or more anomalous transmission prediction models may comprise at least a contextual analytical model configured to generate the prediction associated with the outbound digital transmission based at least in part on historical digital transmission activity data based on a plurality of past digital transmissions originating from the monitored enterprise management system. Responsive to the prediction corresponding to an anomalous transmission prediction, performance of one or more data exfiltration mitigation actions to mitigate risk of data theft may be initiated.

The invention relates to the technical field of digital management, in particular to a data analysis system based on digital enterprise management. The system comprises a data acquisition module, a model establishment module, an inventory adjustment module and a risk assessment module. According to the invention, competitor data, market promotion data and raw material supply chain data are collected through the data acquisition module, a neural network model for predicting enterprise sales is established by using the model establishment module, and the inventory adjustment module calculates the order quantity of the product by using an economic order quantity model according to the predicted enterprise sales. The maximum inventory level of an enterprise is determined through a reorder point algorithm and safe inventory setting, the risk assessment module comprehensively analyzes various indexes by using an analytic hierarchy process to obtain a comprehensive risk assessment result, an inventory optimization strategy is formulated according to the assessment result, and the risk of the enterprise is optimized. The inventory turnover rate is improved, the customer satisfaction is improved, and the market competitiveness is enhanced.

An information technology system generally includes a cloud-based management platform with a micro-services architecture deploying a set of adaptive intelligence facilities that can be configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform and a set of data storage facilities that can be configured to store data collected and handled by the platform. The data can be related to at least one of the value chain network entities and the features of the platform. A set of monitoring facilities can be configured to monitor the value chain network entities. The platform can be configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

Techniques for wildcard based private application access are disclosed. In some embodiments, a system, a process, and / or a computer program product for wildcard based private application access includes receiving a request for access to an application over a secure access service edge (SASE) network for a user associated with an enterprise; determining if the request for access to the application matches a wildcard (e.g., the wildcard can be configured by an administrator of the enterprise for matching a fully qualified domain name (FQDN) for the application); and automatically configuring access information (e.g., IP address, protocol, and destination port) for the application that matches the wildcard.

The invention discloses a label management method and system for enterprise management and government affair service, and the method comprises the steps: obtaining target management data, carrying out the preprocessing of the target management data, building a multi-dimensional model for simulating an enterprise operation environment according to the preprocessed target management data, and generating an initial label, the multi-dimensional model for simulating the enterprise operation environment is constructed for the target management data to generate the initial label, the multi-dimensional change frequency of the target management data is monitored in real time based on the generated label, intelligent management of the label can be realized, the update period of the label is dynamically adjusted through the data change frequency of each dimension, and the user experience is improved. The state of the current label is evaluated according to the real-time state of the target management data, and the change trend of the label is predicted in combination with multi-modal time sequence prediction analysis and a self-adaptive threshold adjustment mechanism, so that the accuracy and timeliness of the label are improved, the management difficulty caused by the complexity of the data is reduced, and the user experience is improved. And the data management efficiency is improved.