59 results about "Enterprise services" patented technology

The invention discloses a monitoring data trend analysis and decision suggestion generation method based on a large language model agent. The method comprises the following specific steps: step 1, constructing a domain knowledge enhancement module; 2, developing and registering a callable tool set, and compiling a series of performance functions by using Python; 3, constructing an agent arrangement platform and a core agent, and creating a master control agent in the agent arrangement platform as a scheduling center of an analysis task; step 4, executing an intelligent agent analysis process, and when an analysis request is received, enabling the intelligent agent to autonomously run according to the following process; 5, system integration and interface calling are carried out, the whole agent is packaged into RESTful API service and deployed in an enterprise server, by introducing a large language model agent architecture, an RAG enhanced reasoning mechanism and a callable tool set, the technical transition from passive data display to active insight generation is achieved, and the method has the advantages of being high in practicability and easy to popularize. And the analysis efficiency, the decision accuracy and the system intelligence level are remarkably improved.

The invention relates to the crossing field of artificial intelligence technology and e-government, and discloses an e-government enterprise service intelligent response method based on AI cue word engineering, comprising: constructing and maintaining a government knowledge graph and a scenarized cue word template library; receiving and analyzing a service request input by an enterprise, and extracting an enterprise feature tag and a business intention; selecting a target cue word template based on the matching degree of the enterprise feature tag and the scene tag of the template in the scene cue word template library, and generating an adapted cue word template; retrieving associated knowledge data from the government affair knowledge graph according to the service request, and filling the adapted cue word template with the retrieved associated knowledge data to generate a structured cue word; inputting the structured cue word into an AI model to drive the AI model to generate a government affair service response; and collecting multi-dimensional evaluation feedback of government affair service response, and carrying out iterative optimization. The demand understanding precision is improved, and the risk of core information misjudgment is reduced.

The invention relates to the technical field of enterprise service digitalization and intelligent management, in particular to an enterprise management method and management platform based on dynamic portraits. The method comprises the following steps: S1, accessing and standardizing multi-source heterogeneous data, calculating a source-level quality score and generating an evidence chain; s2, constructing a portrait map and a time axis taking an enterprise as a main node; s3, the rule engine and the LLM-RAG output numbers in parallel; s4, consistent arbitration and belief propagation; s5, event-driven dependency subgraph increment recalculation is carried out; s6, carrying out portrait-strategy matching and task linkage; s7, carrying out closed-loop write-back and auditable playback; and S8, multi-tenant fine-grained permission. According to the scheme, the problems of data fragmentation and insufficient timeliness and traceability are solved, and the linkage of portrait credibility, interpretability and real-time decision is realized.

The invention relates to the field of enterprise service management, and discloses an enterprise service management system and method oriented to multiple service scenarios, and the method comprises the steps: carrying out the real-time capturing of an operation event generated by each service module in a distributed storage node, disassembling the event into an operation type, a data influence range and related resources; calculating the dynamic weight of the potential impact of the event on the service chain in combination with the delay, resource competition and synchronization sensitivity of the event on the whole service chain; for the high-risk events and the corresponding nodes, simulating local state changes generated in the distributed storage environment after the events are triggered; key nodes and operation events are integrated, and the event execution sequence and resource allocation are dynamically adjusted in combination with node processing capacity, storage bandwidth and synchronous delay constraint; and performing real-time state correction and operation priority adjustment on the key nodes through the generated coordination strategy. The method has the advantage of keeping the real-time consistency of the service state.

The invention provides a contract payment risk prediction system, method, device and medium, the system comprises an enterprise terminal, a cloud and an enterprise server terminal, the enterprise terminal is used for collecting contract payment prediction data, performing data desensitization on the prediction data, and transmitting the desensitized prediction data to the cloud and the enterprise server terminal; the cloud end is used for performing joint training according to the desensitized prediction data to obtain an incremental data set, and issuing the incremental data set to the enterprise server end; the enterprise server side is used for performing structured conversion on the desensitized unstructured prediction data to obtain an unstructured prediction label; establishing a risk conduction rule and a data set according to the unstructured prediction label and the desensitized structured prediction data; training according to the risk conduction rule, the data set and the incremental data set to obtain a risk prediction model; and performing contract return risk prediction according to the risk prediction model. And through a risk conduction rule and a three-terminal architecture, the prediction precision is improved, and the contradiction between data security and model optimization is solved.

The technology described herein is directed towards automatically scaling cloud provider resources allocated for one enterprise's service based on the resources being used or expected to be used by another enterprise's service, in which there is a value chain-based load-dependency relationship between the two enterprises' services. In one example implementation, a first prediction engine determines a predicted workload for a first enterprise service, and sends that information to a load manager that allocates resources for the first enterprise service based on the prediction. Based on the load-dependency relationship, the first prediction engine sends the predicted workload to a second prediction engine, which predicts a second predicted workload for a second enterprise's service, and sends the second prediction information to a load manager that allocates resources for the second enterprise service based on the second prediction. The automatic scaling is done without sharing any enterprise-sensitive data among the enterprises.

Methods and systems are provided for generating alerts in association with a request for credit data or an update to credit data. For example, information identifying an individual may be received from a credit bureau when an individual applies for a product or service from a requesting entity. The requesting entity may be a product or service provider that requests credit data of potential purchasers of its products. An electronic notification may then be sent in association with an event object generated by a notification system that is broadcast for delivery to one or more other systems, where the event object may be broadcast substantially in real time, such as via an enterprise server bus, relative to the requesting entity's initial request.

The invention relates to the technical field of deep learning and big data, in particular to a business opportunity mining method and device based on big data and a medium, and the method comprises the steps: obtaining transaction type data and enterprise basic data; preprocessing the data, and forming an enterprise clothing data center on the basis of the data; pre-training an enterprise user portrait model ECPM and a commodity representation model PRM by using the transaction type data; a multi-dimensional enterprise portrait is constructed through the model, a deep neural network model ResDeepFM is trained in combination with enterprise basic data, an algorithm model based on transaction type data and enterprise basic data is constructed, the model can automatically carry out business opportunity mining and re-purchase prediction, and the user conversion rate and the re-purchase rate are improved; and a device and a medium that run the above program.

The invention discloses a DNC machine tool networking communication terminal which comprises a DNC machine tool networking communication terminal which is connected with an enterprise local area network in a wireless and wired mode and connects enterprise servers ERP, MES, a modem server, a modem client and CAD / CAM to the enterprise local area network, a plurality of sets of devices in a workshop are connected with the DNC machine tool networking communication terminal through a serial port server, and the DNC machine tool networking communication terminal is connected with the enterprise local area network through the serial port server. The invention belongs to the technical field of precise high-speed motor bearings, and aims to solve the problems that management is disordered and production links cannot be supervised in place in the prior art. The technical effects are that communication is established between different devices and an upper layer data acquisition system, bidirectional concurrent communication between one server and multiple devices is realized, and an information communication channel is provided for data acquisition and application in a product production and processing process.

The invention provides a one-stop management system for scientific and creative intelligent AI enterprise policy services, and belongs to the technical field of computer information technology and artificial intelligence. Aiming at the understanding problem of spoken appeals of users and government affair standardized data, the system collects and enhances spoken samples through a multi-modal semantic understanding training module; training an AI model for accurately understanding the user appeals; the policy intelligent analysis and precise matching module constructs a dynamically updated full-amount policy library, and realizes precise matching of appeals and policies through vocabulary, semantic and regional dimension weighted calculation; the whole-flow online declaration and approval module realizes cross-department data sharing and flow intelligentization; the fund intelligent measurement and calculation and non-inductive cashing module measures and calculates fund based on a multi-factor model and uses a block chain for evidence storage; and the dynamic monitoring and risk early warning module realizes policy timeliness reminding and risk early warning. According to the invention, full-process intelligence of policy service is realized, enterprise service efficiency and government administrative efficiency are improved, and development of the department and creative industry is assisted.

The invention discloses an enterprise mailbox splitting and deploying method, device and equipment fused with a large model and a medium, belongs to the technical field of enterprise information security and artificial intelligence crossing, and aims to solve the technical problems of how to realize intelligent processing of mails in an enterprise mailbox, accurate identification of security risks and the like. The technical scheme adopted by the invention is as follows: the method comprises the following steps: splitting a secondary subsystem of an enterprise mailbox: splitting the enterprise mailbox system into the secondary subsystem which supports deployment, operation and maintenance in an enterprise server, and the safety of enterprise data isolation is high; the enterprise mailbox system is split into a manufacturer first-level mailbox system and an enterprise second-level mailbox subsystem; the manufacturer first-level mailbox system is deployed on a mailbox manufacturer cloud server, is used for SMTP sending, IMAP receiving and public authentication service, and interacts with an external mailbox system; deploying a second-level subsystem; and managing and interacting client mails.

Traffic from a remote endpoint of an enterprise is processed by a cloud-based module performing a security service, such as SWG, ZTNA, or CASB. The traffic is forwarded back to a server of the enterprise for transmission over a leased line to a destination endpoint. The remote endpoint, cloud-based module, and server may be in a first jurisdiction and the destination endpoint may be in a second jurisdiction such that cross-border controls are performed more efficiently by a provider of the leased line.

Embodiments of the inventive concept include solid state drive (SSD) multi-card adapters that can include multiple solid state drive cards, which can be incorporated into existing enterprise servers without major architectural changes, thereby enabling the server industry ecosystem to easily integrate evolving solid state drive technologies into servers. The SSD multi-card adapters can include an interface section between various solid state drive cards and drive connector types. The interface section can perform protocol translation, packet switching and routing, data encryption, data compression, management information aggregation, virtualization, and other functions.

The invention discloses a government and enterprise business system construction method, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: constructing a government and enterprise MCP service platform, and based on the government and enterprise MCP service platform, converting an API interface of a government and enterprise business system and a data intermediate station into an MCP service interface set; constructing a special government and enterprise knowledge base by utilizing a retrieval enhancement generation technology, and establishing a large and small model collaboration mechanism based on the MCP service interface set and the special government and enterprise knowledge base; and based on a large and small model cooperation mechanism, performing task allocation and cooperative execution of a plurality of agents by utilizing an agent cooperation technology so as to construct a government and enterprise service system. According to the method, a safe and efficient government and enterprise service system is constructed through the MCP service interface, the RAG enhanced knowledge base, large and small model collaboration and multi-agent collaboration fusion, and the decision accuracy is enhanced.

This invention provides a digital asset and certificate management system, user terminals, and a digital asset and certificate management method that link digital assets on the blockchain with off-chain personal information. [Solution] In the digital asset and certificate management system, when the Web server 200 receives a user information issuance request from the user terminal 100, the user information storage unit issues identity document data based on the user information associated with that user and stores it in the off-chain 100a on the user terminal 100. The corporate server 400 issues digital assets in response to requests from the user terminal 100 and stores them in the blockchain 300. The identity document data and the digital assets are associated with a DID which is a combination of a wallet address generated based on the SNS authentication process performed on the user terminal 100 and a blockchain ID based on the blockchain 300.

The embodiment of the present application discloses an implementation method of an enterprise service platform capable of carrying multiple third-party applications, which comprises: performing first authentication on an enterprise intending to access a third-party application in the enterprise service platform, and managing the content of the service of the platform accessing the application, wherein the platform accessing application is a third-party application of the enterprise intending to access the enterprise service platform through the first authentication; performing second authentication on a platform user applying for the service of the platform accessing the application, confirming the platform user as a platform authentication user when the second authentication is passed; and realizing the interaction between the platform authentication user and the platform accessing application. The method and system design a unified interface specification of the platform, avoid the need of each enterprise to separately connect and promote, greatly reduce the development workload and development complexity; meanwhile, the access of the third-party application can effectively reduce the repeated research and development of the platform product, so as to realize the cooperation and win-win of the platform business and the third-party service developer.

A method comprises storing a plurality of rules by which a rules engine is configured to determine an application to perform an enterprise function for the enterprise related service, wherein the application is a shared application, determining, based on a first rule stored in the data store, the shared application to perform a first enterprise function, wherein the first rule indicates that first enterprise function is capable of being performed by the shared application that is shared across a plurality of different enterprise servers, determining, by the rules engine in the communications system, based on a second rule stored in the data store, the shared application to perform a second enterprise function, where the second rule indicates that second enterprise function is also capable of being performed by the shared application that is shared across the different enterprise servers.

The invention discloses a data processing method and device based on an enterprise service system and a medium, and relates to the technical field of enterprise data processing, and the method comprises the steps: collecting multi-source enterprise business data, carrying out the preprocessing, carrying out the statistics of a front-and-back dependency relationship, and the mapping information between an intervention variable and a result variable, and generating a causal element candidate data package; performing topology rearrangement and cross-enterprise service collaborative scheduling on the meta-scheduling graph structure, recording a data calling relation and resource occupation information, and generating a task execution data set; and extracting index information from the task execution data set, recalculating the actual causal influence intensity, updating the dynamic weight and the meta-scheduling graph structure, and generating an enterprise service data processing report. According to the invention, the coordination management of the task execution sequence and the resource use condition in a complex service scene is realized, and the overall task processing efficiency and the service response capability are improved.

The application discloses a lattice-based digital signature method and system for power data value-added services, and first, a first terminal generates a signature private key and a signature verification public key, wherein the first terminal is a power gateway in a region where a user is located; then, the first terminal generates a legal signature by using a HashTree and a rejection sampling method in an interaction process with a second terminal, and generates a related path by using BuildAuth; the second terminal is a power consumption user; finally, a third terminal calculates a target commitment value by using the signature, restores a root node of a commitment tree by executing RootCale, and verifies the legality of the root node; the third terminal is a power enterprise service system center. The application has good compatibility with the power enterprise service system, and not only enables the power enterprise service system to have the ability to resist quantum computer attacks, but also can improve the operation efficiency of the whole service system. This enables the power enterprise to timely formulate flexible and economically applicable electricity price packages according to the electricity consumption information of the user.

This invention discloses a method and system for adapting and optimizing enterprise-specific functional modules, relating to the field of intelligent enterprise services. The method includes the following steps: First, based on basic enterprise customer data, a scenario-based module library is invoked for scenario-based service module adaptation processing. This involves adding or removing specific functional modules or confirming error-free operations to generate a final adaptation scheme for the scenario-based service modules. Second, the final adaptation scheme is optimized for access control security based on an enterprise privacy and security protection list. Third, during cross-departmental collaborative work, the collaborating departments must access the collaborative central module to request collaboration, authorizing the target collaborating departments to synchronize data and complete collaborative work orders. Fourth, the corresponding specific functional modules are iteratively optimized based on customer feedback. This achieves precise adaptation of suitable scenario-based service modules according to actual customer needs, significantly improving the customer experience.

The embodiment of the invention discloses a resource allocation method and device, electronic equipment, a storage medium and a program product, and aims to solve the problem that in the prior art, the utilization efficiency of a single resource is maximized, and systematic constraints on resource allocation fairness are lacked, so that when multiple departments compete for shared resources, the resource allocation efficiency is greatly improved. The problems that resource distribution is unbalanced and the service quality of low-priority departments is difficult to guarantee are easily caused. The method comprises the following steps: acquiring real-time resource demand data of each department in a government and enterprise service system; inputting real-time resource demand data into the trained resource allocation model to obtain a resource allocation strategy; and performing resource allocation on each department in the government and enterprise service system according to the resource allocation strategy.

This invention relates to the field of enterprise consulting service technology, specifically to an AI-based enterprise service system and method, comprising a context construction module, a knowledge organization module, a matching and recognition module, a path organization module, and an output generation module. It establishes a correspondence between service request phrases and operational verbs, constructs structural groups based on constraints, reorganizes semantic content by matching enterprise knowledge fragments, filters execution text to generate case comparisons, identifies temporal logic to establish a sequential linked list, and pairs task information to generate consulting response text. This invention clarifies consulting intent by constructing phrase correspondences and binding semantic boundaries, extends knowledge fragments through overlap comparison and semantic reorganization to improve question adaptability, optimizes case matching through keyword and service object dual filtering, and establishes a sequential linked list to map task execution order, enhancing the coherence and logical flow of responses.

The application discloses a kind of enterprise data trusted exchange method and system on supply chain, it is related to enterprise data exchange technical field, and the data terminal of each enterprise on supply chain is provided with trusted interaction authentication module, and multiple trusted interaction authentication modules are numbered;Data relay server is set.The trusted interaction authentication module of the application executes data trusted exchange multi-party authentication mechanism to verify the legality and security of data interaction request;At least three cross-verification is used, and the corresponding trusted interaction authentication module based on the network address communication of data interaction request is numbered, and the legality and security of the enterprise server of interaction cipher text verification data interaction request are verified, to avoid the data leakage risk caused by the target enterprise special setting of directional communication fraud;After passing through trusted exchange multi-party authentication mechanism, more secure enterprise data trusted exchange method on supply chain is constructed, to ensure the safe interaction of supply chain data.

The present application relates to the technical field of enterprise digitization service, in particular to a construction method of enterprise service application system based on Internet, which firstly constructs a minimum atomization system base, completes the standardized atomic encapsulation of the minimum granularity of business capability and the construction of full-dynamic microkernel architecture; secondly, the full-dimension operation carbon footprint of the user is collected without sensing and semantic analysis is completed, which is converted into structured business intent data; then a unified demand decision hub is built, two types of demands of function growth expansion and optimization contraction are synchronously identified and priority quantization sorting is completed; finally, double-line self-adaptive optimization is executed in parallel based on the atomization base, and all optimizations are completed in the way of hot deployment. The present application realizes the dynamic adaptation of enterprise service system and business development, and solves the problem of rigidity of traditional system architecture, disconnection between iteration and business demand.

The invention discloses an automatic generation method of an enterprise service field instruction set and a related system, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring an enterprise information data set; performing semantic prediction training on a preset base model based on the enterprise information data set to obtain a first semantic prediction model; obtaining an initial enterprise service field instruction set and generating an expanded instruction set; performing fine adjustment on the first semantic prediction model based on the expanded instruction set to obtain a second semantic prediction model; obtaining a question and answer score data set, and training based on a pairwise algorithm to obtain a reward model; and taking the second semantic prediction model and the reward model as a strategy network and a value network of reinforcement learning, updating strategy network parameters based on a time difference error, and finally obtaining a pre-training dialogue type large language model. By implementing the technical scheme provided by the invention, the question and answer accuracy and the nature degree of the model in the enterprise service field can be effectively improved.

Distributed resource configuration, management, and control are provided through a frontend user interface, an application programming interface (API), and backend operations. Collections of configured and managed resources provide cloud services from a cloud environment to enterprise servers. A user provides resource definitions for resource types through the user interface. The backend operations translates the definitions into one or more low-level resource management API calls, which cause the resources to be configured, deployed, reserved / created, and controlled on one or more distributed devices. The configured resource of the distributed devices provide the cloud services to the enterprise servers. In an embodiment, a single user session via the user interface permits the user to deploy multiple instances of a resources within a corresponding environment.

The invention discloses a service capability evaluation method and device, a medium and a product. The method comprises the steps of obtaining a knowledge graph associated with a first object; calculating an upstream risk influence coefficient of the upstream business object on the first object according to the map; calculating a dependency coefficient of the first object on the second object according to a first path from the first object to the second object; the second object is a business object of which the business interaction importance exceeds a preset importance threshold; determining a third object with a business risk event in the map, and determining a business risk influence coefficient of the third object on the first object; and determining a business capability evaluation result of the first object according to the three coefficients. According to the technical scheme of the invention, the problem that the current business capability assessment is lack of comprehensiveness is solved, the business capability assessment has a global view, the influence of a core enterprise, a business interaction enterprise and a risk event associated enterprise on the business capability of the enterprise is quantified, and the accuracy of the business capability assessment is improved.

A logic graph generation method, applied to the technical field of artificial intelligence (AI). By giving a plurality of texts in a tree‑structured connection describing service inference logic, derivation elements indicating an inference direction, and a rule set on which service inference depends, a corresponding target rule can be accurately matched, in the rule set, for each text on the basis of the plurality of texts and the derivation elements. Then, decision logic nodes and conclusion nodes in the tree‑structured connection are generated one by one by using the semantic understanding capability of an AI model on the basis of the guidance of the texts and the derivation elements and the constraints of the target rules, so as to obtain a logic graph applied to service inference. In addition, nodes in a tree‑structured connection in the logic graph can effectively reproduce the complexity of enterprise services and the dependency‑based long‑chain inference logic, thereby improving the construction efficiency of the logic graph while ensuring the availability of the logic graph.

The invention relates to the technical field of enterprise service digitalization and intelligent management, in particular to an inter-enterprise matching management method and management system based on a knowledge graph. According to the method, aggregation cleaning and entity disambiguation are carried out on multi-source data such as industry and commerce, productivity, equipment, product / process, qualification, policy, geography, risk and visit, and an enterprise knowledge graph is constructed according to chain position constraints; a demand is analyzed into a demand sub-graph, dual-channel matching (graph path scoring and RAG semantic scoring) is carried out on the demand sub-graph and a candidate enterprise supply sub-graph, and consistency arbitration is carried out by a rule layer and a learning layer. The system generates a traceable evidence chain and hierarchical confidence, supports event-driven incremental recalculation and cockpit / mobile terminal closed-loop learning, and improves the accuracy, interpretability and timeliness of matching.

The invention relates to the technical field of big data intelligent analysis and recommendation, and provides an enterprise service-oriented big data intelligent recommendation and matching platform. The platform comprises a data acquisition and fusion module, an enterprise demand analysis module, a multi-dimensional feature modeling module, a recommendation and matching engine and a feedback and iterative optimization module. The system constructs an enterprise and service provider knowledge graph fusing multi-source information by collecting government public data, a third-party database, enterprise internal data and user behavior logs; a natural language processing technology is utilized to convert service requirements input by an enterprise into semantic feature vectors, and multi-level matching is carried out in combination with structured feature vectors of the enterprise and a service provider. The recommendation and matching engine generates a comprehensive recommendation result based on feature similarity, semantic correlation, collaborative filtering prediction and knowledge graph reasoning, and the feedback and iterative optimization module adjusts model parameters according to user clicking, transaction and scoring behaviors to realize continuous self-learning and optimization.