198 results about "Enterprise services" patented technology

The invention relates to the technical field of government and enterprise services, and discloses an intelligent adaptation system and method for government and enterprise services, and the method comprises the following steps: carrying out the structural analysis of an original policy text through a deep learning model, including word segmentation, word vector generation, dominant condition extraction and implicit condition reasoning, and finally constructing a policy knowledge graph; the multi-source heterogeneous data is integrated, dynamic enterprise feature vectors are generated through spatial-temporal feature construction, industrial association diagram modeling and spatial-temporal diagram convolution calculation, the enterprise state change gradient is detected, and the abnormal fluctuation risk is recognized; and mapping the policy condition vector and the enterprise feature to a hyperbolic space, calculating a hyperbolic distance and converting the hyperbolic distance into a matching degree, superposing timeliness attenuation, data freshness punishment and state mutation filtering, and outputting a final matching result. According to the invention, a closed-loop system of policy analysis, accurate matching, resource allocation and feedback optimization is finally constructed, the financial resource waste rate is compressed to a low level, and the government affair service efficiency is improved.

The invention discloses a digital intelligent service system for enterprise services, which belongs to the field of enterprise digital services and comprises a user demand analysis module, an intelligent data adaptation module, a service resource matching module, a service efficiency evaluation module and a user-friendly interface. The user demand analysis module is used for semantic analysis and classification decision making; the intelligent data adaptation module identifies and analyzes the protocol type, and establishes an intelligent data transmission channel; the service resource matching module carries out resource pre-screening and accurate matching; the service efficiency evaluation module tracks an identifier and captures a four-dimensional index system in real time, and the dynamic evaluation unit realizes service state grading early warning through a sliding window algorithm; according to the invention, accurate demand identification is realized, the response speed and the data transmission efficiency are improved, the user satisfaction is improved, the service matching accuracy is enhanced, the resource utilization efficiency is optimized, the service capability and the customer experience are improved, and maximization of economic benefits and social values is realized.

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 enterprise service intelligent recommendation, and discloses an enterprise service matching method and system based on a large language model, and the method comprises the following steps: S1, receiving an enterprise service demand text input by a user, carrying out the semantic analysis of the service demand text through the large language model, and obtaining a semantic analysis result; constructing a service semantic graph containing service nodes and semantic relationships; s2, establishing a plurality of agents representing different roles in the enterprise, wherein each agent corresponds to one service preference; s3, based on the service semantic graph and the service preference, node embedding is carried out by using a graph neural network, and the service combination potential is estimated; and S4, performing multi-target matching optimization according to the service preferences and the combined potentials of the multiple agents, and outputting a service matching result. A graph neural network is adopted to model service node embedding, so that a combination relationship among services is efficiently captured in a graph structure, and automatic learning of non-uniform importance among the service nodes is realized by introducing a multi-layer graph attention mechanism.

The invention belongs to the technical field of cloud storage, and particularly relates to an enterprise service management cloud storage system which comprises a system initialization module, a distributed data storage module, a data encryption module, an authority control module, a multi-level cache module, a data backup and recovery module, a log monitoring module and a task scheduling and dynamic extension module. The distributed data storage module is responsible for dividing data in enterprise service management into a plurality of data blocks; the data encryption module is used for encrypting the enterprise service data stored in the cloud; the authority control module is responsible for managing the access authority of the user to the data; the multi-level cache module accelerates data access by configuring a local cache and a global cache; the data backup and recovery module is responsible for backing up the stored data regularly; the log monitoring module records various operation logs of the system; and the task scheduling and dynamic extension module is used for dynamically distributing calculation and storage resources according to the priority of the task and the utilization rate of the system resources.

The invention provides a user digitization level-based digitization transformation service recommendation method and a user digitization level-based digitization transformation service recommendation system. The method comprises the following steps: constructing a five-dimensional evaluation model; acquiring multi-source data in real time; inputting the multi-source data into a five-dimensional evaluation model, and evaluating the digital level of the enterprise based on a preset evaluation algorithm and weight; constructing an industry knowledge graph comprising enterprises, service providers, solutions and technical standard nodes; the digital service is matched based on a mixed recommendation algorithm, the mixed recommendation algorithm comprises a collaborative filtering algorithm, a content matching algorithm and a knowledge graph reasoning algorithm, and a recommendation result is generated through a vectorization retrieval and optimization algorithm; and constructing a capability matrix including technical capability, implementation experience and service capability dimensions, and recording historical performance data of the service provider through a block chain technology to dynamically evaluate the capability of the service provider. The actual digital level of an enterprise can be accurately evaluated, and the accuracy of service provider matching is improved.

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 discloses an enterprise cooperation intelligent ecological management system based on an industrial internet in the field of online cooperation between enterprises, and the system comprises: 1) a multi-tenant system: realizing data isolation between tenants in a form of one tenant for each enterprise, and supporting an upper application to provide differentiation functions for different tenants; according to the method, manual operation is avoided in a data automatic docking mode, so that the collaboration efficiency and quality are improved, the collaboration enterprises can manage and control shared data, data of different enterprises are isolated in a multi-tenant mode, and the collaboration efficiency and quality are improved. Therefore, the business application on the system can be quickly expanded from serving one enterprise to serving multiple upstream and downstream enterprises on the technical level; under the scene that the user cooperatively uses the application, the enterprise participating in the cooperation can control the account number of the user participating in the cooperation, and the security of the data is improved to a certain extent.

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 discloses a service control method based on voice analysis and a large language model, relates to the technical field of voice interaction, and solves the technical problems that in the prior art, an enterprise service system needs a user to manually fill in form fields item by item, so that operation is tedious and low in efficiency, and a process is interrupted due to lack of an intelligent completion mechanism due to lack of necessary parameters. According to the invention, a full-closed-loop control chain from the voice instruction to the service operation is constructed, the intention of the user voice input is analyzed through the fine-tuning large language model, and the service interface is dynamically matched, so that the system function operation is directly driven by the voice, and a two-way cooperation mechanism of voice input-system operation-result feedback is formed; and meanwhile, a dynamic parameter completion technology is adopted, the interface field needing to be filled is automatically verified in the parameter extraction stage, missing parameters are completed through context completion or voice guidance, the operation bottleneck of traditional form item-by-item filling is eliminated, missing of the field needing to be filled is avoided, and the service execution efficiency and the process robustness are remarkably improved.

The invention belongs to the technical field of enterprise services, and particularly relates to a deep learning-based enterprise and policy information matching recommendation system and method.Enterprise feature vectors are constructed and converted into semantic embedding, so that enterprise attributes form continuous expression in a deep space; semantic unit division and context-aware vector generation are carried out on the policy text, and the policy semantic center is adjusted by fusing the overall distribution trend of the enterprise, so that the reality adaptability of the policy text is improved; a multi-view representation mechanism is introduced, and a policy support direction is described from different dimensions; and finally, generating a comprehensive matching score in combination with the confidence coefficient of semantic distance conversion and the explicit condition satisfaction degree. According to the method, the limitation of keyword matching is broken through, the alignment of enterprises and policies on the semantic level is realized, the discovery capability and matching accuracy of potential applicable policies are remarkably improved, and more enterprises which conform to conditions but are not completely consistent in expression form can be effectively identified and corresponding policies can be recommended.

The invention relates to the technical field of enterprise service processing and automatic deployment, in particular to a cloud integrated enterprise service processing method and system, and the method comprises the steps: obtaining a customer identifier of a target customer associated with an automatic deployment process, and enabling the customer identifier to be associated with a preset business management entity; according to the customer identifier, obtaining a preset business state mark used for representing whether the target customer is in a business sensitive period or not; when the business state mark indicates that the target customer is in the business sensitive period, stopping the execution of the automatic deployment process; and transferring the deployment decision right related to the automatic deployment process to the corresponding service management entity. The method has the advantages that automatic deployment can be dynamically adjusted, and interference to commercial activities of customers is reduced.

The invention discloses an enterprise service platform intelligent management and control method and system based on function analysis, and relates to the technical field of platform intelligent management and control, and the method comprises the steps: collecting the calling frequency, resource occupancy rate, response time delay, user behavior track and abnormal event data of a function module, carrying out the standardization and time sequence verification, and constructing a multi-dimensional operation data flow. Constructing a functional behavior model based on a graph neural network, extracting module operation features and structural dependence, dynamically generating a functional thermodynamic scoring graph, and realizing visual display of module loads and abnormal hotspots; and constructing a functional twin copy for the key module, simulating a management and control strategy in an isolated simulation environment, and obtaining feedback. Constructing a multi-level management and control strategy generation matrix based on feedback, generating and optimizing candidate strategies, dynamically deploying, continuously monitoring and automatically adjusting; the system adopts a hierarchical rollback mechanism to realize strategy self-adaptive rollback in case of exception, so that stable and reliable operation of the platform is guaranteed.

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 discloses an enterprise service post function analysis and management method based on big data, and relates to the technical field of data mining, and the method comprises the steps: obtaining the big data of applicants based on a database API interface of a talent recruitment platform, and constructing an applicant database; based on the function range of the enterprise vacant posts, task types covered by the function range are analyzed, and the function requirements of the enterprise vacant posts are quantified; performing association matching with an applicant database according to the function requirements of the enterprise vacant posts, screening out applicants meeting the function requirements, substituting the applicants into role positioning of a team where the enterprise vacant posts are located, and analyzing the degree of adaptation between each candidate person and the team where the enterprise vacant posts are located; and marking the candidate person with the optimal adaptation degree, and generating an enterprise vacant post recruitment task. The method has the advantages that the degree of fitting between the enterprise recruiters and the posts is improved, and meanwhile the cooperation level between the whole teams is maximized.

The invention relates to the technical field of data processing, and particularly discloses an enterprise service data analysis method and system based on cloud computing, and the method comprises the steps: carrying out the logic verification of data through a business logic rule, and dividing the data into coincidence logic data and non-coincidence logic data; based on the data conforming to the logic, calculating a timeliness offset coefficient of the data, and evaluating the timeliness of data updating; by comparing data records of the ERP system and the AP I interface, a consistency coefficient is calculated, and the consistency of data sources is evaluated; comprehensively analyzing the timeliness offset coefficient and the consistency coefficient to obtain an accuracy coefficient of the data, and dividing the data into accurate data or inaccurate data according to the accuracy coefficient; accurate data is sent to a cloud computing system for real-time updating, a Hash algorithm is used for carrying out Hash calculation on the data, and the integrity of the data in the transmission process is ensured by comparing Hash values before and after sending.

The invention discloses an enterprise service supply chain traceability and intelligent contract management system based on a block chain, and relates to the technical field of block chain and supply chain management. According to the invention, by using the data encryption and certificate generation module and the cross-enterprise trust mechanism establishment module, the problems of data islands and high trust cost are solved, and the collaboration efficiency and integration capability of the supply chain are significantly improved; through a zero-knowledge proof algorithm and a block chain technology, the balance between data security and privacy protection is guaranteed, and the security of supply chain management is enhanced; by means of the intelligent contract execution module, the resource scheduling optimization module, the data loop optimization module and the like, intelligence and dynamic optimization of the supply chain are achieved, the cooperation efficiency and the intelligent level are continuously improved, and the market competitiveness and the sustainable development capacity of enterprises are enhanced.

The invention relates to the technical field of customer interaction service, discloses an enterprise service accurate docking method and system based on artificial intelligence, and realizes accurate matching between enterprises and services by introducing a probability calculation and optimization mechanism of a candidate docking relationship on the basis of a knowledge graph structure. According to the method, the stability and the accuracy of the matching result can be improved by integrating the attribute characteristics, the historical behavior data and the multi-path information under the condition that the multi-source data is incomplete or noise exists, and the recommendation result can adapt to changes of enterprise requirements and service supply in a self-adaptive mode by combining dynamic probability updating and iterative convergence operation. Meanwhile, by introducing a negative sample rejection strategy in the recommendation process, the negative influence of historical low-success-rate docking and abnormal docking conditions on subsequent recommendation is effectively inhibited, so that the practicability and credibility of a recommendation list are improved, and finally more efficient, accurate and sustainable decision support is provided for enterprise service docking.

The invention relates to the technical field of enterprise services, in particular to an architecture-as-a-service system and method based on the TOGAF standard, and the method comprises the steps: receiving enterprise architecture data information inputted by a user based on a technical architecture through an architecture-as-a-service platform composed of a business architecture, a system architecture and the technical architecture, graphic data are assembled based on the enterprise architecture data information; based on the system architecture, generating view data according to the graphic data and a graphic layout algorithm, and judging whether the generated view data is a process implementation graph or not; and based on the business architecture, when the generated view data is a process implementation graph, performing quadratic programming by using a graph layout algorithm to generate new view data, and generating an architecture view from the new view data by using a graph editor. Therefore, the problems that an existing enterprise architecture implementation scheme is low in level coverage rate, a modeling tool is difficult to adapt to a complex enterprise environment, unified information management is lacked, and the connection degree of the enterprise architecture and landing implementation is low are solved.

A heat supply industry indoor temperature intelligent control and settlement system based on the Internet of Things is composed of a local controller (11), a user client (12), an Internet of Things terminal control unit (13), an enterprise digital platform (14), a communication network and an enterprise server. According to the system, a traditional fixed-rate collection mode is broken through, a novel mode of performing dynamic charging according to the actual energy consumption of a user is realized, and the user can check own energy consumption data and expense details in real time through the client, so that the system is more transparent and fair; according to the system, an advanced intelligent control algorithm is adopted, intelligent adjustment can be conducted according to factors such as temperature preference of a user, indoor and outdoor temperature difference and historical energy consumption data, and the more comfortable and energy-saving heat supply effect is achieved; the enterprise digital platform can deeply analyze and mine a large amount of collected data, discover potential energy-saving optimization points and service improvement directions, and provide support for decision making of enterprises.

The invention provides an enterprise-level micro-service retry treatment method, and belongs to the technical field of enterprise service data processing. The method comprises the following steps: defining RPC links of all modules of a service, and loading the modules adopting the RPC links in a service system according to a preset rule; initializing RPC link information of each module and pre-storing the RPC link information in a Redis database; a value corresponding to the RPC link information is set to be serialized JSON (JavaScript Object Notation) data; disassembling the RPC link to perform service management and monitoring; converting each disassembled calling path into an independent key value pair, and maintaining request success count and failure count in a time period; and monitoring a request path of a user requesting to obtain the target service module, and determining a retry request according to a predefined retry success rate threshold. By limiting the success rate of the single-node service retry request and reasonably distributing the time-out time of the whole link, the retry success rate is improved, and the stability of the system link is ensured.

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 discloses a credible exchange method and system for enterprise data on a supply chain, and relates to the technical field of enterprise data exchange, a credible interaction verification module is arranged on a data terminal of each enterprise on the supply chain, and a plurality of credible interaction verification modules are numbered; and a data relay server is arranged. A trusted interaction verification module executes a data trusted exchange multi-party verification mechanism to verify the legality and security of a data interaction request; at least three-party cross verification is adopted, a credible interaction verification module corresponding to a number verifies the legality and security of a data interaction request enterprise server based on network address communication and interaction ciphertext of a data interaction request, and the risk of data leakage caused by directional communication cheating specially set based on a target enterprise is avoided; after a credible exchange multi-party verification mechanism is passed, a safer enterprise data credible exchange method on the supply chain is constructed, and safe interaction of supply chain data is ensured.

The invention discloses an enterprise service demand prediction and resource optimization configuration system based on artificial intelligence, and relates to the technical field of artificial intelligence. According to the method, the multi-source heterogeneous data is integrated through the multi-modal data fusion algorithm, the demand change rule is accurately captured through the time sequence prediction algorithm, the demand prediction precision is remarkably improved, and the problem that the demand perception ability is limited due to insufficient data utilization in a traditional method is solved; the resource optimization allocation algorithm is combined with the comprehensive utility function of the resource utilization rate and the customer satisfaction, the resource combination is dynamically adjusted, the resource allocation efficiency is greatly improved, the defect that a traditional resource allocation mode is static and lack of flexibility is overcome, and the user experience is improved by continuously monitoring customer interaction and service feedback data. Model parameters are dynamically updated, a resource allocation algorithm is optimized, continuous optimization of resource allocation is achieved, the problem that an existing method lacks an effective feedback mechanism is solved, long-term efficient operation of a system is ensured, and more competitive operation support is provided for enterprises.

Systems, methods, and computer readable medium are provided that relate to, among other things, establishing data structures that quantify test-determined parameters related to identifying and improving core decision making skills, implementing adapters that interface with enterprise application services (and data) to identify signals relevant to the parameters from the user activity within the enterprise service, implementing in conjunction with these feature a private online workspace, and providing training through the workspace based on the signals determined from the (other) enterprise services. Signals can be automatically determined by interfaces to other enterprise application services such as email, employee feedback, employee evaluation, and other systems.

The invention relates to a system for realizing intelligent classification processing of enterprise service assets based on AIGC. The system comprises a service asset data acquisition module for automatically extracting service asset API interface information from a code library, a document and a log; the service asset data preprocessing module is used for cleaning, formatting and standardizing the acquired service asset API data; the service asset registration module officially registers the collected service asset API interface information to a service asset library; the service asset intelligent classification module automatically classifies service asset API interfaces by using a natural language processing technology; and the feedback and optimization module is used for collecting user feedback. By adopting the system, the method and the device for realizing the intelligent classification processing of the enterprise service assets based on the AIGC, the processor and the computer readable storage medium thereof, the manual intervention is reduced, and the error rate is reduced; and the burden of a service asset management team is reduced, and the intelligence and automation of enterprise operation are promoted, so that the wide popularization and continuous innovation of digital application are accelerated.

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.

Arrangements for remotely developing an application are provided. A computing platform may receive a request to build an application. The computing platform may receive the request through an API gateway. The computing platform may use a smart contract to identify a first node as a parent node and a plurality of nodes as child nodes. The computing platform may direct the child nodes to transmit virtual copies of dependency packages to the parent node. The parent node may build the application after receiving the dependency packages. The computing platform may detect that the application has been successfully built and may automatically publish the application at an enterprise server.

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.

Authentication using a mobile wallet by receiving, at an enterprise server and from a pre-screened user device, a request to access an account on the enterprise server, and sending, to a security server from an enterprise server, a wallet card request to push a wallet card to the pre-screened user device. A valid screening condition of the pre-screened user device is determined, and a wallet card may be generated and pushed to the valid pre-screened user device. Responsive to a request, a passcode may be generated and pushed the generated code to the pre-screened user device wallet. Responsive to confirming, at the security server, that a passcode received from the pre-screened user device wallet matches the generated passcode pushed to the pre-screened user device, a match indication may be transmitted to the enterprise server to initiate authentication of the pre-screened user device for accessing the enterprise server.