2190 results about "Business logic" patented technology

The invention discloses a multi-source heterogeneous data fusion system based on an RAG. According to the method, through deep knowledge fusion and a dynamic cognitive evolution mechanism, the decision-making intelligence level in a complex data environment is remarkably improved, and equipment operation parameters, environment indexes and a domain knowledge base are deeply associated to form a panoramic data view with space-time continuity. A generative enhancement mechanism endows original data with a self-evolution characteristic, industry empirical rules and real-time situation awareness are injected while the fidelity of the original characteristic is maintained, so that a decision model can capture micro data fluctuation and follow macroscopic business logic, accurate balance between risk early warning and resource scheduling is realized, and the risk early warning efficiency is improved. The dynamic adaptation characteristic enables the system to autonomously update a knowledge system and optimize a decision path in a complex and changeable industrial environment, and a post response mode of a traditional static analysis model is converted into an intelligent center with prospective pre-judgment and real-time regulation and control capabilities.

The invention discloses an enterprise big data mining method and system based on artificial intelligence, and the method comprises the steps: carrying out the dynamic mode alignment through employing a multi-mode hypergraph neural network according to enterprise multi-source heterogeneous data, and generating a time-space consistent multi-mode joint embedded tensor; inputting the multi-modal joint embedding tensor into an orthogonal adversarial manifold learning module, and generating a low-dimensional compact semantic embedding vector with enhanced category separability; performing space-time causal association mining on the semantic embedding vector, and outputting a space-time causal meta-path map containing the recessive commercial logic; and inputting the space-time causal element path map into a dynamic game adversarial interpretation framework, and finally outputting an enterprise-level intelligent decision map with anti-factual robustness. By utilizing the embodiment of the invention, the multi-modal data can be efficiently integrated and intelligently analyzed, and the accuracy and effectiveness of the mining result are improved.

The invention provides a construction method of a double-path collaborative decision network for multi-agent collaborative path optimization, and the method comprises the steps: obtaining multi-source heterogeneous real-time business data and historical business data, and constructing a standardized data set; converting the standardized data set into a dynamic environment input sequence; at least two domain agents perform full-process business logical reasoning based on the dynamic environment input sequence to generate a decision chain structure covering the full life cycle of the business; implementing full-flow path planning simulation based on the decision chain structure, and generating an initial path scheme including multi-resource collaborative scheduling; the environment perception intelligent agent puts the initial distribution path scheme into an actual logistics distribution scene for execution, and loads real-time environment change information to determine a logistics decision chain weight; and the logistics strategy agent optimizes the initial distribution path scheme based on the logistics decision chain weight to obtain a logistics business knowledge graph so as to construct a logistics double-path collaborative decision network.

The invention relates to the technical field of code security analysis, and discloses a control flow analysis and retrieval enhancement-based large model code security review method, which comprises the following steps of: analyzing an incremental code to generate an abstract syntax tree and data flow analysis information; retrieving local knowledge base association business rules and historical vulnerabilities based on grammatical features, and generating a context enhancement prompt; calling a large language model to jointly analyze codes and contexts, identifying vulnerabilities and outputting a structured report; and combining with a historical false alarm data optimization result and then integrating to a development assembly line. Multi-source information is fused through an RAG technology to enhance semantic understanding, and control flow node tracking and cross-version semantic association are combined, so that the problems of a traditional tool business logic vulnerability detection blind area, incremental code analysis failure and high false alarm rate are solved, synchronous improvement of security examination accuracy and efficiency is realized, and the method is adaptive to an agile development scene.

The invention provides a dynamic order making method and device based on multiple rounds of dialogues and electronic equipment, and belongs to the technical field of intelligent interaction. The method comprises the steps of guiding a user to gradually fill in a form through a multi-round dialogue mechanism, generating a question based on a business logic dependency relationship, and mapping a natural language answer of the user into a structured field value in combination with semantic analysis and field dependency management; integrating a business rule engine in a multi-round dialogue mechanism process, executing field-level, logic-level and process-level multi-dimensional verification in real time in a user input process, and dynamically adjusting a verification strategy based on context; constructing and adaptively adjusting a form structure in real time based on a dynamic form generation engine, multi-round dialogue contents and business rules; based on a low-code configuration development platform, in combination with a preset intelligent interaction component and a visual tool, an intelligent filling system supporting natural language interaction is constructed. And the intelligence, flexibility and accuracy of form filling are obviously improved.

The invention relates to the technical field of natural language processing, and provides an LLM-based complex language question SQL query generation method and system, and the method comprises the steps: responding to a target language question request; splitting a requested target language question sentence into one or more core clauses through a language model LLM, converting the core clauses into semantic vectors, calculating a matching score of each core clause and a question normal vector of each table in the schema knowledge document, performing sorting and screening according to the score size and a score threshold, and returning a recall result of the core clauses; combining and sorting the recall results of all the core clauses, and screening to generate a recall result of the target language question; and generating a Prompt through a Prompt script, and calling an LLM (Language Model) to automatically generate an SQL (Structured Query Language) query statement. The method does not depend on field fine tuning, does not need to reconstruct a wide table, and effectively solves the problem that a target table is difficult to position when a large language model processes coded value mapping, multiple tables and complex business logic by constructing a virtual table and splitting a question sentence into clauses containing complete semantics and matching the clauses.

The invention relates to an AI-based automatic adaptation method for a work approval process, and the method comprises the steps: collecting multi-source approval data, and carrying out the preprocessing of the multi-source approval data, and forming standardized approval data; analyzing the system rule text by using the pre-trained AI large model, and extracting a structured approval rule comprising a trigger condition, an approval role and a process node sequence; matching a basic examination and approval template according to the form field and the user permission information, and dynamically generating an adaptive process comprising multiple stages of examination and approval nodes, aging parameters and an additional examination and approval link; and through resource scheduling optimization and time domain correlation analysis, establishing an optimized mapping relation based on flow execution characteristics such as approval timeliness deviation and node skipping frequency, and outputting a visual flow chart and execution parameters. A business logic writing mode is replaced by automatic analysis of an AI large model on an unstructured rule; the dynamic template matching and continuous iterative optimization mechanism can quickly respond to business changes, and resource scheduling optimization and automatic process generation shorten the implementation period and reduce the operation and maintenance cost.

The invention relates to the technical field of gas station equipment monitoring, in particular to a gas station equipment monitoring system and method based on the Internet of Things. The method specifically comprises the steps of collecting multi-dimensional operation parameters of equipment and business process node dynamics in real time, constructing a bidirectional coupling graph model of the equipment and business, and extracting high-order features of physical association and business logic interaction between the equipment by using a graph convolutional network; predicting equipment fault risks by adopting a dynamic health scoring mechanism, backtracking a risk propagation chain in combination with an attention mechanism, and mapping multi-dimensional data into interpretable fault diagnosis information through an abnormal semantic generation network; a double-layer reinforcement learning game mechanism is introduced, an inner layer coordinates equipment maintenance and a risk suppression strategy, an outer layer overall plans service efficiency and a safety target, optimization actions such as resource scheduling and oil product allocation are generated, and a perception-diagnosis-decision closed loop is formed. According to the method, full-time perception of the equipment state, active fault early warning and cross-domain collaborative optimization are realized, and the operation and maintenance efficiency and safety of the gas station are remarkably improved.

The invention discloses a network access permission dynamic management and control method and system based on behavior analysis, particularly relates to the technical field of network security, and is used for solving the problem of systematic service interruption caused by neglecting implicit association between permission and service logic in the existing dynamic permission strategy. The method comprises the following steps: constructing a permission dependency graph by collecting real-time behavior data of a user and equipment in real time, identifying hidden dependency nodes which have indirect data streams with a target interface but are not explicitly called, and generating a hidden path weight coefficient in combination with a historical behavior log; based on the real-time behavior characteristics and the map topological structure, quantizing the outage probability of permission adjustment on dominant and hidden dependency paths, and dynamically generating an alternative access interface and a data transfer interface; when an alternative path abnormal operation is detected, an original path is traced back through a permission dependency graph, and permission configuration is iteratively optimized; the continuity of the business data flow is maintained while the dynamic contraction of the authority is ensured, and the interruption of the associated service caused by local authority change is avoided.

The invention discloses an intelligent data query method and device based on a large model and a medium, and relates to the technical field of large model application. The method comprises the steps of receiving a natural language query text input by a user, and determining a target interface group from pre-associated interface groups through a preset business line classification rule and a keyword matching algorithm; inputting the natural language input text into a pre-trained large model to analyze a parameter set required by interface calling; wherein the parameter set comprises at least two of the following items: a time range field, a business main body field and a statistical dimension field; calling an API interface in the target interface group according to the parameter set, and obtaining original business data; performing context supplementation on the original business data based on the business logic rule to generate a standardized data result containing business background description; and reconstructing the standardized data result according to a display format preset by a user, and outputting a combined visual result containing charts, tables and text description.

The invention provides a front-end automatic testing method of a large language model based on LLM, and belongs to the technical field of front-end automatic testing. The method comprises five core steps of test intention recognition and semantic analysis, page structure analysis and target element positioning, automatic generation of a test operation sequence and assertion logic, test script execution and dynamic regression verification, and test feedback analysis and continuous training. The strong semantic understanding and context reasoning capabilities of the LLM are utilized to perform semantic analysis and simulation on a front-end page structure, a user behavior process and business logic, so that automatic generation and maintenance of a test script are realized. According to the method, the test coverage rate and the generation efficiency are greatly improved, the maintenance cost is remarkably reduced, the readability and the intelligence of the test case are enhanced, and the method is suitable for the modern Web front-end application test of rapid iteration.

The invention discloses a comprehensive algorithm for distribution and scheduling of AGV (Automatic Guided Vehicle) carrying tasks. S1: an SCADA system; equipment communication, data acquisition and storage, protocol analysis and logic processing; s11, the communication protocol, the address and the communication rhythm of the equipment are configured through the system, data acquisition data are stored in ES (ElasticSearch), the data acquisition data are stored for one year and backed up regularly, and the system achieves service logic processing through an LUA script. The carrying efficiency of the AGV is integrally improved, and the execution time of carrying a single task by the AGV is shortened by 10%; the production capacity of a workshop is improved, and the carrying rate exceeds 96% under the condition of no interference; the workshop logistics cost is reduced, and the overall number of AGVs is reduced by 10%; and the first pass yield of material carrying is improved by 15%. The deep fusion data analysis capability enables the whole system to predict demands more accurately, avoid conflicts, optimize the overall production efficiency, optimize the production process, reduce waiting and resource waste, and improve the flexibility of path planning and the reliability of task completion.

The invention belongs to the technical field of financial data management, and particularly discloses a financial multi-source protocol self-adaptive fusion system based on AI semantic understanding and a knowledge graph, which comprises the steps of avoiding fusion errors caused by semantic misunderstanding through deep semantic analysis and a conflict resolution decision based on the knowledge graph; when protocol version updating is detected, incremental learning and model adjustment are carried out based on a newly added sample and historical experience, and a fusion protocol standard is dynamically updated, so that the high adaptive capacity to dynamic change of a financial protocol is realized; an exception monitoring and repairing mechanism is introduced, data missing, format errors and other exceptions are found in time and repaired online based on historical data modes and business logic, and negative influences of abnormal data on downstream risk control, transaction decision making and other key business links are avoided; through deep collaboration and information feedback among the intelligent modules, an intelligent system capable of self-learning and evolution is constructed.

The invention provides a modular construction method of a low-code visual interaction system, and relates to the technical field of low-code development. The method comprises the following steps: constructing an edge cognitive topology network, packaging each node into a reconfigurable meta-module, analyzing a user operation track by using an intention perception interface to generate a meta-intention vector, and carrying out context correlation analysis through a semantic behavior chain to generate a potential behavior sequence. And establishing an asymmetric coupling relationship based on an implicit context mapper, forming a dynamic semantic resonance field, triggering distributed intention resonance and activating a matching element module, and generating a collaborative logic cluster under the constraint of edge computing node resources. And dynamically adjusting an interaction rule and a resource allocation strategy of the logic unit. According to the method, automatic construction and high-precision intention adaptation of complex business logic are realized, the elastic expansion capability and the real-time response efficiency of the system in an edge computing scene are improved, and the risk of logic fault and resource scrambling caused by node isomerism is reduced.

The invention discloses a rural industry agricultural product full-process traceability method based on a block chain. The method comprises the following steps: collecting agricultural product full-life-cycle process data and environmental parameter data through Internet of Things sensing equipment; preprocessing the agricultural product traceability data by using a space-time agricultural data compression technology; storing the compressed key traceability data in a block chain network, and storing high-capacity original data through a content addressing interstellar file system; maintaining the consistency of the block chain network based on an adaptive agricultural season consensus algorithm; automatically executing data verification and business logic processing through the smart contract; the authenticity of agricultural product traceability information is verified based on a zero-knowledge proving technology, and meanwhile peasant household sensitive data are protected.

The invention relates to a dynamic CPU scheduling method, system and device based on a CPU busy state and a medium. The method comprises the steps that busy degree indexes of all CPU cores are dynamically generated through monitoring; service logic to be executed is analyzed into a task set, and task attributes which cannot be changed are distributed to tasks; determining respective target CPU cores based on busy degree indexes of all current tasks, generating binding parameters, and writing the binding parameters into task attributes of the corresponding tasks; executing the allocation logic according to the task attribute of each task, and submitting the task to a running queue of the target CPU core to wait for execution; and when the single execution duration of the task in the target CPU core reaches a preset threshold value or is terminated by external interruption, resetting the corresponding task into the operation queue. According to the invention, breakthrough improvement is realized in three dimensions of hardware resource utilization rate, system real-time performance and scheduling security, and a solution with response speed and stability is provided for task scheduling in a computing environment.

The invention discloses a test case generation method and device, equipment and a medium, and relates to the technical field of software testing, and the test case generation method comprises the following steps: storing request data subjected to structured processing into a seed case library; the request data are various types of request data in the operation process of the service system; determining a cue word according to the target information based on the large model, and generating an initial test case through the cue word and a generation rule; the target information comprises characteristics of request data in the seed case library, an interface document of the tested software and a business logic specification; executing the initial test case according to the test request of the tested software, performing statistical analysis on the code coverage rate of the tested software in the execution process, and screening the initial test case according to an analysis result to obtain a screened test case; and after the execution is finished, filtering the screened test cases according to the first execution result of each test case to obtain a target test case. Therefore, the testing efficiency and quality can be improved, and automatic testing is achieved.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health, lease and the like, and discloses a distributed multi-terminal data cooperative processing method, which comprises the following steps: receiving a structured data unit containing business logic generated by an initiating terminal, and transmitting the structured data unit to a business verification server, receiving verification result data returned by the service verification server, and performing security encapsulation processing on the verification result data to generate security encapsulation data; calling a distributed communication service interface of the distributed operating system, and detecting a connectable device set in a network domain where the initiating terminal is located; and establishing a multi-path transmission link based on the transmission capability parameters of the terminal devices in the connectable device set, and distributing the security encapsulation data to the target terminal device group through the multi-path transmission link. According to the invention, efficient distribution and synchronization of data are realized through a multipath transmission link, the dependence on a background system is reduced, the timeliness and consistency of data synchronization are improved, and the maintenance cost of the system is reduced.

The invention relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention relates to the field of webpage design, and discloses a webpage generation method and system, computer equipment and a storage medium, the webpage generation method comprises the following steps: sending a webpage configuration request to a server at a client and receiving dynamic configuration data and business logic data returned by the server; a predefined node generation rule is matched according to the function module identifier to generate and mount an interactive page element set, and then a browser rendering engine is triggered to generate a visual webpage; meanwhile, when the user operates on the webpage and packages and submits operation data to the server, the server can update the business logic data and the configuration state and return the business logic data and the configuration state to the client again, and continuous dynamic adaptation and real-time updating of the webpage are achieved. Compared with the prior art, the method has the advantages that the compiling quantity of front-end adaptive and compatible codes is effectively reduced, redundant codes and complexity are reduced, the flexibility and expandability in a multi-environment and multi-device scene are enhanced, and the webpage rendering performance and the user interaction experience are remarkably improved.

Example implementations include a method, apparatus, and computer-readable medium configured for implementing a workflow using a large language model (LLM). A workflow automation application sends a first prompt to a large language model (LLM) to transform a first input data source in a first format to a second format. The workflow automation application sends a second prompt to the LLM to define multiple steps of a workflow starting on the data source in the second format. The workflow automation application sends a third prompt to the LLM to define execution of business logic for each step of the workflow. The workflow automation application receives, from the LLM, output data indicating that each step of the workflow has been executed.

The invention relates to a large model business logic processing method and system based on workflow engine and domain knowledge fusion, and is suitable for automatic processing of complex multi-node and multi-branch business processes. According to the method, natural language input of a user is analyzed through a large language model, a service intention is recognized, and the service intention is converted into an executable task process through a workflow engine. And the workflow engine dynamically adjusts an execution path according to rules and data in the domain knowledge graph to realize efficient parallel processing of tasks. The system integrates a workflow engine, a domain knowledge graph and a large language model, supports real-time data processing, rule matching, conflict resolution and decision optimization, is suitable for the fields of water conservancy, medical treatment, finance and the like, automatically generates and executes a complex business process, and optimizes a decision process. The system improves the accuracy and execution efficiency of business decision through deep fusion of domain knowledge.

The invention discloses a reliable communication system based on a publishing and subscribing mode in a satellite-ground weak network environment, and aims to solve the problems of high round-trip delay, limited link bandwidth, high instantaneous packet loss rate, easiness in blocking and switching influence of links and the like in a data transmission process between a low-orbit satellite and a ground station. A publishing and subscribing model is introduced into an application layer to realize service logic decoupling, deep fusion with an underlying enhanced QUIC transmission protocol is realized, and mechanisms such as message persistence, breakpoint resume, forward error correction and connection migration are combined, so that stable, orderly, complete and safe transmission of service data can still be ensured under the conditions of link instability, quality fluctuation and communication interruption, and the service data transmission efficiency is improved. And high availability and continuity of a satellite-ground communication task under a weak network condition are realized.

The invention provides a code generation and evaluation method and system based on RAG and a multilevel decision tree, and the method comprises the steps: integrating project related design documents, and constructing a knowledge base capable of semantic retrieval through a vectorization technology; associating business demand description with related documents in the knowledge base based on an RAG technology, and performing demand semantic enhancement to generate a technology demand cue word; receiving the technical requirement cue word by adopting a large language model so as to generate a complete code conforming to business logic; constructing a four-level decision tree evaluation system, and sequentially executing code quality scanning, deployability verification, dynamic test verification and demand satisfaction verification through an evaluation assembly line to generate an evaluation result; and generating an optimization suggestion according to the evaluation result so as to trigger an iteration generation process when the code does not pass the verification, thereby solving the problems of disjunction between code generation and business requirements, low verification efficiency and insufficient iteration optimization.

The invention provides an intelligent form processing method based on multi-modal verification and large model feedback, and relates to the technical field of artificial intelligence and data processing, and the method comprises the steps: obtaining a corresponding business rule from a relational database through a rule extraction and matching request based on user input data; error verification, service logic verification and cross-field and cross-method verification are carried out on the user input data and the corresponding service rules, and verification results are obtained; based on the verification result, natural language feedback is generated through the large model to provide error prompts and improvement suggestions.

The invention provides a power domain control software architecture and a controller thereof, the software architecture comprises an application layer developed based on an AUTOSAR standard, a runtime environment and a basic software layer, the application layer comprises a plurality of modularized software components, the software components package service logic functions of the controller to be integrated, the software components are provided with an AUTOSAR-based ARXML file definition interface, and the AUTOSAR-based ARXML file definition interface is used for defining the service logic functions of the controller to be integrated. According to the method, the interface compatibility can be improved, seamless integration of application layers across controllers can be realized, and the development parallelism is enhanced by kernel deployment of independently packaged software components according to the real-time performance of functions. The runtime environment generates an inter-core communication agent code, constructs a virtual bus and routes a service request of an application layer component to the basic software layer, and the basic software layer provides system-level service for the application layer through a virtual bus interface of the runtime environment, so that the application layer does not need to directly operate hardware, software and hardware decoupling is realized, and the service performance of the application layer is improved. And smooth realization of software fusion and integration is promoted.

The invention relates to the technical field of data processing, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses a payment business processing method based on a process engine, which comprises the following steps: standardizing an input / output interface, packaging business logic into business nodes, and storing the business nodes in a node library. And selecting nodes, configuration information and a connection relationship according to service requirements, and generating a process model. And starting the process engine, and converting the process model to obtain a process instance. And extracting node information and a connection relationship, scheduling node execution, obtaining real-time data, and generating a dynamic adjustment decision. And according to the decision and the real-time data, adjusting a process instance execution path and node parameters, scheduling node execution, completing payment service processing, and storing process instance information in a historical record library. By standardizing the interface, packaging the service nodes, generating the process model and dynamically adjusting the execution path and parameters, the payment service processing is efficiently completed, the flexibility and efficiency are improved, and the process information is convenient to store and trace.

The invention relates to the technical field of project intelligent management and control, and discloses a multi-agent power grid project intelligent monitoring, management and control and evaluation system and method, and the system comprises a mixed data resource library, a multi-agent system, an index calculation module and a process control module. The mixed data resource library stores structured business data and unstructured documents; the multi-agent system comprises an information extraction agent, an index design agent, a code generation and treatment agent and the like, works cooperatively, and converts a high-level natural language management and control rule into an executable code; the index calculation module is responsible for executing codes according to a scheduling strategy and calculating quantitative indexes; and the process control module automatically identifies risks and executes management and control operations such as process locking according to the index result and a preset threshold value. According to the invention, the complex business logic can be automated and coded, the accuracy and timeliness of risk identification are improved, and refined and prospective intelligent management and control of the power grid project are realized.

The invention discloses a robot unstacking and stacking support system based on a 3D visual system, and belongs to the technical field of 3D visual systems. Comprising a 3D visual data acquisition preprocessing module, a target positioning priority analysis module, a box type intelligent identification matching module, a dynamic path planning obstacle avoidance module, a self-adaptive stacking strategy generation module, an information binding tracing management module and a feedback optimization module. The problems of low efficiency and conflict caused by a single grabbing sequence in traditional stacking and unstacking operation are solved, the priority weight is dynamically adjusted by combining the inclination angle of the carton, weight distribution and reachability verification, and the optimality and safety of the grabbing action of the robot are ensured.

The invention discloses a power data management method based on a block chain, and relates to the field of block chains, and the method comprises the steps: initiating a data request; randomly selecting a preset number of oracle machine nodes; segmenting a private key of the request task into a plurality of parts by adopting a threshold key segmentation method; acquiring requested power data from an external data source; verifying the obtained power data; a Byzantine fault-tolerant consensus mechanism is adopted to achieve a consensus for the power data; signing the verified power data through a threshold signature method; the oracle machine management contract performs aggregation verification on the received signature data; the oracle machine management contract returns the power data passing the aggregation verification to the intelligent contract of the data requester; the intelligent contract of the data requester obtains the returned power data through an event monitoring mechanism, and executes corresponding business logic and state updating according to the power data; aiming at the low fault-tolerant capability of an oracle machine in power data management in the prior art, the fault-tolerant capability of the system is improved.