26 results about "Knowledge rule" patented technology

The embodiment of the invention provides an intelligent agent memory indexing method and system based on intention recognition. The method is applied to the technical field of artificial intelligence and comprises the steps of obtaining real-time question-answer data, and performing preliminary intention classification on the real-time question-answer data by utilizing a domain knowledge rule library; extracting a structured description from the real-time question and answer data after the preliminary intention classification, performing deep intention analysis in stages, and outputting a standardized intention description text; according to the standardized intention description text, acquiring an Agent operation context, performing multi-dimensional retrieval to obtain an adaptive strategy, executing the adaptive strategy, and returning a strategy evaluation result; according to a strategy evaluation result, carrying out microscopic feedback and macroscopic feedback to update a strategy library; the Agent operation context is obtained through the following steps that semantic features of a standardized intention description text are captured, and the Agent operation context corresponding to the deep semantic features is recorded based on a fine-grained metadata labeling system. According to the invention, a complete closed loop from intention identification to strategy multiplexing to strategy optimization is realized.

The invention provides an intelligent prediction and data traceability management method for a lubricating system, and the method comprises the steps: obtaining the multi-source operation data of the input, operation and disposal stages of a lubricating medium in the whole life cycle, building the cross-stage data association based on a unique batch identifier and space-time metadata, and forming a traceable operation data chain. According to the method, dynamic correlation characteristics among equipment operation efficiency, lubricating medium states and environmental conditions are quantified through a multi-dimensional correlation analysis module, and a state evolution decision model is generated in combination with a knowledge rule base. Based on the model, the system calls a risk assessment knowledge base to generate oil replacement prediction suggestions, a component risk priority list and a maintenance strategy report. A maintenance result is fed back to the operation data chain, decision model updating and strategy optimization are driven through a knowledge base self-adaptive calibration mechanism, closed-loop optimization of intelligent lubrication management is achieved, and the fault tracing efficiency, prediction reliability and the maintenance cost-effectiveness ratio are effectively improved.

The invention provides an intelligent optimized arrangement method for a shield construction site. The method comprises the steps that S1, component parameter modeling is conducted on various components of a shield construction site, a knowledge rule base is established, and a component semantic map is established; s2, spatial modeling of a shield construction site and component matching are carried out; s3, on the basis of parameter modeling and rule modeling, constructing a composite target function fusing multiple types of engineering attributes, and outputting a component arrangement scheme meeting a global optimization target, spatial constraint and engineering logic; s4, setting a man-machine collaborative arrangement interface to support a user to participate in adjustment; and S5, based on the component arrangement scheme and the user feedback behavior, calling an arrangement inference engine fusing a historical sample and a feedback learning mechanism, and optimizing the arrangement strategy again. According to the method, the arrangement efficiency and the space utilization rate are improved, the arrangement requirements of complex and irregular sites can be met, multi-target collaborative optimization and conflict automatic detection are achieved, and the method has the arrangement man-machine collaborative adjustment and strategy self-learning capabilities.

The invention belongs to the technical field of electric power intelligent operation and maintenance, and relates to an electric power equipment fault prediction knowledge graph updating method which comprises the following steps: continuously receiving multivariate heterogeneous information from an electric power system operation environment, and applying at least one technology of natural language processing, time sequence analysis and statistical pattern recognition. Extracting new knowledge elements to form a to-be-verified new knowledge set, performing semantic consistency verification on the to-be-verified new knowledge set and an existing knowledge graph, detecting logic conflicts between the to-be-verified new knowledge set and existing knowledge triples and rules through logical reasoning, and obtaining a to-be-verified new knowledge graph based on a context environment where the logic conflicts occur. A conflict resolution strategy is automatically selected and executed by evaluating evidence reliability of new and old knowledge, and an optimized knowledge rule is generated, so that an existing knowledge graph is dynamically updated, the problems that an updating mechanism of the existing knowledge graph is lagged and evolution of context-related knowledge is difficult to process are effectively solved, and the reliability of the knowledge graph is improved. And the knowledge graph is pushed to be converted from static storage to dynamic evolution intelligent agents.

The invention relates to the technical field of artificial intelligence, and discloses a formula optimization method and device, equipment and a storage medium, and the method comprises the steps: carrying out the semantic deconstruction of a target demand in a multi-modal natural language form through a large language model, converting an obtained formula parameter into a differentiable semantic physical quantity, and carrying out the semantic deconstruction of the target demand; according to the method, knowledge rules are extracted from semantic physical quantities through a cognitive distillation algorithm, the semantic physical quantities are verified, the verified semantic physical quantities are optimized through a minimum weight loss function, and then a target formula is obtained through semantic decoding and physical field conversion. According to the method, the formula parameters are accurately extracted through semantic deconstruction, the problem that in the prior art, complex requirements are inaccurately understood is solved, the formula parameters are converted into differentiable semantic physical quantities, mathematical operability is provided for the optimization process, knowledge rules are extracted through a cognitive distillation algorithm for verification, it is ensured that data are reasonable and feasible, and user experience is improved. The defects of lack of an effective verification mechanism and easy optimization deviation in the prior art are overcome.

The invention discloses an industrial network fault intelligent diagnosis method and system based on multi-source data, and the method comprises the steps: synchronously collecting data from a plurality of data sources of an industrial network, and extracting a time sequence statistical feature, a flow entropy feature and a protocol conformity feature to form a multi-dimensional feature vector; establishing a dynamic baseline model by adopting a sliding window online learning method, and calculating a comprehensive anomaly score for anomaly detection; the fault suspicion degree is calculated based on the equipment incidence matrix and the fault propagation model to realize fault source positioning; carrying out fault type identification and root cause analysis by adopting Bayesian reasoning and a knowledge rule base; and outputting a structured diagnosis report containing the fault source, the type, the root cause and the disposal suggestion. According to the invention, early warning, accurate positioning and intelligent diagnosis of industrial network faults are realized, and the operation and maintenance efficiency, safety and reliability of the industrial control network are significantly improved.

The invention discloses a power transmission line visual monitoring data intelligent analysis and early warning method, and particularly relates to the technical field of power system state monitoring, and the method comprises the steps: S1, constructing a dynamically updated power transmission line space-time heterogeneous graph, S2, carrying out risk dynamic simulation based on a space-time graph attention network, and S3, fusing data prediction and knowledge rules through an objection game mechanism, and carrying out early warning. And S4, triggering graded early warning according to the risk grade, and driving optimization of the model and the strategy by using feedback data of early warning disposal. According to the method, the problems of early warning lag and decision stiffness in the prior art are solved, dynamic prediction and deduction of line risks, intelligent fusion decision and continuous self-optimization of the system are realized, and the perspectiveness, accuracy and operation and maintenance efficiency of early warning are remarkably improved.

The invention discloses a content security reasoning auditing method based on knowledge enhancement. The method comprises the following steps: establishing an attribute system based on illegal content in a pre-established standardized knowledge rule base, and structuring the attribute system into an attribute set; a prompt text is generated based on the attribute set and used for calling a teacher model to generate candidate answers through reasoning, and a synthetic data set is established through the candidate answers and the standardized knowledge rule base; using the synthetic data set to guide the student model to supervise and fine-tune to obtain a content category judgment and explanation text, and then optimizing the student model through reinforcement learning training; reasoning explanations are generated through the student model after training optimization and used for manual rechecking and responsibility tracing, and finally violation category labels are output. On the premise of ensuring detection precision and robustness, judgment reasons and bases are explicitly output, transparency and consistency of auditing results are enhanced, reasoning and deployment cost is remarkably reduced, and comprehensive requirements of an internet platform in the aspects of real-time performance, compliance and interpretability are met.

The invention discloses a knuckle test fixture intelligent design system based on a knowledge rule base and a design method thereof, and relates to the field of automobile part tests. The system comprises a data input module, a central processing engine, a knowledge rule base module, a parameterized CAD integration module, a CAE integration module and a result output module. Design knowledge, experience and rules are subjected to software processing and algorithm processing, full-process automation and intelligentization of knuckle test fixture design are achieved, a traditional design mode which depends on manual experience and is low in efficiency and uneven in quality is thoroughly changed, the design period is greatly shortened, and the design quality and consistency are improved.

This invention discloses an incremental data fusion method for the Industrial Internet based on granular attribute reduction and sparse autoencoders, belonging to the technical field of the Internet of Things (IoT). The method includes: dynamically reducing the attributes of incremental Industrial Internet data within the current time window based on granular and extensional decision-making; inputting the dynamically reduced numerical sequence into a multi-layer fully connected network after normalization and detrending processing to extract textual and temporal features; concatenating the textual and temporal features to obtain a joint input, which is then input into a sparse autoencoder for unsupervised representation learning, finally outputting a fused feature matrix directly supplied to the upper-layer Industrial Internet intelligent analysis platform; and triggering a knowledge rule incremental update mechanism when a new batch of incremental data is detected, updating the affected information granules and local model parameters in the new data.

The invention belongs to the field of computer application, and discloses a multi-agent target matching collaborative analysis method based on an RMAPPO algorithm. The method comprises the following steps: (10) constructing a task scene scenario, and obtaining basic environment state information in a scene; and (20) constructing an opposite-side index system according to expert knowledge rules. (30) constructing a model data set and performing preprocessing; and (40) introducing a recurrent neural network to solve the problem of partial state observability. And (50) centrally training the value network and decentrally training the strategy network according to the RMAPPO algorithm. The method has the beneficial effects that the data set is constructed according to task scenarios, and the memory ability of the model is enhanced by introducing the recurrent neural network. According to the method, different intelligent agents can be accurately operated to perform different tasks, especially on the premise of ensuring that people are in a loop, an auxiliary decision-making reference scheme can be made for a decision-making person, and the timeliness and accuracy of modern command control are improved.

The application discloses an industrial decision support system and method based on a large model and artificial intelligence, and belongs to the technical field of industrial intelligent decision-making.The system comprises the following steps: collecting industrial field data streams in real time and preprocessing to generate a structured time series dataset; using an industrial state analysis large model to extract a characteristic vector sequence, combining a sliding time window division and a neural Granger causality test algorithm to generate a causality knowledge rule set; using a neural differential equation framework to predict equipment state data prediction values; performing risk benefit assessment based on the prediction values to generate a structured decision instruction; after executing the instruction, comparing the prediction values and measured value deviations by using a sliding window cumulative error statistical method to dynamically optimize the model; and through the degenerative modeling and dynamic prediction of the causality rule constraint, and the closed-loop verification of the sliding window cumulative error statistical method, high-precision prediction of the equipment state, risk tracing and model self-adaptive optimization are realized.

The application belongs to the technical field of electrical configuration automatic generation, and discloses an electrical configuration automatic generation method and system based on artificial intelligence; the system comprises a knowledge base management module, an expert reasoning module, a configuration file generation module, an electrical rule editing module, a Web rendering engine module and a user interaction interface module, obtains a device knowledge data report, reasons the electrical design based on the device knowledge data report, obtains a reasoning result report, converts the reasoning result report into a configuration file recognizable by the rendering engine, obtains an electrical configuration configuration report, obtains a rendering state feedback report, integrates all functions of the system, and provides a unified user interface, supports device model establishment, knowledge rule editing and configuration data binding, and overall, the application has the remarkable advantages of high design efficiency and quality improvement capability, good configuration standard unification effect and flexible system rule adjustment.

The application provides a kind of mechanical and electrical pipeline conflict detection and adjustment method and system based on BIM model, belongs to the field of mechanical and electrical installation.The technical scheme is: analyzing BIM model to generate component geometric parameters, generate mechanical and electrical pipeline conflict data;According to the mechanical and electrical pipeline conflict data, the conflict type is automatically classified, and the mechanical and electrical pipeline conflict sorting data is calculated;According to the mechanical and electrical pipeline conflict type and the mechanical and electrical pipeline conflict sorting data, the knowledge rule engine is called, and the conflict adjustment strategy is matched;Perform pipeline position adjustment action, update BIM model data synchronously;Record the conflict processing result after adjustment to case base, and update the strategy of knowledge rule engine based on case base data.The beneficial effects of the application are: by constructing the whole process mechanism of conflict detection and adjustment based on BIM model, the automatic and intelligent processing process from identification to adjustment of mechanical and electrical pipeline conflict is realized, the frequency of manual intervention is effectively reduced, and the constructability and collaboration efficiency of BIM model are improved.

The application relates to the technical field of ship process design, in particular to a ship complex structure three-dimensional process design method, a computer storage medium and equipment. The application extracts key process features and processes them by interpreting the three-dimensional model of the ship complex structure to be subjected to process design, then obtains process design knowledge rules of the model process features based on complex structure process knowledge system reasoning, then performs process intelligent design based on the process design indication rules, realizes process automatic reasoning and design, improves the knowledge reuse rate, reduces repetitive labor, shortens the manufacturing cycle of products, and solves the technical problem of low process design efficiency of the ship complex structure.

The application relates to the technical field of DSS optimization configuration, and particularly discloses a DSS optimization configuration method and system based on influence rules among fitting performance indexes, which comprises the following steps: collecting key performance index data and constructing priori knowledge rules; after pre-processing and index calculation are performed on the key performance index data, data of the performance index data is supplemented based on configurable items; the supplemented data is grouped and summarized to obtain a plurality of performance index data relationship mapping tables; the configurable items in each performance index data relationship mapping table are linearly fitted with the performance index data to obtain a first influence rule; whether the first influence rule conforms to the priori knowledge rules is judged; if not, the configurable items are subjected to multiple linear fitting with the performance index data to obtain a second influence rule; and the configuration of the distributed storage system is adjusted based on the first influence rule or the second influence rule. The method can help users to adjust the storage system configuration and realize load balancing.

The invention belongs to the technical field of embedded system security and reverse engineering, and particularly relates to a firmware extraction system and method based on debugging interface rule reasoning. The system comprises a debugging interface identification module, a rule reasoning module, a communication link establishment module and a firmware data extraction module, the identification debugging interface module is used for collecting electrical characteristics of firmware; the rule reasoning module comprises a reasoning engine unit, an explanation unit, a meta-level reasoning unit, a cognitive architecture and a knowledge rule unit; the communication link establishment module sends a control command based on a reasoning result; the firmware data extraction module is used for accessing a storage area of firmware segment by segment and saving the storage area as a file; and the rule inference module is used for carrying out probability inference by adopting a data training model in combination with a historical protocol library. According to the system and the method, the link success rate is improved, the signal integrity is ensured while the matching precision is greatly improved, the stability is enhanced, and the robustness is improved.

This application provides an automated government approval system and method based on artificial intelligence. It breaks down the government approval process into multiple independently operable functional modules. Through artificial intelligence and data-driven technologies, it intelligently models key aspects of government processing, such as material handling, rule judgment, process planning, and result generation. By collaborating between modules, it achieves an automated, efficient, and interpretable approval process. This provides a comprehensive solution integrating artificial intelligence information processing capabilities, policy knowledge rule engines, and process automation mechanisms. It offers comprehensive advantages such as flexible adaptation, high processing efficiency, traceable results, and controllable strategies, and can promote the development of government approval systems towards a new stage of efficiency, fairness, and intelligence.

The invention discloses a temporary employment scheduling and community autonomy method and device based on a block chain, and a storage medium. The method comprises the steps of performing data mining on historical service data when service personnel serve a community based on a data mining and machine learning algorithm to obtain a service knowledge rule, and constructing a knowledge base according to the service knowledge rule; setting an optimization problem and constructing an intelligent optimization scheduling model based on knowledge driving based on the optimization problem; obtaining a to-be-scheduled service order task, and obtaining a service personnel order receiving allocation scheme of the to-be-scheduled service order task according to the intelligent optimization scheduling model based on knowledge driving and all service personnel registered in the system, so as to realize order receiving of the to-be-scheduled service order personnel; and meanwhile, the order receiving record of the service order task to be scheduled is uploaded to the block chain. According to the invention, the problems of difficulty in personnel identity verification, poor personnel scheduling efficiency and the like in the existing resident autonomous cross-community process can be solved.

The invention discloses a distribution network interoperation risk prevention and control method and device, electronic equipment and a storage medium, which are used for solving the technical problems of rule coupling, low efficiency and response lag in a current distribution network interoperation prevention and control mechanism. Receiving a distribution network interoperation instruction, and generating a to-be-verified task based on the distribution network interoperation instruction; constructing a double-source knowledge rule base according to the to-be-verified task; and executing hierarchical knowledge rule verification based on the double-source knowledge rule library, and outputting an operation decision result of the to-be-verified task based on a hierarchical knowledge rule verification result. Therefore, decoupling verification of the rule is executed through hierarchical modeling, accurate positioning of violation levels and error reasons is achieved, the calculation amount is reduced, and the rule verification efficiency is greatly improved.

The invention discloses an intelligent index generation method and system for an operation scheme of a color-coated product. The method comprises the following steps: constructing a release and operation standard knowledge base of color-coated products, a standard specification database of various coatings and a conversion rule database; obtaining user requirements of the color-coated product, converting the user requirements into corresponding standard numerical values through the conversion rule database, matching the standard numerical values with data stored in the release and operation standard knowledge base and the standard specification database of various coatings, and outputting a recommendation result; detailed content of the recommendation result is obtained and serves as new knowledge rule data to update the release and operation standard knowledge base after manual auditing is passed. According to the method, the metallurgical knowledge base automatic index taking the core code of the user demand as the keyword is constructed, and the index rule between the metallurgical specification knowledge base and the user demand is automatically designed based on the business specification and the data similarity identification; according to a rule algorithm and a priority algorithm, automatic association from a user demand to a process control parameter and a release standard is realized, and authenticity, accuracy and timeliness of data are guaranteed.

The invention relates to a dangerous chemical experiment site risk identification method and device based on knowledge rules. The method comprises the following steps: constructing a rule relationship between a factor set and frequent factors, calculating co-occurrence intensity and an original score of each factor, and generating a prior confidence coefficient; constructing a space-time expansion Petri net model according to the factor set and the frequent factor sequence; calculating a time trigger factor, a space conduction factor and an effective contribution degree; and iteratively updating the confidence coefficient of the space-time expansion Petri net library by taking the priori confidence coefficient of the starting point library as an initial value until convergence, and generating final risk factor confidence coefficient distribution for judging the strength of the accident risk chain of the current place. In order to solve the problems that a traditional model ignores time accumulation and coarsens spatial propagation, a time accumulation rule and a spatial conduction rule are introduced at the transition side at the same time, the triggering time, the diffusion range and the diffusion intensity of the risk are quantified, and identification, early warning and evaluation of the accident risk of the dangerous chemical site are achieved.

This invention discloses a structured closed-loop verifiable ordinal deviation driving system, method, and medium operating under a fixed number of parallel channels. The system maps input data to an actual ordinal vector A and generates a theoretical reference vector T from seed states and rules. Two mirror-related deviation components D1 and D2 are constructed using a mirror operator M, and a deviation metric D is synthesized according to a hedging rule. The system further calculates the hierarchical closure degree and updates the next cycle parameter P(n+1) using a self-explanatory scheduling function F based on a stability window criterion, completing the closed-loop reconfiguration of mapping parameters and hardware resources (gating, sleep, and rhythm). The interface module supports a handshake protocol based on "deviation reachability," and the security module uses abnormal jumps in the deviation distribution as intrusion and tampering detection criteria. Without gradient training, its inference process is based on constraint satisfaction and consistency checks of the deviation convergence criterion. The output is determined by the closure degree and stability window criterion, constituting reproducible deterministic logical inference rather than statistical prediction based on probability distribution. Knowledge rules can be loaded into the channel array through lookup tables, ordinal positions, and impedance or weight configurations to achieve bias-converged logical reasoning output, and can be optionally applied to data compression and encryption authentication scenarios.

The invention provides a model-based quantitative evaluation method for an engineering material refund risk. The method comprises the following steps of 1, performing data fusion and processing; step 2, risk assessment of the hybrid model; step 3, risk assessment of the hybrid model; step 4, carrying out risk visualization and early warning; step 5, optimizing feedback; and step 6, intelligent decision support. Through application of the knowledge rule model, the risk index fusion calculation and the setting of the risk index fusion calculation, a dynamic and multi-dimensional risk feature recognition model is added, and the capability of pre-judging the future refunding risk is improved.

The invention belongs to the field of artificial intelligence, and discloses an electric power emergency knowledge graph construction method based on a large model and a related system.The method comprises the steps that public data and internal data in the electric power emergency field are obtained and divided into different data sets according to needs; the multi-source heterogeneous electric power emergency knowledge can form structured and learnable data input after preprocessing, and the integrity and efficiency of knowledge acquisition are improved. According to the method, the base large model is obtained and field knowledge fine tuning is carried out on the base large model by adopting a low-rank adaptation technology, so that the model can fully learn a terminology system and a knowledge rule in the electric power emergency field, and has higher recognition and understanding capabilities when entities, relationships and attributes in an electric power emergency scene are processed; the lightweight fine tuning mode reduces the model training cost and improves the adaptation degree of the model to domain knowledge, so that the problem that the unstructured text is difficult to accurately analyze is effectively solved.

The invention discloses a digital public transportation system passenger flow intelligent prediction method based on deep learning, and the method comprises the following steps: S1, collecting historical passenger flow time series data, public transportation topology, external events and knowledge rules, and forming a data set; s2, symbolizing passenger flow time series data and constructing a self-evolution multi-scale graph structure; s3, identifying an abnormal driving factor through causal inference, and generating anti-fact scene data; s4, extracting multi-source features by adopting an improved TsFresh module fusion compression reconstruction mechanism; s5, inputting the features into a multi-expert cooperative system, and performing knowledge distillation fusion modeling; s6, combining knowledge rules with neural symbols to generate knowledge guide features; and S7, outputting a public transport passenger flow prediction and scheduling optimization scheme by using a game theory mechanism. According to the invention, the public transport passenger flow prediction and intelligent scheduling level in a complex scene is improved.