42 results about "Knowledge rule" patented technology

The invention discloses a light industry supply chain multi-modal data fusion analysis method based on deep learning, and the method comprises the following steps: carrying out the cleaning and standardization processing of text, image, audio and video data collected in a supply chain environment, and constructing a standardized multi-modal data set; then, a special feature extraction network is adopted to generate each modal feature vector, and a feature incidence matrix is constructed through cross-modal correlation analysis; feature weights are dynamically adjusted in combination with a domain knowledge rule base, multi-modal feature interaction is achieved through a cross-modal attention fusion network, and unified fusion features are generated through a self-attention mechanism; and finally, constructing a supply chain decision model, and mapping the fusion feature into a supply chain state evaluation result and an optimization parameter. According to the method, knowledge rule constraint and a deep attention mechanism are fused, supply chain situation awareness precision and decision reliability can be effectively improved, and technical support is provided for intelligent management of the light industry supply chain.

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 electromechanical pipeline conflict detection and adjustment method and system based on a BIM model, and belongs to the field of electromechanical installation. According to the technical scheme, the method comprises the steps that geometric parameters of a BIM model generation component are analyzed, and electromechanical pipeline conflict data are generated; conflict types are automatically classified according to the electromechanical pipeline conflict data, and electromechanical pipeline conflict sorting data are calculated; calling a knowledge rule engine according to the electromechanical pipeline conflict type and the electromechanical pipeline conflict sorting data, and matching a conflict adjustment strategy; executing a pipeline position adjustment action, and synchronously updating BIM model data; and recording the adjusted conflict processing result to a case library, and carrying out strategy optimization updating on the knowledge rule engine based on case library data. The method has the beneficial effects that an automatic and intelligent processing process of electromechanical pipeline conflicts from identification to adjustment is realized by constructing a conflict detection and adjustment full-process mechanism based on the BIM model, the manual intervention frequency is effectively reduced, and the constructability and the cooperation efficiency of the BIM model are improved.

The invention provides an automatic government affair examination and approval system and method based on artificial intelligence, and is used for disassembling a government affair examination and approval process into a plurality of function modules which can operate independently. Intelligent modeling is carried out on key links such as material processing, rule judgment, process planning and result generation related to government affair handling through artificial intelligence and data driving technologies, and an automatic, high-efficiency and interpretable whole examination and approval process is realized through cooperation among modules; therefore, a comprehensive solution integrating artificial intelligence information processing capability, a policy knowledge rule engine and a process automation mechanism is provided, the comprehensive advantages of flexible adaptation, efficient processing, traceable result, controllable strategy and the like are achieved, and the government affair approval system can be promoted to develop to a new efficient, fair and intelligent stage.

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 discloses a walking dysfunction intelligent evaluation system and method based on a flexible electronic technology, and the method comprises the following steps: S1, setting a multi-modal flexible sensor array, and synchronously collecting multi-modal data; s2, synchronously aligning the multi-channel signals by using an edge calculation unit to obtain a standardized gait feature tensor; s3, based on the dynamic Bayesian network, forming a high-dimensional hidden state probability distribution sequence of a complete gait cycle; s4, outputting a gait anomaly typing result and a walking dysfunction risk level based on the echo state network; s5, in combination with a knowledge rule engine and historical gait data, performing multiple verification and dynamic Bayesian network model parameter adaptive updating on a gait anomaly typing result; and S6, generating a rehabilitation evaluation report according to the gait anomaly typing result and the walking dysfunction risk level. According to the method, dynamic Bayesian network time sequence modeling and an echo state network intelligent discrimination algorithm are combined, and active detection and grading evaluation of walking dysfunction are achieved.

The invention provides an intelligent log assertion reasoning method and system driven by a knowledge graph, and relates to the technical field of log reasoning, and the method comprises the steps: carrying out segmentation processing and semantic analysis on heterogeneous log data, constructing a knowledge graph space, and establishing a time sequence and a causal relationship between log event nodes; and a multi-hop reasoning mechanism is adopted to generate a fault diagnosis report and a processing suggestion, and an automatically optimized operation and maintenance knowledge rule set is realized. According to the invention, the fault diagnosis accuracy can be improved, the fault processing time is shortened, and intelligent operation and maintenance management is realized.

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 relates to the field of in-vehicle infotainment testing, in particular to a stability testing method, system and device for an in-vehicle infotainment system and a medium, and the method comprises the steps: learning a first weight corresponding to each display area, support operation and a second weight corresponding to each support operation by using a Q-learning algorithm according to a priori knowledge rule set; executing a test step for a predetermined number of times, the test step comprising: determining a target area based on the first weight according to a predetermined rule; determining a target operation corresponding to a target area according to the second weight; and executing the target operation on the target area, judging the execution validity, and generating a stability test report according to an execution result. According to the characteristics of the vehicle machine system interface, the test event is intelligently generated, and the exploration probability is dynamically adjusted, so that the test coverage rate is improved, the test efficiency is also improved, and invalid and repeated tests are reduced.

The invention relates to a medical information extraction method and device, electronic equipment and a storage medium, and can obtain medical information valuable for constructing a medical database, so that the data quality of the medical database can be improved, and the medical information retrieval efficiency of a user can be improved. The method comprises the following steps: acquiring medical literatures; segmenting the medical literature to obtain segmented literature statements; based on knowledge rules in the medical field, the segmented literature statements are classified to obtain classification tags of the literature statements, and the classification tags of the literature statements are medical information or non-medical information; and performing information extraction on the literature statement marked with the medical information to obtain a medical entity and an entity relationship in the literature statement marked with the medical information, and storing the medical entity and the entity relationship in the literature statement marked with the medical information.

The present invention proposes a new power system planning method based on the fusion of knowledge rules and evolutionary algorithms, including: establishing a new power system planning model; specifically: determining the planning boundary parameters of the new power system, constructing the objective function of the power system planning model, and constructing the planning constraints and operation constraints of the power system planning model; utilizing the relative independence of the planning layer and the operation layer to transform the new power system planning model into a planning and operation double-layer sub-model to reduce the complexity of a single new power system planning model; utilizing a genetic algorithm guided by knowledge rules to solve the planning and operation double-layer sub-model to output the optimal planning scheme for the new power system. The present invention ensures that the planning model is closely integrated with the system operation requirements to avoid separation, and at the same time combines the large-scale optimization capability of the genetic algorithm with the rapid search capability guided by knowledge rules in the model solution, achieving a balance between comprehensiveness and rapidity in optimization and supporting large-scale new power system planning.

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 present invention proposes a method and device for configuring a rule database for a fixed pollution source treatment condition monitoring system. The method establishes a data set and an operation object instance to achieve two-way binding between the data set and the operation object instance, thereby building a knowledge rule database. Based on the pollutant emission indicators of the terminal outlet of the fixed pollution source treatment, the operating status of the treatment facility, and the key operating parameters of the treatment condition, a process knowledge rule table is obtained, and the process knowledge rule table is converted into a standard data file and added to the database to complete the configuration of the knowledge rule database. In combination with object-oriented programming design, relevant rule configuration controls and a rule management UI interface are developed. The present invention integrates a time-series database, background customized service development, and visual rule configuration. The visual rule configuration control calls the corresponding service to complete the rule configuration, effectively improving the automatic monitoring capability of the working condition parameters of key pollution source discharge units.

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 invention relates to the technical field of DSS optimal configuration, and particularly discloses a DSS optimal configuration method and system based on an influence rule between fitting performance indexes, and the method comprises the steps: collecting key performance index data, and constructing a priori knowledge rule; performing preprocessing and index calculation on the key performance index data, and performing data supplementation on the performance index data based on configurable items; grouping and concluding the complementary data to obtain a plurality of performance index data relation mapping tables; performing linear fitting on configurable items in each performance index data relation mapping table and each piece of performance index data to obtain a first influence rule; and judging whether the first influence rule accords with a priori knowledge rule, if not, performing multivariate linear fitting on the configurable item and the performance index data to obtain a second influence rule, and adjusting the configuration of the distributed storage system based on the first influence rule or the second influence rule. The method can help the user adjust the configuration of the storage system and realize load balancing.

The invention relates to the technical field of environment intelligent interaction, in particular to an environment intelligent interaction cognition system and method based on data driving, and the method comprises the steps: in a qualitative cognition stage, firstly carrying out the dimension reduction and structural classification of high-dimensional data through employing a PAM clustering algorithm, determining the optimal clustering number through a contour coefficient method, and achieving the preliminary organization of the data; then, an FP-Growth algorithm is used for mining association rules among features, an interpretable knowledge rule base is constructed, in the quantitative cognition stage, a qualitative analysis result is mapped back to original data, a POS-BP neural network model is adopted, the initial weight of a neural network is automatically optimized through a particle swarm optimization (PSO) algorithm, the convergence speed and prediction precision of the model are remarkably improved, and the prediction accuracy of the model is improved. And finally, accurate prediction of key indexes (quality, energy consumption, cost and the like) in the manufacturing process is realized through the optimized BP neural network. According to the method, effective mining of massive, high-dimensional and low-quality data in the continuous manufacturing industry is realized through a divide-and-conquer strategy.

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 provides a root cause link determination method and device for a service fault, equipment and a storage medium, and relates to the technical field of automatic fault processing. The method comprises the following steps: acquiring service state information related to a business service fault under the condition that the business service fault exists; the service state information is processed based on a preset causal discovery algorithm to generate an initial causal graph corresponding to the business service fault, the initial causal graph comprises at least one initial causal edge, and each initial causal edge is composed of at least two service nodes and a causal direction between the at least two service nodes; the initial causal graph is verified based on a preset knowledge rule base, a root cause link corresponding to the business service fault is determined, and the preset knowledge rule base comprises at least one preset causal rule. The method can efficiently and accurately position the fault root cause path.

The invention relates to the related technical field of text auditing, in particular to an official document rule auditing method supporting custom. The method comprises the following steps: acquiring a rule text input by a user; performing intention recognition and problem property judgment on the rule text, and generating a user-defined rule based on content meeting preset intention requirements and problem properties in the rule text; obtaining to-be-examined contents; reviewing the to-be-reviewed content based on the custom rule to obtain a first reviewing result; reviewing the to-be-reviewed content based on a preset text rule to obtain a second reviewing result; reviewing the to-be-reviewed content based on a preset knowledge rule to obtain a third reviewing result; and summarizing the first review result, the second review result and the third review result to obtain a target review result.

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.