145 results about "Rule processing" patented technology

The invention relates to the technical field of operation and maintenance management and control, and discloses an intelligent building remote operation and maintenance management and control system based on a large model and a cloud edge collaborative architecture, and the system comprises a configuration subsystem which is used for carrying out the reverse proxy and persistent connection configuration of edge gateway equipment in an intelligent building, and obtaining a TLS encryption bidirectional communication channel; the construction subsystem is used for constructing a cloud-edge-end three-layer cooperative computing architecture; the conversion subsystem is used for performing unified semantic abstraction and conversion on the heterogeneous protocol data to obtain semantic data in a standard JSON (JavaScript Object Notation) format; the rule processing subsystem is used for performing rule processing through a near-end decision engine of the edge gateway to obtain a local intelligent response result; the multi-dimensional analysis subsystem is used for carrying out multi-dimensional analysis through a cloud large model engine to obtain an equipment health score, a fault prediction result and an energy optimization strategy, seamless fusion and interoperation of heterogeneous system data are achieved through the system, and then global optimization and linkage control of the intelligent building are achieved.

This disclosure describes a natural language question (NLQ) query service within a service provider network that provides row level security (RLS) for autocomplete during entry of NLQs and fuzzy matching in NLQ answering. The rules take the form of per-user predicates such as Tim can only see rows with region=US. In configurations a complex extraction and preprocessing pipeline to extract distinct combinations of values against RLS predicate “rule keys” is used. Those distinct values are indexed along with grouped rule keys to enable pushing down predicates at auto-complete time. This enables pushing part of RLS rule handling to ingestion time of a dataset rather than handling all RLS rule handling at query time, enabling meeting of latency goals. In configurations, a single logical document of unique cell values is split into multiple documents with a subset of rule keys to handle scalability limits.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

One embodiment of a method for processing data includes performing one or more operations to determine a performance of one or more predefined rules based on data that is received and one or more first predictions generated using the one or more predefined rules, performing one or more operations to determine a performance of a trained machine learning model based on the data and one or more second predictions generated using the trained machine learning model, processing the data using the one or more predefined rules to generate one or more third predictions, processing the data using the trained machine learning model to generate one or more fourth predictions, and generating one or more fifth predictions based on the one or more third predictions, the one or more fourth predictions, the performance of the one or more predefined rules, and the performance of the trained machine learning model.

A method, system, computer program product of providing a FiniteStateMachine (FSM) with embedded business rules, processing data & events, where each state is individually saved to blockchain ledger, a data record including a unique identifier / name, a set of input field(s) or input data slots, state rules in a rule language, and set of output field names or output data slots, method including: executing of a Smart Contract by identifying state by id or name, loading corresponding Component State from blockchain, adding / executing code for processing the State's behavior, where the code includes rules for the state in the rule language (e.g., but not limited to. Open Policy Agent, Rego, Prolog, DataLog, RuleML, Drools Rule Language, NRules, and JavaScriptObjectNotation(JSON), etc.). System includes processor(s), application program interface (API(s)) for communicating, blockchain, rule processor, and memory. This enhances transparency, immutability, and secure execution of business rules within FSM architecture.

The invention discloses a semantic analysis and generation method and system of an operation order, a medium and equipment, and belongs to the field of power system automation. The method comprises the steps that a task text of power grid operation is acquired, semantic analysis is conducted on the task text according to a generative model, and an analysis result is obtained; performing logical reasoning on the analysis result according to a preset knowledge graph to obtain a corresponding operation sequence; wherein the knowledge graph comprises nodes and edges between the nodes, each node comprises power grid equipment and an operation state, a running state and an operation rule of the power grid equipment, and each edge comprises an operation relation and a regulation dependency constraint between the nodes; processing the operation sequence according to a preset formatting rule to obtain a corresponding first operation ticket; and based on a preset operation rule set, performing logic verification on the first operation ticket to obtain a final operation ticket. Therefore, by implementing the method and the device, the problem that logical reasoning and semantic understanding capabilities are insufficient when the operation ticket is generated in a dynamic scene in the prior art can be solved.

Systems and methods are provided for rapid user equipment route selection policy rule processing. A method includes determining an applicable routing policy based on user equipment route selection policy (URSP) rules for an established protocol data unit (PDU) session and storing PDU session information with the established uplink PDU session in a cache. The method further includes examining subsequent uplink packets for PDU session information and checking the cache for an established PDU session with matching uplink PDU session information. The method additionally includes routing the subsequent uplink packets to the established PDU session having the matching PDU session information, causing the subsequent uplink packet to be processed in accordance with the applicable routing policy as previously determined based on the URSP rules.

The invention provides a business rule automatic processing method and system based on natural language understanding. The method comprises the following steps: extracting text content from an original document containing business rules and preprocessing the text content; utilizing a BERT-based metadata extraction model to extract structured metadata from the preprocessed text content; confirming and perfecting the structured metadata; on the basis of the confirmed metadata, a rule execution flow chart and / or a test case are / is automatically generated and used for verifying the correctness of rule logic; and according to the target format, converting the verified rule into a corresponding symbolic representation through a template engine to obtain a defined rule. According to the invention, the automatic conversion of the business rule from the natural language description to the symbolized executable form is realized, so that business personnel can directly use the natural language to define and maintain the business rule, the manual conversion cost is reduced, and the accuracy and efficiency of rule processing are improved.

The invention discloses a hybrid intelligent system architecture combining rule driving and probabilistic reasoning and an operation method thereof. The system comprises a rule processing and debugging module, a probabilistic reasoning and candidate generation module, a hierarchical decision and fusion module and a semantic data packet management and circulation module. The system adopts a processing mechanism of'rule first and probability second ', logical reasoning and error elimination are carried out through a rule module, and then candidate results are generated and sorted in a limited space through a probability module. The semantic data packet is used for bearing information of each module. Model behaviors are constrained by using a digital natural normal form, so that the system improves the long text understanding ability, reduces the language model illusion occurrence rate, and optimizes the resource use efficiency.

Examples of the present disclosure describe systems and methods for behavioral threat detection definition compilation. In an example, one or more sets of rule instructions may be packaged for distribution and / or use by a behavioral threat detection engine. As an example, a set of rule instructions is compiled into an intermediate language and assembled in to a compiled behavior rule binary. Event linking is performed, wherein other rules launched by the rule and / or events that launch the rule or are processed by the rule are identified, and such information may be stored accordingly. The behavior rule binary may be packaged with other rules associated with identifying a specific behavior. The packaged behavior rule is distributed to one or more computing devices for use with a behavioral threat detection engine. For example, the threat detection engine may execute the behavior rule using a rule virtual machine.

The invention discloses a dynamic rule engine loading method which specifically comprises the following steps: acquiring multi-source heterogeneous data by utilizing a StreamSets big data aggregation technology, performing incremental capture and real-time synchronization on the multi-source heterogeneous data by utilizing a CDC technology, and uniformly aggregating the multi-source heterogeneous data into a medical insurance thematic database ISS; the data processing module is used for carrying out data processing on multi-source heterogeneous data in the medical insurance thematic database ISS by utilizing a data rule condition specified by the data base module, and storing the processed data in a metadatabase, and a rule configuration panel calls a component and a script module according to a business logic demand to carry out extraction and rule processing on the data in the metadatabase; wherein the rule processing is realized by rule modeling in the data base.

The invention discloses a data conversion method and device based on machine learning, a medium and equipment, and the method comprises the steps: obtaining original data, and carrying out the preprocessing of the original data; performing feature selection on the preprocessed data based on a feature selection algorithm; inputting the data after feature selection into a corresponding trained rule generation model based on a data type to generate a data conversion rule; wherein the rule generation model is constructed and trained according to different data types; acquiring a real-time data stream, optimizing the rule based on the trained rule optimization model, and / or adaptively adjusting the rule according to the real-time monitoring data; and processing the data through the adjusted rule, and performing anomaly detection and correction on the data after rule processing. According to the method, the automatic rule is generated through real-time data pattern recognition, rule optimization and comprehensive data detection and correction are carried out in a self-adaptive mode, and the efficiency and accuracy of data processing are remarkably improved.

The present invention relates to the field of positioning engines, specifically a positioning method for a rule engine combined with AI intelligence, which includes the following steps: S1, building an AI model, and deploying the AI ​​model to the server or cloud platform where the existing rule engine is located; S2, preprocessing the rule conditions to adapt them to the requirements of the AI ​​model; S3, integrating the AI ​​model into the rule engine as a pre-step for rule matching; S4, after processing the rule conditions through the AI ​​model, quickly searching and matching in the rule base of the rule engine to complete positioning and matching; S5, result feedback and optimization, feeding back the rule execution results to the AI ​​model, and continuously optimizing the AI ​​model. The present invention significantly improves the efficiency and accuracy of rule processing, enhances the flexibility and scalability of the system, improves the stability and reliability of the system, and reduces labor costs and maintenance costs.

The invention discloses a command detection and processing method and device, equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: carrying out the global identifier adding operation of an original operation log of target detection equipment, and obtaining an identified operation log; processing the identified operation log to obtain structured data, and establishing a threat knowledge graph based on an attack command of attack feature description information obtained from a preset vulnerability library and the sensitive path; generating detection rules by using a command mode obtained by analyzing the command information based on the threat knowledge graph and a historical attack frequency of the command information, and screening the detection rules to obtain an effective rule set; processing the command information to obtain a syntax tree, and obtaining a target risk score based on a preset risk scoring rule and the structured data scoring syntax tree; and determining a response rule in the effective rule set based on the risk score grade of the target risk score, and processing the command of the command information by using the response rule. Therefore, the command detection and processing efficiency can be improved.

The invention provides a rule processing method. The method comprises the following steps: determining a corresponding target rule file according to a preset inquiry type; and dynamically adjusting rules in the target rule file according to the attribute information of the inquiry type to obtain a target rule. According to the scheme, firstly, only the target rule file related to the inquiry type is obtained, the number of the matched rule files is reduced, and secondly, the processing mode of the rules in the target rule file is dynamically adjusted, and the response time consumption of rule matching is shortened.

The invention provides a large language model question and answer rule packaging method, medium and system, belongs to the technical field of large language models, and forms a rule graph by constructing a plurality of lightweight neural networks, each network corresponding to a rule type and calculating the similarity between rules. After a question is received, firstly, an initial answer is given by utilizing a large language model, then the correlation degree between the initial answer and each rule is calculated, and a most relevant rule processing sequence is found out. And inputting the initial answer into each lightweight network in the sequence in sequence, performing regularization processing, and finally outputting a regularized result as a final answer of the question. According to the method, extensive knowledge of a large language model and the special rule processing capacity of a small neural network are combined, and answers better meeting the requirements of questioners can be given. The whole process is high in automation degree and transparent to questioners, and the problem that in the prior art, the coupling degree between a rule module and a large language model is high and is difficult to flexibly adjust and expand can be solved.

The invention provides a low-code platform data modeling method and system based on the Dataset technology, and relates to the technical field of low codes, and the method comprises the steps: obtaining configuration information of a target business object, generating structured metadata according to the configuration information, and generating an application component comprising a strategy evaluation engine according to the structured metadata; during the running period of the application component, the engine monitors the data state in real time and dynamically adjusts the behavior or relation structure of the application component when the strategy rule condition is met, and the generated source code does not need to be modified; in order to improve the large-scale rule processing efficiency, the method comprises the following steps: extracting a condition expression and an adjustment instruction in a strategy rule, identifying a dependency relationship, calculating a structure and time sequence association weight according to the dependency relationship, generating a predictive mixed weight, and executing clustering; according to the scheme, the strategy evaluation performance is improved, dynamic service self-adaptive modeling is supported, and the method is suitable for a digital transformation scene.

The invention relates to the technical field of data processing, and discloses a rule-based business identification method and device, equipment and a storage medium, and the method comprises the steps: obtaining the identification information of a target object, and extracting a target field associated with the object from a database based on the identification information; inputting the target field into a preset rule set, and executing matching through conditional logic to obtain a rule matching state set; determining a target rule expression according to the matching state set, and calculating the target rule expression to generate a corresponding service identifier processing result; and establishing association between the processing result and the target object to form corresponding service identification information. According to the invention, structuring, automation and high efficiency of service identifier generation can be realized, and the problems of delay and inconsistency of manual configuration are avoided; the service judgment accuracy is improved, and the flexibility and expansibility of rule processing are improved, so that the system can quickly adapt to complex and changeable service scenes.

The invention provides an Internet of Things event center distributed alarm processing method based on Redis Stream, belongs to the field of Internet of Things (IoT) data processing, and realizes millisecond-level event processing by utilizing the high-performance streaming consumption capability of the Redis Stream and combining a distributed rule matching engine and a cross-device data dynamic association mechanism. Comprising the following steps that: Internet of Things terminal equipment writes data into Redis Stream according to a stream: Model: DeviceId mode to form an independent data stream; an event consumer reads the data from the stream, performs matching based on a preloaded rule, and supports cross-device linkage calculation through a metadata relation table and an SQL template; real-time duplicate removal and full-life-cycle management are carried out on alarm events through a dual convergence mechanism based on a database and a cache, and repeated alarms are eliminated; and finally, through multi-channel push and push state write-back, a closed-loop alarm system is constructed. The system adopts a layered architecture, comprises an event acquisition layer, a rule processing layer, an event management layer, a push service layer and an asynchronous processing layer, has the characteristics of high throughput, strong orderliness and high reliability, and can effectively improve the alarm processing efficiency and accuracy in a large-scale Internet of Things scene.

The invention discloses a rule-based data processing method, which comprises the following steps: extracting data from a plurality of data sources based on preset data source configuration, the data source configuration comprising connection parameters and access protocols of the data sources; mapping the extracted data to a preset feature variable, so that a corresponding relation is established between the feature variable and a data item of the extracted data; creating a plurality of rules through the rule configuration template, and constructing a rule flow tree by using the plurality of rules; performing rule processing on the data according to the rule flow tree to obtain a rule flow tree processing result; and generating data processing decision information according to a rule flow tree processing result. The invention further provides a related device, electronic equipment and a computer program product. According to the method disclosed by the invention, the problem that data acquisition and rule execution are separated in the prior art is solved, and the condition relationship and the execution sequence of a plurality of rules are defined by introducing the rule flow tree, so that complex business logic requirements can be better met.

The invention discloses an NL2SQL ambiguity elimination method based on a large language model and rules in the technical field of natural language processing and database query. The NL2SQL ambiguity elimination method based on the large language model and the rule comprises the following steps: generating structured ambiguity mapping through the combination of the large language model and the rule, and covering a sentence pattern structure and pattern matching ambiguity; the ambiguity is clarified in the form of selection questions through user interaction and rule processing of a user interaction interface, and selection mapping is generated; and rewriting the natural language query and database mode based on the selection mapping, and outputting a final unambiguous problem and an unambiguous mode. According to the NL2SQL ambiguity elimination method based on the large language model and the rule, the accuracy and user friendliness of an analysis result are improved through a structured disambiguation framework, and clear SQL statements can be generated by inputting clear natural language query into an analyzer.

Examples of the present disclosure describe systems and methods for behavioral threat detection definition compilation. In an example, one or more sets of rule instructions may be packaged for distribution and / or use by a behavioral threat detection engine. As an example, a set of rule instructions is compiled into an intermediate language and assembled in to a compiled behavior rule binary. Event linking is performed, wherein other rules launched by the rule and / or events that launch the rule or are processed by the rule are identified, and such information may be stored accordingly. The behavior rule binary may be packaged with other rules associated with identifying a specific behavior. The packaged behavior rule is distributed to one or more computing devices for use with a behavioral threat detection engine. For example, the threat detection engine may execute the behavior rule using a rule virtual machine.

The present invention provides technology with which it is possible to assist creation of a security report that suits a reporting partner and a purpose. A security report creation assistance system according to the present invention includes a processor 103 and a storage device 105. The storage device 105 stores a rule table 113 that retains rules corresponding to a report object and a report type. The processor 103 acquires the report content, report object, and report type inputted by a user, reads a rule corresponding to the report object and report type inputted by the user from the rule table 113, and generates a report on the basis of the rule read from the rule table 113 and the report content inputted by the user.

The embodiment of the specification discloses a kind of station risk identification rule processing method, device and equipment, the method comprises: obtaining the site information of target network site to be identified;Based on the preset site identification demand information, corresponding prompt information is constructed, based on prompt information, using first large language model, generate first risk identification rule for target site based on site information;The first risk identification rule is extended and handled, and the rule population for target site is obtained;Each rule in the rule population is evaluated by a plurality of different modes by a preset multi-modal large model, to obtain the evaluation result of rule population;Based on the evaluation result of rule population, the rule population is iteratively optimized by difference evolution algorithm, to obtain the optimized rule population, based on the optimized rule population, determine the risk identification rule for target site.

This application discloses a rule processing method, a communication device, and a network side device, and belongs to the field of wireless communication. The rule processing method in embodiments of this application includes: receiving, by a first device, first information sent by a first network function, where the first information includes at least one of the following: a first mapping rule and a first allocation rule; and the first information is used by the first device to perform a first operation based on the first information, where the first operation includes at least one of the following: forwarding data sent by a second device to a network side device; allocating an IP address to the second device; and sending the IP address to the second device, where the second device interacts with the first device through a non-3GPP access technology.

The present invention belongs to the field of electronic design automation technology, and specifically relates to a multi-module collaborative electronic product design rule checking system, comprising a bill of materials (BOM) processing module, a rule processing module, and a core processing module. The core processing module receives a standardized bill of materials (BOM) output by the BOM processing module and reads a corresponding rule directory from the rule processing module based on the attributes of the standardized BOM. The core processing module then performs a design rule check on the schematic diagram based on the read rule directory and the standardized BOM, and outputs a check report. This invention improves design efficiency and quality while shortening lead times and reducing R&D costs.

The invention relates to the technical field of automatic operation and maintenance of a power system, and discloses a semantic recognition and automatic execution method, system and device for audit rule configuration and a medium, and the method comprises the steps: receiving an input natural language rule, carrying out semantic analysis through a knowledge graph and a template library, and generating a structured intermediate representation; converting the data into a regular agent, and carrying out logic conflict detection and resolution; performing simulation evaluation in the digital twin sandbox; deploying to a production environment, and performing parameter optimization by utilizing reinforcement learning; and finally, performing feedback and manual intervention through monitoring to realize incremental learning and continuous optimization of the knowledge graph and the analytical model. According to the method, the full-life-cycle intelligent closed-loop management of the rule is realized, and the reliability, the adaptability and the execution efficiency of rule processing are greatly improved.

An endpoint protection system implementing a new blocking strategy allows a user to specify an arbitrary number of protection rules through a user interface. In user mode, the protection rules are compiled into a single expression tree, which is then compiled into byte code. In kernel mode, the byte code is dynamically loaded in memory (e.g., kernel space) and the assembler validates the byte code and performs a plurality of security checks, then ultimately assembles the byte code into machine code that is native to the processor. Because complex detection / protection logic is compiled in user mode, the invention allows for highly expressive and powerful protection rules. Further, because complex detection / protection logic is not manually written in kernel mode, but validated then evaluated via simple machine code instructions in the privileged mode, the invention is safer and will not slow down the entire operating system.

The application relates to a contract auditing rule processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: in response to the current contract content input into a large model, displaying a contract auditing result on a contract auditing page; the contract auditing result is an auditing result output by the large model after auditing the current input contract content according to the current contract auditing rule; in the case that the score information input into the contract auditing result meets a preset score condition, determining a target result quantity; the target result quantity is the number of target auditing results output by the large model in succession for different contract contents under the current contract auditing rule; the target auditing result is a contract auditing result whose score information meets the preset score condition; in the case that the target result quantity meets a preset quantity, archiving the current contract auditing rule. The method can improve the effectiveness of the contract auditing rule.

The invention relates to the technical field of software development, and discloses a service process generation method and device, terminal equipment and a storage medium, and the method comprises the steps: obtaining rule data issued by a business system, and verifying the rule data; obtaining configuration information in the rule data, and generating a rule processor corresponding to the rule data according to the configuration information; and constructing a process model, and configuring the rule processor into the process model to form a service process. Therefore, the user can form the service process of the custom rule by configuring the rule data, and the function of customizing the service process is provided.