89 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.

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.

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.

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.

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 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 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 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.

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.

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.

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 application discloses a hybrid scheduling system and method based on clothing flexible production line rules and machine learning. The system contains rule algorithm module, machine learning algorithm module and coordination and decision module, through rule processing deterministic task, machine learning optimization dynamic scene, and using dynamic weight adjustment to realize collaborative scheduling. The method includes initialization, rule preliminary scheduling, machine learning optimization, coordination decision and production monitoring steps, introduces priority quantization model, equipment matching degree formula and dynamic weight algorithm, solves the problem of insufficient adaptability of traditional scheduling. The application combines rule algorithm and machine learning algorithm, realizes reasonable allocation of production task and optimization scheduling of equipment, improves production efficiency, resource utilization rate and product quality of clothing flexible production line, and reduces production cost.

The present application relates to the field of resource access control, and specifically relates to a fine-grained resource access control method based on ACL linked list, comprising the following steps: constructing an ACL linked list comprising a hierarchical structure, the ACL linked list comprising a main linked list storing core resource attributes and a sub-linked list defining subdivision dimension rules, and configuring permission inheritance and priority rules between the main linked list and the sub-linked list; processing a resource access request, extracting multi-dimensional attribute information by parsing the resource access request, performing hierarchical indexing and locating a target rule partition based on the multi-dimensional attribute information, dynamically loading the target rule partition in storage and performing matching; monitoring access frequency of each rule partition, and dynamically optimizing storage strategy and ACL linked list structure. The present application solves the defects of traditional ACL in control granularity, dynamic adaptability, execution efficiency and resource consumption, and provides a fine-grained resource access control method with high security, high performance and high maintainability.

The invention provides an ACL rule processing method and device, a product, equipment and a medium. The method comprises the following steps: generating a source code based on a pre-configured ACL rule according to a preset conversion rule; compiling the source code to generate a dynamic link library; a dynamic link library is loaded, a target function is obtained from the loaded dynamic link library, the message matching logic is used for determining whether the data message conforms to an ACL rule or not, and the action execution logic is used for determining a message processing instruction corresponding to the ACL rule; and for the data message to be processed, executing the message matching logic to obtain a matching result, and executing the action execution logic corresponding to the matching result, so that the real-time analysis or compiling overhead of the ACL rule can be saved, the compiled message matching logic and action execution logic can be directly executed, any field in the message structure is flexibly quoted as the matching content, and the matching efficiency is improved. The support range of the message matching logic is expanded by using the self-defined matching function, so that the message processing efficiency and expandability are improved.

The invention provides an active customer group judgment method and device, equipment and a storage medium, and can be applied to the technical field of big data technology and financial science and technology. The method comprises the following steps: in response to the obtained information of a to-be-judged customer, extracting a judgment rule of a candidate customer group category from a preset active customer group rule table; forming a customer group rule chain according to the priority of each judgment condition in the judgment rules of the same candidate customer group category; utilizing rule processors corresponding to the judgment conditions in the customer group rule chain to sequentially judge whether the customer information meets the judgment conditions or not according to a customer group rule chain sequence; and determining a target customer group category of the customer according to a result whether the judgment condition is met or not output by the rule processor.

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 embodiment of the application discloses a mass rule processing method and system based on an Internet of Things platform and a storage medium. The method belongs to the technical field of the Internet of Things and comprises the following steps: a rule distribution management service calculates rule hash values of each rule according to an obtained user identifier and saves the rule hash values to a database rule table; the rule distribution management service generates a rule hash partition list required to be loaded by each rule processing service in a striping manner according to a preset hash partition rule, saves obtained rule configuration information, sends the rule configuration information to a corresponding rule processing service, and sends full-amount rule configuration information to a message dispatch service; the rule processing service loads rules corresponding to the rule hash partition list from the database rule table according to the rule configuration information; and the message dispatch service distributes received reported information according to the full-amount rule configuration information and a user identifier in the reported information. The embodiment of the application can improve the processing efficiency of mass rules.

This application provides a low-code platform data modeling method and system based on Dataset technology, relating to the field of low-code technology. The method acquires configuration information of a target business object, generates structured metadata based on the configuration information, and generates an application component including a strategy evaluation engine. During the operation of the application component, the engine monitors the data status in real time and dynamically adjusts the behavior or relational structure of the application component when the policy rule conditions are met, without modifying the generated source code. To improve the efficiency of large-scale rule processing, the method extracts conditional expressions and adjustment instructions from the policy rules, identifies dependencies, calculates structural and temporal correlation weights accordingly, generates predictive hybrid weights, and performs clustering. This solution improves strategy evaluation performance, supports dynamic business adaptive modeling, and is suitable for digital transformation scenarios.

The present application is applicable to the technical field of big data mining, and in particular relates to a mining method and apparatus for an entity enhancing rule, a device, and a medium. In the method, rule construction and rule mining are performed on a candidate predicate set; an optimal preset number of rules are found and mined by means of iteration, search and pruning operations according to a known target; candidate rules are screened by setting upper and lower bounds of gain scores and confidence, effective rules are determined on the basis of gain improvement, and the rules which have been processed and cannot reach optimal performance through extension in the future are excluded, thereby constraining the rules for the next iteration, reducing unnecessary interference, and avoiding redundant rule processing in a plurality of iterations; and processing efficiency is effectively improved due to reduction of the rule processing amount.

The invention relates to the technical field of computer software, in particular to an intelligent voice broadcasting and information processing system and method based on JavaFX. The system comprises a dynamic Kafka consumer module, a message content identification and grouping module, an intelligent broadcast scheduler module, a dialect rule processing module and a JavaFX graphical interface module. The method comprises the steps of system initialization and dynamic connection, message consumption and grouping, intelligent broadcast scheduling, real-time rule processing and alarm life cycle management. Flexible information source switching is achieved by dynamically configuring Kafka connection, repeated broadcasting is avoided based on message identifier grouping in combination with cyclic grouping scheduling and a strategy of taking one for one in each group at a time, dialect rules and speech speed parameters are configured online to improve expansibility, and state visualization is achieved through a three-state list and color identification. The auditory fatigue is remarkably reduced, the system flexibility and the information processing efficiency are improved, and the method is suitable for real-time information prompt scenes such as command centers and monitoring large screens.

A rule processing system uses rule objects that self-define contexts where they apply. When an event is received, a rule set for evaluating the event can be creating by filtering a group of rule objects according to whether the detection rules contained therein are applicable to a context for the event. Each rule object may also contain refinement rules that further define how a detection is reported when a detection is generated by the detection rule(s) contained in the rule object. This architecture can significantly reduce the processing time required for performing detections in heterogenous computing environments such as a large enterprise network, where a large rule set must be applied to a high volume of events having different types and sources.

The invention discloses a rule processing method and device based on a graph structure, a storage medium and electronic equipment, and relates to the technical field of rule engines, the method comprises the following steps: disassembling and analyzing an initial flow chart conforming to a preset flow chart format to obtain a corresponding graph rule file; based on the graph rule file, reconstructing to obtain a to-be-adjusted flow chart; and adjusting the flow chart to be adjusted to obtain an adjusted flow chart. According to the method, the existing flow chart is disassembled and reconstructed through a flow chart recognition technology, and then rule arrangement is performed, so that support is provided for personalized formulation of a user, and the service management working efficiency is effectively improved.

The invention discloses a traffic flow prediction method based on a self-organizing fuzzy recursive random configuration network, and the method comprises the steps: fusing multi-source traffic and environment data, and constructing an input feature vector through abnormal value processing, normalization and periodic time coding; then, a fuzzy rule antecedent structure is automatically constructed based on input features, and an independent recursive random configuration sub-reserve pool is configured for each rule and is used for depicting local nonlinear time sequence dynamics; a candidate rule generation and supervision screening mechanism is designed and introduced to screen fuzzy rules, rule processing is executed through residual projection gain, and self-organizing evolution of a network structure is achieved; and updating the weight of the output layer online by adopting a recursive least square algorithm. And finally, based on the rule triggering weight, performing weighted fusion on the output of each sub-reserve pool to obtain a traffic flow prediction result.

The invention relates to the field of resource access control, in particular to a fine-grained resource access control method based on an ACL (access control list), which comprises the following steps of: constructing an ACL comprising a hierarchical structure, the ACL comprising a main chain table for storing core resource attributes and a sub-chain table for defining a subdivision dimension rule, configuring permission inheritance and priority rules between the main chain table and the sub-chain table; the method comprises the following steps: processing a resource access request, extracting multi-dimensional attribute information by analyzing the resource access request, performing hierarchical indexing based on the multi-dimensional attribute information, positioning a target rule partition, dynamically loading the target rule partition in storage, and performing matching; and monitoring the access frequency of each rule partition, and dynamically optimizing a storage strategy and an ACL linked list structure. According to the invention, the defects of the traditional ACL in the aspects of control granularity, dynamic adaptability, execution efficiency and resource consumption are overcome, and the fine-grained resource access control method with high security, high performance and high maintainability is provided.