15 results about "Action rule" patented technology

The current Forwarding Action Rule (FAR) is a rule which can be presented to a user plane element by a control plane element by including a container for header enrichment. The user plane function (UP

The invention belongs to the technical field of behavior regulation mining, and particularly relates to an Apriori algorithm-based action rule mining method, which comprises the following steps of: 1, performing data cleaning on situation data, finding out available data, and classifying action rule data into conditions and actions; in the second link, the action rules of each category are called item sets, if the number of times that the various rules appear together in the item sets is larger than the minimum support degree threshold value, the item sets are integrated into frequent item sets, and the evaluation criteria of frequent item set mining comprise the support degree and the confidence degree; according to the method, for data such as situations, a frequent item set library of action class rules is constructed based on an Apriori algorithm, and mining of the action class rules is achieved through frequent item sets.

The application relates to the technical field of artificial intelligence equipment, in particular to a campus competition scoring robot based on audience emotion auxiliary judgment, which acquires competition action data of a participating object through a motion capture and posture sensing module, generates action rule confidence data based on rule matching, and introduces an audience emotion auxiliary judgment mechanism when the rule confidence is in a preset rule boundary interval; crowd emotion modeling and differential analysis are carried out on audience reaction data to obtain audience emotion difference data, and the audience emotion difference data are weighted and fused with the action rule confidence data to form a final scoring judgment result. The scheme effectively alleviates the uncertainty of judgment of rule boundary actions, improves the fairness, rationality and consistency of the scoring result, reduces the risk of controversial judgment, and enhances the intellectualization and credibility of the campus competition scoring system under the premise of maintaining the dominant position of the competition rules.

The invention discloses an AI native ERP enterprise management software system and a management method, the system comprises an AI management consultant module, a management rule knowledge base module, a data processing module, a risk identification module, an action generation module and an execution driving module, and the AI native ERP enterprise management software system and the management method cooperatively realize data acquisition, index analysis, risk identification, action suggestion and execution landing. The data processing module is used for disassembling multi-dimensional management indexes based on a PVM price sales volume mixed analysis model; the risk identification module retrieves and matches a risk rule through an LLM model or an RAG vector to generate a structured risk item; the action generation module matches the action rules and outputs action suggestions; the execution driving module is associated with the to-do rule to generate business to-do; and the AI management consultant module undertakes the demand and the task. The management theory and practical experience are converted into configurable rules, full-link driving of AI on enterprise management is achieved, the management efficiency and decision scientificity are improved, and personalized requirements of enterprises of different industries and scales are met.

The problem is to achieve work efficiency and ensure safety of actions of control objects in different control systems, i.e., in an environment where heterogeneous systems are mixed. A safety control server (5) for controlling actions of control objects in heterogeneous systems under a plurality of control systems has an action adjustment section (5330) for outputting an action adjustment instruction for adjusting actions to a control object in a control system different from the control object, in the case where a conflict occurs in which the action of the control object deviates from an intended control result, an action plan adjustment section (5320) for adjusting a pre-stored action plan indicating an action rule for avoiding a conflict of the control object and for functioning, in the case where the adjustment of the action satisfies a prescribed condition, and a safety rule adjustment section (5310) for adjusting a pre-stored safety rule indicating an action rule for avoiding a conflict of the control object, in the case where the adjustment of the action plan satisfies a prescribed condition.

A building system including one or more storage devices storing instructions thereon that, when executed cause the one or more processors to store a data structure of a digital twin of an entity of a building, execute a learning algorithm to construct a trigger rule and an action rule as an input to the learning algorithm, store the trigger rule and the action rule in the digital twin, determine that the trigger rule of the digital twin is triggered by comparing at least some of the information of the data structure against one or more conditions of the trigger rule by querying the data structure for data values and comparing the data values against the one or more conditions of the trigger rule, determine an action of the action rule of the digital twin responsive to determining that the trigger rule is triggered by executing the action rule, and cause one or more devices to operate based on the action determined by the action rule.

Systems and methods for validations related to software are provided. In some embodiments, a method for building software with software action logging includes accessing a target object type. In some embodiments, the target object type includes one or more object properties. In some embodiments, the method further includes accessing an action type. In some embodiments, the action type includes one or more action parameters and is associated with one or more action rules. In some embodiments, the action type is associated with editing a target object of the target object type. In some embodiments, the method further includes generating an action log type that includes at least one of the one or more object properties and at least one of the one or more action parameters. In some embodiments, the action log type further includes an action context associated with an action of the action type.

A network node can be configured with a packet detection rule (PDR). The PDR can allow the network node to identify one or more extended reality (XR) flows. The network node can be configured with a forwarding action rule (FAR). The FAR can include information about how the one or more XR flows should be routed. The network node can invoke a user plane function (UPF). The UPF can be configured using information from the PDR and / or the FAR. The network node can establish a protocol data unit (PDU) session. The network node can identify one or more XR flows indicated in the PDU session based at least on its configured PDR. The network node can forward the one or more XR flows according to the information in the FAR.

The invention belongs to the technical field of computers, and particularly relates to an intelligent decision-making method and device, equipment and a medium, and the method comprises the steps: responding to request information outputted by a user, and determining a reasoning result corresponding to the request information through a large language model; the reasoning result is sent to an action analysis module, a corresponding action instruction output by the action analysis module is received, and the action instruction is determined by the action analysis module based on a preset action rule set and the reasoning result; according to the action instruction, calling a corresponding target tool to execute the action instruction, and obtaining an execution result returned by the target tool; based on historical output information, a final decision result corresponding to the request information is determined through a large language model, the final decision result is output to the user, and the historical output information comprises the request information, the reasoning result, the action instruction and the execution result. According to the method, the execution reliability of the system is improved, the security management and control capability of the system is enhanced, and the maintainability and iteration efficiency of the system are improved at the same time.

The invention provides an action execution result summarization method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: in response to a situation that a scene rule is triggered, determining an asynchronous action rule contained in the scene rule; writing timing messages corresponding to the asynchronous action rules into a first message queue; sending a calling instruction to the corresponding asynchronous equipment according to each asynchronous action rule; writing a calling result message returned by the asynchronous equipment into a second message queue; performing message consumption on the timing message in the first message queue and the calling result message in the second message queue, and determining an execution result of each asynchronous action rule; and summarizing scene execution results according to the execution results of the asynchronous action rules. According to the method, a reliable and efficient mode with overtime management can be provided to obtain the execution result of the asynchronous action rule, and the reliability and stability of result summarization are enhanced.

This invention discloses a simulation evaluation method for automotive infotainment system (IAS) automated testing equipment, belonging to the field of equipment simulation testing technology. The method includes collecting operational test data, processing it using a deep convolutional neural network model to obtain test task data, constructing a test action sequence, mapping the test action sequence to control commands, and optimizing the control commands according to a preset action rule library. The method drives the IAS automated testing equipment to execute the control commands to complete the test operation, records the operation response time and interface state change data during the test, calculates the motion trajectory deviation value, and calculates the test accuracy score. When the test accuracy score is less than a preset threshold, optimized parameters are added to the preset action rule library, and a new test is triggered. This invention uses the TCP / IP communication protocol to achieve precise control of the testing equipment and continuously optimizes the test strategy by dynamically updating the preset action rule library, thereby improving the efficiency of IAS automated testing.

The application discloses a motion spatiotemporal perception quality evaluation method based on text rule prompt learning, and mainly solves the problems of great evaluation limitation and low accuracy rate in the prior art.The implementation scheme is as follows: action videos of different fitness methods are collected and labeled according to requirements to divide training set and test set; based on the existing CLIP model, a motion spatiotemporal perception quality evaluation network based on text rule prompt learning is constructed; time process clues of action rules are mined, and video action features and rule text features which have a direct influence on evaluation results are extracted; the video action features and the rule text features are subjected to multi-modal fusion; the training set is used to train the motion spatiotemporal perception quality evaluation network based on text rule prompt learning; test data is input into the trained quality evaluation network to give an action score and a result of whether the action is standard.The application reduces the cost of action quality evaluation, improves the accuracy of evaluation, and can be used for fine-grained sports fitness action quality evaluation.

The invention discloses an automatic red team penetration testing method, system and equipment based on partial order planning, and the method comprises the steps: firstly extracting state information of a target system, constructing an action rule, extracting an action, and then defining a target space, a state space and an action space of an attack graph; reasoning an attack purpose in a target space through a recursive mode by applying a partial order planning reasoning engine, adding actions capable of realizing the attack purpose and updating sequence constraints between the actions, and constructing a complete attack plan and an attack graph capable of realizing the attack purpose; and constructing an atomic attack behavior evaluation model in combination with DREAD and CVSS to carry out threat evaluation on each attack path in the attack graph, carrying out screening by adopting an attack path screening algorithm to obtain an optimal attack path, and carrying out automatic penetration testing to verify the network security of the target system. The automatic red team penetration test process is realized, the attack steps can be automatically executed to efficiently verify the security state of the target network, and the penetration test efficiency and accuracy are remarkably improved.

The present invention provides a navigation method, device, robot and storage medium implemented based on a knowledge-guided mapless navigation model. The mapless navigation model is obtained by training a pre-constructed DDPG algorithm model based on multiple action rules in a knowledge system, and has stronger generalization ability compared with using only the DDPG algorithm for navigation. During the training process, fusing the guiding action instructions and the policy action instructions can reduce the randomness of action selection of the DDPG algorithm model, so as to quickly obtain valuable data, improve the learning efficiency, and in an environment with sparse rewards, the mobile robot can interact with the environment under the guidance of knowledge instead of randomly, avoiding falling into local optima and ensuring easy convergence in an environment with sparse rewards; and inputting both the policy action instructions and the combined action instructions obtained by fusion into a preset loss function reduces the uncertainty of the corresponding loss function when using only the DDPG algorithm, thus making the learning process more stable.

The problem of ensuring safety and work efficiency for the actions of a control target is addressed in an environment in which different types of control systems coexist. A safety control server controls the actions of respective control targets in differing systems and includes an action adjustment unit that, for each control target of differing control systems, outputs to a control target an action adjustment instruction for adjusting the action of that control target when a contention occurs in which the action of that control target deviates from an assumed control result. An action plan adjustment unit, when the adjustment of the action satisfies a prescribed condition, adjusts an action plan indicating an action rule for achieving a function and avoiding a contention in a control target; and a safety rule adjustment unit adjusts a safety rule indicating an action rule for avoiding a contention in the control target.