277 results about "Dynamic mapping" patented technology

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and particularly relates to a neural network large model efficient reasoning method based on multiple GPGPUs. The method aims to solve the problems of high communication overhead, non-uniform load, low resource utilization rate, high data transmission delay and the like among multiple processors. Dividing a calculation task into a plurality of sub-graphs through static analysis and mixed granularity partitioning of a model calculation graph; distributing the sub-graphs to the optimal GPGPU based on a weighted cost function in combination with heterogeneous resource perception and a dynamic mapping strategy; a global pipeline scheduling plan is constructed by using communication topology perception, and calculation and communication overlap are maximized; data are loaded in advance through a host side hierarchical caching and asynchronous prefetching mechanism, and transmission delay is hidden; multi-stream concurrent execution and event-based lightweight synchronization are adopted on each GPGPU, so that waiting overhead is reduced. According to the method, the reasoning delay can be remarkably reduced, the throughput and the hardware utilization rate are improved, and the method has good adaptivity and expandability.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention relates to the technical field of distributed computing and computing power scheduling, in particular to a computing power resource fusion method based on a distributed flow pipeline, which comprises the following steps of: disassembling a user computing power request into a flow pipeline unit for packaging computing logic, an input / output interface and a resource demand label; meanwhile, computing power types, real-time load rates, memory occupancy rates, network round-trip delays, geographic positioning and energy consumption data of cloud edge end nodes are collected, a hierarchical topology network framework is constructed based on the collected data, node computing power available values are calculated, an inter-node transmission cost matrix is generated, a fault probability prediction model is constructed, and a fault probability prediction model is constructed. And finally, analyzing a data dependency relationship of the pipeline unit through a four-dimensional joint decision engine, executing dynamic mapping, and preferentially mapping the high-computing-power demand unit to a GPU cluster node, so as to realize non-interruption reconstruction during pipeline topology operation. And the global computing power resource utilization rate, the task operation efficiency and the service stability are improved.

The invention discloses a text and image fused online comment toxicity detection and filtering method and system, and relates to the technical field of natural language processing, and the method comprises the steps: extracting a semantic vector of a comment text through a RoBERTa-Marge model; the visual features of the image are extracted through an OfficientNet-V2 model; aligning heterogeneous modal features by adopting a double-flow contrast loss function; self-adaptive decision making of culture sensitivity: loading a regional sensitive rule table according to a user IP address, and dynamically adjusting symbolic semantics; calculating an intimacy correction factor based on the social relationship between the publisher and the receiver; and performing context weighted toxicity scoring, calculating a user historical behavior weight, and outputting a final toxicity probability. According to the method, a deep dynamic mapping mechanism of text and visual features is constructed, heterogeneous features are extracted through RoBERTa-Large and OfficientNet-V2 double-flow architectures, semantic space alignment is forced by utilizing comparative learning, and the problem of image-text splitting detection in the traditional technology is solved.

The invention provides a chip security key protection method and system capable of resisting side channel attack, and relates to the technical field of chips, which comprises the following steps of: obtaining a key operation request, performing address space mapping based on a dynamic mapping rule to obtain encrypted key data, decrypting the encrypted key data, fragmenting key intermediate data, and allocating an independent processing channel to perform decoupling operation; and injecting a time-varying noise signal to cover the association between the key and the physical characteristics, and finally performing recombination and reverse conversion to generate a final key output. According to the method, side channel attacks such as power consumption analysis and electromagnetic analysis are effectively resisted, and the security of key storage and operation processes is improved.

The invention relates to the technical field of data import, in particular to an intelligent Excel data import method and system based on dynamic mapping, and the method comprises the steps that the system traverses all Excel fields for each database field, calculates the feature similarity and field matching degree of the Excel fields, and generates a field mapping template set according to all field matching results; reading the Excel content according to rows, cleaning each row of content into a standard value, performing quality scoring, and outputting a target format data structure set; screening a structure alignment data row and a field structure exception record set which conform to a threshold value; the system reads the structure alignment data rows line by line, processes the extraction value of each field and outputs a legal data set; and extracting a field sequence set, searching whether fields marked as primary keys or unique keys exist in the field sequence set, and combining the fields into a uniqueness judgment set to judge whether the currently written Excel data exists in the target database table.

The invention belongs to the technical field of artificial intelligence, is applied to the field of financial science and technology and the field of medical health, and discloses a rule engine configuration method, device and equipment based on machine learning and a storage medium. The method comprises the steps that historical calculation data and performance indexes in historical calculation tasks are collected, and a training data set is constructed; a machine learning model is trained, and algorithm parameters of the rule engine are dynamically optimized through the training data set; configuring an expression and a user-defined function of a rule engine, and realizing analysis and execution of scripts through JEXL (JavaScript Exchange Library); establishing a dynamic mapping relationship between the algorithm configuration library and the business variables, mapping algorithm variable names and business variable names, and generating configurable algorithm rules; and analyzing task load and resource requirements based on a pre-trained machine learning model, distributing system resources, and repairing abnormal behaviors in a calculation process. According to the invention, the problems of low processing efficiency, high maintenance cost, unreasonable resource allocation and poor expandability in the prior art are solved.

The invention provides a flow and form decoupling cooperative processing method, and relates to the technical field of business flow management, and the method comprises the steps: constructing a layered architecture, separating a flow control layer from a form service layer, defining a flow node jump rule by the flow control layer based on a BPMN standard, and dynamically defining a form structure by the form service layer through JSON Schema; a form identifier is declarated in the process definition file, a corresponding form template is matched through a dynamic mapping service according to context information, and the context information comprises a process instance state, a user role, a device type and a service type; establishing a data sandbox isolation mechanism, generating a unique data space identifier for each process instance, and managing data access authority through a multi-dimensional isolation strategy of a tenant level, a service level and a process instance level; deploying an event bus communication mechanism, triggering two-way communication between a process engine and a form engine through a predefined event type, and ensuring the consistency of cross-system operation by adopting a transaction compensation mechanism.

The invention discloses an electric power multi-protocol data conversion method and system based on object model dynamic mapping, and the method comprises the steps: receiving an original protocol message, recognizing a protocol type, extracting a logic name as a unique identifier of equipment, extracting an object identifier as a measurement point identifier, and extracting an electric energy array as a measurement point value; according to the protocol type and the equipment unique identifier, matching a corresponding object model template from a unified object model library; mapping the equipment unique identifier, the measuring point identifier, the measuring point numerical value and the acquisition timestamp obtained by analysis to corresponding fields in a template; and generating an object model data object conforming to a standard format, packaging the object into a message, executing an encryption authentication mechanism of a network layer and a transmission layer through the power Internet of Things security access gateway, and sending the message to an Internet of Things management platform. According to the invention, standardized, automatic and secure access of different protocol terminals is realized through protocol analysis and the unified object model library.

The invention provides a document adaptive conversion method and device based on a multi-modal large model and a medium, and belongs to the technical field of data processing. The method comprises the following steps: inputting a multi-modal document into a pre-constructed multi-modal large model, wherein the multi-modal large model comprises a multi-modal joint framework fusing a ViT visual model and an LLM model; using a ViT visual model and an LLM model to respectively extract multi-scale visual features corresponding to non-text content and semantic features corresponding to text content, and using a cross-modal attention mechanism to bidirectionally align the multi-scale visual features and the semantic features to obtain a multi-modal document; carrying out self-adaptive blocking on the multi-modal document by using a self-adaptive blocking strategy, and modeling a relative position relationship between blocks; establishing a dynamic mapping rule from a document element to an HTML tag, and mapping the multi-modal document into a front-end interaction component in combination with a natural language instruction of a user; and dynamically rendering the front-end interaction component in an on-demand mounting and resource isolation mode. The problem of insufficient document restoration capability in the prior art can be solved.

The invention discloses a distributed energy storage control method and system for a smart power grid. The method comprises the following steps: S1, collecting and preprocessing operation parameters; s2, local features are extracted and compressed, and a power grid feature set is constructed; s3, performing frequency domain separation on the power grid feature set, and performing decoupling reconstruction; s4, constructing a dual-channel control vector, and forming a mixed instruction matrix; s5, inputting the mixed instruction matrix into a sliding window adversarial mapping unit, constructing a state transition tensor through historical control records, and executing dynamic mapping suppression; s6, determining a target control behavior of the current time slice, outputting and synchronously executing charging and discharging instructions, and recording feedback data; and S7, network parameters and mask weights are updated according to feedback data, and a continuous control closed loop is formed. Accurate decoupling and cooperative control of high-frequency disturbance and low-frequency trend of the power grid can be realized, and response speed, control precision and energy utilization efficiency of energy storage scheduling are remarkably improved.

The invention discloses a dynamic mapping virtual mailbox multiplexing control method and device and a storage medium, and the method comprises the steps: creating a plurality of software queues in an RAM, taking each software queue as a software carrier of a logic mailbox, and dividing the logic mailbox according to message priority or CAN ID; when the idle physical mailbox is detected, dynamically selecting a message to be sent from all the software queues, and loading the message to be sent to the idle physical mailbox; and after detecting that the sending of the physical mailbox is completed and the interruption is triggered, selecting the next to-be-sent message from all software queues and loading the next to-be-sent message to the physical mailbox which triggers the interruption, so that the utilization rate of physical mailbox resources of the CAN controller can be effectively improved, the problem of scheduling starvation of low-priority messages caused by accumulation of high-priority messages is avoided, and the service life of the CAN controller is prolonged. The instant multiplexing of the mailbox and the fair and efficient message sending are realized, and the system resource utilization efficiency and the communication reliability are obviously improved.

The invention discloses a transformer substation global digital twinborn fusion system, which comprises a data fusion module, a model fusion module and an artificial intelligence fusion module, and is characterized in that the data fusion module is used for collecting multi-source data of a transformer substation, preprocessing the data and performing data fusion on the preprocessed data; the model fusion module is used for constructing a bidirectional dynamic mapping model of a transformer substation physical entity and a digital virtual body, constructing a multi-field model and constructing a model increment updating mechanism, and the artificial intelligence fusion module is used for correcting the deviation of an AI model, performing simulation verification on the output of the AI model and generating a visual alarm. According to the method, the technical problems of incomplete data fusion, poor model fusion collaboration and insufficient AI and digital twinning fusion depth in the existing transformer substation digital twinning system are solved, real-time bidirectional accurate mapping of the transformer substation physical entity and the digital virtual body is realized, and the operation and maintenance efficiency and the decision-making precision are improved.

The invention discloses an IO optimization method and device based on distributed storage, equipment and a storage medium, and relates to the technical field of distributed storage. Comprising the following steps: constructing a distributed metadata index layer between a client and a metadata server, and maintaining a dynamic mapping relationship between a path prefix and the metadata server, so that when the client initiates a file access request, a target metadata server can be directly positioned based on the mapping relationship; and whether the request is routed to the temporary metadata server for load balancing or not is dynamically determined according to the real-time load information of the temporary metadata server. Therefore, the problems of metadata request amplification caused by step-by-step analysis of a tree directory and non-uniform load of a metadata server caused by static directory binding in related technologies are solved; the technical effects that the metadata interaction delay is remarkably reduced, the throughput of the system in massive small file and burst IO scenes is improved, and a single metadata server is prevented from becoming a performance bottleneck are achieved.

The invention discloses a cross-platform instruction intelligent adaptation engine based on a large model, and relates to the technical field of instruction adaptation, and the cross-platform instruction intelligent adaptation engine is characterized in that an initial instruction generated by the large model and a target platform identifier are subjected to joint analysis to deeply deconstruct a core user intention contained in the initial instruction and the target platform identifier, and an intention-driven multi-vector retrieval mechanism is introduced; and dynamically calling context fragments highly related to the intention and the target platform from a knowledge base. And the context fragments and the initial instruction are intelligently recombined to construct a final cue word rich in platform adaptability knowledge, so that the generative large model is guided to directly output a final execution instruction accurately adaptive to the target platform. Therefore, by establishing a dynamic mapping and injection mechanism from the general intention to the specific platform knowledge, the general generation capability of the large model and the specific execution specification of the heterogeneous platform can be deeply coupled, so that the automatic and high-precision intelligent adaptation of the cross-platform instruction is realized.

The invention relates to the technical field of servers, in particular to a matching management method and device for device identifiers, and the method comprises the steps: constructing a target dynamic mapping table, and obtaining a target access peripheral device based on a media access control address of the target access peripheral device and topological information of a peripheral component interconnection bus; registering a context association relationship corresponding to the target access peripheral equipment in the target dynamic mapping table, and acquiring an actual equipment identifier of the target access peripheral equipment after the target access peripheral equipment is initialized; and determining a mapping relationship between the temporary device identifier and the actual device identifier according to the attribute information, and updating a context association relationship corresponding to the target access peripheral device by using the mapping relationship. Therefore, the problems of EID mapping failure, poor compatibility and high maintenance cost caused by equipment replacement or restart in a traditional BMC static management mode are solved, and the automation degree, reliability and cross-manufacturer compatibility of peripheral equipment management are remarkably improved through dynamic sensing and an intelligent mapping mechanism.

The invention discloses an agent scheduling and distribution system based on a user interaction scene, provides a page-level agent scheduling system for the first time, and breaks through the technical bottleneck that a traditional general AI model cannot adapt to a fine-grained scene. The method comprises the following steps: a function identification module, which is used for identifying a website or an application function module where a user is currently located; the page analysis module is used for analyzing the content structure of the current page and the user interaction context in the function identification module to obtain page features; the agent scheduling module is used for selecting and binding a corresponding agent from an agent resource pool according to the page features; and the life cycle management module is used for controlling the state storage, freezing or releasing of the intelligent agent when the page is loaded, switched or exited. The technical problems that although multi-agent cooperation is involved in the prior art, but most of the multi-agent cooperation is limited to back-end task scheduling, and dynamic mapping based on page granularity is not achieved are solved.

The invention discloses a regulation-work package bidirectional association and dynamic mapping method and system, and relates to the technical field of enterprise compliance management and task management informatization. The method comprises the following steps: constructing a standardized law and regulation library and a work package attribute library, disassembling a law and regulation file into compliance requirement points, and determining multi-dimensional attributes of a work package; forward mapping and reverse tracing binding of the compliance requirement point and the work package are realized through a mapping matching model; performing integrity, consistency and rationality verification on the mapping relation; monitoring the regulation change and automatically updating the mapping relation; and visually displaying the mapping relationship and monitoring compliance execution. The system correspondingly comprises a regulation management module, a work package management module, a bidirectional mapping module and the like. According to the method, closed-loop management and control of law and regulation requirements, task execution and compliance evidence are realized, the problems of unidirectional and static mapping and rough granularity in the prior art are solved, the compliance management and control accuracy and the dynamic adaptation capability are improved, and the compliance risk of an enterprise is reduced.

The invention discloses a mainboard function automatic detection method and device, and relates to the technical field of computer hardware detection. The method comprises: receiving a user instruction through a graphical interface to generate a test queue; distributing an independent thread for each test item to execute hardware detection, and dynamically mapping the position of a physical port; analyzing a test state in real time and updating interface feedback; comparing the actual hardware parameters with preset parameters; and hot update of the test script is supported. The device comprises a graphical interface module, a multi-thread execution engine, a scripted hardware detection module and the like. The mainboard function detection device can perform efficient, automatic and visual detection on the mainboard function, and is suitable for quality control of mainboard production, maintenance and research and development links.

Provided are a memory device and a method for dynamically mapping address thereof, in which the memory device includes a memory including a plurality of memory regions, an address register in which at least one of a memory device different from the memory device or a host stores a device address for accessing the memory, and an address translator configured to receive a first interrupt, and in response to the first interrupt, convert a physical address of the memory mapped to the device address.

The invention relates to the technical field of software, in particular to an automatic construction method based on dependency topology analysis and dynamic orchestration, which comprises the following steps of: reading and analyzing a predefined configuration file to construct a service dependency relationship graph; wherein nodes in the service dependency relationship graph represent services, and directed edges represent dependency relationships among the services. According to the method, the predefined configuration file is read and analyzed to construct the service dependency relationship graph, so that automation of dependency management is realized, and manual identification errors are avoided; through dynamic mapping of services and construction tasks, the problem of conversion from a logic architecture to physical execution is solved; based on collaborative optimization of topological sorting and a priority strategy, the construction efficiency and the resource utilization rate are improved, and time consumed by a critical path is shortened. Therefore, full-link automation from dependency identification and task mapping to intelligent arrangement is completed, and the efficiency bottleneck and error risk caused by manual intervention are effectively solved.

The invention discloses a BIM-based spatial structure and business structure data fusion method. The method mainly comprises the following steps: constructing dynamic hooking of a BIM model and a spatial structure; dynamic hooking of the BIM model and the business structure is constructed; and fusing data of the space structure and the service structure. According to the method, flexible fusion of space structure and service structure data is realized through a plurality of configuration methods of real-time bidirectional dynamic mapping and hierarchical fusion of BIM model components, space structure and service structure data, and bidirectional synchronous updating of the space structure and service data and automatic updating of a fusion tree can be triggered through monitoring of BIM model changes. And the management efficiency of space structure and service structure data is effectively improved.

The invention provides a memory management method and device based on dynamic mapping and a medium, and relates to the field of data processing. The method comprises the following steps: processing data in different data organization formats by adopting different memory management modes; storing the received data in a first format and then converting the received data into a second format for storage; defining a plurality of logic memory regions corresponding to different processing stages, and establishing a mapping relationship between the logic memory regions and the physical memory unit; and in response to the periodic trigger signal, updating the mapping relationship to circularly change the logic region corresponding to the physical memory unit. According to the method, the memory utilization rate is improved through dynamic loop mapping, read-write conflicts are solved through format conversion, and the system performance and flexibility are improved.

The invention discloses an AI business process automatic generation method based on AI model combinatorial optimization, and the method comprises the following steps: constructing a multi-level AI capability decoupling and reconstruction module, and carrying out the bottom-up hierarchical modeling and top-down modular decoupling, dynamic mapping of an AI atomic power layer, an AI modular production capacity layer, an AI general capability layer and an AI application business layer is realized; an elastic AI capability combinatorial optimization module is constructed, AI capability combinatorial optimization oriented to three dimensions of data, features and models is carried out based on service quality requirements, and multiplexing, combination and arrangement of AI capabilities are realized through evolutionary computation optimization driven by an agent model; and an AI business process automatic generation module is constructed, an AI service containerization deployment scheme is generated and optimized based on data-driven process mining and a hyper-heuristic algorithm, and AI business process automatic generation is realized. According to the invention, the adaptability and execution efficiency of the AI technology in a complex scene can be improved.

The invention discloses a multi-platform data distribution method and system based on dynamic mapping, and relates to the technical field of computer data distribution.The method comprises the steps that a source manuscript is obtained, a unified skeleton structure is established, and the source manuscript is embedded into the unified skeleton structure; a difference patch is generated when a manuscript operation node is triggered, a standard difference set is formed through standardization and serialization processing, and a dependency graph is constructed based on the set. And selecting a minimum patch subset meeting the target platform data requirement in the dependency graph through a genetic algorithm, and applying the minimum patch subset to the unified skeleton structure to obtain an updated manuscript instance. And mapping the update instance into a target platform data mode field by field, splitting the update instance into a difference patch sub-sequence according to a dependency relationship, packaging the difference patch sub-sequence into batches in combination with quota constraint, and distributing the difference patch sub-sequence after adding an idempotent order number and a version number. After distribution, receipt information is collected, and batch strategy parameters are dynamically updated. Repeated adaptation logic is avoided, cross-platform consistency and compliance are ensured, and distribution efficiency and expandability are improved.

The invention relates to the technical field of computer graphics and three-dimensional modeling optimization, in particular to a 3D model lightweight method and system based on point cloud hierarchical slicing and dynamic mapping mapping.When the 3D model lightweight method and system are used, the storage efficiency, the rendering performance and the visual fidelity are improved by establishing a point cloud and mapping bidirectional coupling mechanism, and the 3D model lightweight effect is improved. In the aspect of storage efficiency, an efficient geometric and texture association storage system is constructed by relying on a map curvature adaptive segmentation and dynamic slice binding technology, an excellent geometric and texture compression effect is realized, the storage cost of a high-precision three-dimensional model is greatly reduced, and in the aspect of real-time rendering performance, through a dynamic loading strategy during operation, the real-time rendering efficiency is improved. By combining efficient visibility screening acceleration and an intelligent resource preloading technology, the real-time scheduling efficiency of the model is greatly improved, and the working efficiency of scenes such as design iteration and model editing is remarkably improved.

The invention provides an AIGC generation method and system based on multi-modal content copyright traceability, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly constructing a copyright feature topology comprising an original creation material ownership core node, a creation evolution association edge and an authorization constraint surface, then anchoring the copyright feature topology to a parameter dimension of AI GC generation logic, and carrying out the copyright feature topology; a copyright generation logic anchoring relation is formed, a copyright generation logic symbiotic structure is generated, nodes, edges, surfaces and generated logic parameters form dynamic mapping and are adjusted in real time, the logic symbiotic structure is used for driving AIGC content generation, generated content feature dimensions carry mapping marks and are synchronously updated, finally, the mapping marks are analyzed, and the copyright generation logic symbiotic structure is generated. And reconstructing a dynamic mapping relation and inverting copyright feature topology to realize effective copyright traceability of the AIGC generation content.

The invention provides a dynamic mapping method in a closed environment and an automatic driving vehicle, and the method comprises the steps: obtaining an initial map of the closed environment, and dividing the initial map into a plurality of first cells; determining the occupancy probability of each first cell according to the initial map to obtain a first probability; acquiring environment sensing data through a sensor; updating the first probability according to the environment perception data to obtain a second probability, and determining a first information entropy of each first cell according to the second probability; and taking a first cell where the autonomous vehicle is located as a root node, determining an increment of the first information entropy as a reward function value of a Monte Carlo tree, searching and planning a driving path through the Monte Carlo tree, and updating the initial map according to the environmental perception data in the driving process. The method solves the problem that in the prior art, due to the existence of static and dynamic obstacles in a closed environment, automatic driving measurement is difficult to adapt to environment changes to achieve self-adaptive map information updating.