877 results about "Response time" patented technology

The invention relates to the technical field of cooperative control, in particular to a multi-machine cooperative control method and system for a bridge cable hoisting device, and the method comprises the following steps: collecting tension change characteristics through a distributed sensor array, extracting indexes through a sliding window algorithm to generate a response time window, and generating a cooperative time window through time sequence overlapping and speed trend adjustment. Calculating a compensation value by combining inertial parameters and acceleration to generate a trigger window, detecting tension and displacement difference, inputting the tension and displacement difference into a dynamic threshold function for screening and fusion, outputting a synchronous trigger condition, and generating a synchronous control instruction by encoding a device number and a tension change rate after timestamp alignment. According to the method, multi-node tension synchronous acquisition and normalization processing, dynamic response generation through a sliding window, boundary matching action triggering adjustment through an overlapping algorithm, response lag correction through inertia compensation, tension and displacement difference real-time detection, dynamic threshold screening and triggering condition fusion and timestamp alignment packaging instruction consistency are carried out; and the cooperation precision and the system robustness are improved.

The invention relates to the technical field of data storage, can be applied to business scenes such as medical health and financial science and technology, and discloses a multi-Pool storage resource adaptive writing method which comprises the following steps: monitoring available storage space, read-write input / output rate, average response time and data access frequency of each Pool; dynamically adjusting the current write-in weight of the Pool according to the monitoring result; converting the current write-in weights of all Pool into probability values, and constructing a discrete probability distribution table; selecting a target Pool through a pseudo-random number generator according to the probability distribution table; and writing the to-be-stored data into the target Pool. According to the method, the running state of each Pool is continuously monitored, and the writing weight is adjusted in real time, so that the problem of resource allocation rigidity under a fixed weight strategy is avoided; in combination with a discrete probability distribution table constructed based on normalized weight and a pseudo-random selection mechanism, selection of the target Pool has certain load balancing elasticity, and data can be more effectively guided to be written into the Pool with light load and excellent performance.

The invention relates to the technical field of state monitoring, in particular to an inner package production data real-time monitoring and processing system which comprises a running state acquisition module, a performance deviation evaluation module, an abnormal behavior recognition module, a task scheduling optimization module and a load balancing optimization module. According to the invention, through a cooperative acquisition mode of operation data such as temperature fluctuation, pressure change and flow velocity stability, real-time perception of dynamic collection and high-frequency change of the operation state is realized, and by means of a cross discrimination strategy of distribution uniformity and response time, non-representative data segments are eliminated, and the accuracy of performance abnormity identification is improved. Through time coupling analysis of gradient tracks and offset, abnormal behavior fragments are locked in advance, forward recognition of trend instability is achieved, intervention windows are dynamically screened, actual effect and synchronization of response are ensured, a real-time matching mechanism based on loads and demands is adopted, task priority and processing channel distribution are optimized, and intervention efficiency and scheduling adaptability are improved.

The invention relates to the technical field of artificial intelligence, in particular to a scene application operation maintenance platform based on an artificial intelligence model, which comprises a reasoning state sensing module, a load balancing scheduling module, an intelligent resource allocation module, a deployment configuration adjustment module and a scene feedback integration module. According to the method, by sensing and reasoning process resource consumption and response time migration trend, abnormal tasks caused by resource fluctuation can be accurately recognized, the state monitoring sensitivity is improved, task allocation records are analyzed to construct conflict structures, the resource conflict recognition and visualization capability is enhanced, the model operation level and the GPU vacancy rate are combined, a task migration path is optimized, and the task migration efficiency is improved. Node resource balance is realized, storage frequency, memory margin and network delay are fused during deployment, migration stability is guaranteed, drift tasks are identified through response logs and call records, an operation and maintenance list is generated, operation and maintenance accuracy and multi-scene matching capability are improved, and reasoning task stability and resource scheduling intelligent level are enhanced.

The invention relates to the technical field of computers, discloses an intelligent scheduling method and system for server cluster load balancing, and aims to solve the defects of the existing server cluster load balancing technology in response lag, non-uniform resource utilization rate, service quality guarantee, global optimization capability, fine-grained state perception and scheduling decision. The method comprises the following steps: collecting server state and request feature data, constructing a cluster state and service capability model, and predicting a load trend; and generating an optimal routing strategy by using deep reinforcement learning and multi-objective optimization, and issuing adjustment request distribution. The system comprises a data acquisition module, an application request feature acquisition module, a state sensing and modeling module, a load prediction module, an intelligent scheduling decision module and an instruction execution module. By adopting the technical scheme, the resource utilization rate can be improved, the response time can be reduced, the throughput can be improved, the system stability and elasticity can be enhanced, and the operation cost and energy consumption can be reduced.

The embodiment of the invention provides a voice conversation method and system, electronic equipment, a storage medium and a program product. One method is implemented as follows: a voice dialogue stream (which can be an inquiry voice stream input by a user for a target commodity) of a user is subjected to streaming response, a plurality of dialogue text segments are generated in a segmented manner in the streaming response process, and the dialogue text segments are output in real time after being generated and stored in a first cache queue; and when it is detected that the reply text fragment is stored in the first cache queue, the stored reply text fragment is converted into a reply voice fragment in real time and played. The streaming response may be performed by an agent. Visibly, according to the response of the voice dialogue stream of the user, the dialogue is played while being generated, the waiting time from text to voice synthesis can be greatly shortened, and therefore the response time can be shortened.

The invention relates to the technical field of protection device testing, in particular to a verification method of an intelligent comprehensive protection device, which comprises the following steps of: extracting starting time and response time in a tripping state transition sequence of the protection device, calculating execution delay and action segment length, and comparing and identifying an abnormal link based on a state number sequence; the method comprises the following steps: realizing analysis and standard reconstruction of a state logic structure, forming an ordered tripping path, introducing a long short-term memory neural network to carry out time alignment on a state change sequence and a current signal segment, identifying a potential mismatch or abnormal change trend in a state chain through linkage analysis of a state-current abrupt change position, and reconstructing a state path. According to the method, path continuity and dynamic adaptability are enhanced, a random forest is adopted to take device numbers, the number of jump segments and response residual errors as multiple inputs for training modeling, device scores are output through an integrated tree voting mechanism, and recognition sorting of abnormal frequency significant devices is achieved.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention relates to the technical field of online learning, in particular to an interaction-based mobile English learning system, which comprises a grammar error interaction recognition module, an error item pushing frequency regulation and control module, a learning load dynamic monitoring module, a learning difficulty dynamic adjustment module and an English skill interaction aggregation module. According to the method, the error event density change in the English learning interaction information of the user is dynamically monitored, the error event time sequence is established by using the error labeling field, the error type classification and the timestamp, and the learning content pushing frequency adjustment object is accurately determined according to the error density change amplitude, so that efficient error item accurate intensified training is realized; meanwhile, according to the synchronous change amplitude of the task response time increase rate and the answer accuracy change rate, the change condition of the learning load of the user is recognized in real time, the difficulty level of the learning content is dynamically and automatically adapted, dynamic regulation and control between the learning difficulty and the learning state of the user are formed, and the learning acceptability and effect of the user are improved.

The invention discloses a multi-queue scheduling method based on total and incremental reasoning overhead merging, which comprises the following steps of: firstly, calculating the total resource demand and memory overhead of a task according to the input length and the size of a predictor model in the process of performing total reasoning on a reasoning task; distributing tasks to queues with different priorities according to the total resource demand in combination with response time requirements and task importance; dynamically scheduling tasks according to a priority queue sequence, adjusting an execution sequence in combination with a system load, and preferentially executing tasks in a high-priority queue; distributing a time slice for each task, monitoring task execution time, reducing the priority of overtime tasks, moving the overtime tasks into a secondary priority queue, and triggering new task scheduling; and after preemptive scheduling, the key value cache of the latest scheduling task in the waiting queue is swapped from the accelerator memory to the CPU memory, and is swapped back to the accelerator memory before the task is re-executed.

Latency, response times, and efficiency improvements for data querying are provided herein, particularly in the context of querying large database systems and data tables from disparate data sources. There are provided systems and methods for automatic query and data retrieval optimization through procedural generation of data tables from query patterns. A service provider may utilize different computing services for query processing and data retrieval for different applications and services used by internal and / or external users. Instead of querying large database systems and numerous data tables, pre-aggregated data tables may instead be used and searched by procedurally generating such tables based on precomputation rules and query patterns. Once patterns have been identified in queries, corresponding data may be aggregated from data sources in a pre-aggregated data table. Query optimization rules may then be used to have these data tables queried in place of their original sources.

The present disclosure relates to the technical field of Graphics Processing Units (GPUs), and in particular to a GPU scheduling method and apparatus, and a storage medium. The method comprises: acquiring a migration command, the migration command being used for migrating a workload of a source Virtual Machine (VM) from a source GPU core to a target GPU core; on the basis of the migration command, establishing an association relationship between the source VM and a corresponding hardware identifier on the target GPU core; and on the basis of the association relationship after the migration, using the target GPU core to process the workload from the source VM. According to embodiments of the present application, while the VM is not shut down, the target GPU core is used to process the workload from the source VM, and thus the workload of the source VM is migrated from a high-load source GPU core to an idle target GPU core, so that the thermal migration between multi-core GPU cores can be achieved, the load between a plurality of GPU cores can be balanced, the pressure of a high-load GPU core can be reduced, the response time can be shortened, and the throughput rate can be increased.

The invention discloses a service request scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: selecting at least one candidate service instance for processing a business request from a server cluster based on the business request to be scheduled; obtaining computing power resources and network resources of each candidate service instance; selecting a target service instance from the candidate service instances based on the computing power resources and the network resources; calculating an optimal path from the current service node to the target service instance based on the network resources; and forwarding the service request to the target service instance based on the optimal path. According to the technical scheme provided by the invention, the response time and the processing delay can be reduced, and the service quality and the user experience satisfaction can be improved.

The invention discloses an unstructured storage hierarchical strategy optimization method based on machine learning, and relates to the technical field of data storage management, and the method comprises the steps: monitoring a file access event in real time, generating an access log, and extracting a multi-dimensional feature data set; utilizing the trained multi-dimensional decision tree model to distribute a corresponding storage hierarchy for the storage object to obtain a storage hierarchy decision; according to a storage level decision, distributing the storage objects to different storage layers, and carrying out resource configuration and storage operation; monitoring the access condition of the storage object in the new storage hierarchy in real time, and collecting file access performance, storage cost and response time to obtain feedback data; by monitoring the file access event in real time and extracting the multi-dimensional feature data set containing the basic attribute, the access behavior and the context information, efficient response of hotspot data and reasonable utilization of resources are ensured, and the overall performance utilization rate and the cost effectiveness of storage are remarkably improved.

The invention relates to the technical field of data exchange, in particular to a markup language-based data exchange method and system, and the method comprises the following steps: determining a processing priority, monitoring a load and adjusting a processing sequence, optimizing IO response time, recognizing a redundant field and evaluating a key risk, and executing key updating based on the number and complexity of data packet labels. And the tag analysis accuracy and efficiency are optimized. According to the invention, through accurate analysis of the markup language in the data packet, scheduling, transmission and analysis in the data exchange process can be optimized, and evaluation of the number of labels, the complexity of the nested structure and the field length can be realized, so that each data packet can dynamically allocate priority according to the characteristics of the data packet, thereby effectively improving the data transmission efficiency, reducing the delay and improving the data exchange efficiency. By monitoring the load and the transmission opportunity of the data packets in real time, the processing sequence of the data packets can be dynamically adjusted, and resource waste caused by overload of the data exchange system is reduced.

The invention relates to a response performance test method and system of a microswitch. The method comprises the following steps: inputting a multi-pulse trigger sequence to a microswitch contact and collecting contact response original data; performing digital filtering processing on the contact response original data to obtain contact response time sequence data; comprehensive response time parameters including contact mechanical action starting time, electrical initial contact time and electrical stable contact time are calculated according to the contact response time sequence data; performing temperature compensation and contact rebound characteristic extraction on the comprehensive response time parameter to obtain contact rebound characteristic evaluation data; according to the contact rebound characteristic evaluation data and the comprehensive response time parameter, the response performance stability of the microswitch under the high-frequency operation condition is calculated, and a reliability evaluation result is generated. According to the invention, the problem of time sequence measurement error caused by the influence of contact jitter in the traditional test is effectively solved, and the accuracy and consistency of contact response time measurement are remarkably improved.

The embodiment of the invention discloses a capacity expansion and shrinkage method, system and device for a database cluster, a storage medium and a computer program product. The method comprises the steps that response time information of the database cluster with the capacity to be expanded and shrunk for different types of data access requests is obtained; based on the response time information and the type of the data access request, determining a capacity expansion and contraction strategy for the database cluster; and based on the capacity expansion and shrinkage strategy, carrying out capacity expansion and shrinkage processing on the database cluster. Therefore, the capacity expansion and shrinkage strategy of the database cluster is determined by the database cluster according to the response time information of different types of data access requests and the types of the data access requests, so that a mechanism for automatically triggering the capacity expansion and shrinkage of the database cluster based on the business requirements of the database cluster and the business processing requirements of the database is realized; the intelligence of triggering capacity expansion and shrinkage of the database cluster and the efficiency of capacity expansion and shrinkage of the database cluster are improved.

The invention discloses a WebGL-based mass data particle system rendering method and device, and belongs to the technical field of computer graphic processing. The method comprises the following steps: firstly, selecting the type of a particle emitter according to scene requirements, then constructing a primitive object based on points or quadrangles, and configuring three-dimensional coordinates and texture parameters; aggregation rendering is carried out on a single-frame primitive array through a WebGL vertex shader and a fragment shader, and the drawing calling frequency is reduced; the method comprises the steps of dynamically adjusting particle attributes, including position compensation, color gradient and explicit-implicit control, determining a rendering form and playing time of an animation according to requirements of an actual application scene, and supporting fine configuration of a single particle, including texture animation, dynamic scaling and rotation parameters. When a large amount of data is processed, on the premise of ensuring the rendering quality, the rendering efficiency can be remarkably improved, the consumption of a memory and GPU resources can be reduced, the response time of the system can be optimized, and meanwhile, the interaction experience of a user can be improved.

The invention discloses a path isolation method, electronic equipment, a storage medium and a product, and relates to the technical field of network communication, and the method comprises the steps: obtaining the first time consumption of each path in a plurality of paths, the first time consumption being used for indicating the read-write response time consumption of the path; determining a target path in the plurality of paths based on the first time consumption of each path in the plurality of paths; and in response to the fact that the first deviation frequency corresponding to the target path in the first preset time window is greater than the preset deviation frequency, isolating the target path, thereby solving the technical problem that a path with poor link quality cannot be isolated in a related scheme, achieving the purpose of isolating a path with poor link quality but not having read-write overtime or read-write failure, and improving the reliability of the path. And the influence of the path with poor quality on the disk performance is avoided.

The invention relates to the technical field of response time testing of electro-hydraulic proportional valves, and discloses a response time testing method and system of an electro-hydraulic proportional valve, and the method comprises the steps: collecting multi-point pressure, step response, pipeline length and medium parameters, and unifying the time reference to obtain an original pressure sequence; constructing an inverse filter spectrum to remove damping interference so as to recover a sharp peak value; lagging offset is calculated according to pipeline and medium parameters, time delay compensation is carried out, and an alignment sequence is obtained; intercepting a non-stationary segment to eliminate residual delay deviation; constructing a response dynamic data set, extracting harmonic frequency domain representation, and reconstructing a pressure sequence through inverse transformation and superposition of compensation factors; and calculating a deviation integral quantity, adjusting a dynamic compensation parameter, performing weighted correction, and outputting an optimized reconstruction response sequence. According to the method, accurate reconstruction of the real pressure response of the proportional valve can be realized, so that the accuracy and the reliability of response time testing are improved.

The invention relates to the technical field of load balancing optimization, in particular to an intelligent computing center load balancing and energy-saving optimization method and system, and the method comprises the following steps: obtaining task execution duration and resource state data, calculating a multi-dimensional index, carrying out the weighted analysis, dynamically outputting a regulation and control scheme, and achieving the load balancing and energy-saving optimization of an intelligent computing center. According to the method, response time comparison based on a service level protocol is introduced, a task execution duration distribution structure is clearly recognized, the abnormal task recognition precision is improved, and the judgment accuracy of an overload trend is enhanced by comparing resource utilization rate changes of adjacent periods in the aspect of node state monitoring; in the task allocation process, the available memory of the node and the resource adaptation degree are combined, real performance load performance is obtained through energy efficiency and throughput data cross analysis, task allocation better conforms to the actual energy consumption level, the resource waste risk is effectively reduced, the response flexibility of a scheduling strategy to system load changes is enhanced, and the task allocation efficiency is improved. And the task execution efficiency and the energy efficiency distribution accuracy are improved.

Systems and methods are provided to improve data retrieval from a cache memory by using semantic matching to retrieve data from the cache memory. The system includes a two-tiered cache system, with a first tier implementing “key-value” pairs, and a second tier that includes a table that is configured as an artificial intelligence (AI) search indexed source. When a new input does not have a matching “key” at the first tier, the system performs a semantic search at the second tier of the cache to determine if relevant data is stored in the cache. The current systems and methods increase the likelihood of obtaining data for queries from the cache memory, reduce the response time to the queries, improve search consistency, reduce computing resource utilization, improve system performance, and reduce costs.

The invention discloses a method and a system for realizing hosted API automatic testing by an API gateway, belongs to the technical field of computer software, and aims at solving the technical problems of how to realize non-intrusive and automatic API testing, ensuring that monitoring and verification can be directly carried out on an API flow level, improving the testing efficiency and the coverage rate and improving the testing efficiency. According to the technical scheme, the method comprises the steps of test case generation, wherein an interface structure is automatically analyzed based on an API document, and a basic test case set is generated; flow capturing: monitoring the actual calling condition of the API in real time, and recording the specific content of each request and the corresponding response data; carrying out flow playback: carrying out playback test based on the captured historical flow, and verifying the compatibility of the API; assertion verification: assertion judgment is performed on indexes of the return value and the response time through an assertion mechanism, and abnormity is found; and test report generation: aggregating test result data, generating a test report containing success rate, response time and error log detailed information, and supporting HTML / PDF / JSON export.

The invention relates to the technical field of real-time kernel performance testing of special chips, in particular to a special chip real-time performance evaluation method based on an FPGA (Field Programmable Gate Array), which comprises the following steps: (1) aiming at a real-time system on an ASIC (Application Specific Integrated Circuit) chip, setting an external interrupt source and an interrupt service program through a hardware interrupt controller; (2) two tasks with the same priority are created, scheduling of the tasks is achieved through time slice rotation, and a real dynamic load scene is simulated; (3) configuring an external FPGA (Field Programmable Gate Array) to generate a square wave signal to trigger external interruption of the ASIC chip, receiving a feedback signal, and connecting the FPGA and the GPIO through an external physical connecting line to form a hardware interruption triggering-response closed loop; and (4) realizing nanosecond timestamp capture through a counter built in the FPGA, acquiring time delay data, calculating interrupt response time, and transmitting the time delay data to a PC (Personal Computer) through a UART (Universal Asynchronous Receiver / Transmitter) to calculate a maximum value, a minimum value, an average value and a data jitter range of test data. The method can effectively solve the problem that the traditional internal closed-loop test cannot reflect the real interruption randomness and the dynamic load.

The invention relates to the technical field of data processing, in particular to a data processing method and processing equipment thereof.The data processing method comprises the following steps that on the basis of multi-source data to be processed, identification parameters and access timestamps of data sources are verified, access data are extracted through a data interface, and the data are stored in a temporary buffer area after the data type is verified to be matched with the format; and obtaining access verification data. According to the method, the authenticity and the timeliness of the data are ensured by checking the identification parameters and the access timestamps of the multi-source data, the data quality is improved, data connection is performed by using a security protocol, the protection strength of the data in the transmission process is increased, processor and memory resources are dynamically allocated, a resource allocation strategy is adjusted according to the task priority, and the resource allocation efficiency is improved. The data processing speed and response time are improved, the data processing process is optimized by adjusting the data processing parameters, the processing delay is reduced, and the data processing accuracy and speed are improved.

The invention relates to a method and a device for reducing response time delay of a deep learning model and a storage medium, which are applied to the technical field of artificial intelligence, and comprise the following steps: acquiring a historical load through sliding window sampling, acquiring initial load prediction data by using a dynamic joint prediction mechanism based on the historical load, obtaining an error compensation value through the corresponding actual load data sequence, and performing error compensation on the initial load prediction data to obtain final load prediction data; determining the total number of instances required for a period of time in the future based on the final load prediction data sequence; the number of instances which are currently operated is adjusted to match the total number of instances required in a period of time in the future, so that the response time delay of large-scale deep learning model reasoning work is reduced; through accurate workload prediction and resource scheduling, the response time delay of model reasoning can be effectively reduced, the achievement rate of a service level target is improved, and the service cost is reduced.

The invention relates to the technical field of machine learning, in particular to a cooling pump optimization algorithm of a central air-conditioning water chilling unit based on a knowledge graph, which comprises the following steps of: acquiring an alignment relationship between a pump start-stop signal and current response time, extracting an action section and a path node sequence to construct a logic sequence, and identifying control signal alternation and direction deviation to extract a path limit number, and screening response delay and a load fluctuation section to correspondingly adjust an identification area. According to the method, behavior mapping between the pump action and the signal time sequence is constructed, alternating signals and direction deviation fragments in a control chain are extracted, a path limiting identifier is generated to be used for restraining control interference, a trigger area is regulated and controlled in combination with matching of load response and an equipment action section, and an accessible path is screened to replace a limited section; the path structure is recombined, instruction chains are sorted, the matching capability of path scheduling and the response collaboration of linkage nodes are enhanced, and the control process is promoted to be kept continuous and stable in a differentiated state.

The global event monitoring unit (100) comprises, characterized in that, an accumulator module (110) which comprises at least one mode to receive an input signal for at least one event, a logger module (120) to measure time interval for said at least one event, and a handler module (132) to enable generation of interrupts for the at least events logged by the logger module (120). The handler module (132) is shown within the logger module (120) but the same is possible to be separate. The at least one mode comprises a Port signal Block (102), internal signal Multiplexer (Mux) block (104), a Module Special Function Register (SFR) block (106) and a host (Central Processing Unit (CPU)) (108). The timing measurement between global events is performed precisely along with optimization of the system response time using the measurement results and the system performance evaluation becomes easy and effective.

The invention relates to a fine-grained dynamic unit task scheduling strategy, aims to solve the problem of real-time scheduling of DAG (directed acyclic graph) tasks on a multi-core platform, and belongs to the field of real-time embedded system task scheduling. Static priority distribution is adopted in an existing priority preemptive scheduling method, the parallel execution degree of tasks is limited, and response time is possibly increased. According to the method, the DAG vertex is divided into a plurality of unit nodes, the independent priority is allocated to each unit node, execution is performed with the shorter worst case execution time (WCET), the priority is dynamically adjusted, the limitation of the static priority is broken through, the task parallelism degree is improved, and the calculation limit of the worst response time (WCRT) is optimized. Meanwhile, a unit node priority distribution algorithm is designed, and the task scheduling efficiency is further improved.

Methods and systems are presented for reducing computer power consumption and speeding system response times by dynamically generating and deploying one or more small language models (SLMs) to facilitate automated interactions with users. A context is derived for a chat session based on an utterance submitted by the user and other contextual information associated with the chat session. A SLM is generated specifically for the chat session based on the context. The SLM can be generated by extracting one or more portions of an internal structure of a large language model (LLM), or by merging two or more pre-generated SLMs. The SLM is deployed to generate content for the chat session. When it is detected that the context has changed, the SLM can be updated by incorporating additional parameters from the LLM to continue facilitating automated interactions with the user during the chat session.