1209 results about "Data migration" patented technology

The invention discloses a laser processing parameter autonomous generation system and method based on digital twinning, and relates to the field of digital twinning, and the method comprises the steps: collecting and preprocessing processing data in real time through a multi-source sensor; processing and analyzing the task instruction by using a natural language, extracting a constraint condition and forming a structured demand; a processing parameter candidate set is generated through a Transform model in combination with historical data transfer learning, and virtual processing is performed by means of multi-physics field simulation; an improved non-dominated sorting genetic algorithm is adopted to dynamically optimize the parameter weight, and a global optimal parameter combination is obtained through digital twin iteration verification; and after full-process virtual processing verification and task demand comparison, parameters are adaptively corrected, and model parameters are continuously optimized according to physical and simulation data deviation after actual processing. The method has the advantages that the digital twin is used as a core, multi-source real-time data, the AI algorithm and multi-physical field simulation are fused, and autonomous generation, multi-target optimization and virtual-real closed-loop iteration of laser processing parameters are achieved.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

Provided in the present disclosure are a task scheduling method applicable to a large language model (LLM), and a computing device, a storage medium and a computer program product. The method comprises: when determining that a target task processing node currently executing a target task meets a resource reporting condition, a target second task scheduler sending a target running resource of the target task processing node to a first task scheduler; when determining that the target running resource meets a task migration condition, the first task scheduler determining a migration task processing node from among a plurality of task processing nodes, and sending node information of the migration task processing node to the target second task scheduler; and the target second task scheduler determining the migration task processing node on the basis of the node information, determining the target task currently executed by the target task processing node, as well as task data of the target task, and migrating the target task and the task data in batches to the migration task processing node for execution, so as to obtain a task processing result of the target task.

The invention discloses a memory data migration method and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: determining the access heat of a storage device accepting the access of a computing node based on a memory access task executed by a switching controller and an address mapping table, so as to determine a source device and a candidate data unit, according to the access parameter of the candidate data unit and the state parameter of the storage device, calculating a performance score after the candidate data unit is stored in the storage device so as to determine the data unit to be migrated and the target device, so that cold and hot data identification from the perspective of a computer fast interconnection memory heterogeneous system is realized; the rationality of data migration is improved on the basis of quantitative performance scores; according to the method, the to-be-migrated data unit is controlled to be migrated from the source device to the target device, and the address mapping table is updated, so that the data migration task is prevented from influencing the host work of the computing node, and a cold and hot data migration scheme without being perceived by the host in the computer fast interconnection memory heterogeneous system is realized.

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention relates to the technical field of data storage, and discloses an intelligent hierarchical storage method and system for cold and hot data of a solid state disk, and the method comprises the steps: collecting spatial position change data and vibration state data, and carrying out the preprocessing to obtain a multi-dimensional feature data set; performing quantification processing on the influence degree of the spatial position change data and the vibration state data to obtain an interference weight value; constructing a joint feature matrix based on the interference weight value and the historical access record, and predicting hotspot data distribution; if the matching degree of the hotspot data distribution and the current storage layout is lower than a preset matching degree threshold value, migrating the high-frequency access data to a low-delay area to obtain an optimized storage layout; when the access response time is larger than a preset time threshold value, log data are generated, and delay load features are extracted; and carrying out secondary adjustment on the optimized storage layout to obtain a final dynamic storage scheme. According to the method, dynamic optimization of the storage performance of the solid state disk can be realized, and delay loss caused by cold and hot data mismatching is reduced.

The invention discloses a cross-device memory dynamic complementary utilization method based on CXL protocol interconnection and a computer system. The method comprises the following steps that an ECC module of a memory detects data in the data transmission process; when a data error is detected, the error is positioned by the ECC module, the error counting module corresponding to the position is updated, and an error report is generated; when the counting value of the error counting module reaches a set threshold value, the area corresponding to the position is marked as a failure area; evaluating the states of all memories based on the spatial dimension, the health dimension, the time delay dimension and the load dimension, and selecting a target memory according to an evaluation result of the states of the memories; distributing a new logic address for the failure area based on the error report and a selected target memory; the logic address is transmitted to a target memory through a CXL protocol, and an address mapping table is updated at the same time; and after the mapping is successful, migrating the data of the failure area to the healthy area of the target memory by the system through a CXL protocol.

The embodiment of the invention discloses a distributed data transfer method and device based on fault prediction and a medium, belongs to the technical field of data migration, and solves the problem that when a distributed system breaks down, the timeliness of task completion is seriously influenced. Comprising the following steps: performing health degree evaluation and stable operation duration prediction on nodes in a distributed system through a preset fault prediction algorithm to obtain a node fault prediction result; wherein the fault prediction result at least comprises a node in which a fault is predicted to occur, fault occurrence time and a to-be-transferred task corresponding to the node in which the fault is predicted to occur; performing priority analysis on the to-be-transferred tasks based on the time sequence diagram neural network to obtain task priorities; determining a target node based on the multi-dimensional features corresponding to the nodes and the association relationship between the nodes; and transferring the task to be transferred to a target node through a preset multi-stage progressive transfer strategy according to the node fault prediction result and the task priority.

The invention relates to the technical field of cloud storage, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a load and message queue regulation-based migration method, device, equipment and medium. Monitoring message backlog amount and consumption rate to generate message queue state information, generating migration regulation and control parameters based on the two types of state information, determining concurrency level and issuing rhythm by combining object size classification information, reducing issuing frequency and preferentially scheduling small objects when the message backlog amount is increased and oversize objects exist, and distributing the tasks to the working nodes according to the concurrency level and the issuing rhythm to execute migration, and adjusting migration regulation and control parameters based on execution state updating information. According to the method, concurrency and rhythm are regulated and controlled in real time, object size classification is combined, the problems that traffic is uncontrollable and throughput is affected by large files are solved, and efficient and stable data migration is achieved.

The invention discloses a load balancing optimization method and device, electronic equipment and a storage medium, and relates to the technical field of load balancing optimization, and the method comprises the steps: carrying out the differentiation processing of a data migration task according to a file storage threshold value, and combining a migration performance index and server resource occupation information collected in real time, the task allocation strategy is adjusted through the dynamic task allocation algorithm, so that the reasonable allocation of the files with different sizes and the dynamic load optimization in the migration process are realized. The technical problems that in the prior art, server overload and resource idling coexist due to a single migration server or a static task allocation strategy, high delay and data inconsistency risks are generated due to network congestion and uneven task allocation in the migration process, and the overall performance and reliability of a system are affected can be solved. The technical effects of optimizing the load balance degree, reducing overload and idle conditions of the server, reducing migration delay, ensuring data consistency and improving the overall performance and reliability of the system are achieved.

The embodiment of the invention provides a model reasoning cache management method based on software and hardware collaboration, which comprises the following steps: a user side sends a model access request, executes reasoning calculation, and stores a newly generated key value cache in a video memory cache layer; in the video memory cache layer, updating the access popularity corresponding to each unit-level key value cache, and sorting according to the access popularity from high to low to form a first popularity sequence; and updating the hierarchical access popularity corresponding to each hierarchical key value cache, and sorting according to the hierarchical access popularity from high to low to form a second popularity sequence. And when the space utilization rate of the video memory cache layer reaches a set threshold value, determining and evicting a unit-level key value cache and / or a hierarchical key value cache to be evicted based on the first heat sequence and / or the second heat sequence. The cross-layer data migration efficiency is optimized by utilizing the characteristics of a multi-level memory and through mechanisms such as cache elimination prediction and the like, and efficient utilization of a video memory, reduction of reasoning delay and improvement of the throughput capacity of a system are realized.

The invention belongs to the technical field of artificial intelligence chip and robot control, and relates to a heterogeneous calculation and control fusion chip control system and method, a robot and a storage medium. A GPU, an NPU, a BPU and an MCU are integrated in a central large core cluster, a communication network subunit and a memory control subunit are matched, and a low-delay data channel is constructed in a chip, so that perception, cognition and control instructions can be directly interacted, the cross-chip communication delay is reduced, and the real-time performance is improved. The three-level cache module is accessed through the memory control subunit, high-bandwidth sharing and centralized scheduling are achieved, data migration is reduced, the bandwidth bottleneck is relieved, and the complex task processing efficiency is improved. A trust root subunit is arranged in each unit, chip-level trusted verification and access isolation are provided, and the reliability under key action control and security sensitive scenes is enhanced.

Methods, systems, and devices for data management are described. A data management system (DMS) may receive a request to migrate data from a source data storage entity to a target data storage entity managed by the DMS. The DMS may verify that the data from the source data storage entity is compatible with one or more constraints associated with the target data storage entity and may identify one or more properties of the source data storage entity. The DMS may create the target data storage entity in accordance with the one or more constraints and properties of the source data storage entity. The DMS may provide a user with access to the target data storage entity, and a migration tool may be used to migrate the data to the target data storage entity. On completion, the DMS may commit the migration and activate the target storage entity.

The invention discloses a data migration method and device, computer equipment and a storage medium, and realizes a data migration processing mode oriented to an unstable network environment by combining neural network prediction, decision tree anomaly recognition and a distributed cache mechanism. Therefore, the data transmission stability and the migration task completion rate in the cross-network environment are obviously improved. Firstly, a pre-trained neural network is utilized to carry out deep prediction on a real-time network state, and a potential performance reduction trend is identified in advance, so that a migration process can actively adjust a path or a strategy before a risk occurs. And secondly, by performing decision tree identification on a data packet sending sequence, the system can position a potential interruption point based on fine-grained time sequence characteristics, and quantify the risk in an interpretable manner, so that migration control can be accurate to a specific data packet level. Finally, under the condition that the risk is unacceptable, the system automatically activates a distributed cache mechanism, and the durability and the restorability of the data are guaranteed in a redundant copy storage mode.

The invention provides a data migration method which can be applied to the technical field of artificial intelligence, and relates to application of a large model in the fields of information security and financial science and technology. The data migration method comprises the steps that in response to a migration request for migrating target data in a first database to a second database, a target agent is called to split the migration request into N migration tasks, the database structures of the first database and the second database are different, and N is an integer larger than or equal to 2; calling a target agent to sequentially execute N migration tasks based on a preset tool set, so as to migrate the target data in the first database to a second database; wherein at least one non-target tool in the preset tool set is masked in advance based on the to-be-executed target migration task, and the target agent is configured to perform tool calling based on the masked preset tool set. The invention further provides a data migration device and equipment, a storage medium and a program product.

The invention discloses a cross-heterogeneous database data migration method and system, and the method comprises the steps: building a multi-data-source connection among multiple data sources, and enabling the multi-data-source connection to be used for representing the incidence relation between a source database and a target database; obtaining a source data table based on the migration sql, and querying source data corresponding to the source data table from a source database; converting the source data based on a preset model mapping rule to obtain target data; determining a target database into which the source data is migrated according to the multi-data source connection, and writing the target data into the target database to realize data migration; and performing data verification on the target database according to the checking rule to obtain a migration result, and generating a result report according to the migration result. According to the method, the source data is converted into the target data through the preset model mapping rule, migration obstacles caused by structural differences among heterogeneous databases are effectively solved, the consistency of the data is ensured through the checking rule, and the accuracy of data migration is improved.

The invention discloses a memory management method and related equipment, and relates to the technical field of data storage, the method comprises the following steps: based on access information of memory pages of a target operating system, determining a dynamic popularity score of each memory page; on the basis of the current memory pressure level and the dynamic popularity score of the target operating system, executing a corresponding hierarchical data migration strategy so as to migrate different memory pages to the corresponding main Swap storage space or auxiliary Swap storage space; adjusting a compression algorithm applied to the auxiliary Swap storage space based on the current central processing unit utilization rate of the target operating system; and detecting real-time states of the main Swap storage space, the auxiliary Swap storage space and the physical memory, and adjusting a hierarchical data migration strategy and a compression algorithm based on a detection result. Through dynamic popularity perception, hierarchical data migration, adaptive compression regulation and cross-layer collaborative optimization, the memory utilization rate, the exchange efficiency and the overall system stability can be improved under different system loads.

The invention provides a cold and hot data dynamic migration method and device, a storage medium and computer equipment. Multi-dimensional feature data corresponding to business data to be migrated is obtained. And then inputting the multi-dimensional feature data into a preset intention prediction model to obtain the access probability of the business data in a future period of time, and accurately quantifying the expected popularity of the data. And then obtaining a current access probability threshold value, and performing cold and hot storage migration on the business data according to a storage area where the business data is located according to comparison between the dynamically calculated access probability and the access probability threshold value. In the process, the future access probability of the data is accurately predicted through the extracted multi-dimensional feature data, so that the change of a service access mode is dynamically adapted, the dynamic balance between the storage cost and the query performance is realized, and the flexibility and the adaptivity of cold and hot data migration are improved.

The invention relates to a cold and hot data migration method and device, computer equipment, a readable storage medium and a program product, and relates to the technical field of software development. The method comprises the steps of obtaining customer information in response to a trigger operation for a front-end application; inputting the customer information into a pre-trained customer behavior prediction model to obtain a customer behavior prediction result; comparing the customer behavior prediction result with a cold and hot data knowledge graph to obtain associated data corresponding to the customer behavior prediction result and a predicted cold and hot data type of the associated data; generating a migration strategy for the associated data according to the actual cold and hot data type of the associated data and the predicted cold and hot data type; the migration strategy is used for migrating the associated data to a first memory where hot data is located or migrating the associated data to a second memory where cold data is located. By adopting the method, the identification accuracy of cold and hot data can be improved.

The invention discloses an AI-based distributed big data intelligent storage management method, and relates to the technical field of storage management. The method comprises the steps of generating an initial feature matrix by obtaining observation data of all storage devices in a distributed storage cluster, and inputting the initial feature matrix into a pre-training life prediction model to obtain residual life; equipment with the service life lower than a first safety threshold value is judged as high-risk equipment, and data of the high-risk equipment is included in a to-be-migrated source data set; the devices with the service life higher than a second safety threshold value are included into a to-be-migrated target device set; and generating a migration plan according to the source and target data sets and executing migration. By predicting the residual life of the equipment, advanced identification and active data migration of the high-risk equipment are realized, the data loss risk is reduced, and the utilization rate of the health equipment and the overall cluster storage resource efficiency are improved.

The invention belongs to the technical field of data storage, and particularly relates to a cold and hot data intelligent hierarchical storage and migration method based on deep reinforcement learning, which comprises the following steps of: recording basic original information, analyzing service calling dependency and time sequence association, and establishing a dynamic data relation graph so as to capture change characteristics of a data access mode; the method comprises the steps that firstly, value penetration calculation and migration cost prediction are combined, a migration instruction is generated through reinforcement learning so as to optimize a data migration strategy, and finally the distribution and migration effects of storage resources are optimized through atomization migration and a feedback closed-loop mechanism, so that efficient utilization of the storage resources and remarkable improvement of system performance are achieved. The method not only can dynamically adapt to the change of a data access mode, but also can continuously optimize a migration strategy through reinforcement learning, thereby improving the overall performance and resource utilization rate of a storage system.

The invention discloses a three-normal-form automatic modeling method and system, electronic equipment and a storage medium, and the method comprises the steps: constructing a structured domain knowledge base, and injecting three-normal-form design experience for an RAG framework; business semantics are clarified in real time in combination with user demand input and multi-round dialogue interaction of model questions; a double-stage AI recommendation engine is adopted to match user requirements and knowledge base semantic vectors, a high-precision model change scheme is generated, automatic normal form optimization is conducted on a recommendation scheme, DDL scripts, ER diagrams, data migration schemes and the like conforming to the normal form are dynamically output, integration is conducted in a sandbox environment, and finally closed-loop automatic modeling of'requirement-design-verification 'is achieved. The method is used for solving the problems that traditional data modeling is low in efficiency and high in error rate and cost rate, the problem that model knowledge updating needs full-amount or increment fine tuning, and the cost is high, and the problem that business semantic understanding is insufficient in three-normal-form modeling is solved.

The invention discloses a data migration method and system based on business object mapping and a storage medium, metadata of a source system and a target system is obtained, and the metadata contains a data table group, a field and a field attribute; respectively integrating the data tables with the incidence relation in the two types of systems into a source business object and a target business object based on business semantics; and establishing a mapping relationship between the source business object and the target business object, migrating the data in the source business object to the target system based on the mapping relationship and the conversion rule, and verifying the migrated data. The efficiency and accuracy of data migration are improved, the consistency of a migration structure and business semantics is ensured, and semantic deviation and configuration errors caused by a traditional table level migration mode are reduced. And a migration scene of complex service data between heterogeneous systems can be better adapted.

The invention provides a self-adaptive transformation and migration method and system for an internal website, and relates to the technical field of website migration. Comprising the following steps: constructing a hierarchical credential adaptation architecture, establishing a dynamic authority-data mapping mechanism, realizing knowledge base-data migration linkage and deploying an adaptive security protection chain. The method is used for realizing collaborative improvement of compatibility, security and knowledge service efficiency of an internal website in a credential environment through hierarchical credential adaptation, dynamic permission and data association, knowledge base linkage and self-adaptive security protection; besides, through cooperative work of the hardware driving adaptation module and the software interface conversion module, automatic identification of hardware features and interface features, precise analysis of adaptation requirements and dynamic generation of configuration files are realized. The compatibility of an internal website to different models of servers, autonomous controllable components and software interfaces in a credential environment is remarkably improved, the adaptation period is shortened, and stable operation of the system is guaranteed.

The invention discloses a heterogeneous database migration strategy generation method and device and a medium, and the method comprises the steps: respectively obtaining structures and constraint conditions of a source database and a target database, carrying out the structured conversion of the structures and the constraint conditions, and generating a structured parameter set; performing risk analysis on the target database constraint condition to generate a migration verification data strategy; acquiring a migration verification data set according to the migration verification data strategy; in a preset isolation environment, executing a data migration operation on the migration verification data set, and recording behavior parameters generated in a data migration process; performing risk analysis on the behavior parameters to generate risk assessment parameters; and according to the risk assessment parameter, matching a corresponding migration strategy type from a preset migration processing rule set to generate a migration strategy parameter which can be directly applied to formal migration. The defects that in the prior art, manual experience is relied on, and risk discovery is lagged are overcome, and the certainty and overall efficiency of the migration process are remarkably improved.

The invention discloses a distributed storage data active migration method and system based on intelligent health prediction, and relates to the technical field of distributed storage system operation and maintaining.The method comprises the steps that multi-dimensional health state indexes of all nodes in a distributed storage cluster are periodically collected; calculating the health score of each node through a multi-time scale dynamic model based on the health state indexes; when the health score of any node is continuously lower than a preset active migration threshold value, marking the node as a high-risk node, and triggering an active migration process for all data copies on the node; constructing a multi-target optimization cost function for the to-be-migrated data, and selecting a candidate node with the lowest comprehensive cost from the healthy node pool as a migration target according to the cost function; and migrating data on the high-risk node to a selected target node at a resource-aware dynamic rate, atomically updating metadata after data migration succeeds, and then asynchronously cleaning old data copies on the high-risk node.

The invention discloses a coating quality prediction and optimization method and system based on multi-modal data fusion, and the method comprises the steps: S1, fusing coating parameters, environment data, equipment states and historical quality data, and generating a global feature matrix; s2, inputting the matrix into a GBDT model containing physical prior constraints, and outputting a quality risk probability and a key index; s3, target parameters are generated through a genetic algorithm, Q-Learningg and Pareto screening; s4, performing closed-loop feedback on updated data; s5, cross-line adaptation is achieved through transfer learning and domain adaptation; and S6, iteratively training the model in batches. The system comprises a data fusion module, a quality prediction module, a parameter optimization module, a closed-loop control module, an overline adaptation module and a self-evolution module. The method aims at solving the problems that traditional coating overline parameters are difficult to reuse, small sample modeling cost is high, prediction is separated from process logic, and parameter adjustment lags, so that overline adaptability and prediction stability are improved, small sample cost and energy consumption are reduced, and the method is suitable for high-end coating scenes such as automobiles and aviation.

The invention discloses a garbage collection optimization method and device, equipment and a medium. The invention relates to the technical field of solid state disks, which comprises the following steps that: a global invalid data bit chart is established in a host memory, each table item in the global invalid data bit chart corresponds to a physical block, and a bitmap bit in each table item corresponds to a physical page in the physical block; bit values corresponding to the bitmap bits comprise a first bit value and a second bit value; responding to a data updating event through an access acceleration engine so as to dynamically update the bit value of the corresponding physical page in the global invalid data bit graph; and when garbage collection is triggered, screening the physical page of which the bit value is the second bit value in a to-be-collected source physical block according to the global invalid data bit chart and the access acceleration engine to perform validity verification and data migration, and skipping the physical page of which the bit value is the first bit value. According to the invention, the garbage recycling efficiency is improved.

The invention belongs to the technical field of data processing. The invention discloses a matrix multiplication calculation task processing system, method and device, a storage medium, a program product and a chip, and the system comprises a storage module which is used for storing a preset weight matrix in advance; the linear transformation control module comprises a finite-state machine used for matrix multiplication operation scheduling and is used for generating an input request signal so that the matrix multiplication calculation task processing system can receive an input matrix; the operation module is used for executing matrix multiplication operation on the input matrix and a preset weight matrix to obtain a matrix multiplication operation result and generate an output matrix; and the data flow control module is used for controlling a plurality of groups of data flows formed by the input data in the input matrix according to rows to sequentially enter the operation module according to the input request signal until all the data in the input matrix execute and complete the matrix multiplication operation. The problems that in the prior art, a matrix multiplication accelerator lacks flexibility in a linear layer implementation task of artificial intelligence hardware, the data migration cost is high, and the energy efficiency ratio is low are solved, and the method is suitable for self-defined extension based on an RISC-V instruction set architecture, can be used as a coprocessor of an RISC-V processor, and can be used as a coprocessor of the RISC-V processor. And linear layer calculation in the neural network is accelerated, especially in an artificial intelligence task, the calculation efficiency can be remarkably improved, the power consumption can be reduced, and the real-time processing requirement can be met.