973 results about "Historical record" patented technology

The invention relates to a multi-modal digital employee reception system and method based on edge calculation, and relates to the technical field of artificial intelligence, and the system comprises a multi-modal data collection module, an edge calculation module, a data fusion and decision module, a trigger activation module, an emotion recognition module, an interaction response module and a behavior prediction module. The method comprises the following steps: acquiring voice, image and action information of a user through a high-resolution camera, a microphone array and an infrared sensor; locally processing data by using an edge calculation module, and extracting voice, vision and thermal imaging features; a Transformer model is dynamically optimized through Test Time Training (TTT), multi-modal data are dynamically fused in combination with a multi-head attention mechanism, a comprehensive decision vector is generated, the system can accurately judge the emotional state of a user, user behaviors are predicted based on a fused Token sequence and historical records, personalized services are actively provided, and by dynamically adjusting a trigger threshold value and a weight distribution mechanism, the emotion state of the user can be accurately judged. The false triggering rate and the missed triggering rate are reduced, and the stability and the robustness of the system are enhanced.

The invention discloses a knowledge conversion and fusion processing method and system for massive power grid operation data, and the method specifically comprises the steps: obtaining the attributes, including the alarm type, timestamp and severity, of equipment granularity based on alarm event data collected by a power distribution automation terminal, and carrying out the data cleaning and standardization processing, converting the multi-source heterogeneous alarm data into uniform event granularity representation; fusing the alarm event data and the operation event data, carrying out space-time alignment and causal association on the two types of events based on timestamps and equipment ID attributes, constructing a cross-data-source event semantic link network, and carrying out association analysis on a semantic relationship between the events; and according to a fault diagnosis result, historical records related to fault processing are extracted from the operation event knowledge base, similar fault processing modes are matched, and a specific fault processing scheme is generated in combination with factors of severity and influence range of the current fault.

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

The invention relates to the technical field of vacuum electric furnace monitoring, and discloses a state monitoring system suitable for a vacuum electric furnace. A multi-source sensor array of the system collects multi-dimensional physical signals such as temperature distribution, pressure change and vacuum degree fluctuation in a furnace in real time; a furnace cavity feature reconstruction module extracts sampling point feature parameters and correlates coordinates to construct a three-dimensional dynamic feature field; the process anomaly analysis module calculates a process deviation degree in combination with a preset reference parameter, and marks an anomaly coordinate area; the state transition evaluation module analyzes an abnormal trend according to historical records and predicts a state transition path and rate; the collaborative regulation and control decision-making module generates a multi-stage vacuum maintenance compensation strategy and a heating power regulation gradient scheme according to the multi-stage vacuum maintenance compensation strategy; and the running log feedback module records a strategy execution process, and associates the three-dimensional feature field data to generate a state tracing log. The system can realize comprehensive monitoring of the state of the vacuum electric furnace, accurate abnormity identification, trend prediction, cooperative regulation and control and state tracing, and helps to improve the operation management level of the vacuum electric furnace.

The invention provides a tunnel surrounding rock blasting excavation parameter setting method and system based on a neural network, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining geological characteristic data, blasting excavation historical records and excavation contour data of surrounding rock; sparse subspace clustering processing is carried out according to blasting excavation historical records to obtain a feature data set; performing stress state analysis according to the geological characteristic data, the excavation contour data and the characteristic data set to obtain a dynamic stress state and topological change data of the surrounding rock; performing association rule mining according to the blasting excavation historical record and the feature data set to obtain an optimized decision rule; performing nonlinear finite element analysis according to the dynamic stress state and the topological change data to obtain an equivalent material model of the surrounding rock; and according to the equivalent material model and the optimization decision rule, a blasting excavation parameter setting scheme is obtained. According to the invention, dynamic response analysis is carried out by using the neural network, and real-time monitoring of the surrounding rock under different blasting conditions is realized.

Methods, systems, and computer program products for collaboration systems. A method for identifying selected portions of a set of content objects for use in generating a large language model (LLM) prompt comprises: identifying a content management system (CMS) wherein collaboration activities occur over time and over content objects maintained in the CMS, and wherein the CMS maintains a historical record of occurrences of the collaborator activities over the content objects. Upon receiving a natural language query from a CMS collaborator, reducing a larger corpus of content objects to a smaller corpus of context passages that are used in an LLM prompt. The smaller corpus of passages is formed using a two-phase reduction scheme whereby firstly, selected constituents from the larger corpus of content objects are identified based on CMS metadata; and then, rather than considering the larger corpus, instead considering only the selected constituents when generating the LLM prompt.

A device may receive, at a server system, a free-form query. The device may select two-way communication history records from a database associated with the free-form query to identify selected history records. The device may generate individual summaries of individual history records of the selected history records by processing a set of corresponding individual history records using a chunking algorithm, constructing language responses from outputs of the chunking algorithm using a large language model, and aggregating the language responses. The device may process the language responses using the large language model to generate an individual summary for corresponding individual history records.

The invention provides a self-adaptive recommendation method based on dynamic strategy optimization, and aims to solve the problems of memory bank update lag, retrieval strategy lag and insufficient long-tail article coverage of an existing recommendation system. A time sequence behavior coding module, a dynamic memory storage bank, a strategy control module and a heterogeneous fusion module are designed; and real-time optimized personalized recommendation is realized. Specifically, a time sequence behavior coding module is used for generating a user state vector with time decay, a dynamic memory storage library automatically updates historical records based on a mixed scoring mechanism, and a strategy control module outputs a learnable retrieval number, a weight coefficient and a fusion parameter. The heterogeneous fusion module realizes cross-channel feature enhancement through a dual-channel attention mechanism; according to the method, the problems of preference drift and long-tail recommendation are effectively solved, meanwhile, the online updating efficiency is improved, and more accurate and diversified recommendation results are provided for users.

The invention discloses a dynamic scheduling method and device for mass call requests and a medium, and relates to the technical field of information management, and the method comprises the steps: receiving mass call request information through an information sending device, carrying out the language processing to generate an analysis data set, and obtaining the request types through deep learning classification; performing multi-objective optimization on the analysis data set to generate a dynamic call queue; constructing a time sequence prediction model based on historical records, predicting a call peak, carrying out resource scheduling analysis, and formulating a scheduling strategy; and generating a feedback result after executing the scheduling strategy, synchronizing the feedback result to the call center, and updating the strategy according to response information to realize intelligent dynamic scheduling. The technical problem that in the prior art, the resource scheduling efficiency of the call center in the mass request processing process is low is solved, and the technical effect of optimizing the resource scheduling efficiency of the call center is achieved.

The invention relates to the technical field of ocean salvage ship anchors, and discloses an ocean salvage ship anchor method which comprises the following steps: delimiting a ship anchor preliminary search range according to historical records and environmental data of a ship anchor falling area; acquiring the accurate position of the ship anchor by using a multi-mode composite positioning technology, wherein the multi-mode composite positioning technology comprises a positioning process based on sonar detection, magnetic sensing and underwater image recognition; through a multi-ship collaborative optimization scheduling system, a salvage task is allocated according to ship functions and real-time positions, a salvage ship and a salvage tool are controlled according to an optimized path, and ship anchor salvage operation is executed; and adjusting the salvage path and the operation parameters based on the real-time environment data until salvage is completed. A multi-mode composite positioning technology is adopted, and through combined application of sonar detection, magnetic sensing and underwater image recognition, the technical effect of quickly and accurately positioning the ship anchor in a complex underwater environment is achieved.

The invention discloses a process parameter optimization control method and system for an automobile injection molding part, and belongs to the technical field of data processing, and the method comprises the steps: collecting sample data to train a feedforward neural network based on the historical processing record of a target injection molding part, and constructing a product quality prediction plug-in; performing parameter fluctuation influence analysis according to the historical processing record, and constructing a fluctuation influence analyzer; determining product demand characteristics of the target injection molded part in combination with the automobile use scene; and by taking the product demand characteristics as expectations, optimizing the process parameters by using the product quality prediction plug-in and the fluctuation influence analyzer, and outputting the optimal process parameters for processing control. The problems of large product quality fluctuation and low production efficiency caused by insufficient injection molding process parameter control precision in the prior art are solved. By combining historical records, product quality prediction plug-ins and fluctuation analysis, precise optimization of process parameters is realized, the product quality is stabilized, and the controllability and efficiency of the production process are improved.

The invention discloses an electrical equipment defect identification and alarm method and system based on deep learning. The method comprises the following steps: synchronously collecting and registering visible light and infrared thermal imaging images on the surface of power equipment, and constructing an instance segmentation network comprising a lightweight feature extraction network, a multi-scale feature fusion network and a frequency domain mask prediction branch; enhancing the diversity of training samples by adopting a generative adversarial strategy; based on the graph neural network, analyzing the incidence relation between the defects and the equipment topology and historical records, and deducing the defect causal relation and the risk level; generating interpretable alarm information including the thermodynamic diagram, the natural language report and the maintenance suggestion; real-time detection and deep analysis are realized by adopting an end-side cloud collaborative architecture; and the system performance is continuously improved through a closed-loop optimization mechanism. According to the method, high-precision defect detection under multi-modal data fusion is realized, the robustness and interpretability are high, and the operation and maintenance intelligence level of power equipment is remarkably improved.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

The invention relates to the technical field of data retrieval, in particular to an information technology retrieval consultation system and method. The method comprises the following steps: acquiring user input data; extracting modal key information of the user input data to obtain multi-modal key semantic information data; performing data preprocessing on the multi-modal key semantic information data to generate standard user input data; performing historical retrieval information retrieval on the standard user input data, and when a historical retrieval record is retrieved, performing accurate portrait construction on the standard user input data to generate a user accurate retrieval portrait; and when no historical record is retrieved, performing retrieval reasoning on the standard user input data to generate a user fuzzy retrieval portrait. By combining multi-modal information extraction, accurate and fuzzy portrait construction, cross-context inheritance, dynamic knowledge fusion and sentiment analysis optimization, the problems of low retrieval precision, incomplete information coverage and poor user experience in the prior art are solved.

The invention discloses an intelligent user permission conflict detection method and system, and relates to the field of electrical digital data process.The method comprises the steps that a service flow diagram is constructed based on historical records; extracting a service flow direction sequence of the permission roles from the service flow direction graph, and calculating service association degrees among the permission roles according to the service flow direction sequence; grouping and clustering the authority roles according to the business association degree to obtain a plurality of authority role clusters; calculating a service boundary value of each permission role cluster; determining a target permission role cluster to which the target permission role belongs from a plurality of permission role clusters; calculating the business association strength between the permission role currently owned by the target user and the target permission role; when the business association strength exceeds a business boundary value, generating a first permission allocation control instruction; and when the business association strength does not exceed the business boundary value, generating a second permission allocation control instruction. The method and the device are used for solving the problem of authority conflict in a newly-added business scene, so that the security of the system is improved.

The invention provides a multi-source data management method based on machine learning and a related device, and the method comprises the steps: obtaining a multi-source data set in an emergency command rescue scene, carrying out the data preprocessing of the multi-source data set, obtaining a standardized data set with a unified data form, extracting a data element information set from the standardized data set, and carrying out the processing of the data element information set. And calling a pre-trained blood relationship modeling model to perform association analysis processing on the data element information set, generating a blood relationship description set among the data entries in the standardized data set, and generating a data blood relationship tracking result based on the blood relationship description set. The data consanguinity tracking result comprises a historical record of the full life cycle of the data and cross-data entry dependency graph information. According to the invention, the method can provide a governance support with a data source capable of being checked, a processing process capable of being traced and a clear dependency relationship for emergency command rescue, enables a rescue decision to be developed based on reliable data consanguinity information, and effectively improves the support capability of data governance for the rescue decision.

The invention discloses a network space security risk intelligent identification method and system. The method comprises the following steps: deploying traffic collection equipment at key network nodes and boundary equipment, and collecting network traffic data and log data; extracting network behavior features and log key fields, and constructing a data feature model; performing vectorization processing based on the data feature model, performing traffic classification by using a deep neural network, and identifying a device type and a device identifier; carrying out noise reduction, redundant information removal and standardization processing on flow data in the data feature model to generate standardized data and storing the standardized data in a vector database; entity extraction is carried out on the standardized data, a knowledge graph is constructed, data distribution is analyzed through a Gaussian mixture model, the probability of abnormal behaviors is calculated in combination with historical data, and attack risks are calculated based on Bayesian decision; and generating an alarm according to the calculated attack risk, tracing the historical record of the attack risk in the knowledge graph, sending a blocking instruction, and identifying the security risk of the cyberspace.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a task processing method, device and equipment based on multi-agent cooperation and a medium. And constructing a sub-task dependency graph, sequentially scheduling and executing the sub-tasks, and performing parameter completion to obtain the completed sub-tasks. And tracking the execution progress and the historical record of the completion subtask through a preset progress agent, and generating an executable operation decision by using a preset decision agent. If the task execution does not reach the expected effect, feeding back difference information and a correction suggestion, adjusting an operation decision and generating an updating operation; if the task achieves the expected effect, task completion information is sent to the progress agent, and the state is updated to be task completion. According to the method, the perception accuracy is improved, a task dependence tracking and feedback correction mechanism is provided, and a cross-application automatic task is successfully realized.

The invention relates to a gas leakage early warning method and system for a chemical industry park. The early warning method comprises the following steps: acquiring real-time temperature and pressure data of a reaction kettle area and a storage tank area; obtaining a historical leakage record of a pipeline joint, and determining a diffusion path prediction boundary; extracting a wind direction and a wind speed in a diffusion path prediction boundary, and determining space-time distribution characteristics; analyzing space-time distribution characteristics, determining an overlapping region and setting concentration monitoring nodes; identifying the concentration contribution ratio of the reaction kettle area diffusion characteristic and the storage tank area diffusion characteristic; integrating the operation data of the pipeline joint diffusion characteristics and the loading and unloading area diffusion characteristics, and determining a diffusion path deviation correction coefficient; combining the deviation correction coefficient with the humidity data of the peripheral diffusion characteristics of the cooling tower; and updating diffusion parameters of the separation tower area diffusion characteristics and the public engineering area diffusion characteristics according to a source intensity estimation result. According to the invention, accurate prediction and early warning of gas diffusion in a complex chemical device area are realized, and the safety management level is improved.

The invention provides a security event association analysis and question answering method and system and a medium, and the method comprises the following steps: context-aware query completion: receiving an original query input by a user, obtaining a historical record of a current dialogue, and forming a context enhanced query according to the original query and the historical record of the current dialogue; dynamic task identification: outputting a prediction task model according to the context enhancement query in combination with a dynamic task identification strategy; multi-dimensional retrieval: performing multi-dimensional retrieval on the prediction task model to obtain a final knowledge context packet; and knowledge-driven response generation: generating a knowledge-driven response based on the knowledge context packet, and outputting a final security analysis report after the generated content passes verification. According to the method, efficient, accurate and explainable intelligent association analysis of the multi-source heterogeneous security data is realized by constructing a multi-dimensional knowledge base, designing a dynamic task recognition mechanism and realizing context-aware query completion.

The invention discloses a PDF (Portable Document Format) conversion multiplexing method and device, computer equipment and a storage medium. The method comprises the following steps: extracting metadata features, logic structure features and visual layout features of a PDF document; performing dynamic partitioning according to the type of the PDF document to obtain a plurality of document blocks, and performing hash processing on each document block to generate a corresponding hash value; performing first-level comparison on the PDF document and a PDF document in a historical record according to the metadata features; performing second-level comparison on the PDF document and a PDF document in a historical record according to the logic structure feature; performing third-level comparison on the hash value of each document block in the PDF document and the hash value of each document block in the PDF document in a historical record, and determining whether different document blocks exist in the PDF document or not; if yes, incremental conversion processing is conducted on the difference blocks, the difference blocks obtained after incremental conversion are spliced with the converted parts of the unchanged content in the corresponding PDF document in the historical record, and a complete conversion result is formed. The method greatly improves the conversion efficiency.

The invention discloses a data processing method, a storage medium, electronic equipment and a program product, and relates to the technical field of computers.The method comprises the steps that firstly, data access information is extracted from a solid state disk, and a currently accessed data block is determined; obtaining an access historical record table corresponding to the currently accessed data block, wherein the access historical record table comprises a data block access historical record table and a data page access historical record table; determining target pre-read data based on the weights of the plurality of data blocks and the weights of the plurality of data pages in the access historical record table; and if it is detected that the position of the target pre-read data is not in the pre-read position linked list, loading the target pre-read data into a cache of the solid state disk. Compared with the prior art, the method has the advantages that by maintaining the access history record table and recording the access frequency and weight of each data block and data page, the next data block and data page which are most likely to be accessed can be predicted more accurately, and then the pre-reading hit rate is increased.

The invention discloses a digital twin autonomous optimization system based on building structure quality monitoring. According to the system, structure state data, environmental influence data, material performance data and historical record data are comprehensively collected through a multi-source data collection module; feature data such as a structure degradation rate and a material aging coefficient are extracted through an edge calculation preprocessing module; a digital twinborn body is constructed through a multi-layer fusion module, geometric layer fusion achieves crack grading labeling, physical layer fusion corrects parameters such as material elasticity modulus, and behavior layer fusion updates the structural damping ratio to reproduce dynamic behaviors. Outputting a structure health index, residual life prediction and maintenance suggestion through an autonomous optimization decision module; and in combination with a dynamic iteration updating module, the system is ensured to continuously adapt to the structure change through validity evaluation and parameter adjustment. The accuracy of building structure quality evaluation and the scientificity of maintenance decision making are improved, and the method is suitable for full-life-cycle management of various buildings.

The invention discloses an equipment diagnosis and maintenance method and system based on a large language model, a terminal and a storage medium. The method comprises the steps that multi-source heterogeneous data of target equipment is collected and preprocessed; constructing and continuously updating a historical case vector database containing equipment knowledge; state data of target equipment are monitored in real time, after fault diagnosis is triggered, the most relevant knowledge fragment is retrieved from the historical case vector database according to real-time abnormal data, and a diagnosis context is constructed; inputting the diagnosis context into a large language model for reasoning, and outputting a fault diagnosis report and a maintenance scheme; the fault diagnosis report and the maintenance scheme are presented to a user, and user feedback is collected to optimize the system. According to the method, real-time data, historical records and structured knowledge of equipment can be automatically integrated, a dynamic knowledge base is constructed, accurate and efficient diagnosis of equipment faults is realized by utilizing the powerful reasoning capability of a large language model, an executable maintenance scheme is automatically generated, and the maintenance efficiency is improved.

The invention provides a multi-level page table traversal acceleration method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: inputting a virtual address into a path predictor and a dynamic weight hierarchical cache, and collecting an instruction access proportion, a TLB hit rate and a pre-fetching success rate in real time; performing hash calculation according to a preset bit of the virtual address through a path predictor, and querying a historical record table to obtain a prediction path and a confidence level so as to determine a query range; when the cache is hit, scores of hit entries are calculated, priority ranking and elimination decision making of the cache entries are carried out, and meanwhile, the cache entries are managed by adopting a hybrid replacement algorithm; under a preset condition, starting a prefetch operation through an adaptive prefetch engine, calculating and dynamically expanding a prefetch step length, and preventing memory bandwidth contention through two-stage monitoring; when the instruction access proportion, the TLB hit rate and the prefetching success rate meet conditions, an instruction priority mode is activated, and scores of hit entries are updated by adjusting related calculation parameters.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

Systems and techniques are described for tracking and providing access reports for individual pieces of data managed by a data storage service. A service may generate and store a record of operations performed on a piece of data, such that may be classified as containing sensitive or important data, in a data store. The record may link representations of users and the operations performed by those users to instances of the piece of data, as it is found in one or more data objects within the data store. The data store may link other instances of the piece of data and other operations performed on the piece of data to the first instance of the piece of data. The service may access the data store to produce a history record of the various instances of the piece of data and operations performed on those instances of the piece of data.

The invention discloses an AI marketing advertisement putting optimization system and method fusing multi-dimensional data, and relates to the technical field of data processing, and the system comprises a historical record obtaining module which extracts the historical putting record of a target optimization block; the terminal data interaction module interacts with the advertisement putting management platform based on the historical putting record to obtain multi-dimensional terminal data; a grid division module performs grid division on the target optimization block to generate a putting grid and time sequence label data; the mapping model construction module constructs a label mapping model based on the grid time sequence label data, the historical delivery record and the multi-dimensional terminal data; the advertisement task processing module generates an advertisement label cluster by using a label mapping model according to the advertisement task information; and the putting scheme optimization module realizes the optimal matching of the advertisement task and the label cluster through a multi-objective optimization algorithm, and outputs an advertisement putting scheme, thereby achieving the technical effects of improving the data utilization rate, providing predictive putting optimization and improving the putting income.

The invention relates to the technical field of data processing, and discloses a data processing method and system based on liquid leakage detection. The method comprises the following steps: acquiring a sensor signal and processing to obtain leakage basic data; analyzing signal changes to determine leakage types and influence ranges; matching the risk threshold to determine a risk level; associating historical records to analyze leakage laws; calculating a failure probability to generate a maintenance strategy; and constructing a running state diagram to form a hierarchical management report. According to the invention, leakage management is converted from a traditional passive response mode to a data-driven active prevention mode, optimal configuration of maintenance resources is realized, and the leakage prevention and control efficiency is improved while the maintenance cost is reduced.

The application relates to the technical field of supply chain management, in particular to a supply chain management system based on big data, which comprises a data integration module, a demand prediction module, an inventory optimization module, a collaborative scheduling module and a feedback optimization module. The system realizes supply chain global state analysis and demand prediction through multi-source heterogeneous data collection and a deep learning model, combines dynamic inventory adjustment and cross-regional resource scheduling optimization, and improves the supply chain efficiency. The feedback optimization module further generates optimized control instructions by analyzing and intervening in the time delay of historical records. The application can improve the response speed and resource utilization efficiency of the supply chain and reduce the operation cost.