1215 results about "Data filtering" patented technology

An Artificial Intelligence (AI) based filter apparatus includes an input filter and an output filter protecting a generative AI model and preventing restricted content from being transmitted to user devices. When a user query is received, the input filter determines if the user query can be transmitted to the generative AI model by generating an input risk score for the received user query. If the user query is transmitted and a model query response is received from the generative AI model, the output filter determines an output risk score based on which the model query response may be transmitted to the user. The input filter and the output filter each include a pre-trained language model as a base with additional layers trained to estimate the corresponding risk scores.

The invention relates to the technical field of agricultural internet of things and environment intelligent control, in particular to a greenhouse environment adaptive regulation and control system based on artificial intelligence, which comprises an environment acquisition module used for acquiring multi-dimensional environment data in real time through a distributed multi-source sensor; the central controller is used for generating an optimized regulation and control strategy; the regulation and control execution module is used for driving execution equipment to carry out regulation; the central controller comprises a multi-source data fusion unit, an AI decision-making unit and a dynamic optimization engine which are respectively responsible for data filtering and fusion, generating an initial regulation and control strategy based on a space-time joint AI model, and reconstructing and optimizing the initial regulation and control strategy through a multi-target optimization algorithm. According to the method, the response real-time performance is improved through multi-source sensing and data fusion, predictive regulation and control and multi-parameter cooperation are achieved through the AI model, balance of energy consumption, growth and carbon emission is achieved in combination with multi-target optimization, and long-term self-adaption and strategy iteration of the system are supported.

The invention relates to the technical field of intelligent traffic management, and discloses a road traffic signal lamp management system based on big data, which comprises a data acquisition and fusion module for acquiring traffic sensor data, visual data, GPS track data and external environment data, generating unified traffic state data through space-time alignment, data filtering and multi-modal data fusion; and the flow prediction module is used for predicting the traffic flow of a single intersection and a region by using a time sequence model and a graph neural network based on the traffic state data generated by the data acquisition and fusion module. Through multi-modal data acquisition and space-time alignment, Kalman filtering, Bayesian inference and other technologies, deep fusion of different data sources is realized, the comprehensiveness and accuracy of data are significantly improved, dynamic changes of a complex traffic scene can be truly reflected, high-quality basic data are provided for subsequent flow prediction and optimization control, and a core technical support is formed.

A system for real-time data aggregation and analysis within an Internet of Things (IoT) network, consisting of: a multitude of IoT sensor nodes configured to collect data from multiple sources, each sensor node comprising a data acquisition unit for capturing data in real time and a local processing unit for preprocessing the captured data by performing data filtering, normalization and compression prior to transmission; a centralized aggregation server communicating with the multitude of IoT sensor nodes, the aggregation server comprising: a data aggregation module configured to receive, integrate, and synchronize pre-processed data streams from the multitude of sensor nodes, thereby enabling consolidated data storage; a timestamped metadata indexing unit coupled with the data aggregation module and configured to add metadata to each data entry, including a unique identifier, a geographic location, and a timestamp; an analysis engine integrated into the aggregation server, which includes the following: a data processing unit configured to perform real-time analysis of the aggregated data by applying pattern recognition and anomaly detection techniques and to generate processed results based on predefined parameters; a predictive analytics module that is connected to the data processing unit and configured to perform predictive modeling tasks by extrapolating trends from historical and real-time data; a communication interface connected to the aggregation server, configured to enable bidirectional data exchange with external monitoring devices, user interfaces, or remote management systems via secure communication protocols; and a dynamic load balancing unit embedded in the aggregation server, configured to dynamically distribute computing resources between the data processing unit and the predictive analytics module depending on data volume and complexity.

The invention discloses a three-dimensional point cloud data filtering method and system based on adaptive clustering segmentation and gradient compensation, and the method comprises the steps: carrying out the data preprocessing of three-dimensional point cloud data through employing a statistical filtering method, obtaining the preprocessed point cloud data, extracting the point cloud features in the preprocessed point cloud data, and obtaining the point cloud feature data; according to the invention, a function of establishing multi-dimensional features including point cloud density, spatial autocorrelation and local curvature features by using an adaptive clustering segmentation algorithm based on density spatial distribution and carrying out dynamic clustering segmentation on a ground feature-ground cluster is realized; and residual mixed point clouds can be further separated through a coarse-fine granularity grid grading processing strategy, so that ground point clouds are separated, and the technical limitations of high parameter dependence, insufficient terrain adaptability and poor real-time performance in the prior art are broken through; and the filtering precision and robustness in a complex scene are remarkably improved through multi-dimensional collaborative optimization, and the method is suitable for being widely popularized and used.

A method for annotating images to create a corpus for training a multi-task computer vision machine learning model is presented. The method comprises receiving, at one or more annotation specialist models, a plurality of images to be annotated. Via operation of the one or more annotation specialist models, pre-filtered annotations are generated for the plurality of images. Via operation of a data filtering and enhancement module, the pre-filtered annotations are filtered in accordance with predefined noise criteria so as to output candidate annotations for the plurality of images. The method further comprises, for each of one or more candidate annotations, selectively (1) storing the candidate annotation into the corpus as a final annotation for its associated image, or (2) adding the candidate annotation to its associated image using the one or more annotation specialist models and the data filtering and enhancement module for subsequent iterative annotation and filtering.

The invention discloses a data filtering and noise reduction method based on laser point cloud. According to the method, the multi-scale features are intelligently screened through the random forest algorithm, and the system can automatically generate the optimal filtering rule according to the local geometric characteristics of the point cloud without depending on manual parameter presetting. In a complex urban scene, a fine structure of a high-rise vertical face can be accurately reserved through curvature features, and sparse noise of a vegetation area is rapidly eliminated through a density threshold value. The data-driven adaptive mechanism not only reduces the manual intervention cost, but also greatly improves the scene generalization ability of the filtering strategy, so that the filtering strategy can still keep stable output in a dynamic environment. The dynamic interference is accurately positioned by combining the consistency verification of the motion trails of the multi-frame point clouds and utilizing the time sequence correlation analysis, so that the data reliability in a complex dynamic scene is remarkably improved, and a solid foundation is provided for high-precision three-dimensional reconstruction and real-time perception.

The invention discloses a power grid fault online identification system and method based on big data, and relates to the technical field of power systems, and the method comprises the steps: collecting multi-source data of a power grid system through a sensor; data filtering is performed through a hierarchical storage strategy, a data lake is constructed in combination with multi-dimensional data indexes, and multi-source data is stored in a classified manner; aggregating and converting data in a data lake by adopting a real-time stream processing framework, and extracting high-dimensional features related to a fault by combining wavelet transform with principal component analysis; based on the high-dimensional features, identifying the real-time state of the power grid by using a graph neural network in combination with a deep learning model; and according to the identification result of the real-time state, combining a power grid topological structure, and carrying out fault positioning through a time difference method and a graph theory algorithm. The power grid fault on-line identification system based on big data provided by the invention can efficiently manage and store a large amount of multi-source data in a power grid, and improves the data processing and access speed.

The invention relates to an alarm event root cause analysis method, device and equipment based on a large language model and business topology, and the method comprises the steps: based on an analysis engine, according to an alarm event sent by an alarm system, obtaining an association node set, collecting multi-source data, generating a preliminary context packet, carrying out the extraction of abnormal information according to the preliminary context packet, and carrying out the analysis of the abnormal information. And generating a refined context package by removing irrelevant node information, and generating an interpretable root cause analysis report through large language model constraint and reasoning based on the constructed large language cue word template. According to the method, the initial context packet covering the fault propagation link is constructed based on the service topological graph, rapid modeling of the context is achieved, the high-frequency error mode template is generated through clustering, irrelevant nodes are dynamically pruned to generate the refined context packet, accurate focusing and data filtering are achieved, and the fault propagation efficiency is improved. And embedding the refined evidence chain through a structured cue word template, and driving the large language model to output an interpretable root cause report.

The invention relates to the technical field of data filtering and data fusion, in particular to an improved weighted Kalman data fusion algorithm. The algorithm comprises the following steps: establishing a state equation, calculating a state prediction value at the current moment according to state estimation of current / voltage data at the previous moment to become priori, and calculating an error covariance matrix at the same time; obtaining a prediction covariance matrix at the current moment through matrix operation; calculating an observation noise covariance matrix; calculating to obtain a Kalman gain; calculating to obtain an updated optimal state estimation value; calculating a current optimal estimation value and an optimal estimation value covariance matrix; calculating a weighting factor, and compensating and updating the covariance matrix of the current / voltage data; calculating to obtain an updated covariance matrix; and then loop iteration is carried out on the steps, so that the estimation precision of the system state is gradually improved. The fusion algorithm provided by the invention can improve the data precision and accuracy.

To provide an AI-based filter device for a generative artificial intelligence (AI) model.SOLUTION: An Al based filter device includes an input filter and an output filter protecting a generative AI model and preventing restricted content from being transmitted to user devices. When a user query is received, the input filter determines if the user query can be transmitted to the generative AI model by generating an input risk score for the received user query. If the user query is transmitted and a model query response is received from the generative AI model, the output filter determines an output risk score based on which the model query response may be transmitted to the user. The input filter and the output filter each include a pre-trained language model as a base with additional layers trained to estimate the corresponding risk scores.SELECTED DRAWING: Figure 1

The invention relates to the technical field, in particular to a geothermal resource favorable area prediction method and system, and the method comprises the following steps: collecting electromagnetic and seismic wave data of a plurality of positions of a target area through a sensor based on regional position data, carrying out the correlation of the data and position information, and generating a geological exploration data set through the combination of data cleaning and standardization. According to the method, electromagnetic and seismic wave data of multiple positions are collected and associated with geographic position information, the practicability and reliability of the data are improved, the data quality is optimized in combination with data filtering, the accuracy of stratum structure prediction is improved, a three-dimensional geologic model is constructed and combined with actual drilling data, and the practicability and accuracy of the model are verified; the method improves the accuracy and credibility of model prediction, optimizes the precision and practicability of resource prediction by analyzing the correlation between a geologic structure and geothermal resources, provides scientific decision support for geothermal development, and supports the effective utilization of renewable energy sources and the realization of a sustainable development target.

The invention belongs to the technical field of data management, and discloses a research and development data traceability and collaborative management system based on a block chain, and the system comprises a sensing layer which is used for collecting multi-source research and development data on various research and development tool chains through a multi-protocol collection agent, carrying out the structural packaging of the data to generate metadata, and embedding a data blood relationship tracking mark in the metadata; false data filtering is carried out on the metadata by deploying a trusted execution environment; the chain network layer is used for dividing the filtered metadata into data fragments and respectively storing the data fragments in a distributed storage platform; applying a Hash algorithm to each data fragment to generate a corresponding Hash value; taking the hash values corresponding to all the data fragments as leaf nodes, constructing a fragment hash tree, and further obtaining global root hash; the security of research and development data is ensured, and the cooperation efficiency is improved.

The invention discloses a power distribution network first-aid repair operation safety check scheme generation method and system and a related device. The method comprises the steps that a power distribution network first-aid repair operation safety check document is read and subjected to segmentation processing to form a plurality of text blocks; converting the text blocks into vectors through an embedded model, and storing the vectors into a power distribution network emergency repair operation safety inspection knowledge base; obtaining an input query problem, converting a query problem text into a problem vector through an embedded model, filtering and screening the problem vector by using the power distribution network metadata, and calculating the similarity between the filtered and screened problem vector and a vector in a power distribution network emergency repair operation safety inspection knowledge base by using a similarity measurement method; selecting a target vector construction prompt word from the power distribution network first-aid repair operation safety inspection knowledge base; and splicing the input query question and the prompt word as the previous text by using the large language model, and generating an answer. According to the method, retrieval and generation are combined, the retrieval data correlation is improved through power distribution network metadata filtering, and the effect of a large language model in scheme generation is enhanced.

The invention relates to the technical field of supply chain management, and discloses a commodity supply chain management method based on the Internet of Things. According to the commodity supply chain management method based on the Internet of Things, errors caused by manual data entry are avoided through time sequence analysis and data filtering processing of the cloud analysis management platform, enterprises can accurately predict future market demands, and the inventory turnover rate, the optimal inventory level and the transportation and storage cost are dynamically calculated, so that the inventory management is optimized, and the enterprise management efficiency is improved. In addition, according to the method, by comprehensively evaluating the delivery timeliness rate of suppliers and the quality defect rate of goods, it is ensured that enterprises can select reliable suppliers, the stability and efficiency of a supply chain are improved, the real-time logistics monitoring and dispatching capacity enables goods to be quickly and efficiently delivered to clients, and the efficiency is improved. The customer satisfaction is comprehensively improved, and the competitiveness and market adaptability of enterprises are promoted.

The invention discloses a fault diagnosis and remote monitoring system and method for a solar power supply system, and relates to the technical field of fault diagnosis of a solar system, and the system comprises a heterogeneous multi-mode sensing module which collects the multi-dimensional information of an assembly through a plurality of sensors; the memristor storage and calculation integrated unit is used for realizing data filtering and feature extraction; the multi-scale causal diagnosis engine is used for diagnosing faults by fusing deep learning and causal diagrams; a self-adaptive topology communication network ensures data transmission; a digital twinborn monitoring platform and visual operation and maintenance are adopted, and in addition, an intelligent evolution decision and self-reconfiguration sensor network module is further arranged, so that the intelligence and reliability of the system are improved. Through cooperation of multiple modules, accurate fault diagnosis and positioning are realized, the diagnosis time is shortened, stable data transmission is ensured, self-repairing and autonomous learning capabilities are provided, the operation and maintenance cost can be reduced, the power generation efficiency can be improved, and the reliability and economic benefits of a solar power supply system can be enhanced.

A data processing method, an electronic device, and a storage medium. The method comprises: receiving a query request, wherein the query request comprises a target data table participating in a join operation; on the basis of the query request, executing a logical plan, wherein the execution of the logical plan comprises: constructing a data filtering condition corresponding to column statistical information, the column statistical information being information obtained by means of compiling statistics in advance on a column value range of columns included in the target data table; and executing a physical plan corresponding to the logical plan, wherein the execution of the physical plan comprises: performing partition pruning and / or dynamic filtering on the target data table on the basis of the data filtering condition, so as to generate a processed target data table, and executing the join operation on the processed target data table, so as to obtain an execution result.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and the method comprises the steps: determining a network access mode through a hybrid networking selection algorithm, and generating a communication link through dual-module networking configuration; original monitoring data are processed by using a data framing compression algorithm, and a path is selected and verified through dynamic routing optimization; the modulation format is adjusted through the space-time alignment data of the multi-modal data fusion model and the adaptive modulation technology; optimizing transmission by using an abnormal data filtering algorithm and a dynamic priority scheduling program; and feedback learning mechanism evaluation performance and updating algorithm parameters. According to the invention, network adaptability, data transmission efficiency and reliability are improved, data fusion processing is optimized, adaptive optimization of transmission performance is realized, operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

The invention discloses an external magnetic field active-disturbance-rejection control method and system based on Kalman filtering, and the method comprises the steps: firstly, building an external coil control system to compensate a residual magnetic field in a magnetic shielding cabin measured by a three-axis fluxgate sensor, and enabling the residual magnetic field to be reduced to a normal working range of Quspin; then starting a Quspin sensor, performing data filtering and fusion on the fluxgate sensor and the Quspin sensor through a Kalman filter, and inputting a fusion value and a control quantity into an extended state observer to realize online estimation of a state and total disturbance; and performing feedback control of related linear state errors on the total disturbance estimator so as to compensate a residual magnetic field and magnetic field disturbance in the magnetic shielding cabin. The magnetic field interference resistance of the active magnetic compensation device can be enhanced, the control precision of an external coil control system on the magnetic field in the cabin is effectively improved, and the cost of the magnetic shielding cabin is reduced.

The invention relates to the field of meteorological observation management, and discloses a low-altitude meteorological observation equipment operation and maintenance management method and system based on the Internet of Things, and the system comprises a meteorological sensor array module which is used for collecting low-altitude atmospheric environment parameters as observation data, including temperature, humidity, air pressure, wind speed, wind direction and particle concentration; the Internet of Things communication module is used for transmitting observation data to the edge computing node module in real time; the edge computing node module is used for performing local preprocessing on original observation data, including data filtering, abnormal value elimination and compression; the cloud operation and maintenance management platform is used for storing and analyzing the preprocessed observation data, judging an equipment state according to an analysis result, and generating an operation and maintenance instruction according to the equipment state; and the control module is used for receiving the alarm information and remotely controlling start and stop of equipment. According to the technical scheme, the safety of the low-altitude meteorological observation equipment can be improved.

The invention provides a recruitment patient determination method and device for a clinical test, a computer program product and electronic equipment, and relates to the technical field of medical big data, and the method comprises the steps: obtaining medical data of a plurality of patients, and converting the medical data into structured data; performing natural language analysis on the group entering standard information of the clinical test according to a large language model to generate a structured screening condition; performing data filtering on the structured data based on the structured screening condition through intelligent agents corresponding to a plurality of accommodating and arranging conditions of a clinical test to determine a recall result, matching the recall result based on the plurality of accommodating and arranging conditions, and determining an accommodating and arranging score of the medical data of each patient according to a plurality of matching results; and determining a target recruitment patient corresponding to the clinical test based on the recruitment score. According to the invention, the patient recruitment efficiency and accuracy can be improved.

The invention relates to the technical field of ocean remote sensing and satellite monitoring, in particular to a satellite-borne laser radar ocean water color product quality evaluation and calibration method, system and program, and particularly aims at improving the inversion precision of laser radar ocean water body parameters. The method comprises the steps of multi-source data acquisition and standardization, dynamic space-time matching window optimization, abnormal data filtering, seasonal conversion factor dynamic calculation, multi-source cross validation, consistency analysis, regional interpolation modeling, full-link uncertainty quantification, day and night difference correction and dynamic feedback optimization. By constructing a self-adaptive calibration mechanism and an error control process, the inversion precision and stability of a laser radar water color product are remarkably improved, and high-credibility data support is provided for marine ecological monitoring and carbon flux evaluation.

The invention relates to the technical field of battery temperature control, and discloses a new energy automobile power battery temperature adjusting method, system and equipment, and the method comprises the following steps: synchronously collecting vehicle operation data, traffic information, charging pile state and battery aging parameters, and carrying out timestamp alignment and abnormal data filtering; converting the road test data into a vehicle coordinate system, predicting an aging trend in combination with LSTM, and generating a cross-domain feature vector through feature coding and tensor fusion; and generating a basic temperature control strategy based on reinforcement learning, predicting SOH deviation in combination with digital twinning, dynamically calculating an aging compensation coefficient, and synthesizing an anti-aging enhancement strategy. According to the invention, the maximum temperature is controlled within a safety threshold value by constructing a full-link cooperative temperature control system, so that the effectiveness of a thermal management strategy in a full life cycle is ensured.

The invention relates to the technical field of water quality detection, in particular to a water quality detection method, device and system based on a spectrum technology, and the method comprises the following steps: obtaining spectrum data of a detected sample at a plurality of positions in a water quality detection process; extracting a characteristic peak of the spectral data of each position, and obtaining an abnormal offset coefficient of each characteristic peak; dividing the wave band of each characteristic peak into a rising part and a falling part, calculating the projection distortion coefficient of each part of the characteristic peak, obtaining the distortion index of each characteristic peak with projection and noise in combination with the signal-to-noise ratio of each characteristic peak, and further obtaining the waveform abnormal value of each characteristic peak. On the basis, the influence coefficient of uneven component distribution on the spectral data of each position of the detected sample is calculated, so that the step length factor of the spectral data filtering process of each position is adjusted, the spectral correction data of each position is obtained, and the COD concentration of the detected sample is detected. The accuracy of water quality detection can be improved.

The invention relates to the technical field of distributed computing and networks, in particular to a cloud computing and edge computing collaborative system integration architecture and a collaborative computing method. The method comprises the following steps: an edge processing cache unit filters and caches received original data based on a self-adaptive data filtering strategy and an intelligent cache strategy; the cloud management cooperation unit constructs a time delay energy consumption optimization objective function based on the current cloud node and edge node states, solves the time delay energy consumption optimization objective function by using a multi-objective optimization algorithm, and dynamically cooperates task scheduling and resource allocation; the heterogeneous hardware acceleration unit adopts a hierarchical accelerator scheduling strategy to calculate an optimal hardware matching degree, introduces a calculation acceleration efficiency coefficient, and schedules heterogeneous accelerator resources. According to the cloud computing and edge computing collaborative system integration architecture and the collaborative computing method, efficient collaborative scheduling between cloud computing and edge computing is realized by combining intelligent matching of a multi-objective optimization algorithm and a heterogeneous accelerator.

The embodiment of the invention provides a multi-mode unmanned material checking method based on combination of RFID and machine vision, the method is applied to a mobile module and a camera module, an RFID read-write module is arranged on a mobile trolley, and the method comprises the following steps: in response to movement of the mobile module, the RFID read-write module scans a target material to obtain RFID identification data, and filters and de-duplicates the RFID identification data to obtain a target material; an RFID identification result is determined; dividing a shooting range of the camera module based on a scanning area of the RFID read-write module, and detecting a material box and a material surface sheet label in video image data when the video image data is shot; and identifying the material surface sheet label to obtain material data information, and matching the material data information with the RFID identification result to obtain a multi-modal material matching result.

The invention relates to the technical field of intelligent lock information monitoring, in particular to an intelligent lock information monitoring method and system. The monitoring method comprises the following steps: acquiring state information of the intelligent lock by using various sensors; preprocessing the state information, wherein the preprocessing comprises data filtering, de-noising processing and time synchronization; key feature data of the state information are extracted and analyzed through an analysis algorithm; and analyzing and judging the working state and the fault condition of the intelligent lock by utilizing a multivariable analysis method. According to the intelligent lock information monitoring method provided by the invention, the state information of the intelligent lock is comprehensively collected through various sensors, and the monitoring of the working state of the intelligent lock and the early warning of faults are realized by combining data processing and analysis algorithms, so that the comprehensiveness and accuracy of monitoring are improved.

The invention relates to the technical field of intelligent traffic systems, in particular to a linear intelligent monitoring system for a bridge fabrication machine, which comprises a multi-source sensing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the multi-source sensing module comprises strain sensors, inclinometers, GPS positioning units and machine vision units; the edge computing node is internally provided with a self-adaptive data fusion algorithm and is used for denoising sensor data in real time; the cloud digital twin platform is used for synchronizing the BIM design model and generating a millimeter-level linear deviation early warning signal; the PLC control interface is used for receiving the early warning signal and automatically adjusting parameters of the template system and the walking mechanism; according to the method, environmental noise interference is effectively eliminated through multi-source sensor fusion and a self-adaptive data filtering technology, the monitoring precision is improved to a millimeter level, real-time comparison of construction data and a design model is realized based on a BIM and digital twinning dynamic interaction system, and the timeliness and accuracy of linear control are remarkably improved.

The invention provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention relates to the technical field of tunnel engineering monitoring, in particular to a tunnel health monitoring method and system based on edge computing, and the method comprises the steps: a data collection module obtains the original state data of a tunnel through a sensor deployment network, carries out the primary processing of the original state data based on edge computing, and obtains the actual state data; during the period, the data acquisition module performs data filtering on the original state data based on a state data threshold value, rejects invalid data in the original state data and marks abnormal data in the valid data according to an anomaly detection algorithm, and the remaining unmarked original state data is the actual state data of the tunnel; and the monitoring cloud platform obtains the actual state data every a preset period, and carries out data summarization, data storage and data analysis processing on all the actual state data. The system has the beneficial effects that part of calculation tasks are issued to the data acquisition module, so that the processing burden of the monitoring cloud platform is reduced, and the real-time performance and effectiveness of monitoring and early warning are improved.