791 results about "Materials management" patented technology

The invention provides a supply chain collaborative material management system and method, and relates to the technical field of material management, and the method comprises the steps: receiving a multi-enterprise heterogeneous material data flow, and constructing an industry knowledge graph based on the field-level semantic mapping of a dynamic ontology library; a standardized data table is generated; standardized data is injected into a distributed message queue for parallel processing, dynamic cache partitions are created according to material object labels, timestamp indexes are built in the dynamic cache partitions, a sliding window mechanism is adopted to scan data, and multi-strategy resolution is performed on conflict data in a window; performing conjoint analysis on the cleaned data to generate an enhanced analysis report, and activating a case transfer learning mechanism to update a model when the similarity between new data and historical cases exceeds a threshold value; constructing a material state transfer matrix for the abnormal data, simulating a correction operation in a digital twin environment, and triggering automatic correction; the problems of multi-enterprise heterogeneous data integration, dynamic data processing, intelligent analysis and the like are solved, and the collaboration efficiency and the management level of a supply chain are improved.

The invention discloses a material-based laser scanning system and method, and relates to the technical field of industrial automation, and the system comprises the following components: a laser scanning unit, an environment sensing sensor group, a dynamic compensation and calibration module, a feedback control unit and a visual monitoring interface. According to the invention, the environment change of a scanning site is monitored in real time through the integrated environment sensing sensor group, and the laser scanning data is processed in real time by using an advanced dynamic compensation and calibration algorithm, so that the measurement deviation caused by environmental factors such as vibration, temperature change and dust interference can be effectively reduced; the measurement precision of laser scanning in complex environments such as a coal yard is remarkably improved, meanwhile, the stability of long-term measurement is ensured by continuously optimizing and calibrating model parameters and automatically adjusting laser scanning parameters, and reliable data support is provided for material management.

The invention relates to the field of medical material management, and discloses a hospital material management server, which comprises a data collection module used for collecting material consumption data, the number of patients and operation plans of all departments from a hospital information system and an electronic medical record system, and transmitting the collected data to a prediction analysis module; the prediction analysis module is connected with the data collection module, and the invention also discloses a hospital material management method which comprises the following steps: S1, collecting material consumption data of each department from a hospital information system and an electronic medical record system through the data collection module; by using an adaptive neural network and a virtual twinning technology, accurate demand prediction, inventory dynamic adjustment, distribution optimization, global monitoring and environment simulation adjustment are realized; the interactive feedback module forms closed-loop optimization, the meta-universe cooperation module supports virtualization operation and training, and the defects of a traditional system in prediction, scheduling, monitoring and cooperation are overcome.

The invention relates to the technical field of material management, and discloses a dynamic rule generation and self-adaptive auditing system and method for material management, and the system comprises a rule intelligent extraction module, a rule management knowledge base, an enhanced auditing engine, a man-machine cooperation calibration module and a self-adaptive execution module. The method comprises the steps of automatic rule extraction, rule storage and management, enhanced auditing and reasoning, man-machine collaborative calibration, knowledge base real-time optimization and adaptive routing execution. According to the method, the rule is automatically extracted from the unstructured document, the problem that a traditional system rule depends on manpower and is lagged in updating is solved, dynamic optimization of the rule and confidence is achieved by introducing a man-machine collaborative feedback closed loop, the accuracy and transparency of an audit decision are improved by enhancing reasoning and explainable decision technologies, and the audit efficiency is improved. And the optimal balance between auditing efficiency and risk control is realized through self-adaptive routing execution based on credibility.

The invention provides a material management intelligent warehouse space dynamic management method and system. Specifically, the method comprises the following steps: acquiring information of to-be-stored goods and materials and information of remaining shipping spaces; calculating the matching degree between the to-be-stored material and each remaining bin position, the space utilization rate of each remaining bin position and the operation convenience of each remaining bin position; corresponding weights are set for the matching degree, the space utilization rate and the operation convenience respectively, a shipping space scoring function is constructed, and the shipping space scoring function is the sum of the matching degree, the space utilization rate and the operation convenience; and the remaining bins are scored based on the bin scoring function, and the to-be-stored materials are stored in the bin with the highest score. According to the invention, through intelligent management, in combination with data analysis and dynamic optimization, efficient configuration of storage resources is realized.

The invention relates to the technical field of project cost management, in particular to a hydraulic engineering construction cost control system and method, and the system comprises a material consumption analysis module, an inventory early warning calculation module, a dynamic inventory adjustment module, a replenishment trigger optimization module, and a consumption stability evaluation module. According to the method, the material consumption rate is dynamically monitored, the construction progress and engineering quantity are combined to measure and calculate data, a consumption trend curve is established, the actual use condition is compared, the consumption rate deviation is accurately quantified, real-time evaluation of material use is achieved, the number of supply days is calculated in combination with inventory data, the replenishment period is matched, and the replenishment time is dynamically adjusted; the inventory level is optimized, fund occupation is reduced, the stability is evaluated according to the consumption fluctuation range, the maximum consumption fluctuation value is calculated, the demand is adjusted in combination with replenishment, the inventory early warning threshold value is dynamically corrected, the supply chain breakage risk is reduced, the adaptability of construction material management is improved, and the cost control accuracy is improved.

The invention discloses a demand prediction method and system for aerial material spare parts, and relates to the field of aerial material management. The invention aims to provide the demand prediction system and method for the aerial material spare parts specially for the scene of demand prediction of the aerial material spare parts, the method is improved on the basis of the existing statistical analysis technology, the influence of various factors on the demand is considered in the demand change, the prediction deviation caused by human intervention is reduced, and the prediction accuracy is improved. Based on a time sequence method, a reasonable and effective classification mode and a scientific and accurate demand prediction method for different demand characteristic aviation materials are researched, a more accurate maintenance aviation material spare part consumption prediction model is obtained, an efficient prediction method suitable for aviation material management is provided, the purpose of reducing aviation material waste is achieved, and finally the reduction of aviation material cost is achieved. The method is of great significance to aviation material management and maintenance plan optimization of airline companies.

The invention relates to the technical field of engineering management, and discloses an engineering material management method and system, computer equipment and a storage medium. The method comprises the steps of determining a design bill of materials according to a building information model corresponding to a target project; compiling a construction progress plan according to the work decomposition structure corresponding to the target project; determining a material demand list corresponding to each work package in the construction progress plan; summarizing the material demand list and the time attribute corresponding to each work package to determine a summarized material list corresponding to the target project; determining a material management strategy according to the summarized bill of materials and a supplier database; executing the material management strategy; obtaining business process data generated in a strategy execution process; and adjusting a material management strategy according to the change condition of the supplier database and the business process data. According to the invention, the accuracy of material management can be improved, resource configuration is optimized, and real-time monitoring and adjustment are realized.

The invention discloses a real-time laser coal checking system and method, and relates to the technical field of material management and inventory checking, and the system comprises the following components: a laser detection module, a data processing module, a communication module, a monitoring module and a server module. The laser detection module is adopted to perform high-frequency scanning on the coal pile to obtain the three-dimensional point cloud data of the coal pile, the crown block displacement data measured by the laser range finder and the material and density information of the material pile are combined, the volume and weight of the coal pile are calculated through an algorithm, the outer contour of the coal pile can be determined more accurately, and the accuracy of the coal pile is improved. According to the shape characteristics and the density distribution condition of the coal pile, the coal pile is divided into a plurality of small geometries for volume calculation, and compared with traditional manual measurement or simple geometric shape approximation, the method has the advantages that the measurement precision of the volume and the weight of the coal pile can be remarkably improved; and more accurate data support is provided for material management and inventory checking of enterprises.

The invention belongs to the technical field of medical material management, and particularly relates to a medical material intelligent supply chain management scheme and system based on multi-modal AI. According to the invention, through multi-modal data fusion, full-life-cycle and full-link tracking of materials is realized, information islands are eliminated, management transparency is improved, unexpected consumption and physical damage risks are identified in an early stage, a large amount of overstock or clinical supply interruption is avoided, operation cost is reduced, and based on a dynamic loss threshold, replenishment and cross-warehouse allocation can be realized just in need. The inventory turnover rate and the supply guarantee rate are considered, environment monitoring and validity period information are processed, it is ensured that temperature and humidity sensitive materials are stored under the optimal condition, losses caused by expiration or environment abnormity are reduced, the waste rate and the excessive stockpiling risk are comprehensively reduced, meanwhile, timely availability of key materials is clinically guaranteed, and the service life of the materials is prolonged. And the economic benefit and safety of the medical material supply chain are improved.

The invention provides an electric power material warehouse-in and warehouse-out intelligent management and control method and device based on intelligent carrying, and relates to the field of electric power material management.The method comprises the steps that real-time data in a warehouse are collected through Internet of Things equipment and a sensor, and the real-time data are collected according to the collected real-time data; performing real-time calculation on an advancing path of the intelligent carrying equipment in the warehouse by utilizing dynamic path optimization, and adjusting the advancing path of the equipment according to the real-time environment data, the material storage position data and the carrying task demand data; the intelligent carrying equipment carries out advancing path planning when carrying tasks are carried out in the warehouse, and according to an error correction mechanism, errors in the material warehouse-in and warehouse-out process are corrected; the material demand is dynamically predicted according to the historical inventory data and the external demand data by using the established global task scheduling prediction model, and the priority ranking of the tasks in the warehouse and the dynamic scheduling of the inventory are performed, so that the automatic and intelligent management of the warehouse-in and warehouse-out of the materials is realized, and the material management efficiency is improved.

The invention provides a packaging material intelligent selection method and system based on multi-dimensional data analysis, and relates to the field of material management. Vibration, deformation and temperature and humidity data are collected in real time through an environment sensing module to construct a feature matrix; the data analysis module fuses the historical case library and the logistics task duration to predict the material life, and associates the supply chain inventory to generate residual life evaluation; the decision engine module dynamically distributes cost, safety and environmental protection weights, and combines the supplier credit score to generate a multi-level decision scheme; the execution control module verifies the dimensional tolerance of the material and drives replacement operation, and model calibration is triggered through a 5% deviation threshold value; a humidity sensitive material grading standard and a deformation safety limit value are built in the system, and when the temperature and humidity sudden change exceeds the threshold value, deformation quantity prediction and emergency scheme reconstruction are started; a three-level self-optimization mechanism of environmental perception error, material life error and execution error is established, and closed-loop optimization is realized through sensor calibration, supplier credit recalculation and dynamic adjustment of control parameters.

The invention relates to the technical field of sectional material excess material management, and discloses a sectional material excess material management method and device and electronic equipment, and the method comprises the following steps: S1, setting a minimum available length threshold value of excess materials, which is used for judging whether the minimum available length threshold value is included in an excess material management process or not; s2, the excess materials generated by cutting are recognized, information including the color, the length, the surface state and the profile structure attribute is obtained, and a unique identifier is generated for each section of excess materials; s3, storing the excess material information meeting the conditions into an excess material management database, and associating storage position information of the excess material management database; and S4, after a new order request is received, screening matched excess materials in the excess material database according to an order demand to form a candidate set. According to the method, a structural management technical scheme based on geometric contour reconstruction and matching of the excess materials is adopted, and the identifiability, traceability and reutilization of the excess materials in the machining process are achieved. The technical effects of intelligent classification and efficient scheduling of the processing residual materials are achieved.

The invention discloses a butt joint conveying structure and a three-dimensional printing material management system. The butt joint conveying structure comprises a container, a rotating part and a butt joint assembly. The container is provided with a containing cavity and a butt joint part with a receiving hole, the rotating piece is provided with a first butt joint hole and a first matching part, and the rotating piece rotates between a first position and a second position around a rotating axis relative to the butt joint part and is limited by the butt joint part in the direction of the rotating axis; the butt-joint assembly is provided with a conveying channel used for conveying materials and a second matching part which is connected with the first matching part in an inserted mode and forms rotation stopping matching, so that the butt-joint assembly drives the rotating piece to rotate, and when the rotating piece rotates to the first position, the rotating piece shields the receiving hole, and when the rotating piece rotates to the second position, the receiving hole shields the receiving hole. The conveying channel communicates with the containing cavity through the first butt joint hole and the receiving hole. The three-dimensional printing material management system comprises the butt joint conveying structure. By adopting the technical scheme, the phenomenon that the receiving hole cannot be tightly changed due to falling deformation of the top cover can be avoided.

The invention relates to a platform-based intelligent manufacturing MES system, and the system comprises a plan management module which is used for generating a production plan; the planning and scheduling module decomposes the production plan into specific operation tasks and generates an operation plan scheduling instruction; the document management module is responsible for managing and maintaining various documents involved in the production process; the operation management module converts an operation plan scheduling instruction of the planning and scheduling module into a specific production operation instruction; the automatic acquisition module acquires data of production equipment in real time and transmits the data to the plan management module in real time; the operation information module sends a control instruction to the automatic acquisition module, automatically acquires equipment working information, and feeds back the equipment working information acquired by the automatic acquisition module to the plan management module; and the material management module performs data interaction with the planning and scheduling module. Intelligent, automatic and fine management of the production process can be realized, and the system has important significance for improving the competitiveness of lithium battery copper foil production enterprises.

The invention relates to the technical field of elevator wallboard production, in particular to an elevator wallboard complete production line and a production method. The production line comprises an MES system, a material management module, a plate positioning and shearing module, a plate temporary storage module, a punching and bending module, a quality detecting and gluing module, a plate assembling and welding module and a finished product outputting and boxing module from front to back according to the production sequence, and the plate buffer library is provided with at least one replaceable buffer library arrangement scheme. The plurality of manipulators are connected among the modules; and the central control system is used for overall scheduling, monitoring the production process and adjusting the working parameters of the modules according to production requirements. The technical problems of low production efficiency, low material utilization rate, improper excess material treatment and the like in the prior art can be solved.

The invention provides a risk monitoring method and system based on power grid material supply chain operation. The supply chain management efficiency is remarkably improved through an intelligent risk identification process. The method comprises the following steps: firstly, acquiring power grid material consumption data from a historical database, and calculating the consumption speed and consumption of various materials according to a bill of materials; and key monitoring materials are screened out through the preset consumption monitoring threshold value, so that tedious data processing on all the materials is avoided. And for the screened monitored materials, the system further collects storage, production and logistics data of the screened monitored materials, and evaluates the risk degree by using a pre-trained supply chain risk identification model. And when the material supply chain risk degree exceeds a preset threshold value, defining the material as a supply risk material. The accurate risk monitoring method not only reduces the calculation overhead of data processing, but also can identify potential supply chain risks in time, and provides a more efficient and intelligent solution for power grid material management.

The invention discloses an integrated intelligent production management and control system which comprises a basic data management module, a product process quality management module, a production management module, a material management module, an inspection management module, a quality management module, an equipment management module, an energy management module and a safety management module. The invention relates to the technical field of production management, and discloses an integrated intelligent production management and control system, which carries out custom configuration on various basic data through a basic data management module, and guarantees data exchange and sharing; the product process quality management module runs through the whole crystal production process, improves the production efficiency and the management level, forms a knowledge base and provides scientific data support; the production management module establishes a production management system integrating multiple functions, and the field cooperation and management and control capability is improved; and the material management module realizes full-process material management and control and mobile visual tracking.

The invention discloses a highway engineering construction remote material abnormity management method, which relates to the technical field of material management, and comprises the following steps: collecting material information parameters for analysis, obtaining an analysis result and carrying out abnormity identification, outputting an abnormity identification result, triggering a response mechanism for the abnormity identification result, and carrying out automatic early warning. By constructing a full-process digital highway engineering construction remote material anomaly management system, the problems of multiple types of traditional management are solved, multi-source data are integrated to automatically correct errors and generate accurate data, and the material anomaly management system is established by means of space-time dimension cross validation and construction process compliance analysis. A preset abnormal feature combination library is combined to capture a composite risk, a hierarchical response and a mapping table are adopted to realize differential early warning, responsibility is clarified through receiving verification and block chain evidence storage, and a closed loop is formed by combining treatment with template optimization and dual verification, so that the risk is reduced, and the management intelligence and refinement level is improved.

The invention relates to a material management method and device, computer equipment, a storage medium and a computer program product. Relates to the technical field of data processing. The method comprises the steps of training based on a historical data set of a to-be-managed warehouse to obtain a material management model; the historical data set comprises at least two groups of historical material demand prediction results and historical inventory costs corresponding to the historical material demand prediction results; the objective function of the material management model is the minimum value of the historical inventory cost; obtaining a current material demand prediction result and a current inventory cost of the to-be-managed warehouse; the current material demand prediction result and the current inventory cost are input to a material management model, an output target inventory strategy is obtained, and the target inventory strategy is used for material management of the to-be-managed warehouse; the target inventory strategy comprises an ordering point, an ordering batch and a material quantity threshold value of the next ordering period corresponding to each material. By adopting the method, the management efficiency of the material inventory can be improved.

The present invention discloses a concrete intelligent material system, including a raw material detection module, which obtains aggregate data of sand and gravel aggregates in the raw materials of concrete; a concrete batching module, which is connected to the raw material detection module, obtains aggregate data of sand and gravel aggregates, and adopts a net slurry rheological threshold algorithm to obtain mixing image information; a ratio adjustment module, which is connected to the concrete batching module, obtains mixing image information, and adjusts the ratio of concrete to obtain ratio adjustment information; a feedback module, which is connected to the concrete batching module, and feeds back the ratio adjustment information to the concrete batching module. Through the above scheme, the present invention has the advantages of simple logic, accuracy and reliability, and has high practical value and promotion value in the field of concrete material management technology.

The invention provides a first-aid material tracking method and system based on the Internet of Things. Wherein a unique electronic tag identifier is allocated to each first-aid material, and a material information file is generated; acquiring the position information of the first-aid material and the surrounding environment parameters in real time to obtain an environment adaptability analysis result; setting a safety threshold range adapted to different types of first-aid materials, identifying an abnormal event, recording and updating an early warning mechanism triggering condition to the material information file, and obtaining latest state information; a plurality of potential optimal distribution routes are calculated, a scheme with the highest comprehensive benefit is selected, a path planning strategy is continuously adjusted, and an optimized distribution route is obtained; and constructing a resource allocation visual management platform based on the material information file, the environmental adaptability analysis result, the early warning mechanism triggering condition and the optimized distribution route. According to the technical scheme provided by the invention, the efficiency and reliability of first-aid material management and distribution are improved.

The invention relates to the technical field of material management, in particular to a material counting method and system based on RFID and full life cycle management, and the method comprises the steps: collecting employee data, tool basic information data and image information; verifying the in-warehouse and out-warehouse tools, and recording in-warehouse and out-warehouse information at the same time; when the tools and instruments are delivered out of the warehouse, analyzing the tools and instruments required by the task according to the information of the taking personnel and the task data; and based on full-life-cycle management, using the prediction maintenance model in combination with the use data analysis of the tools and instruments to predict the maintenance requirements and time points of the tools and instruments. According to the invention, fine management of employees and tools is realized, the working efficiency and the safety are improved, the maintenance cost is reduced, and the beneficial effects of improving the operation efficiency and the management level of enterprises are achieved.

The invention provides an intelligent workshop material dynamic pulling control method, system and device, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: firstly, obtaining a basic inventory buffer capacity, an equipment topological graph, production scheduling characteristics, real-time production data and a material warehousing rate of an intelligent workshop; time sequence production features, equipment topological features and production scheduling features are extracted in combination with a multi-modal prediction model, the predicted material consumption rate is output, and the prediction precision is improved; secondly, a real-time inventory water level and a dynamic replenishment threshold value are calculated in combination with a prediction result and equipment operation parameters, production fluctuation and equipment fault risks are dynamically adapted, and inventory overstock or material interruption shutdown caused by a traditional static water level is avoided; and finally, if the real-time stock water level is lower than the dynamic replenishment threshold value, according to the material transportation parameters, constructing a minimum delay path objective function, generating an optimal path, and controlling the automated guided vehicle to execute replenishment, so that timely replenishment is ensured, the stock turnover rate is improved, and the lean level of workshop material management is improved.

The invention provides a distributed production area material management method and system based on radio frequency identification, and relates to the technical field of material management, and the method comprises the steps: determining a plurality of material types according to a material management demand; performing frequency spectrum scanning and signal interference analysis on the target area; introducing a predetermined distribution analysis strategy to carry out frequency band distribution; based on the M working frequency bands, deploying M RFID reader-writers; according to the material distribution sequence, in combination with a label distribution layering mechanism, performing label distribution on the sub-regions; and establishing a target material management scheme based on the first material management scheme. According to the invention, the technical problem that the accuracy of radio frequency identification is reduced and the material management efficiency is further influenced due to interference among devices in a distributed production area in the prior art can be solved, signal interference is avoided through frequency spectrum scanning and dynamic frequency band distribution, the material is subjected to label distribution, and the material management efficiency is improved. And the material management efficiency is improved.

A metering equipment warehouse division demand prediction and scheduling method based on spatio-temporal feature fusion comprises the steps of firstly obtaining and collecting warehouse division data, then establishing a spatio-temporal feature fusion model based on the warehouse division data, and then establishing a spatio-temporal diagram convolution prediction network, the spatio-temporal diagram convolution comprises a diagram convolution layer and a time convolution layer, through extraction and fusion of the two layered features, dynamic weight fusion is obtained, joint modeling of space-time dynamics is realized, and finally, prediction and allocation decision are implemented based on the dynamic weight fusion. According to the method, the multi-modal graph structure fusing the space-time association and the replacement rule is constructed, and a space-time joint modeling architecture and a dynamic feedback mechanism are designed, so that high-precision demand prediction and global inventory optimization are realized; the method systematically solves the core problems of insufficient spatial correlation modeling, dynamic event response lagging, low efficiency in multi-source data utilization and the like of a traditional method, and provides an efficient solution for electric power material management.

The invention provides an automatic material identification and matching method and system for a three-dimensional model and a medium, and relates to the technical field of material management, and the method comprises the steps: constructing a standardized material database, and extracting and standardizing material attribute information; analyzing first material description information in the three-dimensional software model by utilizing an SQL (Structured Query Language), and matching the first material description information with the material attribute information to obtain a first material matching result; extracting second material description information in the project material document by using VBA programming, and matching the second material description information with the material attribute information to obtain a second material matching result; and data integration is carried out through PowerBI, and a project material state visual billboard is constructed. The technical problems that in the prior art, a unified standardized material database is lacked, material description formats in different sources are inconsistent, expression is not standard, a construction material list cannot be accurately generated, and informatization and intellectualization of project material management are limited are solved.

The invention belongs to the technical field of material management, and discloses a management method and device based on material product matching and automatic generation, equipment and a storage medium. The method comprises the following steps: carrying out AI semantic analysis on a material product description of a heterogeneous source, and extracting key parameters of a product; constructing a dynamic product feature matrix based on the key parameters of the product, and performing multi-level matching by using the dynamic product feature matrix and a supply database to obtain a plurality of candidate product matching schemes; determining a target product matching scheme from the plurality of candidate product matching schemes according to a semantic similarity threshold; performing parameter specification alignment processing on the target product matching scheme through a parameter mapping engine; and based on the processed target product matching scheme, establishing a target mapping relationship between the materials and the products, and managing the materials and the products by using the mapping relationship. By means of the method, the mapping relation can be dynamically updated in real time, and meanwhile the accuracy can be improved by means of the constructed mapping relation.

The invention discloses a purchase demand prediction method combined with PMC material management and control, and belongs to the technical field of material management and control data processing, and the method comprises the steps: obtaining a material consumption sequence and corresponding timestamp data, and employing a time sequence decomposition method to separate trend components and seasonal components, and obtaining a consumption rule feature sequence; if the fluctuation amplitude of the consumption rule feature sequence exceeds a preset threshold value, determining the consumption rule feature sequence as a basic demand predicted value sequence; obtaining a market price index sequence, and calculating a correlation coefficient between the basic demand predicted value sequence and the market price index sequence; if the absolute value exceeds a set threshold value, updating a basic demand predicted value sequence through a time sequence model; generating a purchase plan sequence according to the basic demand predicted value sequence and the inventory average value; and generating a final output report. The invention discloses a problem that a current material management and control mode cannot capture key changes in time and is difficult to balance contradictions between inventory and production.

The invention belongs to the technical field of data processing, and particularly discloses a warehouse material management method and system based on an intelligent inspection robot, and the method comprises the steps: collecting environment monitoring data through a data collection device, enabling the environment monitoring data to comprise temperature and humidity information, smoke concentration and an image sequence, and obtaining an original multi-mode signal set; a deep learning model is adopted to extract feature vectors according to the original multi-modal signal set, and multi-dimensional feature representation is obtained and used for subsequent fusion processing; performing priority ranking on anomaly detection from the multi-dimensional feature representation, and determining a high-risk signal subset to identify potential hazards; if the high-risk signal subset meets a triggering condition, image sequence analysis is carried out; the invention aims to solve the problems of complexity of multi-source data and inaccurate real-time identification of abnormal events in environmental monitoring.