152 results about "Decision management" patented technology

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

The invention relates to the technical field of operation and maintenance management, in particular to an asset operation and maintenance decision management platform and management method based on data fusion, and the method comprises the steps: collecting asset full life cycle data, and constructing a standardized asset data set; utilizing ontology modeling and knowledge extraction technologies to construct a dynamic asset knowledge graph containing asset attributes, topological relations and operation states; training a deep learning model to realize asset fault diagnosis, constructing an asset operation and maintenance risk detection algorithm, performing real-time analysis on asset multi-dimensional data, identifying an asset security risk level, and triggering a corresponding operation and maintenance strategy; and converting the diagnosed asset fault and the generated operation and maintenance strategy into a natural language by using a natural language generation technology, and pushing the natural language to an operation and maintenance manager for execution. According to the method, multi-source heterogeneous data is fused, and intelligent operation and maintenance decision-making of the full life cycle of assets is realized.

The invention relates to the technical field of semantic decision management and control, in particular to a multi-modal semantic alignment intelligent decision and risk management and control system, which comprehensively and accurately captures cross-modal semantic association through multi-modal semantic alignment processing so as to generate a plurality of possible reasoning chains with reliability and interpretability. And combining dimensions such as knowledge conflicts and historical risks, calculating reasoning overlapping values to evaluate inter-chain association, realizing multi-dimensional and multi-angle analysis of potential risks, bringing risk fingerprint values and the reasoning overlapping values into a quantitative calculation framework of decision response values, dynamically setting a decision response threshold value, and realizing quantitative calculation of the risk fingerprint values and the reasoning overlapping values. The system can flexibly trigger emergency, early warning or monitoring response according to different risk levels, and refinement and differentiation of risk management and control are realized. The mechanism not only ensures timely disposal in a high-risk scene, but also avoids resource waste in a low-risk scene, and effectively balances risk prevention and control and execution efficiency.

The invention relates to the technical field of hydraulic engineering management, in particular to a hydraulic engineering management method and system based on digital twinning. The method comprises the following steps of obtaining hydraulic engineering design data, hydraulic engineering operation data and hydraulic engineering environment data, and performing digital twin space modeling on the corresponding hydraulic engineering to obtain a hydraulic engineering digital twin space model; performing operation condition virtual simulation analysis and water flow distribution change analysis on the hydraulic engineering digital twin space model, and performing engineering structure stress load analysis and engineering operation risk assessment to obtain hydraulic engineering simulation operation process risk data under different operation and environment change conditions; and performing risk decision management processing on the corresponding water conservancy project to generate a water conservancy project operation process risk management decision so as to execute corresponding water conservancy project operation risk management adjustment work. According to the invention, the management effect and response capability of the hydraulic engineering can be greatly improved.

The invention discloses a host USB structure verification management system based on model fusion, and particularly relates to the field of computer security. The system comprises a data acquisition layer for acquiring a protocol data packet, an electrical signal waveform and equipment logic state information of a USB interface in real time, and generating a standardized multi-modal data stream through time alignment processing; the feature extraction layer is used for extracting modeling features from the standardized multi-modal data stream and encoding the modeling features into a structured matrix; the model fusion layer is used for fusing and outputting joint verification confidence through a protocol rule model, an anomaly detection model and a simulation model; the decision management layer executes a three-level verification strategy in sequence; the user interaction layer provides a visual monitoring and manual feedback interface and supports dynamic model replacement. According to the method, the structural legality of the USB equipment is verified through multi-model cooperation, real-time monitoring and risk level-to-level management of the USB equipment are achieved, the safety and compliance of the host terminal are improved, and the method is suitable for enterprise-level USB equipment protection scenes.

The invention relates to the technical field of industrial data management, in particular to an unmanned aerial vehicle task scheduling intelligent method for low-altitude economy. Obtaining business demand data, equipment state data and environment monitoring data; establishing a business priority dynamic adjustment scheme, evaluating the urgency, potential hazard and environmental complexity of the tasks according to the business risk evaluation matrix, dynamically adjusting the corresponding priorities, and constructing a multi-task cooperation sequence; a multi-target business decision optimization method is adopted to analyze the multi-task cooperation sequence and the equipment state data, load matching clustering is utilized to carry out task preliminary grouping, and a multi-target business decision optimization function is solved to carry out task distribution and business decision management; and carrying out predictive analysis on the river and lake environment risk by adopting a prospective service scheduling scheme, and taking a prediction result as the prospective input of the multi-target service decision optimization method to realize service pre-distribution and decision management. According to the invention, adaptive task scheduling can be realized.

The invention discloses a building hidden carbon emission prediction method based on machine learning, and the method comprises the following steps: S1, building construction activities are comprehensively considered, a building hidden carbon emission structure is qualitatively and quantitatively analyzed, and a main carbon emission source is selected as an output parameter of a target prediction model; s2, the system identifies influence factors of hidden carbon emission of the building, and grey correlation analysis is adopted to select influence factors with large correlation as input features of the target prediction model; s3, making a data collection table, and carrying out data collection for an actual construction project; s4, constructing a construction consumption prediction model by using a machine learning algorithm; s5, building hidden carbon emission prediction is completed in combination with the carbon emission factors; and S6, according to the methods of S1 to S5, using Python to develop a building hidden carbon emission prediction device. According to the method, the hidden carbon emission level of the building can be rapidly and conveniently estimated in the initial design stage of the building, and then low-carbon design optimization, scheme comparison and selection, decision management and the like are supported.

The invention relates to the technical field of databases, in particular to a main and standby automatic fault switching system and method based on database state awareness, and the system comprises a daemon module which is used for monitoring the operation condition of a local database in real time, and periodically publishing and updating the state information of the local database; the synchronization program module is used for regularly connecting the main database and the local database and executing a differential synchronization strategy according to different synchronization requirements and environmental factors; the main library inquiry interface module is used for sending a transmission control protocol message to the daemon module; and the coordination program module is used for executing decisions of the main and standby library roles, managing and issuing main library information, and regularly updating the main library information and database information. The multi-node daemon program, the multi-node synchronization program, the main library inquiry interface and the single instance coordination program are integrated to jointly form the database fault automatic switching system, safe and accurate automatic switching during main library fault is achieved, and the safety of the main and standby automatic switching process is ensured.

The invention discloses an intelligent asset dynamic monitoring method and system based on full life cycle management, and relates to the technical field of asset full life cycle management, and the method comprises the steps: carrying out the twinborn modeling based on the structure attribute information of a target asset, generating an asset virtual twinborn body, recognizing a key part, deploying a sensor, and constructing an asset multi-dimensional sensing network; acquiring asset operation data flow through the network, and mapping the asset operation data flow to twin monitoring and prediction to determine current operation state parameters of assets; constructing an asset full life cycle strategy according to asset management requirements, and matching a current state to obtain a current-stage management strategy of the assets; and determining management parameters based on stage strategy analysis, and performing asset decision management. The technical problems that in the prior art, it is difficult to accurately obtain the state data of all stages of the full life cycle of the assets, and accurate management and decision making of the assets cannot be met are solved, and the technical effects that the state data of all stages of the full life cycle of the assets are accurately obtained, and the accurate management and decision making requirements of the assets are met are achieved.

The invention discloses a sepsis-related acute kidney injury intelligent prediction system and a hierarchical management method, and the system comprises a multi-modal data collection module which synchronously collects an ultrasound contrast video, a urine biomarker and hemodynamic parameters in real time; the spatio-temporal feature extraction module is used for extracting multi-modal spatio-temporal features; modeling a dynamic interaction effect of heart and kidney nodes through a dynamic organ interaction graph network; the self-supervised pre-training module is used for improving the generalization of the model through comparative learning; the dynamic risk prediction module outputs a risk probability curve in the next 24 hours; the hierarchical decision management module is used for generating personalized strategies of liquid management, vasoactive drug adjustment and kidney replacement treatment opportunity; and the interpretability module is used for analyzing the key early warning characteristics based on the SHAP value. According to the method, the problems of deficiency of organ interaction modeling, poor generalization of small samples, stiffness of static decision and the like are solved, and the early warning and precise treatment capabilities of the sepsis-related kidney injury are remarkably improved.

A join decision manager (JDM) generates a data processing pipeline. The data processing pipeline includes at least one join operation associated with build-side row data and probe-side row data. The JDM determines the maximum cardinality associated with the probe-side row data. The JDM determines size of the build-side row data at a decision node of the at least one join operation. The JDM configures execution of the at least one join operation as one of a broadcast join or a hash-hash join based on the size of the build-side row data and the maximum cardinality.

A data processing system implements receiving an access request from the client device of a content requestor to access a content item for which access to the content item is managed by a content access management platform and obtaining access control information. The access control information comprising information associated with a content owner associated with the content item, information associated with the content requestor, and information associated with the content item. The system further implements analyzing the access control information using a first machine learning model trained to analyze the access control information and to output an access determination score, the access determination score representing a level of certainty that the content requestor should be granted access to the content item, determining an automatic access decision to grant or deny the access request based on the access determination score, and notifying the content requestor whether the access request has granted or denied based on the automatic access decision.

The invention provides a micro-grid main grid cooperative switching intelligent regulation and control method and system, and relates to the technical field of power grid regulation and control. According to the method, the historical operation data of the micro-grid and the main grid are collected in real time, and the future operation state parameter set is generated, so that the operation states of the micro-grid and the main grid are accurately predicted. The dynamic stability index of the micro-grid and the power grid health index of the main grid are calculated, the two indexes serve as input, a switching decision management model is applied to generate a switching tendency score, and the switching tendency score is compared with a dynamic decision threshold value in real time to judge whether switching conditions are met or not. According to the method, the collaborative switching control instruction is automatically generated according to the correlation index and the switching mode rule, and the micro-grid is finely pre-adjusted in advance, so that smooth seamless switching between the micro-grid and the main grid is realized when the physical switching action is executed, and the safety, the reliability and the stability of the switching operation between the micro-grid and the main grid are improved.

The invention is applicable to the technical field of power transaction and energy management, and provides a power transaction aid decision management method, which comprises the following steps of: analyzing and predicting a power market environment through an energy large model according to power transaction environment information to obtain an energy prediction result, and performing decision management on the power market environment on the basis of the energy prediction result. The energy scheduling strategy is generated for the electricity market environment through the multi-target optimization model, and the electricity transaction in the electricity market environment is scheduled according to the energy scheduling strategy, so that the decision-making balance and comprehensiveness are improved, the method can better adapt to the complex scenes such as large-scale access of new energy and frequent change of electricity market rules, and the user experience is improved. And the market risk is reduced, so that the method has better adaptability and application prospects.

The invention discloses a photovoltaic base ecological environment effect evaluation system, and relates to the technical field of effect evaluation, the photovoltaic base ecological environment effect evaluation system comprises a data acquisition module, a data processing module, an effect analysis module and a decision management module, and the effect analysis module comprises an ecological sensitive submodule, an environment bearing submodule and an effect synthesis submodule. According to the invention, multiple factors are integrated through the ecological sensitivity sub-module to quantify ecological sensitivity, surface soil crust shear strength is introduced to fill the blank of traditional evaluation, multi-dimensional evaluation is realized, the collaborative level of ecological protection and energy efficiency is quantified through the output result, and the environment bearing sub-module is used to introduce the overground wind direction and underground hydrology, so that the comprehensive evaluation of the ecological protection and energy efficiency is realized. According to the method, the influence of ecological sensitivity and installed capacity can be accurately adjusted to improve the matching degree of photovoltaic development and ecological capacity, calculation results and factors of multiple sub-modules are integrated through the effect integration sub-module, the problem that existing evaluation is inaccurate is solved, and the whole system is based on the artificial intelligence technology and has an excellent intelligent effect.

The invention belongs to the technical field of port operation management, and provides a port project whole-process decision management system based on big data analysis, which comprises the steps of performing verification analysis on a plurality of historical predicted port arrival time through hypothetical moving windows, determining an optimal moving window by using a moving window averaging method and an Euclidean calculation model, and determining the port arrival time according to the optimal moving window; obtaining a model correction factor according to historical predicted time and actual time, obtaining predicted port arriving time, comparing the predicted port arriving time with to-be-loaded departure time, if the predicted port arriving time is not matched with the to-be-loaded departure time, rejecting original stable connection ships, reselecting the ships according to a connection matching degree, and performing re-evaluation until the ships with time dimension connection matching are found from a connection ship sequence, by optimizing a moving window and introducing a correction factor, the port arrival time prediction accuracy is improved, the problem that the port ship scheduling efficiency is effectively improved due to loading and unloading caused by time mismatching is avoided, and the problems of how to accurately predict the port arrival time of the stably connected ship and how to judge whether the port arrival time is matched with the to-be-loaded departure time or not are solved.

The invention discloses a management system capable of planning fire-fighting information of urban residential areas as a whole and an early warning method thereof, and the method specifically comprises the steps: carrying out the multi-level division of a residential area plane and space, setting the residential area plane and space into a plurality of monitoring areas, deploying one or more pieces of fire-fighting monitoring equipment with a unique identity number in each monitoring area, the fire-fighting monitoring equipment is associated to the household information integration platform or the regional information integration platform based on the monitoring region where the fire-fighting monitoring equipment is located, and the information integration platforms at all levels are sequentially gathered and associated to the fire-fighting intelligent decision-making center platform to form a system for daily and disaster decision-making management. The system integrates four sub-systems of detection, information transmission, daily management and disaster situation judgment and early warning, can cover each space of a living area, realizes real-time monitoring of the fire-fighting condition of the living area, rapid data transmission, effective equipment management and accurate fire positioning and early warning, and improves the safety of the living area through cooperative work inside and outside the system. The fire-fighting information management level can be effectively improved, fire disasters can be inhibited, or fire coping strategies can be optimized.

The invention discloses a digital project management method and system based on artificial intelligence, and relates to the technical field of project management, and the method comprises the steps: constructing a project digital portrait and multi-dimensional feature analysis framework, determining an analysis domain boundary of intelligent auditing, and automatically generating an intelligent analysis grid adaptive to multi-source heterogeneous data; and performing grid division on project progress data according to an analysis domain boundary, performing adaptive partition on a project grid through an unsupervised clustering algorithm, dynamically determining a critical threshold value of risk early warning based on a project digital portrait and intelligent analysis grid and comprehensive risk stress analysis, and outputting a risk early warning decision management conclusion containing the critical threshold value. According to the management system, an intelligent analysis grid adaptive to multi-source heterogeneous data is generated by constructing a project digital portrait and a multi-dimensional feature analysis framework, and a risk early warning critical threshold value is dynamically determined, so that real-time perception, accurate analysis and intelligent decision support of the full life cycle state of a project are realized.

The invention relates to the technical field of nuclear radiation detection management, and discloses a nuclear radiation detection management system based on artificial intelligence, comprising: a dynamic sensing module which detects nuclear radiation through quantum sensing to obtain radiation data, captures NV color center fluorescence intensity, analyzes to obtain gamma ray dose rate, constructs a nanoscale olfactory receptor simulation structure, and sends the radiation data to a control module; radioactive gas is detected and cooperatively monitored; the reasoning calculation module is used for analyzing space-time correlation based on radiation data, optimizing algorithm to accelerate result output, adjusting calculation complexity by calculating dynamic weight distillation, and correcting data transmission in real time; the decision management module is used for constructing a four-dimensional causal graph based on the radiation data, performing intervention simulation, evaluating a decision long-term effect, screening an optimal decision, and performing adversarial training based on historical repair case data; and the fusion cooperation module is used for establishing a cross-mechanism federated learning framework, carrying out hash operation on the radiation data, and establishing an NFT (Network Fourier Transform) on a block chain through an intelligent contract for query, verification and transaction.

The invention is suitable for the field of power station equipment management, and provides a power station equipment health management system based on multi-modal perception and hybrid reasoning, and the system comprises a data perception layer which is used for obtaining and preprocessing multi-modal perception data of power station target equipment, and obtaining multi-modal feature data; the hybrid reasoning layer is used for taking the multi-modal characteristic data as input of a pre-constructed hybrid reasoning model to obtain an equipment fault diagnosis result; the decision management layer is used for generating an equipment health management decision according to the diagnosis result and outputting the decision; the hybrid reasoning model comprises a rule reasoning sub-layer, a deep learning sub-layer and a case reasoning sub-layer, the deep learning sub-layer adopts a lightweight Transform model, and the lightweight Transform model comprises a cross-modal attention enhancement module and a fault feature intensified training module. In conclusion, the hybrid reasoning architecture is constructed, so that the fault diagnosis accuracy of the power station equipment is obviously improved, the false alarm rate is reduced, and the technical problems of few unconventional fault samples and difficulty in diagnosis of the power station are solved.

The invention relates to the technical field of artificial intelligence and city updating, in particular to an artificial intelligence assisted city updating decision management system, which comprises an information acquisition module, a city physical examination module and a scheme generation module, the information acquisition module acquires data input by a multi-role user, processes and verifies the data and transmits the data to the city physical examination module; the city physical examination module performs multi-dimensional potential evaluation on the received data based on a machine learning model, generates a visual evaluation result and transmits the visual evaluation result to the scheme generation module; and the scheme generation module generates a structured update strategy and an effect picture according to the evaluation result in combination with the knowledge base, and feeds back the effect picture to the information acquisition module to form a data closed loop. According to the system, a multi-module technology is fused, information collection ensures efficient collaboration of multi-role data, urban physical examination accurately evaluates the land potential, scheme generation gives consideration to compliance and innovation, the system is ensured to be safe, stable and elastically expanded, urban update decision intelligence is realized, and decision scientificity is improved.

The invention relates to the technical field of intelligent manufacturing and supply chain management, in particular to an intelligent management system for titanium plate inventory and production plan collaboration. Comprises: a multi-source data fusion center for a supply chain management system to obtain operation data and generate a collaborative situation original data set; the situation quantitative analysis unit is used for receiving the collaborative situation original data set and generating a market agility demand score and an inventory liquidity current situation score; the collaborative conflict evaluation unit is used for calculating a collaborative conflict index and determining a collaborative conflict situation level; the evolution trend prediction unit is used for generating an evolution sequence of future collaborative conflict indexes; and a dynamic decision management unit for generating an immediate conversion policy in response to the collaborative conflict context level and generating a predictive inventory structure pre-configuration decision. According to the method, the accuracy and objectivity of decision making are remarkably improved, and enterprises can deeply insight into operation core contradictions.

The invention discloses a medical oncology specimen collection and management system, and relates to the technical field of specimen management, and the system comprises a collection and detection unit which is used for collecting biopsy specimens and corresponding specimen data; the tracking quality control unit is used for storing full-process information of the biopsy specimens by utilizing a block chain and dynamically evaluating and tracing quality information of the biopsy specimens; the integrated learning unit is used for constructing a multi-omics database, generating a tumor multi-omics knowledge graph and carrying out dynamic knowledge updating through data mining; and the decision management unit is used for sharing resources and monitoring authorized use records of the biopsy specimens. By integrating the block chain, federal learning and process reconstruction, full-chain upgrading of medical oncology specimens from'collection 'to'application' is realized, tumor diagnosis and treatment are promoted to step forward from'experience-driven 'to'data-driven', digitization, standardization and traceable management of biopsy specimen full-process information are realized, and the efficiency of medical oncology diagnosis and treatment is improved. And data support and decision support are provided for clinical practice and scientific research.

The application discloses a kind of based on Copula function's urban water, energy and environmental system optimization decision management method, comprising: S1. the Copula joint distribution function of water environment capacity and available energy amount in urban water is constructed;S2. the resource optimization allocation and joint risk management model of urban water, energy and environmental system is established;S3. the resource optimization allocation and joint risk management model of urban water, energy and environmental system is solved;The method realizes the optimization allocation and joint risk management of urban water-energy-environment system water and energy under uncertain conditions.Provide urban water-energy-environment system resource optimization allocation scheme and long-term planning scheme of sewage treatment process under different risk scenario combinations on the basis of maximum economic benefit, and help decision makers better realize the trade-off between joint default risk and system benefit, to realize urban risk controllable and water-energy-environment collaborative development provides scientific method.

The invention relates to the technical field of business and financial management, in particular to a business and financial fusion analysis decision management system and method based on multi-modal AI, and the method comprises the steps: configuring deep learning and testing according to financial use and formation information, and obtaining a multi-source contact analysis record; according to the multi-source connection analysis record, feature line and non-feature line difference is configured, and a fusion decision result is obtained; according to the fusion decision result, configuring information-driven machine learning and clustering analysis to obtain a decision value decision record; configuring real-time information analysis according to the real-time decision plan to obtain a real-time financial analysis record; configuring flow analysis according to the real-time financial analysis record to obtain a flow decision result; and configuring real-time information flow analysis and personalized management according to a process decision result to obtain a real-time management result. According to the method, the capability of effectively integrating different information sources is achieved, and the real-time decision-making and long-term financial demand evaluation effects are good.

The invention discloses a ship pipe network programming method based on a visual lightweight modeling technology. The ship pipe network programming method comprises the steps that a pipeline system schematic diagram of a ship pipe network is obtained; constructing a man-machine interaction graphical interface of the ship pipe network according to the schematic diagram of the pipeline system; according to system data of the pipeline system schematic diagram, an Excel database used for storing and managing ship pipe network attribute information data is constructed; integrating data in the Excel database to a man-machine interaction graphical interface, and obtaining an optimized interaction graphical interface for visual lightweight modeling; based on the optimized interactive graphical interface, decision management of the ship pipe network is achieved according to the constructed ship pipe network circulation event mechanism. The problems that a large amount of data needs to be manually input and processed, operation is complex, human errors are prone to occurring, real-time feedback and dynamic adjustment functions are lacked, the repeated utilization rate is low, and pipe network design requirements of different ship types are difficult to meet in a traditional method are solved.

The invention relates to the technical field of organization decision management, in particular to an organization decision method and device based on time sequence prediction and a knowledge graph and a medium, and the method comprises the steps: obtaining a prediction target and a decision demand corresponding to the prediction target; based on the prediction target, obtaining an organization sub-behavior transfer matrix, organization knowledge graph data, index time sequence prediction data and target weight data; based on the decision demand, determining an organization sub-behavior probability matrix according to the organization sub-behavior transfer matrix, the organization knowledge graph data, the index time sequence prediction data and the target weight data; and determining a target decision result corresponding to the decision demand according to the organization sub-behavior probability matrix, thereby realizing accurate locking of the most probable organization sub-behavior mode in a comprehensive and complex information network, considering decision-related factors from a more stereoscopic and more comprehensive perspective, and improving decision-making efficiency. And the accuracy of the target decision result is greatly improved.

The invention belongs to the technical field of reservoir dispatching, and particularly provides a cross-basin water dispatching, supply and distribution integrated decision-making method for guaranteeing ecological flow, which comprises the following steps: constructing a two-stage optimization dispatching model of a cross-basin water supply reservoir system based on a dynamic optimality principle; quantitatively describing ecological water demand processes of a water source area and a water receiving area in the cross-basin system engineering under different ecological water demand levels by utilizing a Tennant method; performing optimization calculation on the optimization scheduling model under different ecological water demand levels by utilizing dynamic planning to obtain a regional water regulation and supply process; constructing a regional water resource optimal configuration model according to the water regulation and supply results; factors such as ecological water demand guarantee degree and decision manager preference are comprehensively considered, and a practical regional water distribution scheme is formulated. According to the method, the water transfer amount and the water supply amount of the cross-basin reservoir system can be effectively determined, meanwhile, the water competition problem of'life, living and living 'of a region is solved, and technical support is provided for reasonability and scientificity of decision making of the cross-basin reservoir system.

The invention belongs to the technical field of building construction, and particularly discloses a viaduct pier ladder cage system capable of intelligently monitoring and early warning risks and an early warning method.The viaduct pier ladder cage system comprises a ladder cage, a wind speed monitor, a laser displacement sensor, a ladder cage intelligent door, an electronic information board, a signal alarm lamp, a ladder cage fence, a console and a master control room alarm; a plurality of laser displacement sensors are installed on the elevator cage and monitor distance parameters with a viaduct pier in real time, a wind speed monitor feeds back the wind speed condition of a construction site in real time, the gradient of the elevator cage is calculated through the distance and wind speed parameters, whether the elevator cage is safe or not is judged according to set conditions, and risks are early warned through an alarm installed at the entrance of the elevator cage. And the risk judgment result is transmitted to a master control room through signal transmission for decision management. According to the invention, the problem that the traditional elevator technology lacks a field monitoring and early warning alarm system when the elevator inclines is solved, and early warning can be provided when the elevator inclines or the use risk exists in severe weather.

The invention relates to the technical field of production decision management, and discloses a factory production decision management method and system based on an MES (Manufacturing Execution System), and the method comprises the steps: obtaining monitoring data through a sensing module, carrying out the analysis and classification, generating a production monitoring information flow, calling a production decision record, and carrying out the multi-dimensional analysis with an overall production target, so as to carry out the prediction simulation; the method comprises the steps of obtaining a reference decision chain, carrying out preliminary material scheduling and task allocation, analyzing decision computing power requirements, identifying idle computing power time periods, positioning abnormal feedback information, carrying out bidirectional equilibrium analysis, generating an expansion decision chain, constructing an expected decision tree and optimizing production scheduling and task allocation. According to the invention, resource allocation is optimized, the capability of dealing with abnormities of factories is improved, intelligent production management is promoted, and the problem of low efficiency of production decision making in the prior art is solved.