134 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 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 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 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 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 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.

The invention discloses a power grid operation decision management system based on artificial intelligence, and the system comprises an application service layer which is used for receiving a power grid operation financial problem text transmitted by a user through a user side, and transmitting the power grid operation financial problem text to a semantic analysis module; the semantic analysis module is used for carrying out natural language understanding on the power grid operation financial problem text to obtain a standardized problem text; the knowledge retrieval module is used for performing vector similarity retrieval in the data resource layer based on the standardized problem text and recalling candidate fragments; the intelligent question and answer module is used for obtaining a dynamic recall threshold value, filtering the candidate segments according to the dynamic recall threshold value, generating decision answers corresponding to the power grid operation financial question text according to the reserved candidate segments through a pre-trained AI model, and returning the decision answers to the user side; the data resource layer is used for dynamically constructing a power grid exclusive knowledge base; and the model optimization layer is used for optimizing the AI model based on the feedback of the decision answer.

The invention relates to the technical field of visualization and diagnosis systems, in particular to an intelligent visualization and diagnosis system for key performance indexes of a 10-kilovolt power grid. Comprising a big data integration and association module, an intelligent decision management module, an intelligent diagnosis and analysis module, a key performance index evaluation module, a multi-dimensional early warning module, an intelligent processing module and a data visualization module, and through cooperative work of all the modules, full-process optimization from data acquisition, processing and analysis to visual display and intelligent processing is achieved. The system not only can help power grid management personnel to quickly master the network operation condition and make a correct decision, but also can find and solve potential problems in advance through intelligent early warning and processing suggestions. According to the comprehensive and intelligent solution, the operation efficiency, reliability and safety of the power distribution network are greatly improved, and powerful technical support is provided for construction and operation of an intelligent power grid.

The invention relates to the technical field of data visualization, and provides an operation and maintenance data visualization method and system based on a smart power grid, which are used for improving the efficiency and the intelligent degree of power grid operation and maintenance visualization so as to improve the timeliness of power grid operation and maintenance decision management, and the method comprises the following steps: collecting a multi-source heterogeneous operation and maintenance data set in the smart power grid in real time; performing cross-modal cleaning and time alignment processing on the operation and maintenance data set to generate a preprocessed data set; executing dynamic feature fusion and correlation analysis on the preprocessed data set to generate an equipment state feature set and an equipment-environment interaction feature set; based on the equipment state feature set and the equipment-environment interaction feature set, generating an interactive visualization element set through a multi-dimensional visualization strategy; performing dynamic rendering and layout optimization on the interactive visual element set, and outputting a power grid operation and maintenance visual interface updated in real time; the power grid operation and maintenance visual interface is configured with a user operation priority response mechanism used for processing interaction conflicts.

The invention is suitable for the technical field of enterprise decision management, and provides an intelligent decision method and system based on a large model, and the method comprises the steps: obtaining a decision target, and obtaining historical data according to the decision target; if the decision target is judged to improve the efficiency, obtaining a production demand, obtaining resource data and obtaining an existing process, analyzing the existing process according to the production demand and the resource data, and identifying unreasonable allocation of low-efficiency links and tasks in the existing process; if the decision target is determined to increase the income, obtaining an existing marketing strategy, analyzing the existing marketing strategy according to the existing product and the existing product price, and identifying whether the conversion rate of the existing marketing strategy reaches the standard; the method comprises the steps of obtaining an optimization result, obtaining an optimization qualified threshold value, comparing the optimization result with the threshold value, and judging whether optimization is qualified or not. The problems that a decision method cannot be provided according to a specific decision direction, improvement suggestions cannot be provided for an existing method, and meanwhile the effect of the decision method cannot be evaluated are solved.

The invention discloses a factory production scheduling decision management system with combination of multi-factor intelligent analysis, which belongs to the technical field of intelligent production decision and specifically comprises an order obtaining and sorting unit, a task decomposition unit, a capacity resource matching unit and a production execution unit. According to the method, a priority evaluation coefficient is obtained based on order information and is used as dynamic priority division guidance to construct a priority initial sequence, and production work orders matched with priority attributes are created according to the priority initial sequence from high to low. And matching and scheduling process tasks of production work orders and productivity resources through reverse reasoning and an intelligent algorithm on the basis of the sub-time windows, integrating synchronous influence factors of'changing orders' in real time in the production process, rejecting and cancelling order products, determining priority attributes based on priority evaluation coefficients of inserted order products, and completing the production process. A local dynamic adjustment method of'front maintenance 'and'rear insertion' is adopted, and a production scheduling mode which is driven by multi-source data, intelligently optimized and dynamically and circularly adjusted in real time is achieved.