60 results about "Intelligent decision support system" patented technology

The invention relates to the technical field of cultivated land resource management, and discloses a data optimization and intelligent decision support system in cultivated land resource early warning, which comprises a cultivated land data acquisition module, a soil index analysis module, a degradation identification module, a land force deduction module, a threshold adaptation module and a decision generation module. The cultivated land data acquisition module acquires cultivated land multi-dimensional attribute data in real time and partitions the data; the soil index analysis module divides soil fertility units and fuses soil indexes to generate resource state vectors; the degradation identification module extracts a core degradation factor, establishes a risk layering model and obtains a data correction coefficient; the land force deduction module identifies the association rule and the dynamic correction factor and calculates the land force attenuation gradient; the threshold adaptation module derives an optimal early warning threshold and generates a risk difference sequence; and the decision generation module analyzes the sequence and integrates the sequence into a regulation and control decision scheme. According to the system, intelligent analysis of cultivated land data, dynamic early warning of degradation risks and accurate decision making are realized, and scientificity and efficiency of cultivated land resource management are improved.

The invention relates to the field of building settlement monitoring, and discloses a multi-source data-artificial intelligence fused building deviation rectification settlement prediction method and system, and the method comprises the following steps: multi-source data collection: obtaining multi-class sensor data related to building settlement, the multiple types of sensors comprise a level gauge, a total station, an underground water level sensor and a soil body pressure sensor, and multi-modal data input is formed; the invention also discloses a multi-source data-artificial intelligence fused building deviation rectification settlement prediction system. The system comprises a data acquisition module, a data processing module, a deep learning prediction module, a model optimization module, an intelligent early warning module and a user interaction module. According to the invention, by dynamically adjusting the settlement risk threshold and introducing a multi-stage early warning signal and an intelligent decision support system, accurate early warning and real-time decision support of the settlement risk are realized, and the adaptability, accuracy and response efficiency of the system are significantly improved.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention relates to the technical field of building informatization and intelligent construction, and discloses a building engineering digital twinning progress management and control method and system.The building engineering digital twinning progress management and control method comprises the steps that multi-source heterogeneous data in the building engineering construction process is collected; carrying out fusion processing on the original data set by adopting a space-time alignment network; updating the digital twin body by adopting an incremental state estimation method; predicting a future progress by adopting a model fusing a graph neural network and a time sequence attention mechanism; a multi-level detection mechanism is adopted to identify progress deviation and analyze a root cause; generating an optimized resource scheduling scheme by adopting a reinforcement learning method; an intelligent decision support system is constructed, and visual display and interaction functions are provided; according to the method, the graph neural network and the time sequence attention mechanism are fused, the progress prediction model capable of capturing the task topology dependency relationship and the time sequence evolution characteristics at the same time is constructed, and the accuracy of progress prediction is improved.

The invention relates to the technical field of safety management, and discloses a hydraulic engineering construction potential safety hazard monitoring method based on Internet of Things, which comprises the following steps: S1, real-time data acquisition: arranging a plurality of sensors at a construction site to acquire data and uploading the data in a wireless communication mode, the method comprises the steps of S1, data acquisition and analysis, S2, data pre-processing and analysis: pre-processing acquired data at a cloud or an edge computing platform, S3, adaptive adjustment: automatically adjusting sensor layout and monitoring frequency according to construction stages and environment changes, S4, multi-source data fusion, S5, alarm and emergency: triggering alarm when monitoring data is abnormal, providing emergency measures by means of an intelligent decision support system, and carrying out emergency management. S6, visualization and decision support; and S7, intelligent feedback and optimization. By adopting a dynamic threshold adjustment mechanism and a multi-modal data fusion technology, a more accurate hidden danger identification effect is achieved, the phenomena of missing report and false report are remarkably reduced, and the effectiveness of early warning is improved.

The invention provides an underwater robot intelligent navigation and environment sensing system based on a multi-sensor fusion collaborative technology. The system integrates a plurality of sensors such as sonar, optical, electromagnetic and the like, and realizes comprehensive data acquisition and real-time perception of an underwater environment. By applying advanced algorithms such as Kalman filtering and a neural network, the system performs synchronization, preprocessing and deep fusion on multi-source data, and the consistency and accuracy of information are remarkably improved. In addition, the system also has adaptive navigation and environment perception capabilities, can dynamically adjust a navigation path and a perception strategy according to fused data, and adapts to a complex and changeable underwater environment. And the intelligent decision support system provides decision assistance for an operator in combination with an environment perception result and a task target. And due to the design of a user interaction and monitoring interface, the usability and real-time monitoring capability of the system are ensured. According to the implementation of the invention, the precision and efficiency of the underwater robot in tasks such as monitoring and exploration are greatly improved.

The invention provides an equipment operation and maintenance intelligent decision support system based on large model reasoning, and aims to improve the accuracy of equipment fault prediction and the intelligent level of operation and maintenance decision. The system obtains equipment operation data in real time through a data acquisition module, performs reasoning analysis on equipment health conditions by using a large-scale pre-trained deep learning model, and predicts the residual service life of equipment and various fault types. The system combines multi-task learning and a multi-objective optimization algorithm, intelligently optimizes operation and maintenance decisions, generates personalized maintenance suggestions, helps operation and maintenance personnel to effectively reduce the downtime of equipment, reduces the maintenance cost, and prolongs the service life of the equipment. The system can be widely applied to the fields of manufacturing industry, energy, traffic and the like, and the accuracy and efficiency of equipment management are remarkably improved.

The invention discloses an intelligent decision support system and method for grassroots social governance based on multi-modal fusion. The method comprises the following steps: S1, acquiring and preprocessing multi-modal data in the field of grassroots social governance; s2, constructing a heterogeneous graph structure according to the data type and the treatment subject category; s3, extracting single-modal features respectively and generating cross-modal dynamic association features; s4, performing collaborative optimization on the heterogeneous graph structure and the cross-modal fusion parameters by adopting a flying fox optimization algorithm; s5, analyzing data in real time according to the optimized heterogeneous graph, and generating potential risk early warning, event trend prediction and event association deduction information; s6, pushing an auxiliary decision-making scheme and a disposal strategy in real time; and S7, continuously optimizing the heterogeneous graph structure by using a feedback result. The decision-making efficiency and accuracy of grassroots social governance are effectively improved.

The invention relates to the technical field of intelligent medical multi-modal data analysis and nursing aid decision making, in particular to a neurology nursing risk dynamic early warning and intelligent decision support system. Comprising the steps that a data acquisition module acquires a physiological time sequence and nursing logs to obtain a multi-modal original data set; the feature extraction module performs nonlinear dynamic analysis on the physiological data to generate a state manifold vector, and performs semantic embedding on a log to generate an intervention state tensor containing spatio-temporal features; the causal reasoning module constructs a dynamic causal map, maps vectors and tensors as nodes, and adjusts transmission weights by using the tensors to suppress non-pathological fluctuations; the decision-making module calculates dynamic risks and difference values based on atlas anti-fact deduction, and generates graded early warning and an optimal nursing path; the feedback module updates the atlas parameters according to the processing result. According to the system, the problem of high-frequency false alarm caused by daily nursing actions of traditional monitoring equipment is effectively solved, the alarm fatigue of medical staff is relieved, and focusing on real critical events is ensured.

The invention discloses a multi-dimensional analysis house building health monitoring and early warning system and method, and relates to the technical field of monitoring and early warning. During operation of the system, various sensors are deployed to monitor the physical states of different materials in a building, collect surrounding environment data and transmit the data to a central processing unit in real time, and through a data fusion algorithm, real-time monitoring and early warning are achieved; the method comprises the following steps: integrating sensor data from different materials into a unified data set, preprocessing and normalizing the data, constructing a multi-material interaction mathematical model, calculating an effective stress sigma eff of a building structure, constructing a building overall health assessment model, and calculating a building health comprehensive index HI; according to the building health comprehensive index HI and the comparison result of the health index of each material and the safety threshold value, the health change of the building is detected in real time, an intelligent decision support system is provided, and building management personnel are helped to carry out long-term maintenance planning and resource optimization distribution.

The invention belongs to the technical field of gas pipelines, and particularly relates to a gas engineering construction quality evaluation system based on big data, which comprises the following steps of: firstly, constructing a defect feature tensor, introducing a space-time grid analysis technology to analyze defect disposal aging interval data and location defect reproduction frequency data, and constructing a defect feature tensor on the basis of a preset quality-efficiency reliability quantification rule; defect features are converted into quantifiable quality-effect scores, then deep fusion of multi-source heterogeneous data is achieved by establishing a space-time incidence matrix of welding defects, welding process parameters and environment monitoring parameters, and on this basis, a fuzzy correlation analysis technology and a preset process compliance quantification rule are adopted to achieve quality-effect evaluation of the welding defects. Complex process parameters and environmental conditions are converted into quantifiable process scores, the process scores and quality-effect scores are integrated to generate a gas pipeline welding quality comprehensive evaluation value of a target contractor, and a quantifiable intelligent decision support system is provided for gas pipeline construction quality management and control of the contractor.

The invention discloses an early warning method and system for coal and gas outburst danger of a mechanical driving face, and belongs to the technical field of coal mine safety, and the method specifically comprises the following steps: collecting data of the working face in real time, constructing a comprehensive early warning model to realize trend prediction and anomaly detection, triggering early warning and evaluating a risk level; a mine GIS is combined, an early warning area is accurately positioned, an intelligent decision support system of a knowledge graph and a rule engine is used, an optimal emergency plan is rapidly matched, and an emergency response plan is generated; according to the system and the method, workers of the heading machine can take refuge or escape measures in time when an accident occurs, so that the mine operation safety and the emergency response efficiency are improved, and the operation effect of the heading machine is improved.

The invention discloses a mental stress assessment and intelligent dredging method and system, and belongs to the technical field of digital medical treatment and health informatics. Multi-modal physiological signals are continuously collected through the wearable device, medical diagnosis level pressure state evaluation is carried out based on the personalized physiological baseline, and personalized pressure indexes and levels are generated; secondly, when it is diagnosed that the pressure level exceeds the standard, the system serves as an intelligent decision support system, the environment and schedule information after privacy protection processing is fused, and an optimal grooming action is dynamically selected from a predefined intervention action library and executed; finally, the system serves as a continuous learning system, the intervention efficiency is evaluated in real time according to feedback data of the pressure index after execution, the decision model is updated, and collaborative self-optimization of the diagnosis strategy and the intervention strategy is achieved. According to the method, medical diagnosis, personalized treatment decision and adaptive learning are integrated, and the systematicness, accuracy and intelligent level of mental stress related health problem management are improved.

The invention discloses an agricultural crop growth parameter collecting, monitoring and tracing all-in-one machine which comprises an all-in-one machine body, and a multi-parameter sensor module, a data collector, a wireless communication module and a power management module are arranged outside the all-in-one machine body. The system comprises an advanced multi-parameter sensor module, a high-efficiency data acquisition and processing system, a reliable wireless communication module, a data processing platform, an intelligent decision support system and a block chain data tracing platform, and is characterized in that the advanced multi-parameter sensor module, the high-efficiency data acquisition and processing system, the reliable wireless communication module and the intelligent decision support system are integrated; key growth parameters such as soil humidity, illumination intensity, air temperature and humidity and carbon dioxide concentration can be monitored in real time, and accurate irrigation and fertilization suggestions can be provided through data analysis. Besides, the system adopts a data tracing platform constructed by a block chain technology, the authenticity and non-tampering property of all monitoring data are ensured, and the transparency and credibility of the whole agricultural production process are enhanced, so that the quality and yield of agricultural products are effectively improved.

The invention relates to the technical field, in particular to a cerebral hemorrhage intelligent decision support system based on deep adaptive feature fusion, which comprises a multi-modal data standardization module, a cross-modal feature fusion module, a focus segmentation calculation module, a hemorrhage type identification module and an illness state grading output module. According to the method, deep association between fusion features and bleeding types is mined through an attention mechanism, related feature indexes are converted into standardized scores by referring to clinical common scoring standards, model attention weight distribution is optimized by combining actual prognosis result deviation, the suitability of illness state grading and the clinical scoring standards is improved, and the probability of illness state grading is lowered. An accurate grading result and prognosis prediction reference are provided for clinical treatment decision and rehabilitation intervention; effective data are screened from multiple types of brain images according to image quality related indexes, clinical text key information extraction and association labeling are combined, data integrity and consistency verification is carried out at the same time, and the standardization degree of multi-source data is improved.

The application discloses an intelligent decision support system based on big data and artificial intelligence, and relates to the technical field of decision support, comprising: a decision data acquisition module, which is used for acquiring decision data, and performing feature extraction and anchoring effect feature marking; a propagation path identification module, which constructs a decision participant opinion influence bipartite graph based on a graph neural network, identifies a core anchor source and a strong cascading propagation path thereof, and outputs an anchoring effect strength value; a weight adjustment module, which is used for generating a weight adjustment factor based on the anchoring effect strength value, generating a weight through iterative adjustment and correlation correction, and generating a decision scheme according to weighted statistics; and a decision correction module, which is used for decomposing a historical decision implementation effect signal to identify a deviation component, generating an counterfactual decision scheme based on causal inference, and correcting a deviation of an actual decision scheme; and effectively solves the information cascade and collective irrationality problems caused by over-amplification of a few opinions or authority influence in traditional group decision-making.

The invention belongs to the technical field of medical information, and particularly relates to a newly diagnosed HIV infected person antiviral treatment intelligent decision support system which comprises a multi-source data integration module, a medicine knowledge module, an individualized decision support module, an interactive output module, a knowledge optimization module and a safety and privacy protection module. The system replaces doctors to query and integrate patient data across multiple systems, clinical time is saved, key information omission is avoided, comprehensive data support is provided for treatment decisions, experience decisions are converted into systematic analysis and graded risk prompts are generated by means of an individualized decision support module, scheme deviation is effectively reduced, the medication risk is reduced, and the treatment efficiency is improved. The method assists the basic level in improving the treatment normalization, allows doctors to input new experience and new achievements summarized in practice into the system after double-blind auditing, and provides continuously iterative knowledge support for the scientificity of the antiviral treatment decision of the newly diagnosed HIV infected person.

The invention discloses an intelligent decision support system for urban water conservancy project multi-source data fusion, and relates to the field of engineering planning. Forming a feature region containing a green belt; analyzing to obtain at least one terrain low point and at least one terrain high point; obtaining an operation labor-consuming index of the distribution block; forming at least one first planning scheme; forming a second planning scheme corresponding to the first planning scheme; and combining the first planning scheme and the corresponding second planning scheme into a third planning scheme, and calculating an operation difficulty value of the third planning scheme. The urban water conservancy project to be repaired is classified to form a feature region containing a green belt, the operation labor-consuming index of a distribution block is obtained, and the operation difficulty value of a third planning scheme is calculated, so that the soil excavation difficulty, the excavation length and the relative position relationship between the water conservancy project and the green belt can be synthesized; and the reasonability of the designed planning scheme is ensured.

This invention relates to an intelligent decision support system for rectal cancer prognosis integrating digital pathological images, belonging to the field of medical image processing technology. The system includes a data acquisition module for collecting multiple sets of sample data; a feature selection module for identifying multiple key medical imaging features of rectal cancer based on the multiple sets of sample data; a model building module for acquiring a pre-trained deep learning model and constructing a rectal cancer prognosis prediction model based on transfer learning, the pre-trained deep learning model, the multiple sets of sample data, and the multiple key medical imaging features of rectal cancer; an image acquisition module for acquiring pre- and post-operative medical images of the rectal cancer patient to be evaluated; and a recurrence prediction module for predicting the recurrence probability of the rectal cancer patient to be evaluated based on the pre- and post-operative medical images of the patient using the rectal cancer prognosis prediction model. This system has the advantage of improving the accuracy of non-invasive prediction of postoperative recurrence of rectal cancer.

The utility model belongs to the technical field of civil engineering equipment, and particularly relates to an intelligent automatic piling vibroflotation system which comprises a vibroflotation device, a multifunctional pile frame, a data collection module, a data transmitting and receiving module, a data processing center, an intelligent decision support system, an intelligent control system, a predictive maintenance module and a user interface module. The first hydraulic rod and the second hydraulic rod are matched with a triangular supporting structure of the base and the beam frame, so that accurate adjustment and high stability of the angle of the mast are realized; the data collection module integrates various sensors, a high-definition camera and a voice recognition module, and collects construction site data in real time; the data transmitting and receiving module utilizes a Beidou cloud system and a 5G internet of things to realize efficient and stable transmission of data; the data processing center is combined with the intelligent decision support system to generate optimized construction parameters based on real-time and historical data; the intelligent control system dynamically adjusts parameters of the vibroflot and the pile frame, and accurate construction is ensured.

The invention discloses an intelligent optimization decision-making method and system for steel structure manufacturing based on a digital technology, belongs to the technical field of steel structure manufacturing, and aims to solve the problems of limitation to single-link optimization, insufficient real-time data processing capability and poor system integration in the prior art, and the method comprises the following steps: S1, data acquisition and sensor arrangement; s2, data cleaning and fusion; s3, constructing a global optimization model; s4, establishing an intelligent decision support system; s5, real-time monitoring and feedback are carried out; s6, system integration and collaboration; s7, scheduling and resource management are optimized; by constructing the multi-link global optimization model, global optimization of the whole steel structure manufacturing process is achieved, links from data acquisition, optimization decision, resource scheduling to process feedback and the like are covered, optimization of the links can be coordinated by comprehensively considering the mutual relation between different production links, and the production efficiency is improved. Therefore, the overall production efficiency and quality are improved, and the method is not limited to optimization of a single link.

The present invention discloses a method and system for early warning of coal and gas outburst hazards in a machine-dug working face, which belongs to the field of coal mine safety technology. The method and system specifically include: real-time collection of working face data, construction of a comprehensive early warning model to achieve trend prediction and anomaly detection, triggering early warning and evaluating risk levels; combining with mine GIS to accurately locate the early warning area, using an intelligent decision support system of knowledge graph and rule engine to quickly match the optimal emergency plan and generate an emergency response plan; in the present invention, tunneling machine workers can take shelter or escape measures in time when an accident occurs, thereby improving mine operation safety and emergency response efficiency, and improving the operation effect of the tunneling machine.

The invention discloses a metro station emergency evacuation route planning method based on a brain-like pulse neural network, and belongs to the crossing field of traffic transportation and intelligent calculation. According to the method, an evacuation environment map structure is constructed on the basis of a brain-like spiking neural network (BSNN), crowd heterogeneity modeling and a personalized path objective function are combined, and the multi-objective path planning method for the subway station large passenger flow scene is provided. According to the method, neural network modeling based on a topological graph, target function design of multi-type passenger demands, a pulse-driven rapid optimization mechanism and a congestion prediction algorithm based on membrane potential change are fused, and adaptive path adjustment and congestion avoidance and diversion among different demand personnel are realized. The system dynamically generates a personalized path through a time sequence control and path state iteration updating mechanism, and obviously improves the evacuation efficiency and the road utilization rate in a high-density people stream environment. Simulation experiments show that the method can shorten the overall evacuation time by about 48% and improve the path utilization rate by 27% in the subway peak period scene, has good response speed and self-adjusting ability, and is suitable for an intelligent decision support system of a multi-scene emergency evacuation task.

This invention relates to the field of enterprise management technology, and in particular to an intelligent decision support system and method based on cognitive logic and contextualized semantics. The method acquires enterprise decision-making data through multi-source data monitoring and preprocesses it to generate cross-modal enterprise scenario cognitive information; analyzes cross-modal correlations between data, integrates cognitive logic reasoning rules and semantic understanding models, and constructs contextualized semantic decision fusion features; trains an enterprise decision quality assessment model based on historical cases to perceive the quality of the current decision-making scenario and outputs decision quality information; judges the execution effect in conjunction with expected goals, and if deviations from the goals are detected, an optimization mechanism is initiated, using a historical knowledge base and multi-objective optimization algorithms to generate candidate solutions and select the optimal solution, thereby achieving intelligent adjustment of the decision-making plan. This method integrates multi-source heterogeneous data and cognitive logic, enhancing the semantic understanding, dynamic evaluation, and adaptive optimization capabilities of the decision-making system, and improving the scientific rigor and real-time performance of enterprise decisions.

The invention discloses an intelligent decision support system for rainfall relevance tunnel deformation dynamic regulation and control, particularly relates to the technical field of tunnel safety monitoring and regulation and control, and is used for solving the problem of local deformation prediction deviation caused by rainfall uniform distribution assumption in the prior art. Rainfall space distribution data and surrounding rock deformation monitoring data of a tunnel coverage area are acquired in real time through a data acquisition module, rainfall time modes are classified and dynamic weight coefficients are allocated by utilizing a classification weighting module, and a high-resolution space grid mapping relation between rainfall capacity and surrounding rock deformation is established in combination with a grid mapping module; furthermore, seepage guidance parameters and deformation diffusion risk parameters of each grid unit are quantified through a seepage analysis module, a health assessment module corrects a grading result based on a historical deformation mode reference library, and finally, a regulation and control generation module outputs a result matched with a risk grade, so that the tunnel safety management and control capability in a complex rainfall scene is remarkably improved.

The present invention relates to the field of enterprise resource planning technology, specifically to an intelligent decision support system based on ERP data, which includes a configuration factor stability determination module, a responsibility intersection node screening module, a path load range determination module, a reverse path verification module, and a cold standby path loading module. In the present invention, by extracting the configuration call frequency and sequence and constructing a stability determination vector, multi-dimensional data association is performed in combination with job task history and material classification, and the responsibility intersection is judged by superimposing department concentration, a configuration combination with continuity and inheritance is generated, call data and module record calculation load boundaries are introduced at the path level, a multi-layer comparison mechanism of the main path and the backup path is constructed, and intelligent recommendation of path switching is realized by screening similar ratios, thereby improving the stability of the configuration combination, the rationality of path selection, and the synergy of resource calls, and enhancing the system's strategy adaptation capability and decision support efficiency in multi-role and multi-path scenarios.

The present invention discloses a high-performance multi-axis automated control platform for component machining, which relates to the field of machining automation technology. It is used to address the problem that the assessment of component machining difficulty often relies on manual experience or simple rules, which can lead to strong subjectivity and inaccurate assessments. Existing manufacturing processes often rely on a large amount of manual intervention and experience, and lack a fully automated, intelligent decision support system. The machining difficulty of each component varies depending on the strength and hardness of the raw materials, as well as the number of machining steps and tolerance requirements. Parts are classified according to their difficulty, and parts that are difficult to machine are processed step by step. The system can automatically select appropriate strategies and algorithms based on actual machining requirements, thereby improving machining efficiency and quality, reducing manual intervention, and increasing production efficiency. The system can also make adaptive adjustments based on real-time data during machining, improving the precision and flexibility of the machining process.

The invention belongs to the technical field of data processing, and particularly relates to an intelligent decision support system based on multiple AI modules. The intelligent quality inspection module outputs data after quality inspection through operations such as text conversion and natural language processing; the data analysis module calls a multi-factor dynamic attribution algorithm to position abnormal data and key influence factors, generates decision suggestions in combination with historical data, and constructs a visual human-computer interaction interface by relying on a generative AI; the knowledge graph module integrates the data after quality inspection and internal and external data of an enterprise, and builds a knowledge graph service database; the intelligent pair training module constructs a real business scene and an intelligent pair training robot based on a large model and a knowledge graph business database, and generates a training task according to decision suggestions. According to the invention, the problems of low decision-making efficiency and low decision-making precision of a traditional decision-making mode in the prior art can be solved.

The invention discloses an enterprise informatization intelligent decision support system based on big data, which belongs to the field of enterprise informatization management and comprises an acquisition processing module, a storage management module, an abnormity early warning module, a dynamic analysis module, a data fusion module, an analysis optimization module, an explanation generation module and a decision execution module. According to the method, decision deviation caused by data errors can be avoided, enterprise risks are comprehensively evaluated from multiple dimensions of manpower, resources, productivity and logistics, scientificity and timeliness of decision are improved, and risk management and decision support requirements of different enterprises at different stages are met; meanwhile, efficient information fusion can be achieved, it is guaranteed that decision basis is comprehensive and accurate, it is guaranteed that the scheme is scientific and reasonable, enterprise operation cost is reduced, reliability and practicability of decisions are improved, and understandability of the decisions is enhanced.

The invention discloses an intelligent grass and animal husbandry logistics intelligent decision support system based on a knowledge graph, and relates to the technical field of logistics management. Through a data driving mode, the risk value of the transmission path is quantified by innovatively combining the reliability index and the collaboration degree value, so that a real-time, efficient and intelligent optimization scheme is provided for a grass and animal husbandry logistics system. The system not only can dynamically update the importance coefficient of the supply chain node, but also can accurately calculate the reliability index of the node by deeply analyzing the historical transportation data of each node, and constructs a cooperative relationship matrix by using the number of common suppliers and the historical cooperation frequency so as to evaluate the cooperation closeness between the nodes. Based on the comprehensive indexes, the system can quantify the risk value of each potential path in real time, and it is ensured that quick response is made in a complex and changeable logistics environment.