75 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 discloses a campus smart canteen management platform based on AI and IOT. The campus intelligent canteen management platform comprises a global sensing system, an intelligent decision support system, a credible tracing system, a safety and supervision system and a user experience optimization system. The global sensing system is used for collecting multi-modal data and carrying out data fusion and preprocessing on the multi-modal data; the intelligent decision support system is used for performing multifunctional analysis on the fused and preprocessed multi-modal data to obtain a management decision; the credible traceability system is used for recording digital traceability information, storing reserved food samples and detecting pesticide residues in real time; the safety and supervision system is used for providing food reserved sample management, food quality event monitoring and analysis, supervision record management and parent applet functions; and the user experience optimization system is used for providing health inspection, health monitoring and personalized suggestions, identity verification and payment modes. In this way, the overall level of canteen management is 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 provides an unknown environment adaptive navigation control system based on deep learning, and aims to improve the autonomous navigation capability of a machine in a complex and unknown environment. According to the system, an advanced sensing module is adopted, multi-sensor data are combined, and a dynamic environment is quickly and accurately sensed through a deep learning algorithm. The online environment modeling mechanism can update the environment information in real time without depending on a pre-constructed map. And by utilizing a reinforcement learning algorithm, the system can autonomously learn and optimize path planning, and meanwhile, energy consumption, time efficiency and safety are considered. An innovative multi-mode obstacle avoidance strategy is adopted, and obstacle avoidance behaviors are dynamically adjusted according to environment complexity and machine states. The intelligent decision support system combines a decision tree and fuzzy logic, and provides fast and accurate decision ability. The adaptive motion control algorithm optimizes path tracking performance and adapts to dynamic characteristics and environmental conditions of a machine. In addition, the collaborative job mechanism supports efficient communication and task collaboration in a multi-machine system.

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 dangerous chemical storage and transportation, and discloses an intelligent dangerous chemical storage and transportation system, which comprises a sensing layer provided with an RFID automatic identification module, an environment monitoring module and a video monitoring and behavior analysis module; the data processing layer is configured with a data acquisition gateway, a data storage platform and a data processing and analyzing module; the application layer is configured with a management platform, an intelligent early warning and alarm system, an intelligent decision support system and a report generation and data archiving system; all the layers are connected through a communication network and are used for realizing real-time monitoring and management of the whole storage and transportation process of the hazardous chemical substances. By integrating technologies of RFID automatic identification, environment monitoring, video monitoring, behavior analysis and the like, real-time monitoring and automatic early warning of the whole storage and transportation process of the hazardous chemical substances are realized. Once environmental parameters exceed standards or abnormal behaviors occur, the system gives an alarm immediately and starts emergency response, the safety of the storage and transportation process is remarkably improved, and accidents are prevented and reduced.

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 relates to the technical field of data analysis and support, and discloses a landscaping intelligent decision support system based on big data analysis. The system specifically comprises a greening data acquisition module used for acquiring growth state data and soil state data of greening plants in a transition area in a garden, and a morphological evolution degree evaluation module used for analyzing and evaluating morphological change conditions of the greening plants in the transition area in the garden, the resource balance degree evaluation module is used for analyzing and evaluating resource occupation conditions among green plants in a transition area in a garden; the decision support model construction module is used for constructing a decision support model; and the decision suggestion output module is used for converting decision support coefficients generated by the decision support model into maintenance suggestions of landscaping. The problem that in the prior art, when plant varieties need to be replaced in landscaping, the growth state and soil conditions of greening plants in a garden transition area cannot be comprehensively monitored in real time is solved.

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 a high-efficiency automatic control platform for multi-axis machining of parts, relates to the technical field of machining automation, and aims to solve the problems that the evaluation of the machining difficulty of the parts always depends on artificial experience or simple rules, so that the subjectivity is high, the evaluation is not accurate enough, and the production cost is high in the existing manufacturing process which always depends on a large amount of artificial intervention and experience. And a full-automatic and intelligent decision support system is lacked. According to different processing difficulty degrees of parts caused by different strength and hardness of raw materials and different processing step number and tolerance requirements, the parts which are difficult to process are processed step by step according to the classification of the difficulty degrees of the parts, and the system can automatically select proper strategies and algorithms according to actual processing requirements, so that the processing efficiency is improved. The machining efficiency and quality are improved, human intervention is reduced, the production efficiency is improved, the system can conduct self-adaptive adjustment according to real-time data in the machining process, and the precision and flexibility of the machining process 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 of emergency communication, in particular to an emergency command and dispatch optimization platform based on satellite super-fusion intercommunication digitization, which is high in reliability, high in throughput and rich in function, and comprises a satellite portable base station used for quickly establishing an emergency command network through a satellite link; the satellite vehicle-mounted base station is provided with a flat plate type vehicle-mounted communication-in-motion antenna; the emergency command portable box is integrated with an audio and video fusion communication command system and can be started by one key; the 4G / 5G mobile base station is used for providing field mobile network coverage; the converged communication system is used for realizing interconnection and intercommunication of multiple communication modes; the security gateway is used for guaranteeing the security and confidentiality of data transmission; the big data processing module is used for collecting and analyzing data from various sensors, cameras and communication equipment and carrying out real-time situation awareness; and the artificial intelligence decision support system performs trend prediction, risk assessment and optimal path planning by using a machine learning algorithm.

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 discloses an intelligent decision support system based on magnetic separation sewage treatment, and particularly relates to the technical field of sewage treatment.The intelligent decision support system is characterized in that an adjustment coefficient is obtained by obtaining water quality data of to-be-treated sewage; a preliminary magnetic field intensity value is obtained by combining a preset standard magnetic field intensity value, and the preliminary magnetic field intensity value is applied to the sewage; obtaining the particle aggregation block size, the pollutant concentration and the adsorbent capture rate of the sewage in a preset time period to obtain a magnetic field evaluation coefficient; the magnetic field evaluation coefficient is compared with the preset magnetic field evaluation coefficient threshold value, and when the magnetic field evaluation coefficient is not smaller than the preset magnetic field evaluation coefficient threshold value, the magnetic field intensity applied during sewage treatment is continuously adjusted to ensure that the magnetic field evaluation coefficient is always smaller than the preset magnetic field evaluation coefficient threshold value, so that the magnetic field intensity can be dynamically adjusted according to actual conditions; the possible sewage treatment effect is optimal, and meanwhile, the influence on the environment is reduced.

The invention provides a mine digital intelligent control device, which relates to the field of mine mining and comprises a data acquisition module, a data processing and analysis module, an intelligent decision support system, an automatic and intelligent control module, a safety monitoring and early warning module and a comprehensive information display platform. By means of the intelligent decision support system and the automatic and intelligent control module, production plans can be optimized, automatic mining and transportation are achieved, and therefore the mine production efficiency is remarkably improved; the safety monitoring and early warning module can monitor the safety risk of the mine in real time, and a safety protection and emergency response system is started in an emergency, so that the personnel safety and the safe operation of mine facilities are ensured; sustainability is improved, the sustainability of the mine is improved, and green mine construction is achieved; and the labor cost can be reduced, the equipment failure rate and the maintenance cost can be reduced, and therefore the overall operation cost of the mine is reduced.

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 invention discloses a big data method for emergency response and disposal of water pollution accidents. The method comprises the following steps: deploying various types of sensor networks in a pollution-prone area; transmitting the real-time data acquired by the sensor to a cloud storage system; monitoring a real-time event mode in the environmental data by using a complex event processing technology, and generating a real-time environmental data model; a machine learning technology and a geographic information system are used to analyze real-time environment data to predict a diffusion path and an influence range of pollutants, and a pollution source position is rapidly positioned; and intelligent emergency response measures are implemented through the intelligent decision support system. A real-time data stream processing technology and complex event processing are used, environmental data can be monitored in real time, emergencies can be quickly responded, the response time can be greatly shortened due to the real-time performance, pollution events can be found and processed earlier, and therefore environmental damage and influences of a water purification system are reduced.

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.