26results about How to "Good decision support" patented technology

The invention provides a bridge girder erection machine intelligent construction control method and system based on machine vision, and relates to the technical field of intelligent control. Image information of a construction site is collected through a plurality of high-definition cameras arranged on a bridge girder erection machine; inputting the image information into a pre-trained deep learning model, identifying and positioning a target in the construction scene, and constructing a digital twinborn model of the construction scene in combination with a graph structure of a construction scene topological structure subjected to spatial perception graph convolution operation; the intelligent decision agent generates an initial construction strategy according to the current construction scene state, an improved Monte Carlo tree is adopted to search and optimize the initial construction strategy, and an optimal construction strategy is generated through multi-objective solution; and after the construction is completed, based on the digital twin model and the whole process data, analyzing the construction quality and efficiency by using a graph neural network, and generating a multi-dimensional evaluation report and optimization suggestions.

The invention relates to the technical field of suspension bridges, in particular to a suspension bridge stress simulation analysis system, which comprises a vibration monitoring module, a cable force prediction and calibration module, an environmental influence analysis module and a structural performance evaluation module, and extracts frequency characteristic values and amplitude information based on vibration frequency data of a suspension bridge steel cable. According to the method, the vibration frequency data is used for deep analysis, potential structure defects are recognized in an early stage, the early warning capacity is remarkably improved, the potential maintenance cost is reduced, the service life of the bridge is prolonged, and the stability and safety of the bridge under variable loads are guaranteed through prediction and adjustment of the real-time cable force state; by integrating environmental data to update the fluid-solid coupling model, the system can accurately simulate the influence of environmental change on the bridge, optimize the structural design, enhance the structural adaptability, improve the economical efficiency and practicability of the bridge design, and optimize decision support, so that the bridge maintenance is more efficient, and the long-term structural safety of the bridge is ensured.

The invention discloses a mangrove forest disease and insect pest remote monitoring and intelligent prevention and control system based on the Internet of Things. A plurality of modules such as an Internet of Things sensor network, an unmanned aerial vehicle inspection system, a high-definition monitoring camera network, intelligent prevention and control equipment and a software platform are integrated; the Internet of Things sensor network monitors environmental parameters and pest and disease damage signs in real time, the unmanned aerial vehicle inspection system provides aerial inspection support, and the high-definition monitoring camera network realizes 24-hour uninterrupted monitoring on the ground; the software platform is responsible for data acquisition, transmission, analysis and early warning release, and provides a remote control interface for management personnel to perform accurate prevention and control; the intelligent prevention and control equipment automatically or remotely executes prevention and control tasks according to the monitoring data and the early warning information; the system also has additional functions of energy management, network communication, user authority management and the like, and continuous and stable operation and data security of the system are ensured. The implementation of the system can effectively improve the intelligent level of mangrove forest pest control, and provides powerful support for protecting a mangrove forest ecological system.

The invention discloses a user experience design method based on a natural language processing technology, relates to the technical field of natural language processing technologies and user experience design, and adopts the natural language processing technology to intelligently collect and analyze user feedback information. The method is constructed on the basis of advanced natural language understanding and sentiment analysis technologies, and an intelligent feedback management system realizes efficient mining and deep insight of mass text data and covers deep information extraction, sentiment recognition and quantification and automatic report generation; according to the application of the natural language processing technology in the field of user feedback collection and analysis, not only is the crossing from passive receiving and manual processing to active detection and intelligent analysis realized, but also the user experience is improved through seamless voice interaction and accurate path guidance. The user experience and the response speed of the enterprise to the user demand are greatly improved, and the intelligent upgrading of the feedback system is really realized.

The invention discloses a cross-domain data fusion method and system for distribution network middle stations based on a knowledge graph, and particularly relates to the technical field of data processing and fusion based on the knowledge graph. Firstly, intelligent gateway equipment is adopted to collect operation, fault, maintenance and other data of all systems and all lines related to distribution network operation, the data are transmitted to a data processing and storage module through a 5G communication protocol port transmission technology, and data cleaning, data integration, data conversion and other operations are carried out; classifying and storing the data, and then constructing a mapping knowledge domain in the distribution network; according to the method, a 5G communication protocol port transmission technology and a composite model are specifically adopted to perform knowledge extraction and knowledge fusion of graph construction, so that the technical problems of data islands and other data processing in a distribution network are solved, data sharing, service sharing and resource integration are effectively realized, and the reliability of the distribution network is improved. The problem that data between distributed systems in the current distribution network field is difficult to cooperate is solved, semantic integration and sharing of data between multiple systems are achieved, and the operation and maintenance efficiency and the intelligent level of a power distribution network are improved.

The invention provides a desulfurization absorption tower system monitoring method, device, equipment and medium, and belongs to the technical field of thermal power desulfurization. The method comprises the following steps: acquiring numerical values of a plurality of monitoring devices of the desulfurizing absorption tower system; based on the emergency level of the data type of the numerical value of each monitoring device and the operation state of each monitoring device, evaluating the numerical value of each monitoring device to obtain a score of the numerical value of each monitoring device; calculating the average value of the scores of the numerical values of the monitoring devices to obtain the score of each sub-device of the desulfurizing absorption tower system, and calculating the average value of the scores of each sub-device of the desulfurizing absorption tower system to obtain the score of the desulfurizing absorption tower system; and the score of the numerical value of each monitoring device, the score of each sub-device of the desulfurization absorption tower system and the score of the desulfurization absorption tower system are transmitted to a DCS system for layered and graded display. According to the invention, real-time monitoring and early warning, efficient fault diagnosis, data integration and sharing and visual display are realized, and the equipment operation management efficiency and operation convenience are improved.

The invention provides a carbonate pore structure inversion method and related equipment, and relates to the technical field of geological prospecting, and the method comprises the steps: obtaining test data, employing a trained deep learning prediction model to carry out pore structure inversion through employing a Bayesian estimation algorithm for the test data, so as to obtain a pore structure prediction result, the deep learning prediction model comprises a Bayesian convolutional neural network layer. According to the technical scheme, the test data are input into the trained deep learning prediction model, and the Bayesian estimation algorithm is adopted to perform pore structure inversion so as to obtain the pore structure prediction result including the confidence interval of the pore structure parameters. Therefore, the uncertainty quantification of the prediction result can be realized, the uncertainty is further expressed, the confidence interval is obtained, and the prediction precision can be effectively improved, namely the accuracy of the pore structure prediction result is improved. The method has great application potential in the field of data-driven pore structure prediction, and better decision support is facilitated.

An intelligent associated hydropower station one-stop cockpit comprehensive management and control method realizes efficient comprehensive management and control of a hydropower station through four methods of exception handling comprehensive information management, cockpit large-screen information flow switching, large-screen switching page management and control and multi-source data fusion. According to the exception handling comprehensive information management method, exception handling cards are managed through a dynamic configuration algorithm, and quick response and accurate handling are ensured; according to the cockpit large-screen information flow switching method, a flexible display mode is supported through stable network connection and safe video flow transmission; according to the large-screen switching page management and control method, timely transmission of key information is ensured through real-time monitoring and an intelligent screen switching strategy; according to the multi-source data fusion method, comprehensive management and fine control of data are realized through combination of a real-time database and a historical database. Through the construction and management of the five databases, the fine management and automatic control capability of the playing content is further improved, and the efficient operation and safety of the system are ensured.

The invention belongs to the technical field of atmospheric science, and discloses a set-variational hybrid assimilation system and method based on a meteorological large model, which adopts a physical constraint mode (RANDOMMV) to perform random disturbance on an atmospheric initial field, quickly generates set members based on the strong deduction capability of the artificial intelligence meteorological large model, and improves the set-variational hybrid assimilation efficiency. The member diversity is ensured; and the sampling error is reduced. On the basis, a set-variation hybrid assimilation method is adopted, and a set-variation hybrid assimilation system is constructed by combining a dynamic set background error covariance and a static background error estimated through an NMC method, so that effective assimilation of the multi-source observation data is realized. According to the method, the physical consistency and stability of the AI ensemble forecast members can be ensured. Long-term average static error information derived from a difference field of historical forecast data and flow dependent background error information of ensemble forecast are combined; and dynamic and static combination enhances the robustness of the hybrid assimilation system.

The utility model relates to the technical field of drainage systems, in particular to a drainage system for a coal processing plant. Comprising a wastewater treatment unit, and further comprises a final treatment water outlet pool, a sampling detection unit, a diversion execution mechanism and a controller, the sampling detection unit comprises a sampling module and a water quality detection unit, and the final treatment pool is communicated with the wastewater treatment unit and used for storing treated coal processing wastewater; the sampling module is used for collecting a coal processing wastewater sample from a final treatment water outlet pool, the water quality detection unit is used for detecting water quality parameters in the coal processing wastewater sample, the shunting execution mechanism comprises a drainage electromagnetic valve and a reflux pump, a channel where the drainage electromagnetic valve is located is communicated with a drainage port, and a channel where the reflux pump is located is communicated with the wastewater treatment unit. The output end of the water quality detection unit is electrically connected with the input end of the controller, and the output end of the controller is electrically connected with the input ends of the drainage electromagnetic valve and the reflux pump.

The present invention relates to the technical field of levee hidden danger detection, and in particular to a state analysis method and system based on a data set. The method comprises the following steps: using a laser radar to detect the stratigraphic lithology of the levee, obtaining a multidimensional dataset of the levee, using GIS software to perform three-dimensional visualization processing based on the multidimensional dataset, and constructing a stratigraphic analysis map; based on the stratigraphic analysis map, using an unmanned boat and a submersible to monitor and analyze the hydrological data downstream of the levee, and generating a high-definition remote sensing image of the downstream levee; based on the high-definition remote sensing image of the downstream levee, using the sensor upstream of the levee to obtain a real-time multimodal dataset upstream of the levee, and using the real-time multimodal dataset upstream of the levee to perform cloud-based aggregation operations on the levee; based on the cloud-based dataset upstream of the levee and the downstream high-definition remote sensing image, using the levee hydrological artificial intelligence algorithm to construct a comprehensive levee decision support system; the present invention improves the efficiency of levee hidden danger investigation and analysis by processing the levee hidden danger dataset.

The invention provides a new energy stand-alone data restoration method based on a clustering algorithm and computing equipment. The method comprises the following steps: processing correct data in historical data of each station; clustering and grouping are carried out according to the processed correct data, and a cluster to which each station belongs is determined; missing data and abnormal values in the real-time data of each station are identified and marked; and repairing the missing data and the abnormal value according to the cluster to which the station in which the missing data and the abnormal value are identified belongs. According to the technical scheme of the invention, the data processing efficiency can be improved, the data restoration time can be shortened, real-time online data restoration can be realized, and the monitoring strength and the maintenance capability of the equipment operation state can be improved.

The invention discloses an intelligent mine unattended system and method, and relates to the technical field of mining, and the system comprises a vehicle information intelligent recognition module which is used for automatically capturing and verifying vehicle information before a vehicle arrives at a wagon balance, and automatically releasing the vehicle to enter a weighing area after the vehicle is confirmed to be correct; and the automatic weighing operation module is used for enabling the vehicle to be stably parked on an unattended weighing platform, enabling the system to automatically start a weighing program, and rapidly capturing and displaying weight data. Real-time monitoring and data management of the weighing process can be intelligently realized, an intelligent identification technology (such as license plate identification and RFID) and a monitoring system are integrated, cheating behaviors in the weighing process are prevented, the transaction fairness is ensured, in-and-out route planning, remote monitoring, data encryption and intelligent analysis are realized, and in-and-out route planning, remote monitoring, data encryption and intelligent analysis are realized. Seamless integration of weighing data and other management systems is realized, automatic circulation and sharing of data are realized, and data query, analysis and decision support are facilitated.

The invention provides an intelligent feature evolution analysis method and system for a time sequence path, and relates to the technical field of feature evolution, and the method comprises the steps: obtaining time sequence path data, and carrying out the preprocessing to generate a standardized feature sequence; performing Monte Carlo sampling and transition probability matrix calculation to obtain a characteristic evolution strength index; setting an evaluation time window by adopting a beam search algorithm, calculating a stability score, and selecting an optimal path branch; and finally, determining an optimal characteristic evolution path based on a weighted combination of the evolution strength index and the stability score, and generating an analysis report. According to the method, the evolution law of key features in the time series data can be effectively identified, and the analysis accuracy and efficiency are improved.

The invention relates to the field of medical treatment, in particular to a model fusion method and system for prognosis prediction of liver cirrhosis, and the method comprises the steps: obtaining the feature data and type label data of historical patients with liver cirrhosis; constructing a data set; constructing and training three types of multi-classification models; calculating three types of multi-classification logarithmic loss values based on a preset multi-classification logarithmic loss formula; based on a preset model fusion weight formula and the three types of multi-classification logarithm loss values, respectively calculating fusion weight values; inputting the feature data of the target liver cirrhosis patient into the three trained multi-classification models to obtain a prediction result for predicting the corresponding type of tag; and obtaining a model fusion prediction result based on a preset model fusion strategy. According to the method, model fusion analysis is carried out on the model output result by fusing the multi-classification logarithm loss formula, the model fusion weight formula and the model fusion strategy, so that the accuracy of the model fusion prediction result is improved, and better decision support is provided for medical staff.

This invention discloses a method and system for assessing diabetic foot based on plantar pressure, belonging to the field of gait and plantar pressure data analysis. The method includes: collecting plantar pressure data from subjects; analyzing and processing the collected plantar pressure data to extract original features related to foot lesions; enhancing and filtering the extracted original features to obtain feature data with high correlation to labels; classifying the obtained feature data into similar subdomain spaces using a multi-subdomain classification algorithm model; the subdomain spaces are multiple and are obtained by partitioning a single domain of a typical plantar pressure feature dataset feature space using a multi-subdomain partitioning method; and performing CPT / VPT regression prediction on the subject's feature data in the subdomain spaces to obtain assessment results. This invention, based on plantar pressure signals, can accurately and effectively assess diabetic foot and provide better decision support for clinicians, thereby improving the rehabilitation and treatment outcomes of diabetic patients.

The invention discloses an industrial Internet of Things platform management method and system based on digital twin drive, and relates to the technical field of new-generation communication technology and industrial digital platform, and the method comprises the steps: obtaining the basic information and operation information of all equipment in a factory; defining a hierarchical industrial Internet of Things element component; carrying out digital coding on the elements, and carrying out digital mapping; carrying out association matching on different elements, and carrying out scene-based configuration; global driving information is added, and global information interaction and bidirectional digital twinning are carried out. By designing hierarchical and modular element components and an identification coding technology, mapping of various entities in a digital space in an industrial Internet of Things scene is realized, a new scene is quickly adapted, the digital twin construction cost of an enterprise is reduced, and the informatization and intelligence levels of links such as design, operation and management of the industrial Internet of Things are improved.

The present invention relates to the medical field, and in particular to a method and system for generating a diagnosis and treatment plan for a brain tumor by weighing a knowledge base and model results, the method comprising: obtaining text evidence information related to brain tumors; building a knowledge base; building and training a diagnosis and treatment plan generation model; building and training a plan scoring model; obtaining medical record text information of a target patient, and generating a recommended plan for a diagnosis and treatment plan for a brain tumor based on knowledge base matching technology and the model generation results of the diagnosis and treatment plan generation model. The present invention dynamically generates personalized auxiliary diagnosis and treatment plans by weighing an accurate knowledge base and accurate model generation results, and using a recommended plan generation technology, which not only improves the accuracy and efficiency of diagnosis and treatment decisions, but also provides better decision support for medical personnel.

The application discloses a kind of space-time perception framework for multivariate time series anomaly detection, belong to abnormal detection technical field, comprising: variable community analysis driven grouping remodeling module, for variable spatial relationship modeling and variable grouping to multivariate time series, and each variable group is respectively reconstructed;Error enhancement module driven by space-time stationarity module, for spatial importance weighting and time stationarity smoothing to reconstruction error, and generate enhanced anomaly score.The application explicitly utilizes the spatial correlation between variables by variable community analysis driven grouping reconstruction, groups strongly correlated variables and independently reconstructs, effectively reduces the noise and interference introduced by weakly correlated variables, and improves the reconstruction quality and model generalization ability.Two modules that capture space-time relationship features work together, significantly improve the accuracy and robustness of anomaly detection.

This invention provides a method, apparatus, equipment, and medium for production prediction based on time-varying reservoir parameters. The method includes the following steps: S1, establishing a variable decline exponential production decline model for tight gas reservoir wells; S2, collecting historical gas production data of tight gas reservoir wells, and confirming the second undetermined characteristic parameter β and establishing a first linear model based on linear fitting of lnq and t / (lnt)β, wherein the slope of the first linear model is -α and the intercept is lnq. i Value; and S3, confirming the theoretical initial output q based on the first linear model. i The invention uses a first undetermined characteristic parameter α to predict gas well production using the variable decline exponent production decline model. This invention better reflects the production decline law of tight gas wells, is simple to operate, and has higher prediction accuracy than traditional decline models with a constant decline exponent. It can be widely used for production decline prediction in tight gas reservoirs.

The application discloses a power transmission line path planning method and system based on natural language and a knowledge graph, and comprises the following steps: a large language model is used to analyze a natural language path planning target, an intention classification result and a named entity are obtained, and are mapped to a business rule knowledge graph for reasoning, and a task step sequence is generated; confirmation or modification of each task step by a user is received, and each target task step is obtained; a large language model is used to retrieve a combination corresponding to each target task step from a GIS operation knowledge graph, and the combination is disassembled into a GIS operator call chain; corresponding GIS tools are called through an MCP protocol, spatial analysis operations in the GIS operator call chain corresponding to each target task step are executed in the order of all target task steps, and a path planning result of a power transmission line is generated. The application realizes full automation, process explainability and human-computer interaction collaboration of power transmission line path planning, and improves the efficiency of power transmission line path planning.

The invention discloses a regional energy economic environment network coupling development evaluation system and method, and the method comprises the following steps: 1, carrying out the arrangement of energy data, economic data and pollutants over the years in the statistical yearbook of each administrative unit in a designated region, and forming analysis panel data; 2, searching and analyzing energy data and pollutant data researched by various authoritative institutions, and determining that the data has a good supporting effect on analysis in 2030, 2050 and 2060; meanwhile, a prediction model about the GDP and the three industries is constructed based on the regional economy development law and compared with related mechanism data, the future regional economy development trend is effectively predicted, development evaluation work of an energy-economy-environment system can be effectively and quantitatively carried out, an iterative optimization mode is formed, and the development evaluation efficiency of the energy-economy-environment system is improved. And better decision support is provided for later regional development policy making.

The invention discloses an on-line real-time monitoring management method and system for transformer substation personnel, and relates to the technical field of transformer substation monitoring management, and the on-line real-time monitoring management method for the transformer substation personnel comprises the following steps: step (1), the transformer substation personnel can monitor real-time pictures and conditions of a transformer substation through a video monitoring system; according to the transformer substation personnel on-line real-time monitoring management method and system, when transformer substation equipment breaks down, fault information of the equipment is recognized and recorded through a fault early warning system, and when the fault information of the transformer substation is consistent with the reason of the last fault information, the fault early warning system sends alarm information and meanwhile, the fault information of the transformer substation is monitored in real time. According to the technical scheme, the alarm record with repeated fault information reasons can be transmitted to the terminal of a worker, the worker can quickly check and overhaul the equipment fault, and meanwhile, the historical fault information of the equipment can be checked according to the fault early warning system of the fault reporting equipment, so that the judgment of the worker on the service life of the equipment fault is improved.

The invention belongs to the technical field of review result quantitative analysis, and discloses a quantitative analysis method for kidney transplantation review result data based on artificial intelligence. First patient reexamination result data are collected and preprocessed, second patient reexamination result data are obtained, and the first patient reexamination result data comprise biochemical index data, image result data and pathological report data; analyzing the second patient review result data to obtain a patient state reference value, and classifying the patient state reference value to obtain a classification result; storing the first patient review result data and the patient state reference value; in general, the method has the remarkable advantages that the auxiliary decision making effect is good, the patient information confidentiality is high, and the ability of assisting doctors in analyzing conditions is high.

The present invention relates to the field of medical big data technology in artificial intelligence, and in particular to a tumor knowledge base construction system and method based on a large model and prompt word optimization, the system comprising: a data acquisition module, a prompt word generation and optimization module, an entity recognition and relationship extraction module, a knowledge graph construction module, a clinical decision support module, and a knowledge base update and maintenance module. The present invention uses prompt words that are initially designed and continuously optimized to ensure that the subsequent large language model LLM accurately and efficiently extracts required information, and uses the large language model LLM to quickly and accurately extract related entities and the association relationship between related entities from target text information, so as to construct an accurate hierarchical tumor knowledge graph database. When the hierarchical tumor knowledge graph database is subsequently applied, a personalized clinical report on recommended treatment plans can be generated by inputting the clinical data of the target patient, thereby providing better decision support for medical personnel.

The invention relates to the technical field of data processing, and discloses an urban indigenous plant leaf area index actual measurement system and method, and the system comprises the steps: S1, a data collection module which is used for collecting leaf image data of urban indigenous plants; s2, the image processing module is used for preprocessing the collected leaf image data and extracting contour and area information of the plant leaves; s3, the leaf area index calculation module is used for calculating a leaf area index (LAI) according to the preprocessed leaf area data, and the leaf area index formula is # imgabs0 #; S4, the storage module is used for storing the collected image data, the processing result and the calculated leaf area index; and S5, a display module which is used for displaying the calculation result of the leaf area index and related image data in real time.