27results about How to "Reduce decision risk" patented technology

The invention discloses an industry report generation optimization method and system based on reinforcement learning and a knowledge graph, and relates to the technical field of industry report generation, and the method comprises the following steps: firstly, collecting industry data from multiple sources, preprocessing the industry data, determining the collection frequency according to the type and importance, then constructing the knowledge graph, and recognizing entities by using a plurality of technologies. Designing a reinforcement learning model, defining space and reward functions such as states and actions, training according to a specific algorithm, analyzing and generating a preliminary report according to user requirements by means of the model and graph selection data, combining an optimization algorithm with a language model and a graph to improve the report, and finally evaluating, feeding back and adjusting a parameter strategy to improve system performance. The method can improve the industry report generation efficiency, optimize the report quality, enhance the depth and accuracy, adapt to different requirements, can be self-promoted, and effectively assists the decision and development of each industry.

The invention discloses a large cylindrical battery health monitoring method and system, and relates to the field of battery health monitoring. The large cylindrical battery health monitoring method comprises the following steps: when a large cylindrical battery is in a running state, continuously acquiring historical battery running state data of a plurality of historical monitoring time periods and current battery running data of a current time period, and preprocessing the historical battery running state data and the current battery running data; analyzing the historical battery operation state data and the current battery operation data of each historical monitoring time period to obtain a historical battery health assessment value and a current battery health assessment value of each historical monitoring time period, and performing analysis again to obtain a current battery health change value; according to the method, the current battery health change value and the preset battery health change range are judged and analyzed, and corresponding measures are taken based on the analysis result, so that the long-term health state of the battery is accurately reflected, potential trends and problems are timely identified, and a stable evaluation result is kept when monitoring is performed in a changing environment.

The invention provides a business analysis aided decision-making method and system based on big data. Wherein real-time data streams from different sources are integrated to form continuous data input streams; dynamically constructing a real-time monitoring model reflecting a service state based on the data input stream, the real-time monitoring model being used for capturing a service state change; when the real-time monitoring model identifies that the service state of the specific service is changed, triggering a scene analysis mechanism so as to evaluate the development trend of the specific service through the scene analysis mechanism, and generating a corresponding scene report; according to the scene report, a preset strategy generation algorithm is applied, and the strategy generation algorithm generates action suggestions adapted to the specific service according to optimal action paths under different scenes; and providing the action suggestions to a decision support interface to assist a decision maker in quickly responding to business changes. According to the technical scheme provided by the invention, the decision-making efficiency and the service response speed are improved.

The invention relates to the technical field of logistics, in particular to a digital twin network traffic prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining historical traffic data of a digital twin network of a logistics circulation center, and obtaining traffic training data from the historical traffic data; constructing an initialization model based on the TimeGPT time sequence model, and performing parameter configuration on the initialization model to obtain a basic prediction model; performing training processing on the basic prediction model based on the traffic training data to obtain a traffic prediction model; acquiring updated traffic data from the digital twin network of the logistics circulation center according to a preset period, and performing updating training on the traffic prediction model according to the updated traffic data to obtain an updated traffic prediction model; acquiring real-time traffic data of the digital twin network of the logistics circulation center, and performing traffic prediction based on the updated traffic prediction model and the real-time traffic data to obtain a predicted traffic result; according to the method, the problem of overfitting of the model can be effectively prevented.

The invention discloses an enterprise management system based on big data analysis, and the system is characterized in that the system comprises a data collection module, a data preprocessing module, a data storage module, a data analysis module, a management decision module, a risk early warning module, and an intelligent cooperation module; the data acquisition module comprises an internal business data acquisition unit and an external market data acquisition unit, and the internal business data acquisition unit has a function of monitoring staff workflow data in real time; according to the invention, through an innovative data acquisition mode, the internal business data acquisition unit monitors the work flow of employees in real time, and the external market data acquisition unit captures multi-source data such as social media, data sources are comprehensively enriched, so that enterprises can know their own operation and market dynamics in multiple dimensions, and the enterprise management efficiency is improved. And a powerful basis is provided for accurate decision making.

The invention discloses a multi-stage human-machine attribute value alignment method and system based on a large model, the method comprising: using multiple large models to obtain the decision attribute weights of the #imgabs1# stage according to the situation information of the #imgabs0# stage, and then using weighted factors to fuse to obtain the machine intelligence weights of each attribute of the #imgabs2# stage; using a group decision method to obtain the human decision weights of each attribute of the #imgabs3# stage; based on the machine intelligence weights and human decision weights of each attribute of the #imgabs4# stage, a loss function is established; by calculating the partial derivative of the loss function with respect to the large model weight factor, using the gradient descent method and updating the weighted factors of each large language model on each attribute in the #imgabs5# stage based on the partial derivative calculation value, for the attribute weights output by the fusion of each large language model in the #imgabs6# stage; looping the above process to finally realize the gradual alignment of human-machine attribute weights. The invention can reduce decision-making risks and effectively improve the accuracy and reliability of artificial intelligence in complex decision support.

The invention discloses a low-delay adaptive code rate control system for a meteorological monitoring video stream, and relates to the technical field of adaptive code rate control in video stream media transmission, and the system comprises a content importance analysis module which is used for carrying out the real-time analysis of a video block to be transmitted, generating a content importance score representing the meteorological information value; and the decision arbitration module is connected with the content importance analysis module and is used for receiving the content importance score, the network state information and the buffer area state information. According to the low-delay adaptive code rate control system for the meteorological monitoring video stream, bandwidth resources can be allocated according to the difference of meteorological information values of video contents, the quality of key monitoring pictures is guaranteed preferentially when network conditions fluctuate, unnecessary code rate switching is inhibited actively through internal look-ahead simulation and risk assessment, and the quality of the meteorological monitoring video stream is improved. Therefore, balance between continuous and clear transmission of high-value information and overall watching fluency is realized in a complex network environment.

The invention provides a management accounting cost control and optimization method based on machine learning, and belongs to the technical field of management accounting systems. The method comprises the following steps: S1, data collection and preprocessing: collecting enterprise historical cost data, and preprocessing the data; s2, feature extraction and selection: extracting features related to the cost by using a machine learning technology, and screening features having significant influence on the cost through a feature selection method; s3, constructing a machine learning model: constructing the machine learning model based on the extracted features; s4, cost prediction and optimization: performing cost prediction on new data by using the trained model, optimizing the prediction cost, and realizing the target of cost control; and S5, decision support and feedback. According to the method, the accuracy of cost prediction is improved through a machine learning technology, a flexible personalized optimization scheme is provided, the model can be continuously optimized, and enterprise managers are helped to make scientific decisions, so that risks are reduced, and profits are increased.

The invention relates to the technical field of digital finance, and discloses an intelligent statistical analysis method and system for economic indicators, and the system comprises a module which improves the data processing efficiency, and achieves the automation of data collection, cleaning and storage through a distributed calculation frame and a data preprocessing module; the module is used for ensuring the accuracy and the consistency of the data, and ensuring the uniform format and the complete content of the multi-source data through a data standardization and cleaning algorithm; a module for realizing intelligent analysis and prediction, wherein a machine learning algorithm is adopted to perform intelligent analysis and prediction; a module for realizing real-time early warning and response and realizing real-time early warning through a threshold rule and an anomaly detection algorithm; according to the invention, full-process automation is realized through an intelligent means, and manual intervention is not needed from data acquisition to result display. The method not only provides the analysis of the current data, but also has the prediction capability of the economic trend, and fills the blank of the existing tool in the field.

The invention relates to an enterprise project informatization management method, device and system and a storage medium. The method comprises the steps of obtaining enterprise project internal data, enterprise project external data, initial fuzzy reasoning parameters and project execution historical data corresponding to a target enterprise; performing dynamic fuzzy reasoning on the enterprise project internal data and the enterprise project external data according to the initial fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data; according to the dynamic fuzzy reasoning data and project execution historical data, identifying a relationship between each execution object and each task detail in the target enterprise to obtain project behavior analysis data; performing structured fusion on the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data; and scheduling fixed resources and human resources of the target enterprise according to the enterprise project fusion data to obtain enterprise project management information. By adopting the method, the resource scheduling efficiency of the enterprise management project can be improved.

This invention relates to the field of flight scheduling technology, specifically providing a method, system, device, and storage medium for collaborative scheduling of low-altitude aircraft. The method includes: edge nodes receiving real-time data from a swarm of drones and generating corresponding scheduling data using a pre-deployed scheduling model; if the edge nodes confirm that the real-time data is environmental mutation data, they upload the environmental mutation data to a regional cloud; the regional cloud matches the environmental mutation data with historical scenario data; if the match is successful, scheduling data is generated; if the match fails, the environmental mutation data is uploaded to a central cloud; the central cloud generates scheduling data based on the received environmental mutation data and returns the scheduling data to the regional cloud, which then distributes the scheduling data to the edge nodes. This invention constructs a low-altitude traffic management system that combines real-time performance, reliability, and self-evolution capabilities.

The application discloses a sintering machine operation and maintenance strategy simulation test and optimization method and system based on digital twinning, relates to the technical field of sintering intelligent operation and maintenance, and comprises the following steps: a simulation environment is constructed; real-time operation data of equipment is acquired, the real-time operation data of the equipment is mapped into a current equipment state snapshot through a digital twinning platform, and the current equipment state snapshot is converted into an initial current state vector; an operation and maintenance strategy to be tested is packaged into a decision function module and dynamically linked to the simulation environment; a simulation engine of the simulation environment is called, each single-strategy simulation deduction cycle in the operation and maintenance strategy is executed step by step with the initial current state vector as a starting point; a state trajectory, an action sequence and an event log are recorded; and a single-strategy comprehensive performance score corresponding to each single strategy is calculated. The method solves the technical problems that in the prior art, an operation and maintenance strategy lacks simulation verification, is directly put into production, results in high trial-and-error cost, and strategy reliability cannot be quantitatively evaluated.

The invention discloses a traffic dynamic shortest path distribution method and system based on a triangular fuzzy number weight, and relates to the technical field of intelligent traffic systems.Fuzzy information in a path evaluation attribute is represented through a triangular fuzzy number, a comprehensive weight is analyzed and calculated in combination with deviation maximization and a moisture value, and the decision reliability is effectively improved; a K-Means algorithm is adopted to perform dynamic path search, and through a representative vertex subset pre-calculation and increment updating strategy, the path retrieval efficiency is remarkably improved; a travel time prediction model related to departure time and real-time flow is established, and accurate response to accidental congestion is realized; and finally, outputting an optimal path scheme through a multi-objective decision-making method. Compared with a traditional algorithm, the speed is improved on a real road network data set, the path distribution accuracy is improved, meanwhile, the path safety, comfort and efficiency are effectively balanced, real-time path planning under a large-scale road network is supported, and an efficient solution is provided for intelligent traffic management.

The invention provides a railway full life cycle data and model integration method and system and a storage medium. Relates to the technical field of railway data management. The method comprises the following steps: acquiring and storing railway data in real time from each railway data source accessed to a platform through a data lake module; the data lake module comprises a data uptake sub-module and a data storage sub-module; the data uptake sub-module is used for importing railway data from each railway business data source in real time based on a data source address and storing the railway data into the data storage sub-module; each railway data source comprises a data source corresponding to each service in the full life cycle of the railway; obtaining railway equipment facility models from each railway data source in real time through the model library module, and storing the railway equipment facility models in the model library module in a document form; the model library module comprises an access interface sub-module, and the access interface sub-module is used for receiving and executing a model library operation request acting on the model library module; according to the invention, the problem of difficulty in integration and sharing of railway full-life-cycle data can be solved.

This invention discloses a method and system for identifying equity structure diagrams based on data processing, relating to the field of data processing technology. The method includes: performing feature recognition on the equity structure diagram to obtain entity features and relationship features; constructing an identification model based on a graph neural network, using entity features as nodes and relationship features as edges; identifying nested relationships in the equity structure diagram through the identification model; wherein, nested relationships include relationships between equity entities; equity entities include shareholders and shareholders; and determining multi-level equity penetration paths, actual control paths, cross-shareholding relationships, and potential equity change relationships in the equity structure diagram based on the nested relationships.

The application relates to the technical field of intelligent factories, and discloses a traditional handicraft and intelligent factory information fusion system and method, which comprises a data acquisition module, a data processing module, a first data analysis module, a backend management module and a second data analysis module.The data acquisition module is used for collecting first production data on a handicraft production line; the data processing module is used for obtaining the first production data, cleaning and deduplicating the first production data to obtain second production data; the first data analysis module is used for establishing a production prediction model based on the second production data to obtain a predicted production situation; the backend management module is used for receiving real-time production preset information; the second data analysis module is used for obtaining the real-time production preset information and obtaining production optimization suggestions; and the judgment module compares the predicted production situation with the production optimization suggestions to obtain a production optimization scheme.The application can effectively fuse the information of traditional handicraft and intelligent factories and improve production efficiency.

The present invention relates to the field of breeding monitoring technology, and specifically to a smart breeding monitoring method and system based on big data. After receiving the breeding data summarized by the breeding farm terminal, the smart breeding management platform uses natural language processing technology to extract key parameters; classifies the key parameters, analyzes the key parameters of the same type, and outputs a first evaluation report; generates a breeding plan recommendation based on the first evaluation report, transmits it to the terminal and the feed-to-weight ratio prediction model, and obtains the first feed-to-weight ratio; after the breeding farm starts to execute the breeding plan recommendation displayed by the breeding farm terminal, it receives new breeding data and outputs a second evaluation report; at the same time, calculates the similarity between the first evaluation report and the second evaluation report; extracts the feed weight from the new breeding data, and calculates the second feed-to-weight ratio based on the feed weight and the received breeding weight; outputs the similarity, the ratio of the second feed-to-weight ratio to the first feed-to-weight ratio to the first evaluation report, and improves product quality and market qualification rate.

The invention provides a real-time bidding and tendering data intelligent analysis system and method, and relates to the technical field of data analysis and bidding and tendering, and the system comprises a data collection module which is responsible for obtaining data from a plurality of bidding and tendering platforms in real time; the data preprocessing module cleans the received data and removes repeated, abnormal and incomplete data records; the index calculation module calculates a bidding activeness index, a price rationality index and an enterprise competitiveness index of a bidder based on the preprocessed data; the comprehensive evaluation module obtains a comprehensive evaluation index of each bidding party by using a preset evaluation model according to the indexes; and finally, deeply analyzing the comprehensive evaluation index by a visual analysis module, and displaying the ranking of each bidding party through a visual tool. According to the method, the data analysis efficiency and the decision support capability in the bidding and tendering process are improved, and better service experience and economic benefits are provided for all parties participating in bidding and tendering.

The invention discloses an interval affine algorithm-based electric vehicle charging bearing capacity calculation method and system. The method comprises the following steps of: obtaining an uncertainty factor data set of an electric vehicle charging load; performing interval uncertainty modeling of the charging load of the electric vehicle to obtain interval representation of the charging load of the electric vehicle; calculating load flow by using an affine algorithm to obtain affine representation of node voltage; according to the affine representation of the node voltage and the set value of the voltage violation index VVI, constructing an interval undervoltage probability IUP model, and calculating to obtain the upper and lower bounds of the interval undervoltage probability; and according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the power distribution network, the accessible number range of the electric vehicles is calculated to serve as the finally obtained electric vehicle charging bearing capacity of the power distribution network. The method aims at achieving accurate calculation of the electric vehicle charging bearing capacity of the power distribution network, and scientific planning is provided for power distribution network optimization so as to achieve flexible operation of the power distribution network.

The invention relates to the technical field of glass seal welding process optimization and defect prediction, in particular to a glass seal welding process optimization and defect prediction method based on deep learning. The method comprises the following steps: firstly, acquiring multi-source sensor electric signal data of a welding furnace in real time, and synchronizing and preprocessing to form a standard time sequence data sequence; and then, inputting the data sequence into a pre-trained multi-task deep learning model, and synchronously realizing dynamic process optimization and early defect prediction by the model through a shared feature extraction network and two parallel task branch networks. After the process is finished, the system associates an actual quality result with process data to form an incremental sample, and performs online fine adjustment on the model based on an intelligent trigger mechanism and an anti-forgetting algorithm, so that the system can adapt to changes of equipment and materials. According to the invention, the transformation of the glass seal welding process from fixed parameter control to real-time closed-loop intelligent optimization is realized, and the product yield, the process stability and the production intelligence level are effectively improved.

The present invention discloses a multi-level and multi-mode dual-objective optimization method for route networks, which belongs to the field of aviation technology. First, a gravity model is used to predict the passenger flow of newly opened routes. Secondly, the route network cost and the aviation accessibility of branch cities are comprehensively considered, and a multi-level and multi-mode dual-objective optimization design model for trunk and branch route networks is constructed, which solves the technical problem of integrating the general navigable short-distance transport route network into the existing trunk and branch route network, and effectively improving the service efficiency and operation efficiency of the route network, ensuring that while reducing operating costs, the aviation connectivity of remote areas is improved, reducing decision-making risks, and being able to achieve more accurate resource allocation, avoid resource waste, and ensure the rationality and effectiveness of network layout. While optimizing the overall efficiency of the network, the present invention can also take into account the aviation connection needs of branch cities, achieving a balance between global perspective and local optimization.

This invention relates to the field of coal mine gas geological disaster prevention and intelligent identification technology, and discloses a method and system for intelligent identification and graded early warning of underground gas geological anomalies in coal mines. The method includes: S1, collecting multi-source heterogeneous monitoring data in the target area of ​​the coal mine and performing standardization processing and spatiotemporal benchmark unification; S2, constructing a multi-dimensional feature fingerprint database based on historical or accumulated standardized datasets; S3, acquiring real-time multi-source monitoring data for the current tunneling or exploration process and extracting its features; based on the multi-dimensional feature fingerprint database, using evidence theory to fuse and calculate multiple extracted real-time feature evidences to obtain a comprehensive confidence assessment result for the existence of a specific type of gas geological anomaly in the undiscovered area ahead; S4, generating graded early warning information based on the comprehensive confidence assessment result and a preset risk level threshold. This invention achieves advanced identification, risk assessment, and targeted early warning of geological disasters such as underground gas outbursts.

The invention relates to an automatic checking system for a factory station table, and belongs to the technical field of data checking. The system comprises a hardware layer and a data layer, wherein the data layer comprises a data access layer and a data storage layer; the data access layer is used for providing a data interface; the data storage layer serves as a database to store data; the service layer is configured with a preset algorithm, and the execution steps of the preset algorithm comprise: obtaining a to-be-checked factory site table, obtaining description information of the to-be-checked factory site table, and carrying out word segmentation processing on the description information to obtain a plurality of sub-character strings; carrying out normative judgment on each sub-character string; performing fuzzy matching on the sub-string and a standard description information base in response to the sub-string conforming to the specification; generating a check report based on the fuzzy matching result; and the display layer is used for displaying the check report. Through the establishment of the system, the automatic checking of the checking report is completed, the checking efficiency can be improved, the workload of personnel can be reduced, and the automatic and intelligent requirements of a modern power grid can be met.

The application belongs to the field of environmental protection and groundwater monitoring, and particularly relates to a groundwater pollution dynamic risk evaluation system based on multi-source heterogeneous data fusion, which comprises: a data integration bus module for synchronous acquisition and space-time alignment of heterogeneous data; a dynamics simulation module for analyzing flow field and solute transport law; a proxy model correction module for realizing second-level prediction by using a physically constrained deep residual network; a risk multi-dimensional representation engine for constructing a dynamic evaluation matrix; and an uncertainty quantification and grading early warning terminal for outputting a risk confidence interval through parameter perturbation analysis. The application realizes deep coupling of mechanism and data driving, and significantly improves real-time performance, scientificity and accuracy of risk evaluation.

The invention discloses a software development demand management system and method, and belongs to the field of computers. The system comprises a demand acquisition module used for receiving demand information input by a user and generating structured demand data based on a preset software customer demand right template and a demand obligation book template; and the intelligent analysis engine is connected to the demand acquisition module and is used for performing multi-dimensional feature extraction on the structured demand data. According to the method, user input is guided through a structured template (right / obligation book), semantic ambiguity is analyzed in combination with a natural language processing technology, accurate alignment of demand description and a business target is ensured, and understanding deviation is reduced from the source. A bidirectional tracking mechanism of the demand verification module realizes dynamic association of demand entries and function design, and eliminates demand omission or realization deviation. The intelligent analysis engine predicts the demand change probability and the influence range based on a machine learning model, automatically marks the technical feasibility and conflict dependency relationship, and assists the team in quickly adjusting the priority.

The invention discloses a method for inhibiting formation thickness interpolation oscillation based on an improved KNN, and belongs to the technical field of geological modeling. Aiming at the problem that a traditional interpolation method is easy to generate a pseudo stratum without geological basis near a stratum pinching line, the method comprises the following steps: firstly, extracting a drilling position, a stratum existence label and thickness; then, on the basis of an improved KNN algorithm, intelligently partitioning a to-be-predicted region into an interpolation effective region and an interpolation ineffective region by calculating a spatial anisotropy weight and constructing a weighted distance formula; then, spatial interpolation is only carried out on the stratum thickness in the effective area, and the thickness is forcibly set to zero in the ineffective area; and finally fusing results of the two parts to generate a final thickness model. According to the method, interpolation oscillation is inhibited from the source, a clear stratum boundary is ensured, a model result is more geologically reasonable, and the reliability of resource assessment is remarkably improved.

The invention relates to a project manpower cost calculation system and a management method thereof, and the system comprises a personnel management module, a personnel scheduling module, a project management module, a manpower distribution module, a risk management and control module, a cost statistics module, an enterprise personnel database and a project database, and is used for maintaining the data circulation and function operation of the whole system. Wherein the system user is an administrator class; according to the project human cost calculation system, human resource management and cost calculation are combined, comprehensive calculation and management of the project human cost are achieved through the automation technology, the system adopts the data integration technology, data such as working hours and overtime conditions of project members can be automatically collected and integrated, real-time tracking and analysis are achieved, and meanwhile the project human cost calculation efficiency is improved. The system can be flexibly adjusted and optimized according to project characteristics and requirements, and helps enterprises to realize reasonable configuration of human resources and accurate control of cost.