1085 results about "Multiple factors" patented technology

The invention provides a pre-control and monitoring method for a whole process of actual grouting engineering based on digital geological model, comprising: extracting discontinuous fracture surfaces in a geologic body structure, to build a underground geologic body structure model; build a multi-source geologic body attribute model, optimizing and solving grouting simulations of a disaster high-risk region by using multiphase flow calculation method, and controlling grouting equipment by using optimized grouting parameters to carry out an actual grouting engineering; obtaining a multi-factor diffusion range of variable coefficients by adjusting physical values of the parameters in the grouting simulation and attribute data structure model by using a control variable method, learning and capturing complex mapping relationship between different parameters by using a neural network, optimizing the grouting parameters in the actual engineering in real-time, so as to realize the pre-controlling, monitoring and optimization of the whole process of the actual grouting engineering.

The invention provides an intelligent inspection operation robot, which comprises a self-adaptive interaction assembly for collecting unstructured features in an inspection environment of the inspection operation robot in real time, converting continuous signals of the unstructured features into discrete interaction decision nodes, and generating an interaction parameter set capable of being iteratively optimized through a cross validation mechanism; the autonomous intention analysis component constructs a probabilistic decision-making program based on implicit features of an operator behavior track of the inspection operation robot, extracts a potential mode of an operation intention through nonlinear correlation analysis in combination with an interaction parameter set, and recognizes an interaction demand which is not clearly expressed; and the collaborative response component constructs a response decision mechanism with self-organizing characteristics, and jointly inputs an interaction parameter set and an interaction demand into the response decision mechanism by establishing a dynamic association rule between different interaction channels of the inspection operation robot and an operator to generate a composite interaction scheme. According to the method, the composite interaction scheme comprehensively considering multiple factors can be generated.

The invention relates to the technical field of data processing, and discloses a motor vehicle safety performance classification method and system based on multi-factor coupling influence. The method comprises the steps of collecting multidimensional safety data of a motor vehicle, calculating a mutual information value and transfer entropy to generate a coupling relation network diagram, constructing a three-layer safety index system to form a comprehensive score, obtaining low-dimensional features through phase-space mapping, determining a dynamic boundary value by applying density clustering, and classifying real-time states to output risk early warning information. According to the technical method for realizing accurate classification and early warning, the defects of neglect of factor coupling relation, lack of scene adaptability, insufficient dynamic prediction capability and the like in the prior art are overcome, and the accuracy and practicability of safety performance evaluation and early warning of the motor vehicle are improved.

The invention relates to an intelligent maintenance decision method and system based on a knowledge graph, and relates to the technical field of intelligent operation and maintenance and decision support and information, and the method comprises the steps: obtaining the operation state data and historical maintenance data of industrial equipment; constructing an industrial knowledge graph and generating an incidence relation model; an optimal maintenance path is obtained based on the analysis of the relationship between the fault type and the maintenance path; optimizing a maintenance task allocation scheme through the optimal maintenance path, the incidence relation model and the maintenance resource constraint condition; carrying out maintenance time window dynamic adjustment analysis to obtain an optimal maintenance strategy; and finally, performing multi-objective optimization iterative analysis to obtain a final maintenance decision. According to the scheme, various factors can be comprehensively considered, and the scientificity and the accuracy of maintenance decision making of the industrial equipment are improved.

The invention provides a north pole atmosphere coupling forecasting method capable of automatically fusing sea ice concentration and thickness, which belongs to the technical field of meteorology, and comprises the following steps: firstly, acquiring GFS background field data and correcting by applying an ice-sea heat exchange correction equation; and then sea ice concentration and thickness information is extracted, and physical consistency verification is carried out based on a thermodynamic equilibrium equation. Performing multi-element fusion through an Arctic ice sea assimilation model, calculating the sea ice surface temperature by applying a polar region sea ice heat flux equation, and establishing a sea ice concentration thickness boundary layer interaction equation set to describe the modulation effect of sea ice on a boundary layer; the method comprises the core steps of performing deep fusion on various sea ice features by applying a feature fusion model, adaptively adjusting the weight based on an ice-gas interface balance index, generating an optimized mode initial field and boundary conditions, and finally setting a parameter scheme to execute an integral program to obtain a forecasting result. The technical problem of low forecasting accuracy caused by insufficient physical consistency of sea ice information is solved.

The invention provides a regulation factor threshold effect evaluation method and device for vegetation response sudden drought characteristics, equipment and a storage medium. Relates to the technical field of drought risk prediction and influence evaluation. The method comprises the following steps: acquiring hydro meteorological data, vegetation productivity data, topographic feature data, soil characteristic data and biological variable data of a target area; constructing a drought suddenness index, extracting a drought suddenness event according to the drought suddenness index, and determining a drought suddenness feature of the drought suddenness event; extracting vegetation productivity abnormal data before and after the sudden drought event, and determining the response characteristic difference of the vegetation to sudden drought; the method comprises the following steps: determining influence factors of vegetation on sudden drought response characteristics, fusing XGBoost, a random forest regression model and an SHAP interpretability analysis method, analyzing regulatory factors of the vegetation response sudden drought characteristics, and determining a threshold effect of the regulatory factors. According to the method, the nonlinear cooperative regulation effect of multiple factors on vegetation response sudden drought characteristics can be disclosed more comprehensively.

The invention discloses an engineering quality safety risk intelligent assessment method and system, relates to the technical field of engineering quality safety assessment, adopts multiple types of sensors to collect data, breaks through the limitation that the traditional structure monitoring only depends on a single index to judge the risk, and realizes cross-parameter and cross-component structure state comprehensive perception. The problem that the structure risk is commonly evolved by multiple factors is solved, and the overall recognition precision is improved. A sliding window and a periodic variable coefficient calculation mechanism are introduced, and the system can dynamically extract fluctuation characteristics of structure response and identify different evolution forms of short-time fluctuation, medium-term creep and long-term mutation. And the defects of insufficient abnormal precursor identification capability and serious hysteresis response in the traditional method are overcome. According to the method, an abnormal mutation index AEI and an evolution acceleration factor EAF are constructed, the formation process and the development path of the risk are identified no longer only on the basis of whether a certain moment value exceeds a threshold value or not, and the risk process perception capability is improved.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention relates to the technical field of building curtain wall keel structure design, and discloses a curtain wall keel structure intelligent optimization design method, which comprises the following steps: firstly, obtaining geometric parameters, load distribution data and material performance indexes, and verifying the geometric parameters, the load distribution data and the material performance indexes; analyzing the verified data to generate multi-dimensional structure characteristic parameters, and constructing a structure optimization reference model; keel structure data are verified in real time according to the model, and abnormal matching of node displacement and stress distribution is quantitatively evaluated; according to a verification result, generating a structural anomaly index to judge a design defect; and when defects exist, optimization design schemes including keel section adjustment, node reinforcement and the like are automatically generated and sequenced. According to the method, multiple factors are comprehensively considered, multi-objective optimization is achieved, the curtain wall keel structure design quality and efficiency can be improved, the structure safety is guaranteed, and the cost is reduced.

The invention discloses a loess slope stability intelligent monitoring method and system, and the method comprises the steps: carrying out the regional differentiation modeling of multi-source data of a target region, and outputting slope change data; according to the period-by-period displacement time sequence data, a slope deformation trend is obtained through prediction based on an LSTM algorithm; and inputting the slope change data, the slope deformation trend and the meteorological rainfall data into a slope stability evaluation model to predict a safety coefficient, and if the safety coefficient is lower than a preset threshold value, issuing early warning information. According to the loess slope stability intelligent monitoring method and system provided by the invention, data such as gradient change and deformation trend are accurately extracted, multi-source data and meteorological rainfall data are fused by means of cross-modal attention, a random forest model is used for learning a multi-factor coupling rule, and finally multi-factor data prediction is integrated to obtain a safety coefficient for early warning. The problem of low early warning reliability caused by difficulty in accurately evaluating the slope stability by fusing multiple factors such as gradient, deformation and meteorological rainfall can be solved.

The invention relates to the technical field of electrical digital data processing, and discloses a statistical entropy and multi-factor fused text similarity data processing method, which comprises the following steps that: a processor extracts substring sets which do not contain maximum common values of a first data sequence and a second data sequence, and calculates the quadratic sum of the lengths of substrings to generate local statistical entropy; traversing the maximum common substring set to obtain storage address indexes of the maximum common substring set in the first data sequence memory space and the second data sequence memory space, and constructing a topological mapping vector of a mapping structure displacement relationship; calculating the total number of inverted pairs of the topology mapping vector by using a merge sorting algorithm, and generating a normalized topology dissipation index; and by taking the local statistical entropy as an information carrier and taking the topological dissipation index as a structural damping factor, executing nonlinear damping modulation operation to obtain a final similarity score, and solving the technical problem that the block-level displacement cannot be identified by linear scanning logic by quantizing topological entropy increase of data distributed in a storage space.

The invention relates to a method for setting power loss of a mountain photovoltaic array. The method comprises the following steps of: establishing a coupling causal spectrum of multi-source factors including terrain, angle, sunlight, temperature, dust and wiring mode to component output; identifying a dominant loss path based on an identification method of a Bayesian network; outputting a structure weight matrix of the power loss, and identifying an inclination angle gt; a temperature gt; a set of influence factors of the wiring topology pair setting; calculating a power loss response index of each component based on the loss path weight; forming a response index thermodynamic diagram by using a space weight interpolation algorithm, and performing array space sensitive partitioning; the high response area is used as a preferential setting target, and the low response area enters an observation maintenance mechanism; by constructing the response index and the correction response potential function based on multi-factor coupling, the power response potential of the component to the setting operation can be accurately identified, the problems of average adjustment and low-efficiency resource allocation in a traditional setting method are effectively avoided, and it is ensured that setting resources are intensively input into a high-response area.

The invention relates to the technical field of green intelligent building planning and design, and discloses a green intelligent building multi-factor fusion planning and design method. The method comprises the steps of firstly obtaining geographic information data and ecological environment parameters of a building site, collecting peripheral multi-dimensional environment data and performing fusion processing, constructing a three-dimensional space topology model, and generating an initial space planning model; and then climate simulation and resource circulation conditions are loaded, a comprehensive planning analysis model is formed, and dynamic collaborative evaluation indexes are output. A multi-factor coordination model and key coordination parameters are obtained by constructing a multi-objective optimization pre-training framework for iterative learning, and then a system collaborative simulation result is generated. And establishing a configuration model to optimize facility deployment parameters to obtain an optimal configuration scheme. And finally, dynamically adjusting parameters according to the efficiency evaluation framework to realize planning control of the green intelligent building. According to the method, multiple factors are integrated, optimization of building planning and design is achieved, and the sustainability and the intelligent level of the building are improved.

The invention provides a factory power load monitoring and early warning method based on digital twinning, and belongs to the technical field of digital twinning and power load prediction, WTConv and i Transform are combined for power load prediction, and the challenge that a traditional power load prediction method is difficult to deal with in a complex dynamic factory environment is solved; in the actual factory operation process, the power load is influenced by multiple factors such as the production rhythm, the equipment operation state and the external environment, so that load data have high time-varying characteristics and nonlinear characteristics; through the combination of WTConv and iTransform, the method can better capture a complex space-time mode and multi-scale features, thereby remarkably improving the accuracy and robustness of power load prediction, providing more reliable load prediction support for a power system, helping enterprises to optimize production scheduling, reducing energy loss, and effectively preventing the load risk of the power system.

The invention discloses an ice condition prediction method based on multi-feature fusion and physical constraint, and belongs to the technical field of hydrological forecasting. Firstly, various types of data of a target area are collected and preprocessed to serve as a data set, features of the various types of data are extracted, feature fusion is conducted on obtained image features, time sequence features and environment features through a multi-head attention mechanism, and a fusion feature vector is generated. Secondly, adopting a physical information neural network model, taking the fusion feature vector as input, taking ice thickness and ice stress as output layers, carrying out constraint by using a composite loss function, carrying out model optimization by using a verification set, carrying out processing through a full connection layer in the network, and carrying out end-to-end training and regularization of a prediction model; and finally, evaluating the final prediction model obtained by training through the test set, and verifying the effectiveness and generalization ability of the final prediction model. The method combines multi-source and multi-mode observation data, can effectively capture complex relations between ice surface changes and various factors, can improve prediction precision, stability and reliability, and can improve model training efficiency and generalization ability.

The invention discloses a large-span continuous rigid frame construction line shape intelligent monitoring method, and relates to the technical field of large-span continuous rigid frame construction, and the method comprises the following steps: S1, data collection; s2, data processing; s3, performing multi-stage intelligent early warning; s4, construction regulation and control; and S5, outputting a result. According to the method, multi-source equipment is used for synchronously collecting data, the real-time performance and integrity of the data are guaranteed, deep learning is used for eliminating anomalies, a neural network is used for constructing an environment influence model and correcting the data, the influence of environment factors on linear data is corrected, digital twinning is combined with a finite element and a neural network to predict a deformation trend, and the prediction precision is improved. A linear deviation trend is pre-judged in advance, a construction scheme is optimized, multi-level intelligent early warning dynamically adjusts a threshold value according to multiple factors, early warning information is accurately issued, risk response timeliness is enhanced, decision suggestions can be automatically generated through construction regulation and control, an evaluation effect is fed back, and the construction process is optimized.

The invention discloses a new energy electric vehicle ordered charging method considering multiple factors, which realizes ordered charging through a closed-loop framework of data driving, multi-objective optimization, intelligent scheduling and dynamic feedback based on multi-dimensional data such as user demand, power grid stability, economical efficiency and renewable energy utilization, and can improve the charging efficiency on the basis of meeting the charging demand of a user. Peak clipping and valley filling of power grid loads, efficient utilization of renewable energy sources and user charging cost optimization are achieved, power grid load fluctuation is reduced, the electric energy quality is improved, the renewable energy sources are utilized to the maximum extent, it is ensured that the requirements of new energy electric vehicle users are met, and meanwhile the economical efficiency and stability of a power grid are achieved. Meanwhile, through an intelligent scheduling system and load prediction, charging tasks are uniformly distributed to night valley periods, so that a power grid load curve is more stable, and the utilization efficiency of power grid resources is optimized; and meanwhile, through an intelligent algorithm and a big data technology, the charging demand prediction accuracy is improved, and charging scheduling is more intelligent and accurate.

The invention relates to a supply chain risk assessment decision method and system. The method comprises the following steps: carrying out single-factor evaluation modality and multi-factor weighted evaluation modality on a risk original data set of each link of a supply chain, and creating a multi-dimensional risk quantification model; according to the multi-dimensional risk quantification model, performing multi-stage time scale evaluation on the risk original data set to obtain a multi-stage risk evaluation result; based on the multi-stage risk assessment result, modeling a supply chain network into a directed graph, and constructing an integer programming model for solving to obtain a risk-oriented path allocation scheme; and according to the risk-oriented path distribution scheme, risk index calculation and early warning grade division are carried out on the multi-stage warehousing system to obtain a collaborative early warning strategy of the multi-stage warehousing system. According to the invention, reasonable distribution of supply chain resources is realized, the congestion risk of logistics nodes is effectively relieved, and the operation efficiency of the whole supply chain is improved.

The invention belongs to the technical field of eye detection, and particularly relates to a myopia progress risk prediction system, which comprises a data acquisition module used for collecting a plurality of biological index data related to myopia progress, including but not limited to ocular axis length, corneal curvature, diopter, age, gender, lifestyle, genetic information and the like; the multi-factor analysis module is used for comprehensively analyzing multiple pieces of biological index data and modeling the individualized myopia progress risk based on a multi-factor interaction relationship; the regression algorithm module is used for collecting multiple pieces of biological index data; by analyzing multiple biological indexes at the same time, comprehensive evaluation of the myopia progress risk is achieved, compared with a traditional method which only depends on a single index for prediction, the system considers interaction of multiple factors, individual differences and environmental influences can be captured more comprehensively, and therefore prediction accuracy and reliability are remarkably improved.

The invention provides a virtual power plant aggregation cost quantification method considering multiple demand response fuzzy dynamic willingness, and belongs to the field of distributed resource aggregation regulation and control. Firstly, a constructed flexible load user dynamic response willingness model quantifies the participation level of flexible loads such as an electric vehicle and an air conditioner cluster under multi-factor driving. And secondly, the constructed wind power / photovoltaic typical scene set can be used for risk quantification considering uncertainty in a virtual power plant aggregation cost calculation risk model. Thirdly, a constructed virtual power plant aggregation cost calculation risk model objective function comprehensively considers resource calling cost, network loss, adjustment deviation penalty and system tail risk loss; and finally, establishing constraint conditions of the virtual power plant aggregation cost calculation risk model, and realizing evaluation of schedulable margins of an electric vehicle cluster and an air conditioner cluster. According to the method, credible input can be provided for the adjustable resource boundary of the virtual power plant, and quantitative representation of the relationship between the aggregation main body adjustment service amount and the cost is realized.

The invention discloses a large-span box girder bridge time-varying reliability evaluation method considering concrete fatigue creep. The method comprises the steps that concrete static creep and shrinkage strain are calculated; calculating concrete fatigue creep strain generated by the vehicle load; constructing a concrete unified constitutive model considering a multi-factor coupling effect; establishing a long-term performance fine analysis numerical model of the large-span box girder bridge; establishing a large-span box girder bridge time-varying reliability calculation method; establishing a limit state equation of reliability analysis; and evaluating the structural time-varying reliability considering the concrete fatigue creep effect. According to the method, the time-varying reliability of the large-span box girder bridge under the coupling action of factors such as concrete static creep, cyclic creep and shrinkage can be accurately evaluated, and technical support can be provided for health diagnosis, maintenance and reinforcement of bridges of the same type.

The invention provides a sludge amount measurement method and system based on a reservoir model, and relates to the technical field of hydrographic survey, and the method comprises the steps: obtaining the operation data, basin hydrological data and reservoir topographic data of a target reservoir; based on the operation data, the watershed hydrological data and the reservoir topographic data, establishing a water balance equation and a sediment balance equation; establishing a reservoir model through a water balance equation and a sediment balance equation, and calculating a first predicted distribution quantity of sediment deposition in combination with a dispatching record and a sediment transportation quantity of a target reservoir; calculating a second predicted distribution quantity of the target reservoir at the prediction moment based on the historical sludge distribution quantity of the target reservoir; and if it is determined that the first predicted distribution quantity and the second predicted distribution quantity are within the preset range, determining that the sludge quantity of the target reservoir at the prediction moment is the first predicted distribution quantity. By introducing a coupling mechanism among multiple factors related to reservoir sludge amount prediction, the accuracy of reservoir sludge amount prediction is improved.

The invention discloses a power demand evaluation method, system and device based on deep learning, and a storage medium, and belongs to the technical field of power system intelligence and energy management, and the method comprises the steps: collecting data of multiple modes, and carrying out the preprocessing of the data; designing an adaptive weighting strategy according to the preprocessed data, weighting different modal data, constructing a deep learning model through weighted fusion data, and training the model; and outputting a prediction result of the power demand, and correcting the output result. According to the method, the dynamic weighting strategy of multi-modal data and a time window correction mechanism are fused, so that the adaptability of power demand prediction to complex environment changes is remarkably improved, and the load fluctuation characteristics of extreme weather can be accurately captured; and in combination with the time sequence modeling capability of the long-short-term memory network, the long-term dependency relationship and the multi-factor coupling rule are effectively learned, and the problem that a traditional model is insufficient in expression of periodic and sudden demand modes is solved.

The invention relates to the technical field of intelligent manufacturing, in particular to a nodular cast iron alloy component self-adaptive optimization method and system based on artificial intelligence, and the method comprises the following steps: obtaining furnace temperature and component parameters, generating a stable comparison legend, screening offset abnormal combinations to form an available set, extracting performance curve characteristics to construct a trend chart, and obtaining a nodular cast iron alloy component self-adaptive optimization result. And superposing the structural parameters and the element distribution positioning segregation area, and outputting feeding correction parameters to generate an optimization scheme. According to the method, normalization processing is carried out on furnace temperature, carbon-silicon ratio and other multi-factor offset values, a component offset map is constructed, trend feature extraction and sorting of response curves under multiple working conditions are combined, component combinations with stable performance are screened out, a mapping relation is established through the structural curvature, the wall thickness slope and the injection rate, and a spatial coincidence map is generated through coincidence analysis. And the feeding direction and the injection rate are inversely calculated, dynamic adaptation of components and the structure is achieved, and the structure uniformity and the performance consistency are improved.

The invention belongs to the technical field of hydropower station operation and maintenance management, and relates to a hydropower station spare part inventory intelligent prediction method and system based on digital twinning. According to the method, the residual life of the key component of the equipment is predicted according to the historical fault data and the real-time monitoring data of the hydropower station equipment, and the operation condition and the health degree of the key component of the equipment can be known more comprehensively and accurately by fusing the historical fault data and the real-time monitoring data. According to the residual life of the key parts of the equipment, in combination with the equipment maintenance plan and the supply chain period, the expected demand of each spare part in the future time period is output, multiple factors such as the residual life of the key parts of the equipment, the equipment maintenance plan and the supply chain period are comprehensively considered, and prediction of the expected demand of the spare part is more comprehensive and reasonable. The safety inventory threshold value and the purchase priority are dynamically adjusted according to the expected demand of each spare part, matching of equipment loss prediction and the spare part demand is realized, the inventory structure is optimized, the spare part fund occupation is reduced, and the inventory management is more flexible and efficient.

The invention relates to the technical field of radar remote sensing, discloses a high-order loose body historical activity detection method based on multi-source DEM data, and aims to solve the problem that the historical activity of a high-order loose body in a glacier coverage area is difficult to accurately and quantitatively evaluate in the prior art. The scheme mainly comprises the steps that drainage basin units are automatically divided based on DEM data, and glacier influence areas are screened; multi-source DEM data are fused, and system errors are eliminated through geographic positioning deviation correction, elevation distortion deviation correction and track mode deviation correction; establishing a penetration depth physical model, and correcting the radar penetration depth in the glacier area by using the C / X wave band elevation difference optimization parameter; calculating an elevation change rate by adopting robust regression; and calculating an activity comprehensive index by integrating multiple factors, and identifying a hot spot region and a time period through probability estimation and spatio-temporal clustering. According to the method, automatic and high-precision quantitative evaluation on the historical activity of the high-position loose body is realized, and the method is particularly suitable for geological disaster early warning and risk evaluation in high mountain areas.

The invention belongs to the technical field of insurance product pricing, and more specifically relates to a multi-factor fusion insurance product dynamic risk pricing optimization method. Collecting multi-source information data, and carrying out data preprocessing; on the basis of travel trajectory analysis of vehicle-mounted OBD data, in combination with high-risk road sections, driving behaviors and environmental factors, a space-time accumulation risk index is constructed, a graph convolutional network is used for modeling a risk transfer effect in a social circle of vehicle owners, mutual influences among the vehicle owners in the social circle are captured, and a neural network based on multi-task learning is used for establishing a social network model. Dynamic risk, social risk features and other related data are comprehensively processed, the risk probability and loss amount prediction are optimized through a shared network layer, and the insurance pricing is generated in combination with external market data, so that the comprehensiveness and accuracy of the insurance pricing are ensured; the method solves a blind area existing in a conventional pricing method, enables the insurance pricing to be more intelligent and precise, and can reflect the actual risk condition of a vehicle owner in real time.

The invention discloses a garment supply chain risk analysis method and system based on artificial intelligence, belongs to the technical field of garment supply chain analysis, and effectively enhances crisis pre-judgment and response capability of a supply chain through a dual mechanism of simulation deduction and dynamic optimization. A built-in game learning framework of the system can autonomously construct a multi-factor coupled complex interruption scene, for example, an extreme condition that a port is closed in a typhoon season, cross-border tax policy mutation and alternative raw material transportation route congestion occur at the same time is simulated. The deduction not only reveals the problem of excessive dependence of a single node, which is difficult to discover by traditional auditing, but also can verify the practical feasibility of a standby scheme, and helps an enterprise to establish a multi-layer defense system. The continuous self-optimization characteristic of the system enables the system to be prominent in response to novel challenges, and when the international logistics network is suddenly adjusted, a supplier recombination scheme considering both cost and time efficiency can be quickly generated, and the potential chain breakage risk is resolved in the germination stage.

The embodiment of the invention provides a risk zoning method and device for helicopter flight, equipment and a storage medium. The method is applied to the technical field of data processing, and comprises the following steps: carrying out classification and threshold division on meteorological elements according to a fusion data set and different flight scenes to obtain a meteorological element threshold breakpoint set; based on the meteorological element threshold breakpoint set, performing frequency statistics and data synthesis on the fused data set to obtain a multi-layer meteorological element frequency data set; obtaining an initial clustering result of each meteorological element through clustering analysis according to the meteorological element frequency data set; according to the initial clustering result, carrying out risk grade division on each meteorological element to obtain a standardized single-element risk zoning result; and based on a single-element risk zoning result, adopting a multi-element fusion method to obtain a comprehensive risk zoning map. In this way, the technical problem that in the prior art, due to the fact that multiple elements jointly participate in clustering, the risk level is difficult to judge can be solved.

The invention provides a tool-based task processing method and device and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: determining a plurality of candidate tools by utilizing similarity between task description and function description of a preset tool; calculating the applicability score of the candidate tool in the current state of the intelligent agent by using the key parameters of the candidate tool and the memory data of the intelligent agent; calculating target scores of the candidate tools according to the applicability scores and the similarity of the candidate tools and historical tool data of the candidate tools; determining a to-be-called tool from the candidate tools according to the target scores of the candidate tools and the collaboration among the candidate tools; retrieving reference memory data from the memory data based on the task description; and calling the to-be-called tool in combination with the reference memory data to execute the to-be-processed task. According to the method, multiple factors can be comprehensively considered, the tool selection range is precisely narrowed, the tool selection accuracy is ensured, and then the task execution accuracy can be improved.