697 results about "Multiple factors" patented technology

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 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 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.

The invention belongs to the technical field of blasting vibration prediction, and particularly discloses a blasting vibration speed attenuation law prediction method based on regression analysis, and the method comprises the steps: laying monitoring points in multiple directions around a blasting source, synchronously collecting the geological parameters and mass point peak vibration speed of each point, and constructing a segmented propagation path; a single dominant propagation section is identified based on adjacent monitoring point data, independent influence of specific geological conditions on vibration attenuation is quantified by combining single-factor control regression analysis, decoupling modeling of geological factors and attenuation behaviors is realized, and meanwhile, a double-factor dominant propagation section is identified on the basis of quantizing the independent influence of the single geological conditions. And deducting a known effect through residual analysis to separate out an independent contribution of another geological parameter, and finally integrating a multi-factor influence function to construct a comprehensive attenuation model. Progressive modeling from single-factor decoupling to multi-factor collaboration is achieved, and the precision and physical interpretability of blasting vibration prediction in a complex heterogeneous stratum are remarkably improved.

The invention relates to the technical field of tourism resource information processing, in particular to a tourism hotel dynamic scoring and recommending method and system based on multi-factor fusion, and the method comprises the steps: constructing a dynamic game portrait containing a user space-time behavior mark and hotel multi-modal attributes, and carrying out the dynamic scoring and recommending of a hotel based on the dynamic game portrait; the method comprises the following steps: analyzing a recessive preference vector of a user and an adaptive strategy vector of a hotel by simulating an interactive game process of the user and hotel attributes, performing dynamic equalization matching on the recessive preference vector and the adaptive strategy vector to generate a hotel situation score, generating an anti-consensus recommendation sequence according to the hotel situation score, and performing recommendation on the hotel situation according to the anti-consensus recommendation sequence. The anti-consensus recommendation sequence is subjected to interpretable packaging, and a final recommendation result with a decision guide clue is output to the user, so that the user can obtain a personalized hotel recommendation list and can also understand logic and diversity values behind recommendation, and the user experience is improved. Therefore, the transparency, the user trust degree and the exploration satisfaction degree of the recommendation system are improved.

The invention discloses a dynamic base station dormancy / wake-up method based on a flow load and a channel state, which improves the accuracy and reliability of dormancy and wake-up judgment by comprehensively considering multi-dimensional factors such as a service load, a wireless channel state, user distribution, adjacent region bearing capacity and the like. By designing a multi-level dormancy and wake-up mechanism and combining flexible management of different functional modules, reasonable balance between energy consumption reduction and service continuity is realized; by introducing a neighbor cell cooperation mechanism and a pre-wake-up strategy, the wake-up time delay is shortened while the coverage and switching stability are maintained. Meanwhile, key indexes such as energy consumption, access time delay, offline rate and switching success rate are continuously monitored in the operation process, and the feedback information is used for dynamically optimizing parameters such as criterion weight, threshold value and residence time. Through a closed-loop adjustment mechanism, the system can keep stable and effective operation in different scenes, and long-term balance between energy-saving control and service performance is realized.

The invention provides a timed task execution optimization method and system. The method comprises the following steps: forming a timed task execution optimization system architecture; determining a corresponding calculation factor to obtain a dynamic priority score; predicting the to-be-predicted task data by using the task prediction model to obtain a task execution time prediction value; analyzing the to-be-executed task queue, calculating the average waiting time, determining a multiple factor according to the task priority and the system load state, and identifying the task with the task execution time prediction value larger than the dynamic threshold value in the to-be-executed task queue as a long task; performing automatic fragmentation processing on the long task, generating a fragmentation result, determining a scheduling sequence based on the dynamic priority score and the task execution time prediction value, and generating a scheduling decision; and performing resource availability evaluation on the resource use data to obtain a node resource evaluation result, and allocating execution resources for the selected task. According to the method, the problems of efficiency and reliability of the timed task execution system in a high-load scene are solved.

The invention relates to the technical field of geotechnical engineering structures, in particular to a multi-factor coupling CFG pile full-life failure probability prediction and optimization method. Comprising the following steps; the method comprises the following steps: S1, integrating resistance degradation, pile-soil interaction, geological evolution and load time-varying characteristics to construct a limit state model; s2, on the basis of the limit state model constructed in the step S1, Monte Carlo simulation circulation is used, the failure probability is calculated, and a CFG pile system failure probability change graph is obtained, S3, parameters in the limit state model are optimized, after each parameter is optimized, the steps S1 to S2 are carried out again, and the optimized CFG pile system failure probability change graph is obtained; according to the method, the accuracy and the adaptability of full-life prediction and optimization of the CFG pile are improved.

The invention provides a robot complex task dynamic arrangement method, belongs to the field of robot task arrangement, and is used for solving the problems of weak dynamic environment adaptation capability, poor multi-robot collaboration and insufficient risk pre-judgment of traditional static task arrangement in related technologies. According to the method, after double matrixes are constructed and fused into tensors, low-rank decomposition is performed, a threshold value is dynamically generated and optimized, task priorities are calculated through multiple factors, resources are allocated in combination with resource constraint and federated learning, synchronous cross-scene adaptation during execution, risk pre-planning and visual feedback optimization are performed, and dynamic adaptation, efficient collaboration and stable execution of complex tasks are achieved.

The invention provides a service deployment and model selection system and method for inference precision perception in an edge environment. The system comprises a user side and an edge server side. The user side comprises terminal equipment and a reasoning request module; the edge server side comprises a service type selection module and a model selection module; the system runs based on multiple time slots; in each time slot, the reasoning task of the user is recorded as a quintuple, and the quintuple represents the task size, the service type, the calculation density under different configurations, the reasoning precision under different configurations and the task priority; the calculation density and the reasoning precision of the tasks change along with different resource configurations; the system model provided by the technical scheme comprehensively considers a plurality of factors such as reasoning delay, reasoning precision and memory limitation, and aims to improve the utilization efficiency of edge resources while considering the user service quality.

The invention discloses an earthquake disaster loss dynamic assessment method and system based on multi-source disaster situation data, and relates to the technical field of earthquake disaster loss assessment, and the method comprises the following steps: S1, obtaining the multi-source original data of an earthquake disaster, and carrying out the preprocessing, the multi-source original data comprises seismic oscillation parameter data and multi-source disaster data; and S2, based on the preprocessed multi-source data, constructing a coupling driving model of the earthquake influence field and the multi-source disaster situation data, and generating a regional disaster damage thermodynamic map. According to the earthquake disaster loss dynamic evaluation method and system based on the multi-source disaster situation data, the earthquake multi-source disaster situation data is acquired, the coupling driving model of the earthquake influence field and the multi-source disaster situation data is constructed, and the influence of various factors on the earthquake disaster loss is comprehensively considered, so that the limitation that a traditional method depends on a single data source is overcome; the real situation of earthquake disaster loss can be reflected more comprehensively and accurately, and a more reliable basis is provided for rescue decision making.

The invention discloses a robot operation arm constraint safety system based on communication perception and a method thereof. The method comprises the steps that communication parameters with an upper computer and the angular speed and control torque of a robot operation arm are obtained in real time; de-noising the communication parameters, and performing evaluation calculation to obtain a network score; judging whether the network score is greater than a preset first threshold value, if so, judging that communication is abnormal and accumulating, if the communication is abnormal in continuous N periods, smoothly switching from an RL online mode to a local TDE prediction mode, and calculating a disturbance estimation value of the current period after mode switching; calculating an impact trend index of the current period based on the current control torque, the angular velocity and the historical period data difference value, and outputting a degradation factor from the disturbance estimation value and the impact trend index through a fuzzy rule; and adjusting the control parameter through the reduction factor, and generating the control torque of the next period. The fuzzy rule is fused with multi-factor decision, and control stability and system responsiveness are both considered.

The invention provides a settlement data over-limit adjustment method in a construction stage, and belongs to the technical field of wind tunnel construction. Monitoring points are installed at key positions of a wind tunnel structure foundation to establish a three-dimensional monitoring network; five special analysis matrixes including a shrinkage creep factor matrix, a load settlement gradient matrix, a sudden change settlement identification matrix, a multi-factor continuous coupling matrix and a construction process parameter matrix are constructed, and the independent contribution value of each deformation component is quantitatively separated through a settlement separation optimization equation set; a settlement overrun evaluation index calculation model is established for risk grade division, and corresponding load control measures, concrete curing condition optimization and construction process improvement operation are selected from a construction process parameter matrix according to an evaluation result. The technical problem that the contribution degree of each component of settlement deformation under the multi-factor coupling effect in the construction process cannot be accurately separated and quantitatively analyzed is solved.

According to the intelligent inventory management and automatic replenishment e-commerce operation method, sales data, inventory data, return data, commodity attributes, supply chain data and market environment data (such as social media, weather data, economic indicators and the like) of an e-commerce platform are integrated, and a data preprocessing method is adopted for cleaning and denoising, so that the quality and integrity of the data are ensured. A multi-dimensional data fusion and deep learning algorithm is adopted, multiple factors such as commodity historical sales data, seasonal fluctuation, market dynamics, user behaviors and the like can be fully considered, and therefore compared with a traditional single prediction model, the demand prediction accuracy can be greatly improved, the inventory overstock or stockout risk caused by prediction errors can be avoided, and the user experience can be improved. By designing a mechanism for monitoring and analyzing external events in real time, emergency situations (such as emergency promotion, market demand fluctuation, weather change and the like) can be quickly responded, a replenishment strategy can be timely adjusted, and inventory crisis caused by the fact that the system fails to quickly respond to external changes is avoided.

The invention discloses a multi-dimensional comprehensive evaluation method for permeability resistance of overlying strata of a working face, and belongs to the technical field of mine engineering and geological safety evaluation. 12 types of core factors such as rock strength, rock core quality, physical and mechanical properties of rock mass, hydrological properties and mining influence characteristics are integrated; a full-process evaluation framework of data acquisition, index system construction, weight distribution, comprehensive modeling and verification optimization is constructed, an analytic hierarchy process and an entropy evaluation method are coupled to determine index weights, a dynamic evaluation model is constructed in combination with a fuzzy mathematics theory, and accurate classification and risk early warning of the permeability resistance of the overlying strata are realized. According to the method, through comprehensive analysis of a plurality of factors, the accuracy of overlying strata permeability resistance evaluation can be improved, so that the underground water permeation path and the permeation amount are predicted more accurately, the uncertainty is reduced, the comprehensiveness and the accuracy of evaluation are remarkably improved, and a scientific basis can be provided for mine water disaster prevention and control and safe mining.

The invention discloses a self-adaptive adjustment method and system based on an energy management system, and relates to the technical field of energy management, and the method comprises the following steps: collecting multi-dimensional heterogeneous data including weather conditions, economic situations, social activities, user behavior patterns and energy price fluctuations; according to the method, the multi-dimensional heterogeneous data is fused, the space-time attention mechanism and the graph neural network are utilized to construct the high-precision prediction model, ultra-short-term to medium-and-long-term energy demand dynamic prediction is realized, the problem of insufficient multi-factor fusion is solved, the hierarchical response strategy is triggered based on the prediction result, and the prediction efficiency is improved. Distributed energy storage, a flexible load and a production end are linked to perform dynamic power adjustment, strategy autonomous optimization is realized through reinforcement learning, the adaptive adjustment ability to a complex scene is improved, an adaptive adjustment closed loop is formed through real-time error calculation and model parameter correction, and adjustment parameters and model weights are corrected in real time, so that the real-time performance of the system is improved. And the stability and accuracy of long-term operation of the system are ensured.

The invention provides a hybrid level enhanced random algorithm based on double-loop optimization and a cross-platform implementation method thereof, and belongs to the technical field of modeling and parameter, and the method comprises the following steps: S1, initialization; s2, setting algorithm parameters; s3, executing internal circulation, and executing local search under a fixed threshold value; s4, judging constraint conditions; when there is no constraint condition, generating a candidate solution; s5, calculating a target function value; s6, judging updating of the matrix according to the target function value; s7, returning to an outer loop, and adjusting a threshold value Th according to the frequency for receiving the new design and the number of iterations; s8, after M iterations are completed, the acceptance rate and the improvement rate are calculated; s9, updating the threshold value Th; and S10, judging a condition for stopping iteration. According to the method, the problems that a general construction method is lacked, local optimum is easy to sink and cross-platform deployment is difficult to realize in a multi-factor mixing level test design are solved.

The invention discloses a natural gas pipeline failure risk assessment system and method based on a multi-factor coupling model, and relates to the technical field of natural gas pipeline safety, and the system comprises an intelligent environment monitoring module, a dynamic technical parameter library, a block chain management assessment module, a coupling calculation engine, an enhanced visualization platform and a self-adaptive early warning module. According to the method, the risk coupling degree is calculated by adopting the risk coupling algorithm improved based on the nonlinear characteristic and the synergistic effect, so that risk coupling quantification is realized; the adaptive capacity of the natural gas pipeline evaluation system is improved through technical innovation integration, full-chain closed-loop management is formed through combination of technologies such as block chain evidence storage, machine learning defect prediction and three-dimensional GIS rendering, model parameters and early warning strategies can be dynamically adjusted according to real-time data, and conversion from passive response to active prevention is achieved.

The invention relates to the technical field of warehousing of bulk grains in a horizontal warehouse, discloses a warehousing method and system for bulk grains in a horizontal warehouse, and aims to solve the problems of uneven accumulation, potential safety hazards and low efficiency in an existing method. A three-dimensional warehouse body coordinate system is constructed through multi-dimensional analysis, and spatial density distribution is simulated; establishing a dynamic accumulation model based on density data to simulate a spatial-temporal trajectory, calculating acting force among particles through finite element analysis to obtain stress distribution, and constructing a stability evaluation system; the impurity rate is corrected through a coupling model by utilizing environment parameters of the internet of things, and a stability evaluation result is dynamically compensated by adopting a multi-factor matrix; a deep reinforcement learning algorithm is applied to optimize a control strategy, an intelligent model is deployed at an edge node, and a joint control automation system realizes closed-loop management. According to the bulk grain warehousing device, the uniformity, stability and efficiency of bulk grain warehousing operation are improved.

The invention relates to the technical field of catalyst processing, in particular to an automatic proportioning control system for catalyst mixed materials. According to the invention, the physical distance of each material conveying pipeline and the opening and closing delay of the control valve are obtained, and the task issuing time point is combined, so that the effective response time intervals corresponding to different pipelines are established, and the judgment of the validity fed back by the weighing sensor is more accurate; furthermore, through dynamic trend fitting between historical matching data and current adjacent pipeline data, it is ensured that accurate substitution weighing information can still be obtained under the condition of abnormal feedback, and the matching precision deviation caused by the abnormal feedback is effectively avoided; the actual emptying state of the residual materials in the pipeline is jointly judged by combining the tail section flow velocity and the tail end storage capacity change, scheduling resource configuration has real-time effectiveness, the pipeline priority is calculated through multiple factors such as the conveying distance, the flow velocity fluctuation and the historical idle period, and the reasonability and accuracy of a scheduling path are improved.

The invention relates to the technical field of semiconductor test equipment, in particular to a full-automatic high-precision probe station, which utilizes multi-physics field coupling sensing and compensation to remarkably improve the positioning precision: by comprehensively collecting multi-physics field data such as a temperature field and a stress field and combining a coupling algorithm to optimize a compensation strategy, multi-factor interference is effectively counteracted, and the accuracy of the probe station is improved. The positioning repetition precision can reach + / -0.05 [mu] m, and the test requirement of the advanced semiconductor technology is met; a dynamic three-dimensional modeling technology and dynamic adaptability enhancement are achieved, a dynamic three-dimensional modeling system with the size affected by multi-physical field data can update the form of a test target in real time, test deviation caused by target deviation or surface fluctuation is avoided, and the adaptability of equipment to a complex test scene is improved. The automation degree is high, the testing efficiency is improved, the whole process of data acquisition, modeling, compensation and testing can be completed without manual intervention, the calibration process is simplified, the testing period is shortened, and the method is suitable for batch detection scenes.

The invention is suitable for the technical field of load prediction, and provides an electric vehicle charging load spatio-temporal distribution prediction method considering multi-factor interaction influence, and the method comprises the steps: obtaining the initial state information of a preset number of electric vehicles; calling a preset travel probability matrix corresponding to the vehicle type of the electric vehicle, and generating a travel target position of each vehicle by using a Monte Carlo method; calculating the shortest path, the driving speed and the driving time of each vehicle from the initial position to the travel target position; determining the residual electric quantity, the current position and the current time of each vehicle; when the residual electric quantity is lower than a preset threshold value, selecting an optimal charging station and recording charging information in combination with charging pile distribution at the current position, real-time capacity of power grid nodes and charging waiting time; generating a charging power space-time distribution matrix; and outputting a final charging load space-time distribution prediction result when iteration meets a convergence condition. And through dynamic coupling modeling and Monte Carlo simulation, the prediction precision of the time-space distribution of the charging load is improved.

The invention discloses an allergic rhinitis diagnosis and allergen traceability system based on dynamic text guidance, and belongs to the technical field of intelligent medical treatment. According to the method, the problems that in the prior art, patient description is inaccurate, allergens are various in variety and have differences, and allergens are difficult to accurately determine and trace are solved, the initial situational sub-graph is verified by introducing periodic features of the patient, the causal contribution degree of the initial situational sub-graph is evaluated through a Bayesian reasoning algorithm, and a mode association result library is formed; according to the method, the crossing of the allergic rhinitis diagnosis and tracing from the traditional static judgment to the dynamic accurate inference is realized, and a personalized and scientific allergen avoidance scheme is provided for patients; through similarity retrieval, environmental information injection verification and calculation of a mode goodness-of-fit score, under the condition that patient symptoms are in multi-factor mixing, a mixed causal graph is created through a graph fusion technology, and the mode goodness-of-fit score is calculated again, so that the accuracy of obtaining an allergen traceability result in practical application is ensured.

The invention relates to the technical field of concrete durability evaluation, in particular to a compound green concrete salt freezing resistance durability evaluation system under a frozen soil environment, which comprises a data input unit, a decision information table storage unit, a rough set analysis processing unit and an evaluation output unit, the data input unit receives a sample data set containing freeze-thaw cycle times, dry-wet cycle times, freeze-thaw-dry-wet cycle times, long-term soaking time, composite salt concentration, gas content and fly ash mixing amount, and the decision information table storage unit stores a decision information table constructed based on a rough set theory. The influence factor combination and the relative dynamic elastic modulus percentage are associated, the rough set analysis processing unit establishes a mapping relation through attribute reduction and rule extraction and quantifies the weight of each factor, and the evaluation output unit outputs a relative dynamic elastic modulus predicted value, durability grade classification and key factor contribution sorting. And the salt freezing resistance durability of the concrete under the multi-factor coupling effect is accurately evaluated.

The invention relates to the technical field of building anti-seismic risk assessment, and discloses a building anti-seismic potential safety hazard assessment method and system based on multi-factor coupling, and the method comprises the following steps: collecting key factors of potential safety hazards of a to-be-assessed building, and endowing each piece of key factor information with a discrete value; constructing a nonlinear coupling model through key factor construction and an interaction effect between key factors; building state information is monitored in real time through a sensor, a damage or collapse possibility index is corrected according to monitoring data to obtain a correction value, and a second comprehensive risk value is obtained through recalculation; and according to the second comprehensive risk value obtained through calculation, building anti-seismic safety risk grades are divided. According to the method, the damage possibility index and the accident consequence severity index are constructed, the comprehensive risk value is calculated according to the damage possibility index and the accident consequence severity index, quantitative modeling evaluation from static attributes to the risk value is achieved, and the method is more scientific and objective compared with a traditional single-factor evaluation method with high empirical and subjective performance.

The invention discloses a control method and system for a midspace wetland simulation system, and belongs to the technical field of wetland simulation and control, and the method comprises the steps: determining preset parameters according to experimental requirements, starting the system, and collecting and preprocessing environmental parameters in real time through a sensor network; establishing a wetland ecosystem dynamic model by fusing machine learning algorithms such as LSTM, Transform and a graph neural network, and predicting an environment change trend; an optimal control strategy is generated in combination with a reinforcement learning algorithm, and parameters of an environment control module are adjusted in real time; the big data analysis technology is used for mining data rules, and system preset parameters are automatically optimized. Meanwhile, a physical model is introduced to calculate a theoretical value to correct a prediction model error, and through space-time fusion modeling, reinforcement learning decision and big data driven optimization, the control problems of multi-factor coupling and strong dynamic nature in wetland simulation are solved, and the environmental change prediction precision and the control strategy optimality are remarkably improved.

The invention relates to the technical field of conversation type artificial intelligence, in particular to a multi-round conversation robot intention hit optimization method. The method comprises the following steps: after a user utterance is received, a system generates candidate intentions through a double-path mechanism; then, each candidate intention is evaluated through a multi-factor scoring algorithm, and the algorithm fuses four key signals: a graph transition probability, semantic correlation, a context coherence score calculated through a cross attention mechanism, and an out-of-domain penalty; and finally, making a decision according to the final scores of the candidate intentions: if the confidence coefficient of the candidate intention with the highest score is high enough and the candidate intention is obviously distinguished from other candidates, confirming the intention; otherwise, a targeted clarification problem is generated to solve ambiguity. According to the method, the accuracy and robustness of intention detection are remarkably improved through collaborative integration of dialogue streams, semantic matching and deep context analysis, and meanwhile, the user experience is improved through intelligent ambiguity processing.

The invention provides a multi-scale low-altitude airspace division evaluation method, which comprises the following steps: acquiring airspace environment data in a low-altitude airspace, the airspace environment data comprising three-dimensional topographic data, obstacle data and airspace management data, and then constructing a low-altitude environment digital model according to the environment data; dividing the low-altitude environment digital model into a plurality of evaluation grids; and acquiring flight data of the aircraft, dividing an evaluation area according to the flight data of the aircraft, the obstacle data and the airspace data, and then evaluating each evaluation grid in the evaluation area. The problems that in the prior art, comprehensive influences of multiple factors such as terrain limitation, airspace management and control conditions and aircraft performance parameters are not fully considered, a comprehensive and accurate airspace evaluation result is difficult to form, and a reliable basis cannot be provided for safe flight of a low-altitude aircraft and reasonable use of the airspace are solved.

The invention discloses a damage assessment method for a shallow-buried foundation bridge under a flood disaster. The method comprises the following steps: constructing a finite element model of the shallow-buried foundation bridge; introducing uncertainty of flood level height, foundation scouring pit length and scouring depth by applying a random Pushover method, obtaining bridge pier top yield displacement of the shallow foundation bridge under multiple groups of scouring working conditions, and calculating first two moments of logarithm of the flood-fighting capacity of the structure to obtain the flood-fighting capacity; performing flood-fighting demand analysis on the response of the shallow-buried foundation bridge in different flood washing environments based on a finite element reliability method; the flood-fighting capacity is compared with demand analysis, the failure probability of the shallow-buried foundation bridge under different working conditions is evaluated, fitting is conducted through the least square method, and a vulnerability curve of the shallow-buried foundation bridge is generated. According to the damage assessment method for the shallow-buried foundation bridge under the flood disaster, the influence of multiple factors such as the flow velocity and the scouring depth on the performance of the shallow-buried foundation bridge can be quantified, and the damage state of each stage of the shallow-buried foundation bridge is effectively assessed.