183 results about "Static model" patented technology

The invention provides a mine water disaster monitoring and early warning method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting multi-source data of a mining area, carrying out the time-space alignment of the multi-source data, and generating a data set, the multi-source data comprising remote sensing data; preprocessing the data, inputting the preprocessed data into a multi-modal fusion network, and extracting surface water body distribution, lithologic permeability, underground water level and structural fracture characteristics to obtain a three-dimensional hydrogeological static model; the hydrological numerical model based on physical driving is coupled with the static model, and the dynamic model is used for simulating the dynamic change of an underground water flow field and a pollutant diffusion path; based on the dynamic model updated in real time, multi-target collaborative evaluation is carried out to evaluate the mining area water resource, ecological and social collaborative effect; and calling an unmanned aerial vehicle to inspect a leakage position or a settlement position in the hydrogeological risk map based on a multi-target collaborative evaluation result. According to the method, the prediction accuracy is improved through the multi-source data.

The invention relates to the technical field of intelligent water affair and urban water supply system informatization, in particular to a water supply pipe network hydraulic model calibration and leakage locating method and system, and the method comprises the steps: obtaining the pressure and flow data of a plurality of monitoring points in a pipe network in real time; taking the model as a boundary condition to drive a hydraulic model to carry out real-time simulation, and calculating a theoretical value; calculating a residual error between a theoretical value and an actual value of the monitoring point; judging a triggering model calibration event or a leakage suspicion event based on a residual error abnormal mode; if calibration is triggered, inverting and updating global parameters of the model through an optimization algorithm to realize self-calibration; if leakage is triggered, a suspected area is determined by combining pressure space distribution analysis, and a leakage pipe section is accurately positioned through analog simulation and correlation analysis. Through closed-loop feedback of real-time data and the model, the model can adapt to changes of a pipe network system, dynamic self-calibration of the water supply pipe network hydraulic model is achieved, and the problem of precision attenuation of a traditional static model is solved.

The invention discloses a VGA (Video Graphics Array)-based 5G miniature optical transceiver module self-calibration method, which belongs to the technical field of optical transceiver module self-calibration, and comprises the following steps: S1, establishing a self-calibrated benchmark reference value through an initialization module and collecting environment parameters and benchmark optical parameters; s2, dynamically collecting and receiving optical power and environment change data, and judging whether deviation exceeds a threshold value or not to trigger model retraining by collecting actual operation data of environment parameters and driving parameters and combining a neural network model prediction target value; integration of a new data set and re-fitting of a non-linear relation enable the model to continuously adapt to the influence of long-term factors; key parameters such as a gain coefficient or a temperature compensation curve of a proportional-integral-differential control algorithm are dynamically corrected, long-term precision and reliability of a self-calibration system are guaranteed, and the problem of calibration failure caused by failure of a static model is avoided.

The invention discloses an offshore wind turbine foundation scouring full-period intelligent monitoring method and system based on multi-source data fusion and a dynamic coupling model, and relates to the technical field of intelligent monitoring, and the method comprises the steps: deploying a multi-source monitoring module, and constructing a finite element model; carrying out load calculation and parameter inversion; and training full-cycle dynamic updating of the washout failure function model. According to the method, a self-adaptive Kriging-Bayesian method is adopted, a Bayesian inversion framework and a self-adaptive agent model are fused to solve optimal soil body parameters, full-period model dynamic updating based on dynamic monitoring data is achieved, a multi-fidelity deep kernel learning model is adopted, three types of data are fused into a training set, full-period intelligent monitoring of offshore wind turbine foundation scouring is achieved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The dynamic identification of soil parameters is realized by combining a self-adaptive inversion framework with a displacement error closed-loop optimization mechanism, and the technical problem that a traditional static model cannot adapt to the spatial-temporal variability of seabed geology is solved.

The invention belongs to the technical field of network security, and provides a network threat real-time detection and defense method and system based on artificial intelligence. The method comprises the steps of multi-modal data acquisition and preprocessing, dynamic graph feature engineering and knowledge graph collaborative fusion, dual-adaptive model training and optimization, streaming real-time detection and anomaly scoring, DRL-driven hierarchical defense response and automatic disposal, and feedback-driven model adaptive updating and block chain auditing. According to the method, a mixed model of OS-ELM + dual-adaptive ridge regression + federated learning is designed, the training speed is higher than that of CNN, and over-fitting / under-fitting is avoided by dynamically adjusting a regularization coefficient; the federal learning realizes data local training and parameter uploading, and solves the problem of privacy disclosure; knowledge distillation enables the model volume to be reduced, edge equipment deployment is adapted while the accuracy is maintained, and the generalization ability is obviously superior to that of a traditional static model.

The invention discloses a low-carbon transformation method, system and device for a building air conditioning system. The objective of the invention is to solve the problems of static model, single target, one-sided evaluation and non-standardized process in the existing reconstruction technology. According to the method, building multi-dimensional data are collected; constructing a building energy consumption prediction physical simulation model coupled with a user behavior prediction model based on machine learning to dynamically reflect a real operation condition; a multi-dimensional objective function is defined, and a Pareto optimal transformation scheme set is generated; constructing an evaluation model based on an analytic hierarchy process and a fuzzy comprehensive evaluation method, and calculating a comprehensive evaluation value of each scheme; and selecting an optimal scheme according to the comprehensive evaluation value, and generating a detailed implementation report. The method can scientifically and efficiently determine the global optimal transformation strategy considering energy conservation, carbon reduction, comfort and economy, and significantly improves the scientificity and comprehensive benefits of building transformation decision.

The invention belongs to the technical field of low-code development platforms, and particularly relates to a low-code platform model construction and dynamic execution method and system based on a hybrid DSL (Digital Subscriber Line), and the method comprises the steps: describing static model information in an application by using a structure DSL in a declarative grammar; defining a behavior DSL in a mounting mode under a corresponding element of the structure DSL; analyzing the structure DSL into a meta-model object tree in the platform, and converting the behavior DSL into an executable expression tree / intermediate representation; jointly driving the meta-model object tree and the expression tree / intermediate representation by utilizing a template engine, and automatically generating at least one application code in a front-end page, a back-end interface and a database script; a common structure DSL and a behavior DSL are combined and packaged into a scene template, and the scene template is stored in a template library so as to be reused by different service scenes, and modularization and templating of service modeling are realized. The complexity of the system is reduced, and the development efficiency and quality are improved.

The invention discloses a primary and secondary fusion ring main unit fault rapid isolation method and system, and relates to the technical field of power distribution network automation. The method comprises the following steps: acquiring topological characteristics of each node of the ring main unit and performing validity verification to generate an effective topological fingerprint; constructing a dynamic time sequence fault tree model based on the topological fingerprints, and obtaining a minimum cut set with time sequence constraints through space-time decoupling; based on the minimum cut set, a fault isolation strategy is generated through reinforcement learning, and safety verification is carried out; and based on the isolation strategy passing the verification, pre-judging a future topology state through topology deduction and generating a trigger signal. Through dynamic topological feature extraction and verification, reliable basic data is provided for the primary and secondary fusion ring main unit, and the adaptive capacity of the primary and secondary fusion ring main unit in a topological change scene is enhanced; by means of dynamic time sequence fault tree modeling, the fault evolution law is accurately captured, the limitation of a traditional static model is broken through, and the accuracy of fault positioning is improved.

The invention discloses a load transfer method, system, equipment and medium considering new energy and load power fluctuation, and belongs to the technical field of relay protection, and the method comprises the steps: taking a switch motion moment as a first stage of load transfer, taking a set time after the switch motion as a second stage of load transfer, building a dual-stage load transfer model, and taking the set time as a second stage of load transfer; obtaining a load transfer scheme; double-stage constraint verification is executed, and operation constraint verification is carried out on the load transfer schemes of the first stage and the second stage; introducing a constraint out-of-limit penalty factor, and quantifying the degree of the load transfer scheme deviating from the constraint condition; constructing a load transfer scheme comprehensive evaluation function, and performing weighted integration; and optimizing the load transfer scheme comprehensive evaluation function by adopting an improved binary particle swarm algorithm, and determining an optimal load transfer scheme. According to the method, the limitation of a traditional static model is overcome, and the problem of operation constraint violation caused by photovoltaic output change is avoided.

The invention relates to the technical field of artificial intelligence optimization operation systems, and discloses a disaster early warning and emergency resource intelligent dynamic allocation system, which comprises an input mapping unit for mapping a non-stationary time sequence data stream into a node feature tensor, and a drift monitoring unit for judging feature distribution drift based on Kullback-Leibler divergence; when the divergence exceeds the limit, the topological evolution controller calculates a reconstructed graph structure by using a Hertz rule and a covariance, and constructs a dynamic pruning threshold according to the synaptic age; the static base and the dynamic residual subnet are operated in parallel by the double-tower reasoning unit, and the output fusion unit generates a deployment instruction. According to the invention, by utilizing a gradient-free covariance driving and synaptic age constraint mechanism, millisecond-level graph topological structure self-adaption under non-stable distribution is realized; the problems of response delay and structure solidification of a static model in a disaster sudden change environment are solved.

The invention relates to the technical field of well site fracturing, and discloses an intelligent fracturing method and system for precise fracture forming, and the method comprises the steps: collecting underground multi-dimensional monitoring data in real time; dynamically constructing and correcting a three-dimensional crack expansion model by using the monitoring data, and outputting a prediction path containing crack geometric parameters; based on the prediction path and the real-time downhole pressure deviation, a control instruction is generated through an optimization algorithm; and the control instruction is executed, the sliding sleeve valve element is driven to move, the flow of fracturing fluid is adjusted, and accurate formation of cracks is controlled. According to the method, by fusing underground multi-dimensional monitoring data and dynamically correcting the three-dimensional crack propagation model, prediction deviation caused by single data dimension and static model solidification in a traditional method is overcome, prediction errors of the crack length and the steering angle are reduced, accurate prediction and control of the crack propagation path are achieved, and the method is suitable for large-scale popularization and application. And the geological targeting property of fracturing construction is obviously improved.

The invention relates to the technical field of new energy power generation, and discloses a generating capacity calculation method and system based on wind and light resources and equipment data. The method comprises the following steps: acquiring and fusing wind and light resource data and equipment performance parameters to construct a standardized data set; based on the data set, a resource-equipment dynamic coupling potential field is established, and the potential field accurately represents the comprehensive power generation potential in the space-time dimension by fusing the resource space gradient and the equipment response characteristics; carrying out triple integral calculation on the potential field, and controlling an error by adopting a self-adaptive algorithm; and finally, realizing self-calibration of the system through model verification and parameter optimization. According to the method, the defects of insufficient data fusion, model staticization, contradiction between calculation precision and efficiency and the like of a traditional method are overcome, and the accuracy, the adaptive capacity and the engineering practical value of generating capacity calculation are remarkably improved.

The invention discloses a U-shaped aqueduct prestress multi-beam cooperative tensioning control method and system. The method comprises the steps that S1, a quantitative derivation model is constructed, based on an aqueduct structural mechanical equilibrium equation, a prestress coupling effect item is introduced for calculation, a prestress coupling matrix is output, and an initial tensioning sequence is derived; the device is simple in structure, and an initial tensioning sequence is deduced by establishing a mechanical equilibrium equation and introducing a coupling effect item; meanwhile, stress / displacement deviation data collected by a monitoring system in real time are fed back to an edge calculation terminal to calculate a control strategy in real time, adjustment is executed by a tensioning device, and therefore a field data closed-loop optimization mechanism is constructed, the tensioning force and sequence can be dynamically corrected, the limitation of a static model is broken through, and the construction efficiency is improved. And a displacement constraint term is introduced as a punishment or control condition in the optimization process, so that deformation overrun of key parts of the structure is effectively prevented, and the precision, safety and intelligent level of prestress construction are remarkably improved.

The invention belongs to the technical field of hydrogeology and intelligent control, particularly relates to an underground water level dynamic regulation and control method and system based on digital twinning, and aims to solve the problems of one-sided perception, static model, extensive decision and single feedback of traditional regulation and control. The method comprises the following steps: collecting multi-source monitoring data and constructing a data set through cross-scale calibration; constructing a multi-field dynamic coupling digital twinborn model, and introducing a dynamic coupling coefficient to deduce a risk evolution field; calculating a variable coefficient of the risk value of each physical field based on the risk evolution field, and generating a comprehensive risk value; and matching the risk threshold to generate a deep and shallow layered regulation and control instruction. The multi-field dynamic coupling breaks the static limitation of the model, the dynamic weight based on the variable coefficient ensures that the regulation and control decision can preferentially respond to the key physical field with high absolute risk level and large spatial fluctuation, and the full-region visual tracking and accurate management and control of the underground water level and the geological risk are realized.

The invention relates to the technical field of computer-aided modeling, in particular to a heating and ventilation equipment energy efficiency optimization method and system based on digital twinning, and the method comprises the steps: obtaining a real-time operation data set, and constructing a mechanism model; obtaining a model prediction data set by using the mechanism model, and training a data reconstruction model; inputting the real-time operation data set into the data reconstruction model to obtain a reconstruction error, and taking the reconstruction error as a state deviation data set; dynamically correcting an adjustable model parameter of the mechanism model based on the state deviation data set, and generating a dynamic calibration digital twinborn model; and finally, executing a multi-target collaborative optimization algorithm based on the dynamic calibration digital twin model, and generating an energy efficiency optimization control instruction. According to the method, the state deviation caused by the performance degradation of the physical equipment is quantified by utilizing the data reconstruction model, and the mechanism model is dynamically corrected by taking the state deviation as feedback, so that the problem that a static model is disjointed from a physical entity state is solved, and high-precision dynamic simulation and energy efficiency collaborative optimization are realized.

The invention belongs to the technical field of traffic management evaluation, and particularly discloses and provides an urban traffic toughness quantitative evaluation method based on multi-mode traffic collaboration, and the method comprises the steps: carrying out the time-space standardization processing of multi-source data of motor vehicles, public traffic, slow traffic and external disturbance events, and generating a standardized time-space data set; based on the data set, respectively calculating a comprehensive transportation efficiency index, an alternative path redundancy index and a connection connectivity index, and constructing a toughness evaluation index set; based on the historical sequence of each index, generating a dynamic combination weight through a subjective and objective combination weighting method; and based on the index set and the dynamic weight at the current moment, calculating a comprehensive toughness index and generating a time sequence thereof to output an evaluation result. According to the method, the defects that in the prior art, an isolated analysis single mode depends on a static model are effectively overcome, and accurate multi-dimensional quantitative evaluation of the real toughness level of the complex traffic system under disturbance is achieved.

The invention relates to the technical field of crane safety monitoring, and discloses a crane hoisting overload state monitoring method and system based on digital twinning, and the system comprises a twinning modeling module, a data acquisition module, a state simulation module, an overload decision module, and a model optimization and visualization module. Deep interaction between a physical world and a virtual space is realized through digital twin bodies, limitation of a traditional monitoring method is broken through, multi-source parameters such as wind speed, load and structural stress are fused for the first time, a dynamic coupling model is constructed, nonlinear correlation of wind load, inertia force and structural deformation is captured, the problem of missed judgment of hidden risks in the traditional method is solved, and the method has a wide application prospect. An accident early warning coverage range is expanded, a deflection calculation model is established based on an improved mechanical theory, high-sensitivity detection of fine deformation of the main beam is realized, compared with a static model, the magnitude order is improved, structural damage accumulation is effectively prevented, deep correlation mining is performed on historical and real-time data, and long-time-history advanced early warning of overload risks is realized.

The invention discloses a protection setting calculation and optimization method based on an intelligent power plant management platform, and relates to the technical field of power system protection. By constructing a platform and equipment communication link, integrally collecting and preprocessing internal and external parameters, and dynamically updating topology and model parameters in combination with a switch state, the problem that a traditional static model is difficult to adapt to operation changes is solved, the timeliness and accuracy of the model are improved, a multi-scene database is constructed in a classified mode, labels are labeled, and the efficiency is improved. Fault current distribution is accurately calculated, constant value adaptability verification is carried out, constant value optimization is carried out in combination with a multi-objective optimization algorithm and sensitivity analysis, limitation of single-scene constant values is avoided, constant value conflicts under different scenes are reduced, reliability of initial constant values is guaranteed, sensitivity analysis is superposed, it is ensured that the constant values are still reliable during parameter fluctuation, and the reliability of the constant values is improved. The risk of protection operation refusal and misoperation is reduced, a constant value is issued, the device state is monitored in real time, evaluation indexes are calculated, the constant value is controllable in the whole process, and stable operation of the device is guaranteed.

The invention belongs to the technical field of education informatization and industrial data fusion, and discloses a system and a method for constructing production and education fusion data based on a knowledge graph. The problems that traditional production and education fusion data is dispersed and heterogeneous and lacks an effective integration and analysis means, a traditional analysis method depends on a static model to cause poor dynamic adaptability and hysteresis, and the production and education fusion degree is difficult to quantitatively evaluate to cause the lack of decision basis are solved. And a course optimization scheme lacks data support, so that the implementation effect is poor. According to the method, the standardized data stream of industry and education multi-source data is constructed, the ontology model of the production and education fusion core elements is established, the production and education fusion knowledge graph is established, the relation weight is dynamically evolved, and the production and education fusion degree index is quantitatively calculated in combination with the semantic similarity and the industry weight factor; and a course optimization scheme is generated based on a knowledge graph topological structure and reinforcement learning simulation, so that intelligent matching, dynamic optimization and decision support functions of a production-teaching fusion relationship are realized.

The invention provides a regulation and control method and system for hole wall instability of a horizontal drill hole in a high ground stress environment, and belongs to the technical field of horizontal drilling. The method comprises the steps that a high ground stress hole wall instability model based on elastic-plastic damage mechanics is established, and a high ground stress correction coefficient, a dynamic damage variable and a multi-field coupling equation are introduced into the model; model parameters are corrected in real time based on measurement while drilling data, and the hole wall stability is dynamically predicted; according to the prediction result, the model prediction error can be greatly reduced through the method, the progressive damage process of the rock mass in the high ground stress environment can be accurately described, and the technical problem of inaccurate prediction of a traditional static model is solved. By correcting model parameters in real time and dynamically adjusting a wall protection strategy, the hole wall instability accident rate is reduced, and the drilling operation safety is remarkably improved.

The invention relates to a self-adaptive model updating method and device, electronic equipment and a storage medium. The self-adaptive model updating method comprises the following steps: acquiring distributed external sample data newly generated by a large language model; clustering processing is carried out on the out-of-distribution sample data to obtain an out-of-distribution sample cluster, and the out-of-distribution sample cluster at least comprises out-of-distribution sample data of the same structure label; and performing search training on the out-of-distribution sample cluster, determining a target adapter, and sending the target adapter to a database of the large language model to update the large language model. Static model updating is converted into a time-driven learning process by setting a self-adaptive updating mode, so that a large language model can be subjected to self-adaptive updating operation according to failure result feedback during reasoning in a training process, the model is easier to adapt, error accumulation is reduced, stable updating is ensured, accuracy is improved, and the training efficiency is improved. And the precision and robustness of the model are enhanced.

The invention discloses a low-temperature pipe network refined hydraulic balance adjusting system. A sensing-analysis-regulation integrated intelligent adjusting system is constructed. The system adopts a mode of combining distributed optical fiber sensing and intelligent nodes of the Internet of Things, can capture multi-dimensional parameters such as flow, pressure and temperature of each section of a pipe network in real time and with high precision, solves the problem of one-sidedness of traditional single-point monitoring data, introduces a dynamic hydraulic model based on deep learning in an analysis aspect, and improves the accuracy of monitoring data. The model can perform autonomous iterative optimization according to real-time monitoring data, accurately predicts the hydraulic distribution trend of the pipe network under different working conditions, and overcomes the defect that a traditional static model is difficult to adapt to the dynamic change of the pipe network. A self-adaptive multi-end linkage adjusting device is innovatively designed, in combination with a model prediction result, millisecond-level cooperative regulation and control can be carried out on executing mechanisms such as valves and pump sets in a pipe network, spanning from local adjustment to overall balance of the whole network is achieved, fault expansion is avoided through automatic adjustment, and the reliability of the system is greatly improved.

The invention discloses a digital twinborn simulation optimization system and method for mining dilution rate and loss rate, relates to the technical field of digital mines, and solves the problem of isolated operation of geology, production and automation subsystems in a traditional system by fusing a geologic model and a production model in a unified platform. And an integrated intelligent system with data intercommunication and business collaboration is formed. And the management of the dilution rate and the loss rate is changed from the traditional post statistics to the instantaneous perception and the abnormal diagnosis based on the real-time data, so that the online monitoring and the active intervention of the production process are realized, and the resource waste is reduced. Parallel deduction and multi-objective optimization are carried out on multiple schemes through a simulation engine, and a global optimal scientific decision scheme under multiple objectives can be provided. Through real-time feedback of an execution result, the twin mining area model is continuously calibrated and optimized, the defect that the precision of a traditional static model is attenuated along with time is overcome, and a reliable basis is provided for long-term optimization.

The invention belongs to the technical field of computer network security, and discloses a power network security knowledge ontology modeling method, which comprises the following steps of: firstly, collecting multi-mode heterogeneous data such as power monitoring, security equipment and threat intelligence in parallel; then, adopting a hybrid strategy, on one hand, constructing a top-layer seed ontology based on a domain standard, and on the other hand, automatically extracting an entity relationship from the text by utilizing a deep learning model; secondly, associating multi-source knowledge and solving conflicts through entity alignment and sequential logic fusion, and generating a consistent and credible global knowledge view; and finally, establishing an ontology self-evolution mechanism based on reinforcement learning, dynamically adjusting knowledge weight and automatically expanding a mode, realizing deep understanding, accurate prediction and self-adaptive evolution of network threats, and improving the active defense level of a power system. According to the method, the core problems of one-sided knowledge acquisition, poor fusion capability, static model evolution incapability and the like in the prior art are solved.

The invention discloses a decoupling design method and system for reliability optimization of an angular contact ball bearing. The method comprises the following steps: determining initial design parameters of the angular contact ball bearing; constructing a reliability optimization design model which takes maximization of the rated dynamic load as a target and comprises probability reliability constraint processed by a penalty function; constructing a design variable and failure probability mapping agent model, specifically, identifying and quantifying uncertainty parameters, constructing a quasi-statics model and a limit state function, calculating the failure probability of a sample point through a probability reliability method, and training the agent model taking the design variable as input and the failure probability as output; and based on the proxy model, solving the reliability optimization design model by using an optimization algorithm, and outputting optimal bearing design parameters. According to the method, the reliability analysis process and the optimization iteration process are decoupled, so that the optimization efficiency is greatly improved while the design robustness is ensured.

The invention discloses an intelligent blast furnace condition trend early warning system and method. The system comprises a data acquisition module, a dynamic detection module, a quality association module and a feedback control module. The data acquisition module generates original feature signals of time-space correlation; the preprocessing module receives the original characteristic signals and filters noise interference through a Gaussian process regression algorithm; the prediction module generates associated characteristics of gas flow distribution and charge level form, and then captures a long-period trend component in the standardized data flow through a time convolutional network; the feedback module receives the furnace condition evolution probability signal and generates virtual blast furnace state data based on a gas-solid two-phase flow and heat transfer coupled physical field simulation model; and the early warning module generates a grading early warning instruction according to the degree of the parameter calibration signal crossing the preset threshold. According to the intelligent blast furnace condition trend early warning system and method, the problem that early warning lags due to the fact that a traditional blast furnace condition early warning method depends on a single parameter, a static model and artificial experience can be solved.

The invention discloses a virtual power plant-oriented deep learning generation power joint prediction method and system, and relates to the technical field of artificial intelligence and power systems, and the method comprises the following steps: obtaining the multi-source operation data and meteorological data of a virtual power plant in real time; carrying out fusion and preprocessing on the data; variational mode decomposition is carried out on the processed data, and feature components are extracted; enabling the feature components to pass through a double-flow depth prediction network to obtain two groups of intermediate prediction results; the network comprises a double-flow structure used for processing operation characteristics and meteorological characteristics respectively, a plurality of interactive gating points are arranged in the double-flow structure, and the double-flow structure is used for dynamically calculating correction weights of the meteorological characteristics to the operation characteristics according to characteristic components; and carrying out weighted average on the two groups of intermediate prediction results to generate a power generation power joint prediction value. The method is used for solving the problems of low precision and poor stability caused by data physical distortion, feature interaction deficiency and model static stiffness in virtual power plant power prediction.

The invention relates to the technical field of power grid project management, and discloses a power grid capital construction investment progress dynamic calculation method based on equipment internet of things, comprising the following steps: constructing a multi-dimensional power grid capital construction typical project library based on multi-source heterogeneous data of historical projects; for a to-be-measured project, selecting a dominant prediction model from a preset financial expenditure method model or an image progress method model according to the starting time of the to-be-measured project, and performing matching from the project library to obtain a monthly progress trend table of a target typical project; dynamically correcting future key milestone nodes of the project based on the deviation between the current actual progress of the project and the target trend table; and based on the corrected key milestone node, fitting the remaining part of the target trend table through a time axis scaling and interpolation algorithm, and generating a future investment progress prediction curve. The problem that traditional prediction depends on a static model and cannot reflect real-time change of a project is solved, and the accuracy and timeliness of investment progress prediction are remarkably improved.

The invention discloses a flexible employment identity recognition method and system based on multi-dimensional risk features, and the method comprises the following steps: obtaining an employment data set, and carrying out the feature extraction of the preprocessed employment data set, and obtaining employment features; constructing a static classification model and a time sequence prediction model, and training the static classification model and the time sequence prediction model by using the employment features to obtain the trained static classification model and time sequence prediction model; and respectively inputting real-time employment data into the static classification model and the time sequence prediction model to obtain a static risk probability and a time sequence risk probability, and judging an employment identity according to the static risk probability and the time sequence risk probability. The employment data is acquired, the static model and the time sequence model are constructed, training is performed by using the employment data, and the employment data of the user is comprehensively judged in combination with the static classification model and the time sequence prediction model, so that the problems of low efficiency and poor timeliness of the existing identification technology are solved.

The embodiment of the application provides a network quality evaluation method, device and equipment and computer storage medium, and relates to the technical field of low-altitude communication. The method comprises the following steps: obtaining static evaluation indexes and dynamic evaluation indexes of a space to be evaluated stored in different storage spaces. Based on the static evaluation indexes and the dynamic evaluation indexes, a static model and a dynamic model are constructed, and then a fusion model is obtained. Based on the fusion model and a preset evaluation method, the network quality evaluation result of the space to be evaluated is determined. The static evaluation indexes and the dynamic evaluation indexes stored in different storage spaces are obtained, and the static and dynamic indexes are differentiated, processed and stored, so as to adapt to the stability of the static evaluation indexes and the periodic change law of the dynamic evaluation indexes, improve the modeling efficiency and the prediction accuracy, and then improve the evaluation accuracy. Through fusing indexes of multiple dimensions, a multidimensional model is obtained, and multiple dimensions are comprehensively covered, so that the evaluation is close to the actual business, and the evaluation accuracy is improved.