267 results about "Scenario simulation" patented technology

The invention discloses an efficient intelligent warehousing system and a storage method, and relates to the technical field of warehousing management, the system comprises the following components: a virtual digital twinborn model construction module, a business scene simulation module, a potential problem prediction module and an operation process optimization module; according to the system, the virtual digital twinborn model in 1: 1 mapping with the actual storage environment is constructed, synchronous updating is carried out in combination with data collected by the sensor network in real time, the system can accurately reflect the dynamic condition of the actual storage environment, and the service scene simulation module is utilized to simulate the real storage environment. The system can simulate warehousing operation processes in different business scenes in a virtual environment, automatically adjust cargo storage positions, equipment scheduling schemes and personnel task allocation, so that the efficiency and precision of warehousing management are remarkably improved, and in addition, the operation process optimization module can predict and improve the efficiency and accuracy of warehousing management according to simulation results and potential problems. The operation process of the actual warehousing system is automatically optimized, and the overall operation efficiency is further improved.

The invention discloses a disaster emergency rescue nursing skill training system based on virtual reality, and relates to the technical field of virtual reality. A high-simulation virtual disaster scene is constructed based on real data, diversified injury condition virtual objects matched with the scene are generated, a secondary disaster risk diffusion effect and emergency prompt information are overlaid in real time, physiological state changes such as blood pressure and respiration generated by nursing operation differences in the rescue process of wounded personnel are dynamically simulated, and the rescue effect of the wounded personnel is improved. The problems of single scene and fixed injury condition in traditional training are solved, real-time and multi-modal feedback of student operation is realized through accurate comparison and hierarchical identification of multi-dimensional operation characteristics, the defects of guidance lagging and general are overcome, a personalized and adaptive training scheme is generated according to a comprehensive evaluation result, the progress is dynamically adjusted, and the training efficiency is improved. And scene simulation, operation practice, accurate feedback and personalized improvement are integrated.

A system and method for predictive analysis, scenario simulation, and decision optimization is provided. The system includes a prediction management system executed on a distributed computing infrastructure, and a prediction engine configured to receive input data, including event parameters, user-defined constraints, real-time data feeds, and historical trends. The prediction engine generates predictive models using algorithms trained on historical event outcomes, assigns probability scores and confidence intervals to potential outcomes, and dynamically updates the models based on new input data. Actionable insights are generated and ranked according to predefined success criteria. A non-transitory computer-readable medium is used to store the predictive models, outcome probabilities, and actionable insights for subsequent analysis and reporting. This system facilitates enhanced decision-making by offering real-time insights and continuously refined predictions, thereby optimizing responses to complex events and scenarios.

The invention relates to the technical field of intelligent risk analysis, in particular to an intelligent risk analysis system of a financial system. The system comprises a data flow analysis module, a relational graph construction module, an abnormal mode detection module, a security boundary monitoring module, a staged strategy planning module, a scene simulation evaluation module, a decision support module and a comprehensive risk management module. A graph database and a graph convolutional network are utilized to deeply analyze a financial entity relationship, reveal a risk association network, improve abnormal behavior recognition through a K-mean value and an isolated forest algorithm, enhance security boundary monitoring through a random forest algorithm, enable risk threshold adjustment to be more dynamic, combine a decision tree with a genetic algorithm, optimize a risk management strategy, and improve risk management efficiency. The system dynamics and proxy model technology in scene simulation evaluation strengthens risk prediction and increases foresight, principal component analysis and risk matrix evaluation are applied in a comprehensive risk management module, a quantitative analysis tool is provided, and more systematic and comprehensive risk management is realized.

The invention discloses a semantic judicial contract compliance automatic generation and examination system based on a DIKWP artificial consciousness model. The system is constructed according to five layers of data, information, knowledge, wisdom and intention, wherein the data layer integrates multi-source information; the information layer enables terms to be semantized and disambiguated; the knowledge layer performs rule matching and compliance judgment by using a legal knowledge graph; the wisdom layer automatically corrects the risk terms based on a fair principle and performs scene simulation; and the intention layer is embedded into targets of all parties to ensure that the contract is consistent with the compliance intention. The system process covers the whole process from intention analysis, knowledge reasoning to intelligent generation and intention negotiation, and finally a compliance contract and an audit report are output. The system is provided with a multi-language semantic adaptation and intelligent negotiation interface, and convenient access can be realized through an API (Application Program Interface). The system realizes integration of contract generation and review, intention driving and decision interpretability, can effectively reduce compliance cost and dispute risks, and is suitable for multiple scenes such as finance, e-commerce, government purchase and the like.

The invention discloses a multi-objective optimization water resource allocation scheduling method, which combines an LSTM-Prophet hybrid prediction model with an attention mechanism, synchronously captures climate periodicity, policy regulation sensitivity and industrial development tendency characteristics in water demand prediction, and improves water demand prediction precision through a genetic algorithm. Through an improved NSGA-III algorithm and a PSO collaborative optimization mechanism, the convergence problem of a high-dimensional target space is effectively solved in combination with a chaotic mapping technology, and the multi-target optimization efficiency is improved by 40% in cooperation with a dynamic weight adjustment strategy of deep reinforcement learning. And multi-scenario simulation verification of a scheduling scheme is realized by utilizing deep coupling of a digital twin system and a three-dimensional GIS, and a real-time feedback correction mechanism of a Markov decision process is realized. A finally constructed entropy weight-TOPSIS multi-dimensional evaluation system is combined with AR visualization and intelligent contract traceability technologies, and a Pareto optimal solution giving consideration to social fair, economic benefits and ecological integrity is provided for decision makers.

The invention relates to the technical field of occupational simulation training evaluation, and discloses a system for college student occupational scene simulation training evaluation. The system comprises a task analysis unit, a dynamic behavior modeling unit, a multi-mode interaction management unit, a state evolution tracking unit, a strategy adaptation unit and a comprehensive evaluation generation unit. The system receives the initial scene description and generates a structured task framework, and behavior parameters are configured for student roles; constructing a multi-modal interaction atlas by fusing aligned voice, action and sight line data in real time; identifying a key decision node, and dynamically tracking a team state evolution path; matching and adjusting strategies in a behavior rule base according to the evolution sequence, and outputting an adaptive intervention scheme; and finally generating a multi-dimensional structured evaluation report. According to the system, deep insight and dynamic and objective intelligent evaluation of complex interaction behaviors in the simulation training process are realized.

The invention discloses a distributed adjustable resource layered dynamic aggregation method and system for a virtual power plant. The method comprises the steps of collecting operation data, environment data, user behavior data and market data of a distributed power supply, a flexible load and an energy storage facility, extracting core features, extracting feature parameters by adopting a quantification method, and constructing an adjustable resource feature database; constructing an operation capability index model, and obtaining a resource comprehensive operation capability value based on the index and the weight thereof; scheduling the resources in a classified, partitioned and graded manner; establishing a three-layer structure of a bottom-layer resource model, a middle-layer aggregation model and an upper-layer optimization model, and constructing a multi-objective optimization model; by executing a marketization transaction strategy, a power grid dispatching response strategy and an income distribution strategy, collaborative optimization operation of the virtual power plant, the power market and power grid dispatching is achieved, and finally multi-scene simulation verification and model parameter optimization are conducted. According to the scheme of the invention, efficient aggregation and collaborative optimization scheduling of adjustable resources are realized.

The invention discloses a burying point analysis system for customer behavior analysis, and relates to the field of customer behavior analysis, the system comprises the following components: a data acquisition module, a digital twin model construction module, a business scene simulation module and a behavior prediction and decision support module; according to the invention, the digital twinborn model which accurately reflects customer behavior characteristics is constructed by using the digital twinborn model construction module and combining a deep learning algorithm and a data mining technology, and the model is imported into a diversified virtual business scene for behavior simulation through the business scene simulation module, so that the customer behavior simulation efficiency is improved. And finally, the behavior prediction and decision support module accurately predicts customer behaviors and generates a personalized decision plan by applying a space-time correlation prediction algorithm and an intelligent decision recommendation algorithm based on a knowledge graph, so that the customer behaviors can be accurately predicted. The functions jointly enhance the insight ability of enterprises for customer behaviors.

The invention discloses a territorial space planning layout method based on multi-target constraint and dynamic weight adjustment. The method comprises the following steps: S1, constructing a multi-dimensional target system comprising a basic constraint layer, a development demand layer and a dynamic adaptation layer; s2, establishing a target space mapping model, generating a basic space unit and endowing the basic space unit with a target correlation coefficient; s3, a dynamic weight adjustment mechanism is designed, and after initial weight assignment, automatic adjustment is carried out when the monitoring data exceed a threshold value; s4, constructing a multi-objective optimization algorithm, and performing coupling calculation to generate an initial scheme; and S5, carrying out multi-scene simulation verification, and outputting an optimal scheme with the highest target collaboration degree. According to the method, the scientificity and adaptability of a territorial space planning layout scheme are improved through cooperation of multiple targets and dynamic weight adjustment.

The invention discloses a method for determining the suitable development area of a large-scale drip irrigation farmland in an underground water shallow-buried area, and the method comprises the following steps: coupling an MODFLOW model and an SWNCM-2D model based on programming software, and developing a coupling model suitable for water and salt migration and crop growth simulation of a saturated zone and an unsaturated zone of the large-scale drip irrigation farmland in the underground water shallow-buried area; according to lysimeter underground water burial depth control test data, calibrating and verifying key parameters of the coupling model, performing multi-scenario simulation of different underground water burial depths, and determining an appropriate underground water burial depth under a drip irrigation condition; setting scenes with different drip irrigation farmland area proportions, simulating by adopting a coupling model, and determining a suitable development area of the large-scale drip irrigation farmland in the underground water shallow buried area by taking a suitable underground water buried depth as a control standard; according to the scheme, through multi-model coupling, multi-process simulation and multi-scene analysis, technical support and quantitative basis are provided for development and field management of large-scale drip irrigation farmland.

The invention relates to a scene simulation-based multi-space-time supply and demand risk identification method for a power system, and the method comprises the steps: generating a database with a space-time label through a data collection and preprocessing unit, and constructing a power grid model; the risk identification module, the scene simulation module and the dynamic mapping module are used for displaying the risk and adequacy data of each region and each time period in the form of a thermodynamic diagram and a distribution diagram to form a power grid fluctuation region; the method comprises the following steps: acquiring different variables in a power grid system to generate dynamic nodes, forming edges among the nodes by an interdependence relationship among the variables, constructing a time-varying relationship network of each time period, combining the time-varying relationship network with a machine learning model, and constructing a prediction model reflecting nonlinear dynamic change of the power grid; a power grid model is adjusted by constructing a time-varying relationship network to carry out a nonlinear dynamic change prediction model, and the evaluation precision of power grid space-time distribution is ensured; the method has the advantages that the time-varying relationship network is established, the prediction precision and the response speed are improved, and risk factors are accurately captured.

The invention belongs to the technical field of data processing, and discloses a deep learning optimization system and method for intelligent equipment. Comprising the steps of collecting data and performing data cleaning to obtain accurate equipment parameters; identifying a target intelligent device resource level based on the accurate device parameters; performing energy consumption scheduling on the target intelligent device based on the resource level to obtain a scheduled intelligent device; performing disturbance simulation on the accurate equipment parameters, and outputting a disturbance stability index; appropriate parameter screening is carried out on the accurate equipment parameters based on the disturbance stability index, and stable equipment parameters are output; performing risk assessment on the stable equipment parameters to obtain parameter risk indexes; performing model matching on the scheduling intelligent equipment based on the parameter risk index to generate a model adjustment strategy; performing scene simulation based on the model adjustment strategy, performing loading optimization on the model adjustment strategy based on a simulation effect, and outputting an optimization model strategy; the efficient deep learning optimization method for the intelligent equipment is realized.

The invention discloses a water resource bearing capacity early warning and response decision-making method, which relates to the technical field of water resource early warning, and comprises the following steps: analyzing water resource bearing capacity, and establishing a water resource bearing capacity system; based on the DPSIRM conceptual model, establishing a dynamic model of the water resource bearing capacity early warning system, performing comprehensive weight calculation on index data of the dynamic model of the water resource bearing capacity early warning system through an analytic hierarchy process and an entropy weight method, and obtaining a comprehensive evaluation result of the water resource bearing capacity in combination with a TOPSIS evaluation model; dividing a comprehensive evaluation result of the water resource bearing capacity, and discriminating key indexes influencing the water resource bearing capacity in combination with a geographic detector model; and taking the key index as the input of a Bayesian network, obtaining an output result, and meanwhile, carrying out variable scene simulation to obtain the influence of different variables on the bearing capacity. According to the method, overload condition risks occurring in different scenes are simulated, and the probability and possibility of occurrence of water resource overload events are calculated.

The invention belongs to the technical field of construction management, and particularly relates to a construction data processing method and system based on big data, and the system comprises a data collection and editing preprocessing module, a time-space correlation modeling module, a multi-dimensional risk deduction module, a decision feedback optimization module and a construction supervision terminal. Multi-source heterogeneous data are efficiently accessed and processed through the data collecting and editing preprocessing module, the efficiency and accuracy of data processing are improved, the space-time correlation modeling module constructs dynamic digital twinborn bodies of construction elements, real-time mapping and correlation analysis of a physical space and a digital space are achieved, powerful support is provided for risk prevention and control, and the method is suitable for large-scale popularization and application. The multi-dimensional risk deduction module carries out risk quantitative evaluation and scene simulation based on the digital twinborn body, potential risks can be found in advance and early warning signals can be generated, the decision feedback optimization module converts risk early warning into a specific disposal scheme, the construction supervision difficulty can be reduced, and a powerful guarantee is provided for smooth proceeding of a construction project.

The invention relates to the technical field of ecological environment management, and provides a multi-scale ecological environment damage assessment model construction and optimization management system, which comprises a multi-scale data acquisition module used for acquiring various basic data related to an ecological environment under different space, time and other scales; the parameter calibration and analysis module is used for verifying and adjusting parameters required by model calculation and evaluation according to the acquired data to ensure that the parameters are adaptive to the actual ecological environment condition; and the scene simulation calibration module is used for setting different ecological environment change scenes, simulating the development process of the different ecological environment change scenes, calibrating simulation logic in combination with the actual situation, and providing a simulation scene for analyzing the ecological environment damage trend and testing the management strategy effect. Through a multi-scale scene simulation and calibration mechanism, the system can accurately simulate ecological damage trends under different environment change scenes, and a reliable simulation scene is provided for testing a management strategy effect.

The invention discloses an ecological vulnerability evaluation method based on road domain landscape pattern optimization, and the method comprises the steps: obtaining expressway road domain environment data as training data, and obtaining ecological environment data; calculating an ecological environment quality index of the ecological environment data, and obtaining landscape structure features in the target area; integrating the landscape pattern index into an ecological sensitive factor, and constructing an ecological vulnerability evaluation model by adopting an SRP structure; performing spatial autocorrelation analysis on space-time evolution of the ecological vulnerability of the road domain according to the ecological vulnerability evaluation index, establishing an ecological vulnerability multi-scene simulation model, performing overlay analysis on an ecological vulnerability evaluation result and a land utilization type, obtaining a specific restoration strategy, tracking space-time evolution characteristics of the ecological vulnerability, and performing road region ecological vulnerability evaluation. Ecological vulnerability influence factors are adjusted based on different scenes, future possible ecological vulnerability risks are simulated, specific restoration strategies are made according to different partition standards, and effective configuration and use efficiency of resources are ensured.

The invention discloses a real-time flood control scheduling method for a water project group. The method comprises the following steps: constructing a component system of project entities, disaster-bearing targets and law and regulation knowledge; real-time water and rain work conditions are obtained, the five-dimensional indexes are fused and combined with the aging correction factor, and a composite risk index is calculated; based on the real-time water and rain work condition and the assembly system, combining a marginal benefit ratio, and dynamically identifying an effective water engineering group; according to the threshold value of the composite risk index, implementing a low-level layered activation strategy, a middle-level layered activation strategy and a high-level layered activation strategy on the effective water project group; and performing multi-scene simulation optimization on the activated project by adopting a dual-mode workflow combining an agent model and a high-precision physical model, and outputting a final flood control scheduling scheme. According to the invention, accurate perception of risks, dynamic optimization of projects and fine configuration of scheduling strategies are realized.

The invention discloses an enterprise market segmentation and positioning method based on data mining, and relates to the technical field of enterprise market analysis and decision making. According to the method, through dynamic relation modeling and real-time interaction mode tracking, in combination with time sequence analysis and an iterative updating mechanism, the market dynamic change can be accurately captured, and the problem that a traditional method is slow in response is solved. Meanwhile, potential relations among data points are deeply mined by adopting information correlation analysis and a graph network algorithm, the limitation of a traditional method is broken through, and more accurate market subdivision is realized; besides, based on prediction scene generation and decision path optimization, a scene simulation technology and external verification data are combined to generate a high-confidence prediction scene, scientific, comprehensive and dynamically optimized decision support is provided for enterprises, and it is ensured that the enterprises keep competitive power in a complex and changeable market.

The invention discloses a subway transport capacity matching and passenger flow collaborative management and control method based on multi-agent reinforcement learning, and relates to the technical field of urban rail transit operation management. The method comprises the following components: S1, a subway system digital twin model construction step, S2, a multi-agent reinforcement learning training environment construction step, S3, an extreme scene simulation and agent training step, S4, a virtual-real data interaction and model iteration step and S5, an actual system deployment and decision execution step. According to the method, a high-fidelity digital twin model of a subway system is constructed, extreme scenes including passenger flow retention caused by rainstorm and instantaneous large passenger flow of a large competition field are simulated in the model, and a multi-stage sampling method based on scene clustering is combined to perform training for ten millions of times; and the decision-making capability and the cooperation efficiency of the multiple agents in the face of complex and extreme conditions are obviously improved.

The invention discloses an equipment protection setting value optimization method and device, terminal equipment and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: constructing a simulation model of target equipment; generating a plurality of operation scenes, and performing simulation analysis on the protection constant value of the target equipment according to the simulation model in each operation scene to obtain a protection constant value simulation value of the target equipment; a genetic algorithm is adopted to optimize reference parameters of the protection setting value of the target equipment, and optimized reference parameters are obtained; and based on the optimized reference parameter, carrying out optimization processing on the protection constant value simulation value to obtain a final protection constant value. Through multi-operation scene simulation and heritage algorithm optimization, a complex and changeable operation environment of the power grid is considered, and the optimized protection setting value can cover different working conditions of the power grid, so that the method can adapt to dynamic changes of the power grid, and the accuracy and reliability of equipment protection setting value optimization are effectively improved.

A model training method and apparatus, and a readable storage medium. The method comprises: inputting multiple sets of verification parameters of a refrigeration device into a scenario simulation model to obtain scenario simulation data corresponding to each set of verification parameters among the multiple sets of verification parameters, wherein the refrigeration device is used for cooling multiple network devices, the multiple sets of verification parameters comprise multiple sets of refrigeration parameters under different boundary conditions, and the scenario simulation data comprises the energy consumption of refrigeration device and temperature data of a region where each network device among the multiple network devices is located; and training a reinforcement model on the basis of the multiple sets of verification parameters to obtain a target reinforcement model, wherein the target reinforcement model is used for determining a target refrigeration parameter on the basis of the current boundary condition of the refrigeration device, the target refrigeration parameter is the one, among multiple sets of candidate refrigeration parameters, that results in the lowest energy consumption of the refrigeration device, and when the refrigeration device executes the candidate refrigeration parameters, the temperature data of the region where each network device is located is lower than a temperature threshold.

The invention relates to the technical field of supply chain management and control, in particular to a supply chain intelligent management and control system and method based on multi-source data fusion, the system comprises a data acquisition module, a data fusion processing module, a digital twin deduction module, a multi-target decision module and a strategy execution and feedback module, and the method completes corresponding management and control steps according to the system. The method comprises the following steps: firstly collecting full-chain multi-source heterogeneous data of a supply chain, generating unified multi-dimensional state data through space-time alignment and multi-modal fusion, then constructing a digital twin model to carry out multi-scene deduction to output a candidate strategy, screening out a comprehensive optimal strategy through a multi-target decision, and finally analyzing and issuing the strategy and collecting execution effect feedback. And closed-loop iterative optimization is formed. According to the method, the problems of isolated data, subjective decision, no deduction system and lack of closed-loop optimization of traditional supply chain management and control are effectively solved, the management and control intelligence and automation level is improved, the supply chain toughness is enhanced, the operation efficiency is optimized, the operation cost is reduced, and full-chain collaborative management and control are realized.

The invention provides a lightweight design method, device and equipment for an intelligent robot with a body and a storage medium, and relates to the technical field of artificial intelligence. According to the method, the scene simulation environment is constructed by collecting the task scene parameters, the robot three-dimensional model is constructed based on the lightweight design scheme, the test cases are generated according to the task requirements, the model test parameters are collected, and comprehensive evaluation and diagnosis of the initial lightweight design scheme of the robot are achieved. Through collection, evaluation and diagnosis of model test parameters, a design scheme is continuously optimized, and the reliability of robot performance is improved; through repeated testing and optimization evaluation, iterative optimization of a lightweight design scheme is realized, so that an optimal optimization design scheme is obtained, the lightweight design level of the intelligent robot with the body is remarkably improved, the convenience and adaptability of the specific intelligent robot are enhanced, and the intelligent robot with the body is more intelligent. And powerful technical support is provided for efficient application of the robot in complex task scenes in the financial and medical fields.

The invention discloses an uninterruptible power supply full-scene test method based on multi-mode simulation, and aims to solve the problems of single test scene, deficiency of composite working conditions, poor collaboration and the like in the prior art. The method comprises the following steps: firstly, constructing a test scene model library containing a multi-mode power grid, a load and an environment simulation model; combining specifications of the UPS to be tested and a test target, selecting a model combination to generate a composite test scene, and defining a rule that parameters change along with time; then, a test platform containing a programmable simulator and a central control unit is built, and the UPS to be tested is connected; the central control unit drives the platform to carry out collaborative testing, and input, output and internal key parameters are collected in real time; and finally, quantitative indexes such as output voltage stabilization precision and THD are calculated to complete evaluation. According to the invention, multi-factor coupling scene simulation is realized, the test authenticity and precision are improved, the period is shortened, and support is provided for UPS research and development optimization and quality detection.

The invention relates to the field of foundation pit monitoring, and discloses a foundation pit monitoring method and system based on a regression prediction algorithm. Comprising the steps that displacement, stress, underground water level, environmental factors and acoustic emission signals are collected, monitoring data are subjected to missing measurement compensation and noise reduction processing, and a multi-dimensional feature sample is constructed; performing fusion modeling by using static regression, LSTM dynamic prediction and spatial correlation regression, introducing an adaptive weight module to realize dynamic weighting, and generating a multi-scenario prediction curve in combination with hysteresis effect correction and scenario simulation; the optimization function and dynamic threshold unit performs early warning judgment and outputs risk levels and disposal suggestions; and furthermore, abnormal data are identified and compensated through a sensor reliability evaluation module, so that the continuity and stability of prediction are improved. The system is composed of a data acquisition terminal, a preprocessing module, a feature extraction module, a regression prediction module, an adaptive weight fusion module, a dynamic threshold adjustment unit, a sensor reliability evaluation module, an early warning judgment module and a visual platform.

The invention belongs to the technical field of electric power metering equipment fault detection, and particularly relates to a metering equipment intelligent evaluation method and system based on extreme scene simulation. Aiming at the defect that the operation condition of equipment under extreme conditions is difficult to simulate in the electric power metering equipment detection in the prior art, the invention adopts the following technical scheme: the metering equipment intelligent evaluation method based on extreme scene simulation comprises the following steps: constructing a test parameter generation model based on a bright electric power large model, learning feature distribution of a historical fault mode through a self-attention mechanism in the test parameter generation model, and obtaining parameters exceeding the design specification of the to-be-tested metering equipment in the simulation extreme scene; deploying a high-precision sensor array; collecting data of the measured metering equipment through a high-precision sensor array in a simulated extreme scene; and performing fault analysis according to the acquired data of the measured metering equipment. According to the method, the single-dimension limitation of a traditional testing method is broken through, and the limit durability of the equipment is evaluated more comprehensively.

The invention provides a multi-scene-driven low-carbon industrial park land utilization elastic planning method and system. The multi-scene-driven low-carbon industrial park land utilization elastic planning method comprises the following steps of S1, data collection and preprocessing, S2, scene setting, S3, multi-scene simulation, S4, elastic index system construction, S5, scheme evaluation and optimization, and S6, planning scheme making. According to the invention, through multi-scene simulation, various possibilities of future development of the industrial park, including factors such as different economic development speeds, policy orientation and environmental changes, are fully considered, so that the planning scheme has adaptability and flexibility under various scenes. In the aspects of low-carbon development and decision support, a multi-attribute decision method based on an evidence theory, an improved particle swarm optimization algorithm and the like are adopted, so that a plurality of targets and indexes are comprehensively considered. Meanwhile, through a real-time monitoring and dynamic adjustment mechanism, adjustment and optimization are carried out according to actual conditions, and effective implementation of a planning scheme is ensured.

The invention relates to the technical field of artificial intelligence and education, and discloses a college student vocational ability assessment system based on artificial intelligence technology learning. The system comprises a data collection module, an industry data docking module, a cross analysis module, a virtual portrait construction module, a sandbox test module and a report generation module. Multi-source heterogeneous data of students are collected and digitalized description is completed, meanwhile, skill quality requirements are extracted in butt joint with industry post requirements, and ability development gaps are recognized through cross analysis. And constructing a dynamically evolvable virtual vocational ability portrait based on the gap, carrying out multiple rounds of pressure test and scene simulation on the portrait in a vocational development sandbox environment, and finally generating an ability evaluation report containing quantitative shortages and advantages. According to the invention, multi-dimensional deep description and dynamic development deduction of student ability are realized, and comprehensiveness and predictability of vocational ability assessment are improved.

The invention provides a smart city data linkage method based on digital twinning, and the method comprises the steps: constructing a fusion modeling layer, constructing a lightweight digital twinning model through CIM, BIM and GIS technologies, packaging a scenarized simulation model through a micro-service architecture, and forming a data linkage space carrier; establishing a space-time-event double-main-line data linkage mechanism by taking the scene as a reference, accessing multi-source data through a stream processing engine and associating the multi-source data to a space coordinate system to form a dynamic data link; a semantic mapping layer based on Ontology unifies data modes and associates and maps the data modes, a cross-domain data linkage channel is constructed, and heterogeneous data semantic-level linkage is achieved; recognizing the state change of the facility through a time sequence model, and generating a cross-department automatic instruction chain in combination with spatial association to form scenarized closed-loop linkage; and constructing a decision support linkage system by using feedback data, integrating a quantitative model and a deduction engine, and converting the data into visual decision information to complete a full-chain scenarized closed loop. The method solves the problems of traditional data dispersion and the like, improves the linkage accuracy and processing efficiency, and provides support for urban governance.