164 results about "Scenario simulation" patented technology

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 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 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 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 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.

The invention relates to the technical field of tunnel engineering safety risk assessment, in particular to a tunnel engineering full life cycle-based safety evaluation method, which comprises the following steps of: integrating multi-source data of each stage of a tunnel full life cycle to form a structured data set and extracting safety features; an influence relation chain between security features in different stages is mined through a pre-constructed multi-stage collaborative analysis network, and a dynamic security state evolution graph is constructed based on the influence relation chain. In combination with historical accident cases, quantitative labeling of risk probability and severity is carried out on state nodes and evolution paths in the atlas, and multi-scene simulation deduction is carried out for key paths. And positioning and grading weak links of the whole life cycle according to a quantification result, and generating a potential safety hazard grading list. According to the method, the transformation of the security risk from static and single-stage assessment to dynamic and cross-stage association assessment is realized, and the insight of systematic risk evolution rules and the accuracy of early warning are improved.

The invention discloses an English listening and speaking interaction intelligent training system based on a large language model, and relates to the technical field of English learning, and the training system comprises a user capability graph module, an interaction feedback module, a personalized learning path generation module, a scene simulation module, an interaction rhythm control module and a community cooperative training module. According to the method, the pragmatic ability, the cross-language communication ability and the linguistic ability are jointly incorporated into the training content, and the adaptive training content is generated in combination with a real cross-language communication scene, so that a user synchronously masters expression specifications and culture adaptation key points in different scenes in the training process; the effect that training content and real communication requirements are deeply matched is achieved, the user is helped to accurately adapt to a native English scene and real phrase habits of people, the requirements of an actual application scene are met, and substantial improvement from basic language knowledge mastering to real communication ability is achieved.

The invention belongs to the technical field of virtual reality organization ideological education, and discloses an organization ideological education application system combined with a virtual reality technology, and an interactive learning development module and a learning assistance and recommendation module can solve the problems. The interaction module creates three types of core scenes: role playing is fused with motion capture and organization related term semantic recognition, and the motion specification degree and the verbal skill accuracy are evaluated in real time; the difficulty of ideological education scene simulation is adjusted according to user roles, new organization members simplify the change process, and basic organization affair personnel add exception handling scenes and can deduce decision influence; multi-person collaboration supports 8-30-person hierarchical collaboration, a branch principal can allocate tasks, basic organization affair personnel can watch data, and points can be synchronized when the tasks are completed; the intelligent recommendation module refines four types of user roles and four types of content tags, and combines an algorithm to deduce a daily list, an NLP knowledge base and a difficulty disassembling tool to assist in real time, so that organization members can participate actively from passive learning, and the learning difficulty is accurately solved.

This invention relates to the field of water conservancy engineering construction technology, and discloses a method and system for controlling blasting vibration during lock excavation. By collecting information on the geological hardness of rock strata and the distance to surrounding buildings, and combining this with finite element analysis to simulate the blasting energy distribution, a dynamic matching mechanism for borehole depth and charge quantity is constructed. This method overcomes the limitations of traditional empirical parameter settings and uses iterative simulation and optimization algorithms to achieve coordinated adjustment of borehole depth and charge quantity. For example, in areas with high rock hardness, by increasing the borehole depth and matching a segmented charge strategy, the blasting energy is uniformly released in the deep rock mass, effectively reducing the peak ground vibration and avoiding structural damage to adjacent buildings. Furthermore, by introducing multi-dimensional data analysis and multi-scenario simulation verification mechanisms, it can flexibly cope with complex working conditions such as uneven rock hardness, topographical undulations, adjacent water bodies, or dense building clusters.

The invention provides a teaching method and system for progressive AI ethic scene simulation in different learning segments, and the method comprises the steps: constructing a progressive AI ethic scene library in different learning segments according to different scene grades and corresponding task requirements; configuring a scene simulation platform and an AI ethic guidance module; based on the AI ethical scene library, the scene simulation platform and the AI ethical guidance module, progressive scene simulation and real-time task guidance are executed; in the process of executing scene simulation and real-time task guidance, collecting and analyzing multi-modal teaching data in real time; and based on an analysis result, generating a teaching evaluation and personalized promotion scheme. According to the AI ethical teaching system, the practicability and immersion of ethical teaching are improved, large-scale personalized teaching is realized, a scientific and comprehensive literacy evaluation standard is established, a data-driven teaching optimization closed loop is formed, and the AI ethical teaching system based on branch learning, scene simulation and intelligent guidance is constructed.

The invention relates to a multi-scenario simulation deduction method for a network public opinion evolution rule, and the method comprises the steps: constructing a multi-scenario simulation deduction system for the public opinion evolution rule, carrying out the simulation fitting of the public opinion evolution rule through the real public opinion data, and carrying out the scenario evolution of the public opinion evolution rule. A multi-scene simulation method based on system dynamics is adopted, in combination with exogenous intervention, a multi-scene simulation deduction system of a public opinion evolution rule is constructed, whether public opinion evolution can be effectively accelerated to enter a fading period or not is observed by adjusting the number of three types of public opinions with high, medium and low influence degree levels, the evolution period is shortened, and the evolution efficiency is improved. And technical support is provided for health guidance of public opinions.

The application provides a smart supply chain risk early warning and resilience optimization management system, and belongs to the technical field of supply chain risk management.The system comprises a data acquisition module, a supply chain graph network construction module, a multi-source data fusion module, a risk identification and early warning module, a resilience evaluation and optimization module and a decision support module.The system builds a supply chain relationship model based on a graph network, integrates multi-source heterogeneous data such as macroeconomics, geopolitics and meteorological environment, realizes the prospective identification of potential risks of a supply chain and the accurate tracking of a risk propagation path, evaluates the resilience of the supply chain based on a scenario simulation method, and generates targeted optimization strategies.The actual application shows that the system reduces the supply chain interruption loss by 65%, increases the inventory turnover rate by 30%, and provides a systematic risk management tool for enterprises.

The invention discloses a noun explanation update-driven file content identification optimization method and system, and relates to the technical field of file content identification, and the method comprises the steps: constructing a layered noun explanation rule base; periodically checking the consistency of noun interpretation data source versions through a lightweight verification protocol, and triggering an incremental updating process when detecting that the version difference exceeds a preset fault-tolerant threshold value; based on a conflict resolution mechanism in the hierarchical noun interpretation rule base, generating a multi-scene simulation training corpus; a multi-scene simulation training corpus is adopted, the file content recognition model is optimized through a hierarchical parameter updating mechanism, and noun explanation recognition is executed on an input file text; and starting the self-correction process of the hierarchical noun explanation rule base based on the training effect feedback of the multi-scene simulation training corpus, and readjusting the priority and the association weight of noun explanation. The technical problem that in the prior art, the dynamic updating effect of noun explanation is poor, and consequently the file recognition accuracy is low is solved.

The invention discloses a personality self-adaption-based personalized communication scheme generation method and device, and the method comprises the steps: receiving a communication scene inputted by a user, and extracting the personality features of the user and the personality features of an interaction object; according to the personality characteristics of the user and the personality characteristics of the interaction object, a behavior disorder diagnosis report is generated in combination with a personality disorder rule base; generating a scene simulation interaction task according to the behavior disorder diagnosis report, and sending the scene simulation interaction task to the user for interaction; collecting interaction data of finishing the scene simulation interaction task by the user, and comparing the interaction data with risk points in the behavior disorder diagnosis report to obtain a comparison result; and an intervention instruction is generated according to a comparison result, and the non-adaptive behavior or psychological state glide of the user is intervened and corrected in real time according to the intervention instruction, so that a final communication scheme is generated, and the behavior change efficiency and continuous motivation of the user in multiple fields such as interpersonal communication, sales, fitness training, learning and growth and the like are improved.

The application belongs to the field of spatial analysis, and is a dynamic multi-dimensional industrial space chain analysis and evaluation method, comprising the following steps: S1, collecting data to form a dynamic multi-source industrial space data set containing extended attributes after preprocessing; S2, fusing the dynamic multi-source industrial space data set with high-precision GIS data, industrial chain basic topology and remote sensing image data, updating the industrial chain topology relationship and optimizing the fusion features to form a dynamic fusion data set; S3, constructing a multi-target industrial space structure algorithm containing economic and environmental protection targets based on the dynamic fusion data set, realizing real-time iterative optimization and integrating an abnormal early warning function; S4, calculating a multi-dimensional agglomeration effect index through the multi-target real-time optimization algorithm, simulating industrial migration paths under multi-policy scenarios and visualizing; S5, carrying out two-dimensional coupling evaluation of sustainability and economy based on the scenario simulation results, generating feedback suggestions and optimizing the previous steps.

The invention relates to the technical field of geological mineral planning, and discloses a geological mineral planning method based on multi-modal data fusion, which comprises the following steps: collecting multi-modal data and carrying out cleaning preprocessing, converting a standardized multi-modal data set into a primary feature vector set with weights and semantic tags, constructing a multi-dimensional feature matrix, and carrying out multi-modal data fusion on the primary feature vector set; s3, establishing a system dynamics model and a general equilibrium model, constructing a dynamic coupling model of the system dynamics model and the general equilibrium model, defining a multi-dimensional scene space, performing simulation prediction based on the dynamic coupling model in the step S3, obtaining a result data set, constructing a multi-dimensional evaluation index system, and identifying an optimal planning path. According to the method, through a complete closed loop from multi-source data acquisition, fusion and model construction to scene simulation and decision output, the problems of data splitting, model isolation and decision qualitative in a traditional planning method are systematically solved, and the systematicness, scientificity and operability of planning are remarkably improved.

The invention provides a method and a device for testing vehicle-mounted event data records. The method comprises the following steps that: a signal simulation system obtains field values of a plurality of fields recorded by vehicle-mounted event data, generates scene simulation data based on the plurality of field values, and transmits the scene simulation data to a signal reporting system; the signal reporting system uploads the scene simulation data to an Internet of Vehicles platform; the analysis system obtains a pull value of the scene simulation data from the Internet of Vehicles platform, and obtains an expected value of the scene simulation data from the signal simulation system; and comparing the expected value with the pull value to generate a comparison report. In the mode, all-around test support can be provided for research and development improvement, type authentication and quality control of the EDR product, the reliability and data recording quality of the EDR product can be improved, and a more reliable data basis is provided for automobile safety accident analysis and responsibility confirmation.

The invention provides an operating room nursing operation intelligent training system and method, and the method comprises the steps: collecting a nursing operation track of a target user in a high-fidelity operation situation simulation environment matched with a target operation type in real time, generating a deviation map of the nursing operation track and a standard operation, and recognizing a space-time operation abnormality feature in the deviation map; determining a pressure load index of the target user when the emergency event occurs according to the combined characteristics of the various physiological signals of the target user on frequency change in combination with heart rate variability, and when the pressure load index exceeds a preset psychological pressure threshold value, guiding the target user to relieve psychological pressure and recording a physiological stress state track of the target user; and carrying out emergency response training on the target user based on the spatio-temporal operation abnormal features and the physiological stress state trajectory pushing special training strategy. By adopting the scheme of the invention, an adaptive intraoperative nursing emergency response capability training system can be constructed to perform dynamic intervention training on the intraoperative emergency response capability of nursing personnel.

The invention discloses a multi-scenario operation method based on a single-node memory database and multi-Agent calculation, and belongs to the field of combat simulation, and the method comprises the following steps: 1, starting a scenario editing tool, operating a multi-scenario editing process, and creating a memory database; secondly, the scenario is edited, and scenario entity information is stored in the memory database; thirdly, storing a multi-scenario file; 4, starting a scenario operation monitoring tool, operating a plurality of scenario operation monitoring processes, and creating a memory database; 5, loading the scenario file, and creating a data blackboard by the memory database based on the scenario file; sixthly, advancing each set of simulation time according to the step length, and managing the data blackboard; seventhly, in the step length period, multi-Agent simulation calculation is executed, and a calculation result is updated to the data blackboard; and 8, judging whether the scenario is ended or not, and if not, turning to the step 6. According to the method, single-node multi-scenario parallel operation is realized by adopting a memory database technology and a multi-Agent technology, and the method has the advantages of simplicity and high efficiency.