127results about How to "Optimize scheduling" patented technology

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention relates to the technical field of dynamic scheduling, in particular to a dynamic optimization scheduling algorithm for a photovoltaic energy storage system, which is characterized in that based on a working environment of the photovoltaic energy storage system, environment illumination intensity data is acquired in real time, average power generation under the same illumination intensity is extracted, and weather conditions in a future time period of a current region are received through weather forecast data; and predicting the generating capacity in a future period to obtain generating capacity evaluation information. According to the invention, by capturing the environment illumination condition in real time and combining the historical power generation data, the future power generation condition of the photovoltaic system is effectively predicted, the load change of the power grid is monitored in real time, and the influence brought by specific activities is considered, so that timely response can be made when the demand of the power grid fluctuates, and the supply and scheduling of electric energy are optimized; according to the invention, continuous monitoring and abnormity processing are carried out on the power generation equipment, and the abnormal condition of the equipment is handled by correcting the discharge capacity in real time, so that the risk in operation of the energy storage system is reduced, and solid technical support is provided for stable operation of a power grid.

The invention discloses a tobacco enterprise composite scheduling method based on multi-agent collaborative optimization, and the method comprises the steps: obtaining the system data of a tobacco enterprise, and obtaining a plurality of corresponding decision-making schemes based on a sales prediction agent, a production plan agent, a purchase stock agent, and an inventory management agent; performing iterative optimization on the decision scheme based on a Nash equilibrium game and a Lupinstein bargaining model, and calculating a system utility value to obtain an optimal decision scheme; according to the optimal decision-making scheme, based on an adaptive mechanism, adaptive optimization is carried out, and an adjusted decision-making scheme of each agent is obtained, so that tobacco enterprise composite scheduling of multi-agent collaborative optimization is realized; the sales prediction agent, the production plan agent, the purchase stock agent and the inventory management agent perform information interaction through an attention mechanism. Interactive cooperation among multiple agents is realized by introducing an attention communication mechanism and a game theory cooperation mechanism, and overall optimization of tobacco enterprise scheduling is promoted.

The invention discloses a new energy power prediction monitoring platform and method for power grid dispatching optimization, and relates to the technical field of power systems, and the method comprises the steps: determining a distribution diagram of a target new energy power station, collecting the historical data and weather forecast data of the power station, and obtaining a plurality of weather forecasts through parallel calculation. And based on weather forecast and historical data, establishing a weather power prediction model. And generating a plurality of power prediction results through the prediction model according to user requirements, performing rolling prediction according to the new energy type, and continuously optimizing the model based on the prediction results. And adjusting a scheduling plan and a power transaction strategy of the power grid through a plurality of power prediction results so as to optimize the utilization of new energy and the scheduling of the power grid. The technical problems of inaccurate new energy power prediction and untimely response in the prior art are solved, and the technical effect of improving the new energy power prediction precision and real-time performance is achieved.

The invention discloses an electric power-traffic collaborative balance scheduling method and system for electric vehicle charging, and belongs to the technical field of electric power systems, and the method comprises the steps: obtaining the travel charging demand information, charging station information and traffic network information of each electric vehicle in real time; constructing a power-traffic coupling operation network model which comprises a power network model, a traffic network model and a coupling network model; using a multi-agent multi-layer graph convolutional learning algorithm to perform power-traffic collaborative balance scheduling, using a preposed graph convolutional network to perform coupling feature representation and environmental state perception on the coupling network model to obtain the coupling feature of each node, and using a charging station as an agent to perform power-traffic collaborative balance scheduling; and inputting the coupling characteristics of the node where the charging station is located into the state space as a part of observed quantity of the intelligent agent, and outputting a charging scheduling decision of each electric vehicle through a target strategy network and a target value network in the intelligent agent. The electric vehicle charging routing behavior can be optimized, and real-time balanced scheduling of the electric power-traffic network is realized.

The invention relates to the technical field of energy storage configuration, in particular to an energy storage configuration method and system for photovoltaic power generation. The method comprises the following steps: acquiring information data of a photovoltaic power generation energy station; performing distributed synchronous energy storage network construction on the photovoltaic power generation energy station information data to obtain a distributed synchronous energy storage network; performing real-time power generation data acquisition on the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; performing photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; performing historical electricity load data acquisition on the distributed synchronous energy storage network to obtain historical electricity load data; and performing load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data. According to the invention, through real-time photovoltaic power generation prediction, load demand dynamic analysis and adaptive energy storage strategy optimization, the accuracy and flexibility of photovoltaic energy storage configuration are improved.

The invention discloses an intelligent centralized control chain distributed multi-energy optimization scheduling device and method, and relates to the technical field of energy management. The method comprises the steps that energy samples are collected, energy characteristic indexes are established, and energy grading is conducted according to the indexes; historical power load data and load influence factor data are collected, and a power load prediction sequence is obtained by using the multi-time scale power load prediction model; collecting power supply and power grid data of the power system, calculating the power supply capability, and obtaining the power supply and demand balance degree according to the power load prediction sequence; and finally, forming a multi-energy optimization scheduling strategy according to the supply and demand balance degree and the energy classification. The device comprises an energy grading module, a load prediction module, a power supply and demand balance degree calculation module, an optimization scheduling module and a real-time monitoring and dynamic adjustment module, and is used for realizing the method. Through grading and optimal scheduling of energy, the energy utilization efficiency is improved, the operation cost is reduced, and the environmental pollution is reduced.

The invention discloses a self-adaptive resource scheduling method for improving cluster reconstruction efficiency, and relates to the field of resource scheduling distribution, and the method comprises the steps: monitoring the resource use data of each node in a cluster in real time through a monitoring agent, and obtaining the health state of the node through a heartbeat mechanism; collecting input task requests, predicting the resource demand quantity of each task, and performing preliminary queue processing on the tasks to obtain a task queue group; setting a resource competition constraint according to the task queue group and the resource use data and the health state of each node, obtaining dynamic resource scheduling corresponding to each task by using an optimization algorithm, and allocating the tasks to each node; a resource scheduling dynamic graph is constructed, a visual interface is set, and the resource scheduling dynamic graph is arranged on the visual interface and used for displaying resource scheduling, providing a user-friendly interface and realizing high-efficiency scheduling of adaptive resources in a cluster reconstruction process.

The invention relates to the technical field of solid waste sorting scheduling, in particular to a real-time monitoring optimization method and device for a solid waste collection, transportation and sorting ERP system. The method comprises the following steps: obtaining a real-time solid waste sorting log based on a solid waste collection, transportation and sorting ERP system; solid waste carrying vehicle tracking is carried out on the real-time solid waste sorting log, and a carrying track distribution visual view is constructed; acquiring an omnibearing monitoring image of a real-time sorting site; equipment sorting behavior visual identification is carried out on the real-time sorting site omnibearing monitoring image, dynamic sorting evolution is carried out, and a dynamic sorting twinborn model is constructed; carrying out multi-time-point solid waste sorting simulation on the dynamic sorting twinborn model, carrying out sorting load demand prediction, and generating a sorting load demand prediction value; and carrying out vehicle load calculation on the sorting load demand prediction value one by one, and then carrying out dynamic vehicle scheduling decision making to obtain a dynamic vehicle scheduling strategy. According to the invention, efficient and real-time solid waste sorting scheduling optimization is realized.

The invention provides a collaborative two-stage multi-runway departure flight ordering method, which is specially designed for relieving the congestion problem of a multi-runway airport in a peak period, ensures that the departure process of the flight is more efficient, and accords with scientific planning. The method comprises the following steps: constructing a multi-runway dynamic push-out rate control model to determine an optimal queuing threshold value of a taxiway; constructing a flight ordering and assigning model with the goal of minimizing flight delay and operation cost; and proposing a continuous Markov chain-based interactive whale algorithm, and carrying out flight push-out sequence and runway resource allocation. According to the method, the departure flight taxiing amount of the airport scene can be accurately identified, taxiway waiting time caused by runway occupation after the flight is launched is reduced, the actual significance is achieved for improving the flight punctuality rate and improving the overall airport operation efficiency, and the current green aviation development concept is met.

The invention discloses a task scheduling method for an intelligent fire-fighting management service platform, and relates to the technical field of fire-fighting informationization, and the method comprises the steps: collecting environment data and position coordinates, constructing a support vector machine (SVM) model, carrying out the task grade division of a monitoring region, and forming a task priority list; extracting a historical inspection record, calculating a deviation value between task completion duration and standard duration in the historical inspection record as an efficiency coefficient, and performing grade grouping on firefighters according to the efficiency coefficient; the position coordinates are converted into task distance parameters, the task matching degree is calculated in combination with the efficiency coefficient, a two-way selection queue is established, and firefighters are matched for the monitoring area according to a task priority list; and recording a task execution track of the firefighter through a positioning module, recording execution data of the task to a historical inspection record, and updating an efficiency coefficient to form a task scheduling mechanism. Through multi-dimensional data analysis and a dynamic evaluation mechanism, the task scheduling efficiency of the fire-fighting management platform is improved.

The invention discloses an air conditioner energy-saving operation optimization scheduling method and system. The method comprises the following steps that position coordinate data of all air conditioner units in a factory workshop are obtained; according to the position coordinates and the relative positions of the air conditioner units, the air conditioner units are divided into a basic air conditioner set and adjacent air conditioner sets; according to the spatial distribution relation of the air conditioning units, an influence relation graph between the air conditioning units is constructed; based on the heat generated in the workshop, the thermal load condition of each area is determined; based on the heat analysis result, the operation parameters of each air conditioning unit are set in combination with the mutual influence relation between the air conditioning units; scheduling optimization is conducted on the operation state of the air conditioning unit based on the operation parameters of the air conditioning unit; according to the air conditioner energy-saving operation optimization scheduling method and system, the problems of low energy efficiency, energy waste, unreasonable scheduling, lack of flexibility and the like of an air conditioner system are solved. Through intelligent optimization scheduling, the operation efficiency of the air conditioning system can be effectively improved, energy consumption is reduced, and the purposes of energy conservation and emission reduction are achieved.

The invention relates to a task scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a submitted target task, and adding the target task to a task priority queue; sorting all the tasks in the task priority queue based on the priority and deadline demand of each task in the task priority queue; task scheduling is conducted on the basis of the sequence of all tasks in the task priority queue, for the current scheduling task, a target computing node is determined on the basis of the transmission duration of data on which the current scheduling task depends and the earliest idle moment of each computing node, and the target computing node is used for completing the current scheduling task. By adopting the method, the task completion efficiency can be improved.

The invention discloses a regional power grid wind power generation power prediction optimization method which comprises the following specific steps: firstly, collecting data such as wind speed and wind direction through meteorological sensors arranged in a wind power plant and around the wind power plant, and carrying out cleaning and de-noising preprocessing; secondly, decomposing the meteorological data into intrinsic mode function components with different frequencies by adopting variational mode decomposition, constructing a convolutional neural network to perform feature extraction on each component, and adaptively adjusting the size of a convolution kernel according to the periodic characteristics of the data; then, a bidirectional long-short-term memory network based on an attention mechanism is used for fusing features, time sequence information is captured, power prediction is achieved through a full-connection neural network, and the weight of the FCNN is initialized through a random forest regression model to improve the initial performance of the model; according to the method, the prediction precision is improved through multi-level characteristic decomposition and a self-adaptive mechanism, the real-time response capability is good, and the requirements of the regional power grid for the precision and timeliness of wind power generation power prediction are met.

The invention relates to the technical field of power grid demand response, and discloses a power grid dispatching demand response system and method.The system comprises a state vector generation module, a state sequence generation module, a demand factor extraction module, a factor correlation analysis module, a weighted feature generation module and a resource dispatching module; the method comprises the following steps: generating a state estimation value of a power grid, carrying out vector coding to obtain a state vector, carrying out splicing and dimension reduction to obtain a state sequence, extracting demand factors from demand data, calculating a relationship among the demand factors through correlation analysis, weighting the demand factors according to factor correlation to obtain demand weighting characteristics, and finally, obtaining a state estimation result. And a load management strategy is generated based on the characteristics and the state sequence, so that resource scheduling is performed on the power grid according to the load management strategy, and the accuracy of power grid scheduling demand response is improved.

The invention relates to the technical field of fire-fighting control, in particular to a fire-fighting linkage control device for petroleum and petrochemical engineering, which comprises a multi-modal data acquisition module, a fire modeling and prediction module, a self-adaptive fire extinguishing control module and a virtual reproduction and optimization module. Dynamically modeling a flame form through multi-modal data fusion and a generative adversarial network; a fire path is accurately predicted in combination with a differential equation network; a path planning algorithm is adopted to optimize equipment scheduling, and modeling parameters and equipment configuration are analyzed and optimized through virtual scene generation and feedback data. According to the method, the accuracy of fire scene modeling and the efficiency of fire extinguishing equipment dispatching are remarkably improved, the problems that a traditional fire extinguishing system is slow in response and low in control accuracy are solved, and a powerful technical guarantee is provided for safe operation of petroleum and petrochemical storage tanks.

The present invention discloses a vertical intelligent take-off and landing field take-off and landing control system, which relates to the technical field of take-off and landing of take-off and landing fields, including an environmental monitoring module, an intelligent flight scheduling module, a vertical catapult take-off module, a take-off and landing field tilt control module, a fault accurate warning and processing module and a flight data in-depth analysis module: an environmental monitoring module: real-time monitoring of environmental parameters of the take-off and landing field, and automatic adjustment of monitoring strategies for extreme weather conditions; an intelligent flight scheduling module: real-time monitoring of environmental parameters and aircraft status, and intelligent planning of the take-off and landing path and time of the aircraft; in the present invention, through the real-time monitoring and strategy adjustment functions of the environmental monitoring module, the system can automatically adjust the monitoring strategy for extreme weather conditions to ensure the safety of aircraft take-off and landing, greatly improving the adaptability and survivability of helicopters in complex environments, especially in places with relatively harsh conditions such as mountainous areas and waters, and can significantly reduce take-off and landing accidents caused by weather factors.

The invention discloses a wood structure building information classification coding and data automatic identification mapping system and method, and the method comprises the steps: 1, creating a traditional wood structure building component-level model, and building a traditional wood structure building database; 2, performing overall view projection on the traditional wood structure building component-level model, matching the model with a traditional wood structure building database, and outputting a regulation code so as to screen corresponding data; 3, picking up the split components, performing isolated view projection, performing feature fuzzy matching with the simplified data pool, screening and defining the components, and outputting identification codes; 4, the traditional wood structure building is subjected to space division and tagging, and positioning codes of the components are generated; 5, the code segments are called according to purposes to be combined, and component codes are formed; and 6, integrating component parameters to carry out attribute addition and mapping, and endowing construction codes. The invention relates to the technical field of historic building information management, and can solve the technical problems in traditional wooden structure building information management in the prior art.

The invention relates to a project performance management and control method, device and equipment, a product and a storage medium. The method comprises the following steps: constructing a performance management and control model of a target project; the performance management and control model is provided with performance nodes set based on a target project, multiple stages of intermediate nodes set stage by stage from the performance nodes, and multiple child nodes set from the tail end intermediate node; respectively determining a weight coefficient of each node at the same level in the corresponding upper-level intermediate node or performance node; and obtaining the performance score of the child node, and determining the performance score of the intermediate node and the total performance score of the performance node step by step based on the performance score of the child node, the weight coefficient of the child node and the weight coefficient of the intermediate node, so as to perform performance management and control on the target project based on the total performance score. Compared with the prior art, the resource allocation and scheduling can be effectively optimized, and unnecessary delay is reduced.

The invention provides an AI-based zero-carbon park energy management method and system, and a medium, and aims at achieving the purposes of efficient utilization and zero-carbon emission of energy in a park through an intelligent scheduling strategy. According to the method, energy consumption demand estimation, carbon emission optimization, multi-energy system management and other technologies are utilized to carry out real-time monitoring and optimal scheduling on various energy use conditions of the park. Specifically, the system collects the power use condition in the park, and performs energy consumption demand estimation and carbon emission calculation through an AI model. And then, according to a demand estimation result and a carbon emission optimization model, an optimal energy scheduling strategy is selected, real-time feedback is obtained after scheduling execution, and a decision model is further optimized. And finally, realizing collaborative optimization of an environmental protection target and economic benefits by adaptively adjusting a discount factor and a weight balance mechanism.

The invention relates to the technical field of production scheduling optimization, in particular to an intelligent load adjusting method based on an intelligent aerial view mobile terminal, and the method comprises the following steps: S1, building a flow database of production processes of different types of to-be-processed products; s2, establishing a production process splitting module, wherein the production process splitting module is used for extracting the product production process in the process database established in the previous step and transmitting the product production process to the intelligent aerial view mobile terminal; and S3, creating a plurality of production orders by the intelligent aerial view mobile terminal according to different types of processed products. According to the process compatibility and the load of the production line, the manufacturing process of the product to be processed and the dependency relationship between the processes can be deeply understood, the production task can be better planned and scheduled, the reasonable combination of the process operations from different orders on different production lines is realized, and the production efficiency is improved. And production tasks are reasonably distributed and arranged according to factors such as running time, resource requirements and equipment availability of each working procedure, so that the production efficiency is improved to the greatest extent.

The invention relates to the technical field of power systems, in particular to a regional power grid heterogeneous frequency modulation resource inertia evaluation method, which comprises the following steps: establishing an aggregation model of system frequency response under disturbance input for a synchronous generator system; aiming at the synchronous generator system, obtaining the critical inertia of the synchronous generator; for a power electronic power supply system, obtaining the critical inertia of a power electronic power supply; for the synchronous generator system, obtaining the actual response inertia of the synchronous generator; for a power electronic power supply system, obtaining the actual response inertia of a power electronic power supply; and evaluating the stability of the power system through the inertia release depth evaluation index. According to the method, aiming at the inertia level of a regional power grid, system frequency responses containing various heterogeneous resources are established, inertia capabilities of different adjustment resources are researched, and factors such as equipment configuration, control strategies and operation conditions of a power system under different control modes are considered, so that the virtual inertia contribution capability when a new energy field station accesses the power grid is comprehensively evaluated.

The invention discloses a measurement and correction method for frequency coupling sequence admittance of a network-forming converter system, and the method comprises the steps: injecting three-phase voltage disturbances with different frequency values into the network-forming converter system for a plurality of times, and obtaining output voltage and current response data; according to the voltage and current response data, obtaining a frequency-coupled sequence admittance measurement result; obtaining a compensation factor of phase correction; and performing phase correction by using the compensation factor to obtain an optimized sequence admittance result. According to the technical scheme provided by the invention, the impedance measurement of the grid-connected system is realized by adopting a grid-connected point disturbance signal injection mode, and the power grid impedance is estimated through the collected impedance data; and phase correction is carried out on the measured frequency coupling sequence admittance coupling component by considering the influence of the power grid impedance, so that the measurement precision of the frequency coupling sequence admittance of the grid-connected system of the network construction type converter is improved, the accurate analysis of the stability of the grid-connected system of the network construction type converter is facilitated, and the scheduling and operation strategies of the system are optimized.

The invention relates to the technical field of power systems, and discloses a half-wavelength controllable tuning method under the background of a dual-carbon and novel power system. Comprising the following steps: system evaluation and demand analysis: collecting power grid information and fusing dual carbon and novel power system demands to determine a tuning target and key parameters; designing a half-wavelength tuning strategy, determining tuning parameters by using a particle swarm optimization algorithm, formulating the strategy, and performing simulation optimization; selecting and configuring half-wavelength controllable equipment, evaluating performance indexes, selecting equipment, and reasonably configuring, installing and debugging; system integration and communication network construction: integrating equipment and constructing a reliable communication network to ensure information transmission; tuning implementation and monitoring are carried out, a control system is developed to send instructions, data are collected and analyzed to evaluate performance, and optimization is fed back according to results. According to the invention, the stability and adaptability of the power grid are enhanced, the intelligent development is promoted, the resource configuration and structure are optimized, the safety and sustainable development are guaranteed, and the challenges of dual-carbon and novel power systems can be effectively handled.

The invention relates to the field of comprehensive energy, and discloses a low-carbon comprehensive energy system optimization planning method considering clean energy, which comprises the steps of determining an optimization planning target, analyzing energy requirements of a system, and determining constraint conditions in physical, technical, economic and environmental aspects; all kinds of energy resources available in the system are evaluated, the potential and availability of all kinds of renewable energy resources are evaluated based on geographical and climate conditions, the supply modes of all kinds of energy resources are analyzed and determined in combination with energy demand, and a multi-energy collaborative supply scheme is provided; establishing a mathematical model of the multi-energy system, simulating the operation condition of the system, evaluating the contribution of each energy form and the influence of the contribution on the stability of the system, and analyzing the influence of different parameter changes on the operation of the system; a comprehensive objective function is constructed according to requirements and objectives of the system, and constraint conditions of system operation are set according to equipment performance, energy efficiency and environmental protection laws and regulations. The method has the advantages of optimizing multi-energy collaborative supply and ensuring stable operation of the system.

The invention relates to the field of power system load analysis, and discloses a non-intrusive electric energy load decomposition method based on a spatial-temporal feature reconstruction technology. According to the method, fast Fourier transform (FFT) is adopted to find a periodic mode of power data, then a one-dimensional time sequence is remodeled into a two-dimensional space-time tensor according to a period, data feature information of the two-dimensional space-time tensor is deeply mined by using a convolutional neural network, and non-intrusive decomposition of the power data is realized. According to the method, the spatial-temporal characteristics of the power data are reconstructed, so that the method can learn the change relation between the period and the period, the power utilization habit of a user is deeply mined, and the load decomposition precision is improved. Meanwhile, the identified power load periodicity corresponds to the power equipment, so that the influence of similar power electric appliances can be avoided.

The invention discloses a load forecasting and scheduling method adaptive to heat user behaviors, relates to the technical field of load forecasting, solves the technical problem that all factors influencing the load are difficult to capture when a linear method is used for processing complex problems in the prior art, and specifically comprises the following steps: collecting historical load data of a source side pipe network, and analyzing a heat user behavior mode; establishing a pipe network load simulation model based on the historical load data and the heat user behavior mode; simulating a real-time operation state of a pipe network by using a digital twinborn technology, and updating heat user behavior data in real time; according to the simulation result and the heat user behavior data updated in real time, the load change of the pipe network is predicted in real time; according to the prediction result, self-adaptive regulation and control scheduling is carried out so as to adapt to the real-time changeable switching requirements of the heat users; and the regulation and control strategy is optimized through a machine learning algorithm, and optimization of a scheduling plan is ensured.

The invention discloses an emergency field data sensing system based on cloud side end processing, which relates to the technical field of emergency data sensing and comprises an identification model construction unit, a target frame detection unit, a global position analysis unit, an influence degree judgment unit, an evacuation path generation unit and an emergency processing unit. According to the invention, local position information contained in an image data frame corresponding to each sub-region is analyzed in an edge server through a global position analysis unit, and the local position information related to each sub-region is converted by using a coordinate optimization algorithm in a cloud server to obtain global position information in a specified scene. By means of the design, on one hand, the accurate position of the target can be obtained, on the other hand, the calculation speed of the system is greatly improved, the requirement for server performance is lowered, and the optimal evacuation path of each crowd in the emergency site is calculated through the influence degree judgment unit and the evacuation path generation unit; and the condition that the personnel are congested during evacuation is avoided.

The invention discloses a cold-chain logistics vehicle planning system. The system comprises the following steps of initial population creation, explosion and spark generation, evaluation and selection, genetic manipulation, pheromone updating, termination judgment and optimal solution output. According to the method, a dynamic pheromone updating and path reward and punishment mechanism is introduced through improvement of pheromone updating, so that the convergence speed of the algorithm is accelerated, and the quality of a solution is improved; the exploration depth of a solution space is increased by improving the crossover operation and considering the length and quality of an introduced path; improvement of mutation operation depends on a self-adaptive mutation strategy, the exploration capability of the algorithm is enhanced, multi-objective optimization is introduced into path planning through the improved firework algorithm to consider transportation cost, temperature control and energy consumption management at the same time, and a dynamic adjustment mechanism is combined to cope with frequently changing terrain and climate conditions in the karst landform, so that the development of the karst landform is facilitated. Therefore, the adaptability of the algorithm in a complex environment is improved.

The invention discloses a reference risk dynamic management method and system, and relates to the technical field of power grid risk dynamic management, and the method comprises the steps: collecting zero-power user data, and building a first station line transformer user relation model; establishing first dynamic monitoring; key equipment in the power grid and the risk level of the power grid after a fault are automatically identified according to real-time operation data, and a reference risk report is updated in a rolling mode. According to the method, automatic collection and integration of zero electric quantity user data and linkage analysis of a power grid equipment and user risk relation are realized by establishing the first station line transformer user relation model, and the operation state change of the power grid is displayed in real time by establishing the first dynamic monitoring system, so that the operation efficiency of the power grid is improved. According to the technical scheme, a power grid manager can clearly master real-time data, key equipment in the power grid can be automatically identified through analysis of the real-time operation data, and therefore rapid positioning of a power-losing area is achieved, the identification speed and accuracy are improved, the requirement for manual intervention is reduced, and high efficiency of risk assessment is ensured.