68results about How to "Improve the ability to solve" patented technology

The invention provides an interactive question and answer task processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including interactive question and answer tasks, each sub-target task is correspondingly issued to each sub-target agent at least including an interactive question and answer agent, and different sub-agents are used for processing different types of sub-tasks; the interactive question-answering agent is controlled to present an interactive graphical user interface corresponding to the interactive question-answering task to the user, an answer result corresponding to the received feedback answer is presented, and the interactive graphical user interface comprises a question and an interactive answer area; an associated sub-target agent in the multiple sub-target agents is controlled to generate a corresponding associated processing result based on an answer result output by the interactive question and answer agent, and the associated sub-target agent is a sub-target agent which depends on the answer result and serves as part of task input information; and determining a target processing result corresponding to the target task based on the association processing result. Through application of the scheme, a better comprehensive task processing effect can be brought under the condition of lower comprehensive cost, especially for an interactive question and answer task in which a user needs to subjectively answer a proposed question and the effect is checked according to an answer result; the interactive questioning and answering agent provides the interactive graphical interface containing the question and the interactive answering area for the user, so that the task requirement of the user can be met more quickly in an interactive answering mode, and the task solving effect is improved.

The invention, which relates to the technical field of artificial intelligence and skill training examination, discloses a multi-crowd adaptive training evaluation system comprising the following components: a user data management module, an RAG module, an answer processing module, a score analysis module, an intensive training module and a question bank updating module. According to the method, comprehensive and accurate assessment of the user ability is realized by constructing a multi-crowd adaptive training assessment system, the system constructs the user ability portrait by collecting historical data and real-time performance of the user in multiple dimensions and utilizing advanced data processing and analysis technologies, and dynamically adjusts the training content and difficulty, so that the user ability assessment efficiency is improved. According to the method and the system, the ability level and the learning requirement of different users are adapted, the personalized training mode not only improves the participation degree and the learning efficiency of the users, but also helps the users to quickly and accurately identify the ability weakness of the users, and powerful support is provided for subsequent improvement.

The invention discloses a solid waste resource utilization management method based on a digital city, and the method comprises the steps: collecting data through deploying an Internet of Things sensor in a solid waste treatment link, building a data set, and constructing a digital twinborn model in a virtual environment; carrying out capability prediction and risk assessment on processing equipment in the model, and dynamically predicting the solid waste output in combination with historical data; the resource efficiency and the processing cost are taken as optimization objectives, a dual-objective optimization function is constructed, an optimal resource processing strategy is solved and generated through a genetic algorithm, and the digital twinborn model is updated by calculating an output error; according to the method, the solid waste recycling efficiency and the treatment economy are remarkably improved, through combination of the digital twinning algorithm and the genetic algorithm, precise scheduling of the equipment capacity is achieved, the treatment process is optimized, the equipment load fluctuation and the operation cost are reduced, and an intelligent solution is provided for smart city solid waste management.

The invention discloses a charging and discharging control system and electronic equipment. The method comprises the following steps: S1, constructing a charging pile multi-target optimization model; s2, initializing a chaotic particle swarm optimization algorithm; s3, each particle in the particle swarm is optimized according to an objective function in the charging pile multi-objective optimization model; s4, carrying out loop iteration until the chaotic particle swarm optimization algorithm reaches a convergence condition; s5, generating a prediction result of the operating environment of the charging pile; s6, updating the current optimal particle and the global optimal particle; and S7, generating a multi-target charging and discharging optimization control strategy of the charging pile according to the optimized global optimal particle, applying the optimization control strategy to a charging pile charging and discharging control execution module, and executing multi-target charging and discharging control of the charging pile. According to the method, the globally optimal solution in a complex scene can be quickly solved, and the efficient operation requirement of the electric vehicle charging pile in a large-scale deployment scene is met.

The invention provides a post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency, and relates to the technical field of emergency logistics multi-target distribution optimization under the background of urban earthquake disasters, and the method comprises the following specific steps: S1, determining a basic hypothesis: considering the actual scene of disaster rescue operation, and making the basic hypothesis for the emergency material distribution problem; s2, establishing an emergency material distribution fairness model; s3, establishing a disaster point emergency material demand emergency degree evaluation index system; s4, establishing a multi-target path optimization model; and S5, solving the established multi-target model by using an improved mixed multivariate universe optimization algorithm. The mixed multivariate universe algorithm based on differential evolution is provided, higher convergence precision and better robustness and uniformity are achieved, the earthquake disaster emergency logistics distribution path optimization accuracy is improved, material demand urgency meeting and distribution fairness are guaranteed, the distribution cost is reduced, and the distribution efficiency is improved. And the disaster victim satisfaction degree and the demand emergency adaptation degree are improved.

The invention relates to a multi-stage collaborative scheduling method for a ship block assembly workshop, and belongs to the technical field of assembly workshop scheduling. The ship block assembly job shop scheduling modeling is an assembly job shop collaborative scheduling problem model considering three stages of processing, transportation and assembly; and solving an assembly job shop collaborative scheduling problem model of three stages of processing, transportation and assembly based on an improved genetic algorithm. Taking minimization of completion time as an optimization target, and converting a part integrity constraint in a machining stage, a time difference constraint of a part in a transportation stage and a flowing water blockage constraint in an assembly stage into control variable constraints in a mathematical model; the strategy is improved on the basis of the genetic algorithm, and the solving capacity of the genetic algorithm for the ship block assembly job shop scheduling problem is improved; the method effectively solves the problem of ship block assembly workshop scheduling, and has solving precision and stability.

The invention discloses a path planning method based on an improved genetic-ant colony algorithm, and belongs to the technical field of path planning. The method comprises the following steps: firstly, initializing a pheromone matrix of an ant colony algorithm by utilizing a genetic algorithm, then executing the ant colony algorithm, updating the pheromone matrix again, selecting an ant path with the maximum fitness value as an optimal path of the iteration, then generating a next-generation population, iterating in this way, and finally calculating the fitness value of the optimal path of each iteration. And selecting the path with the maximum fitness value as a final planned path. According to the method, the pheromone matrix of the ant colony algorithm is initialized by using the genetic algorithm, the quality of an initial solution is improved, and the algorithm convergence speed is accelerated; according to the fitness value, the ant colony algorithm population executing the genetic operation is classified, the understanding stability is further improved, the time loss and the path loss during path planning are reduced, and the efficiency and the accuracy of path planning are improved.

The invention relates to an earthwork allocation optimization method based on a combined problem model framework. The method comprises the following steps: obtaining construction node information, cost information and vehicle-mounted capacity information of to-be-processed earthwork; constructing a linear programming model based on the construction node information and the cost information by taking the minimum cost as a target, solving through a solver to obtain a static deployment scheme, and storing the static deployment scheme as a multi-way tree; constructing a travel salesman-like problem model based on construction node information, cost information and vehicle-mounted capacity information by taking a shortest path as a target, calling data in the multi-way tree, and solving through an ISABO algorithm to obtain a path allocation scheme; and the static allocation scheme and the path allocation scheme are combined and output, and allocation optimization of the to-be-processed earthwork is completed. According to the earth-rock deployment problem and construction optimization method for solving the complex problem based on the combination problem, the functional composition of the complex mathematical problem model is successfully deconstructed, and the difficulty of mathematical modeling is greatly reduced.

The invention provides a group-type multi-role-playing virtual simulation training method and system and a medium, and the method comprises the steps: carrying out the simulation training in response to a virtual simulation training scene selected by at least one teacher user, and determining the number and types of roles participating in the virtual simulation training scene; n of the M trainees apply to participate in the virtual simulation training scene, the N trainees are allocated as roles, the mapping relation between the roles and the N trainees is determined, and scoring permissions of the remaining Q trainees are configured; recording training process data of N trainees participating in group training; obtaining evaluation data of roles played by the Q user students on the N student users; obtaining an evaluation score of each student user participating in the virtual simulation training scene according to the mapping relation; and finally, based on the obtained Q evaluation scores, determining training scores of the user trainees participating in training.

The invention relates to the technical field of classroom intelligent terminals, in particular to a classroom intelligent terminal integrating a micro host, network access and audio acquisition. According to the technical scheme, the classroom intelligent terminal integrating a micro host, network access and audio acquisition is provided. Comprising a main control unit, an acoustic array device, a network interface device, a deep learning audio processing unit, a multi-mode interaction device, an adaptive network technology component, an edge computing unit, a virtual reality and augmented reality device, a block chain module and a voice synthesis and emotion recognition module. The system provides more advanced, intelligent and interactive rich education experience, is expected to improve the learning effect and teaching efficiency, has potential revolutionary influence in the education field, and provides a more flexible, personalized and efficient learning environment.

The invention discloses a robot fault self-diagnosis system and method, relates to the technical field of robot fault diagnosis, and solves the technical problem of low automation degree of a robot caused by incapability of accurate self-diagnosis and self-repair of the robot in the prior art. The method comprises the following steps: collecting real-time operation data of a robot, and extracting real-time error logs and historical fault data from a database; the fault state of the robot is monitored according to the real-time operation data of the robot, a historical fault library is constructed based on historical fault data, the fault state is input into the historical fault library for matching, and mismatched faults, matched faults and corresponding matching repair methods are obtained. Different self-repairing methods are adopted for different faults, the historical fault library is updated, the repairing effect is monitored, and the matched repairing method is updated according to the repairing effect; and when the robot cannot complete self-repair, planning an optimal maintenance path and controlling to go to repair.

The invention discloses an ultrasonic guided wave nondestructive testing method for internal and external corrosion of an electric power tower, and belongs to the technical field of electric power tower detection. In order to solve the problem that the use safety of an electric power steel pipe tower is influenced by inclination of the tower caused by damages such as cracks and corrosion, the corrosion condition in the electric power tower can be quickly and accurately detected through an ultrasonic guided wave technology, and the influence of the corrosion condition on the propagation speed is evaluated by utilizing the reciprocity principle of a time reversal method and modal identification. The specific position and range of corrosion in the tower are accurately positioned, rapid detection and defect scanning positioning are achieved, the detection efficiency of the internal and external corrosion conditions of the power transmission line steel pipe tower is improved, rapid and non-destructive flaw detection of the power tower is achieved, and the detection efficiency is improved. The method is widely applicable to detection of corrosion defects of steel pipe fittings of power transmission and distribution lines under goaf areas, settlement-prone zones and various complex geographical conditions, potential safety hazards of electric power towers are found in advance, the ability to solve sudden problems is improved, and the reliable power supply ability is improved.

The invention provides a ship vibration monitoring method and system based on a data model technology. The ship vibration monitoring method comprises the steps that monitoring information of a ship is collected, the monitoring information is processed through the data model technology, a monitoring model of the ship is constructed, the monitoring model is operated, and the operation state corresponding to each operation device in the ship is obtained; performing health assessment on the ship based on the operation state, determining a plurality of abnormal vibration characteristics of the ship according to the monitoring model when the ship does not accord with a specified health level, and determining an abnormal reason corresponding to each abnormal vibration characteristic, and generating a ship maintenance scheme for the ship according to each abnormal vibration characteristic and the corresponding abnormal reason, and displaying the ship maintenance scheme, so that the operation state and health condition of equipment and a system in a cabin can be analyzed and evaluated, and support is provided for decisions in the aspects of use, operation, control, overhaul, management and the like of the equipment and the system.

The utility model relates to the field of diesel engine crankshaft parts, in particular to a crankshaft of a diesel engine for teaching. The crankshaft of the diesel engine for teaching comprises a support, mounting grooves and clamping jaws, the top of the support is rotationally connected with a crankshaft body, the middle of the support is fixedly connected with a cross rod, a tank is mounted on the cross rod, the mounting grooves are symmetrically formed in the crankshaft body, balancing weights are mounted in the mounting grooves, and the two clamping jaws are symmetrically mounted at the bottom of the support. According to the utility model, the mounting seat and the balancing weight are arranged on the crankshaft body, and the motor is combined to drive the crankshaft to rotate, so that the state change of the crankshaft in the movement process can be intuitively displayed; meanwhile, the transparent tank body on the cross beam and the shaking condition of clear water in the transparent tank body can clearly reflect the uniformity of the crankshaft counterweight and whether defects exist or not, so that students are helped to better understand the importance of the crankshaft balance.

The invention discloses a citrus orchard image data acquisition and annotation method, which relates to the technical field of image acquisition, and further comprises a factor analysis module, a default module, an image data standardization module, an image database module, an abnormal image analysis module, an abnormal root analysis module and a correction module. The system has the advantages that the quality of the acquired citrus orchard image can be obviously improved through the default module, the state and details of the fruit tree can be observed more clearly, the weather state information at the current acquisition time can be acquired by using the image data standardization module, the data is adjusted according to the image definition, and parameter adjustment data is generated, so that the accuracy of the system is improved. The reliability and the stability of image acquisition are improved, excessive influence of external factors such as weather change is avoided, the reason of the abnormal condition of the fruit tree can be analyzed according to the abnormal condition information of the fruit tree by utilizing the abnormal root analysis module, and the efficiency and the effect of problem solving are improved.

The invention provides a virtual simulation practical training platform of an intelligent networked automobile environment sensing system, which comprises a virtual traffic environment construction module, an environment sensing data simulation module, a test module, a practical training teaching module, a data management module and an optimization module, according to the platform, information and corresponding perception data in a virtual environment can be obtained according to mutual cooperation among the simulated traffic environment construction module, the environment perception data simulation module, the test module, the practical training teaching module, the data management module and the optimization module, and a user can design and test a scene. And the practical training and teaching module can carry out corresponding practical operation and experiment, so that the practical ability and problem solving ability of the user are improved, and the stored data can be collected, stored and analyzed through the data management and analysis module, so that corresponding optimization can be carried out.

The invention belongs to the field of model detection and symbol simulation, and relates to a symbol track value counting method and system based on a satisfiability model theory solver and limit model detection. The method comprises the following steps of: taking a hardware description language Chisel and a circuit specification described by extended track counting logic as input, reading the extended track counting logic through a preprocessing module, and performing logic synthesis and SMT (Surface Mount Technology) coding on a Chisel circuit; and the model detection rear-end module verifies the circuit specification based on SMT coding in combination with an STE algorithm and a BMC algorithm. According to the method, the respective advantages of the BMC and the STE are combined to enhance the solving capability of the symbol track value, the efficiency and the reliability of model detection are improved, meanwhile, the standard description capability of the STE is expanded on the Chisel level, and the verification process is greatly simplified.

The invention relates to the technical field of artificial intelligence and evolutionary computing, and particularly discloses a memory annual ring learning-based evolutionary method, which comprises the following steps of: locking elite groups, extracting core declusters, coding task constraint fingerprints, backtracking efficient operators, drawing performance operator portraits, constructing memory annual ring units and storing the memory annual ring units in a memory annual ring library. When facing a new task, analyzing and generating a to-be-matched task fingerprint, matching a historical memory annual ring unit, extracting a core declustering and efficiency operator portrait, remodeling a population basis, generating a dynamic operator weight table, and executing dual-channel collaborative guide evolution; according to the method, through collaborative optimization of the solution space and the strategy space, the convergence speed and the solving performance of the algorithm in a new environment are remarkably improved, conversion from blind exploration to experience guidance is achieved, and the challenges of knowledge migration and experience reuse in dynamic change or staged tasks of a traditional evolutionary algorithm are solved.

The utility model discloses a hole-free shear wall inner wall detachable teaching model, which comprises two vertical plates, a first transverse plate and a second transverse plate, the first transverse plate and the second transverse plate are detachably assembled at the two ends of the two vertical plates respectively, a horizontal overhanging steel bar penetrates through the two vertical plates, a vertical overhanging steel bar penetrates through the first transverse plate, and the second transverse plate penetrates through the second transverse plate. And one end part of the vertical outwards-extending reinforcing steel bar is positioned in the second transverse plate. According to the utility model, visual teaching is realized, so that students can intuitively understand the arrangement and connection mode of the steel bars in the shear wall; the practical operation capability is improved, and the operation steps in the actual construction process are simulated by disassembling and reassembling the steel bars in the model; and the error analysis and solution capability is improved.

This application discloses a method, apparatus, and equipment for optimizing the frequency conversion of fans in a multi-level station ventilation system. The method includes: acquiring a ventilation network model of the multi-level station ventilation system; calculating airflow distribution in the ventilation network model based on a ventilation network solution method, and adjusting the ventilation network model based on the airflow distribution calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions; generating at least one candidate fan frequency conversion control scheme based on the underground ventilation airflow demand and the corresponding set optimization model of the multi-level station ventilation system; and determining the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the underground ventilation airflow demand; wherein the set optimization model is a multi-objective mixed integer linear programming model. This enables intelligent frequency conversion control of the multi-level station ventilation system and significantly improves the solution performance of the fan frequency conversion control scheme.

The embodiment of the invention discloses an AI-based question interaction method and electronic equipment. The method comprises the following steps: receiving a question proposed by a user; understanding the problem through a target agent Agent and outputting a processing result, wherein the target agent comprises a planner, an inference device and an actuator; the target Agent is used for performing the following processing: with solving the problem as a target, performing task decomposition through the planner, and generating an optimal task for solving the problem; reasoning at least two actions required to be executed for completing the preferred task through the reasoner, wherein the actions comprise a result action and a non-result action; the at least two actions are executed through the executor, and in the process of executing the non-result action, the partially completed stage completion result corresponding to the result action is returned to the user. Through the embodiment of the invention, the user question can be more intelligently answered, and the waiting time of the user is shortened.

The invention discloses a unit commitment acceleration method and system based on adaptive calling neighborhood search and a medium. The method comprises the following steps: acquiring unit basic data in a power system; constructing a unit combinatorial optimization model based on the obtained unit basic data in the power system; when the unit commitment optimization model to be solved has the characteristic that the relaxation solution of the problem root node and the optimal solution of the problem meet constraint conditions, calling a relaxation enhanced neighborhood search algorithm; and searching a feasible solution based on a relaxation enhanced neighborhood search algorithm, and performing accelerated optimization on a unit combination problem based on the feasible solution to realize optimal scheduling of the power system. According to the method, the RENS is adaptively called, related nodes are fixedly calculated, and particularly expensive cutting planes are forbidden to be calculated when the RENS is used, so that the solving performance of the unit combination problem can be better improved, and stable operation of a power grid system is facilitated.

This invention provides a method for the continuous evolution of code agents based on structured memory. Addressing the problem that existing code agents cannot adapt to complex repository-level tasks, this invention proposes a human-machine co-evolution framework. The method mainly includes three stages: First, the memory construction stage, which uses a large language model to extract historical code submission records into standardized structured memories and constructs a vector database; Second, the dynamic execution and self-correction stage, where, when handling new tasks, a two-stage retrieval mechanism is used to obtain the most relevant historical experience to the current problem, and a refined sub-agent is introduced to generate test cases and verification lists, providing real-time guidance and correction to the main agent's generation results; Third, the experience internalization stage, where manually verified task solutions are again converted into structured memories and stored in the database. This invention, by constructing a closed-loop evolution mechanism, significantly improves the accuracy and automation level of code generation and defect repair.

The invention provides a multi-unmanned aerial vehicle task allocation system based on a two-stage multi-population wolf pack algorithm, and the system comprises an initialization module which defines the position of an individual wolf in the two-stage multi-population wolf pack algorithm as a multi-unmanned aerial vehicle task allocation scheme, and divides a wolf pack into a plurality of sub-populations, including a common group and a leader group; the wandering module is used for re-ordering task numbers by adopting different wandering frequency calculation methods for the common group and the leader group for wolf detection; the calling module is used for learning the scheme of the current first wolf at different convergence speeds by the common group and the leader group; according to the purse module, except the head wolf, the individual wolves of the common group and the leader group adopt different threshold values to carry out variation or learn the scheme of the low-fitness individual wolves; the individual wolf reconstruction module is used for reconstructing by adopting a roulette mechanism; and the sub-population adjustment module is used for starting a multi-population mechanism after reconstruction is completed, and reforming a leader group. The wolf pack algorithm is improved through multiple mechanisms, and the solving efficiency and accuracy are improved.

This invention relates to the field of computational electromagnetics, specifically to an antenna layout modeling and analysis method based on coupled rays and current basis. The method includes: defining electrically small and electrically large regions of the electrically large carrier antenna layout target and generating mesh data; assembling the system matrix within the electrically small regions; accurately aggregating all illuminated areas within the electrically large regions using backward ray tracing technology; establishing a cyclic iterative mode for the electrically small and electrically large outer surface currents; obtaining the overall stable current distribution on the outer surface based on the residual convergence results of the currents in the electrically small regions; and finally solving for the antenna layout performance characteristics affected by the coupled electrically large carrier currents. This invention effectively improves the accuracy and efficiency problems of the traditional integral-ray hybrid method, and enhances the design efficiency of electrically large carrier antenna layouts.

The present disclosure provides an optimization method and system for joint replenishment and distribution, belonging to the technical field of joint replenishment and distribution; wherein the optimization method comprises: after obtaining a preliminary update population corresponding to the current iterative processing in the exploration stage or development stage of an arithmetic optimization algorithm, creating a newly introduced population corresponding to the current iterative processing, and then selecting half of the individuals with the largest fitness from the preliminary update population and the newly introduced population as the final update population corresponding to the current iterative processing, and using them as the original population for the next iterative processing to perform the next iterative processing; by introducing new individuals in each iterative processing process, the global optimization capability can be improved, thereby improving the solution effect of the joint replenishment and distribution problem.

The utility model discloses an electric power system whole process operation control experiment platform comprising an electric power system power plant operation control experiment system, an electric power transmission line operation control and protection experiment system, a transformer station experiment system, a factory power supply and power distribution operation control and protection experiment system and an electric power system whole process operation control system. The power system power plant operation control experiment system comprises a generator set and a power plant control cabinet, the power plant control cabinet is connected with the generator set, the generator set is connected with the power transmission line operation control and protection experiment system, and the power transmission line operation control and protection experiment system is connected with the transformer substation experiment system. The substation experiment system is connected with the factory power supply and power distribution operation control and protection experiment system, has an integrated function of power generation, power transmission, power distribution and power utilization, and is more reasonable and advanced in software and hardware design, so that students can perform whole-process experiment operation of the power system on the same platform and know operation and control of the power system better; and the safety and stability of the experiment process are ensured.

The invention relates to the field of multi-energy optimization scheduling, in particular to a rural comprehensive energy system multi-objective optimization method considering carbon emission cost and # imgabs0 # efficiency, which comprises the following steps: S1, establishing a rural comprehensive energy system; s2, establishing an electric energy supply model of a solar photo-thermal system, a gas energy supply model of a biomass pyrolysis gasification power generation system and a thermal energy supply model of a biomass biogas thermoelectric system; s3, constructing an optimization solution model of the rural comprehensive energy system; and S4, an NSGA-II algorithm is adopted to solve a Pareto solution set of the optimization solving model, the Pareto solution set comprises a plurality of parameters, and an elite gene retention strategy and rapid non-dominated sorting are introduced in the population updating process of the NSGA-II algorithm to improve the solving ability of the NSGA-II algorithm. According to the method, the technical problem that the rural comprehensive energy system optimization method mainly based on a single target cannot effectively consider the requirements of low carbon, high efficiency and economical efficiency is solved by constructing the multi-target rural comprehensive energy system optimization solution model.

The application discloses a reinforcement learning driven heuristic solving method for multi-ship co-dock maintenance scheduling, and three global search operators are designed for the multi-ship co-dock maintenance scheduling problem. One is a ship crossover operator, which is used to adjust and exchange the maintenance order of the ship. The second is a maintenance task destruction operator, which breaks the original task structure by extracting part of the task from the current maintenance task sequence, providing a larger optimization space for subsequent search. The third is a maintenance task recombination operator, which inserts the extracted maintenance task into the existing maintenance task list to form a new maintenance task order, thereby improving the possibility of obtaining a better scheduling scheme. The execution process of the global search operator is adaptively guided, and a more suitable search operation is dynamically selected according to the state information in the solving process, so as to reduce the invalid search overhead in the meta-heuristic algorithm, improve the utilization efficiency of the computing resources, and further improve the solving efficiency and solving effect of the multi-ship co-dock maintenance scheduling problem.

The embodiment of the present application provides a multi-party multi-objective optimization method for unmanned aerial vehicle path planning, including: obtaining an initial population corresponding to a multi-objective decision function of a planned path of the unmanned aerial vehicle, and performing non-dominated sorting on the individuals to be optimized in the initial population to obtain the non-dominated levels of the individuals to be optimized; sorting according to the non-dominated levels, activating the individuals to be optimized with higher non-dominated sorting using a multi-party adaptive activation operator to obtain a set of activated optimized individuals; calculating the crowding distance information of the activated optimized individuals, and obtaining a set of cloned optimized individuals according to the crowding distance information and the non-dominated levels; generating offspring optimized individuals according to the cloned optimized individuals using a multi-party adaptive crossover operator algorithm; merging the offspring optimized individuals with the initial population to obtain a candidate population; sorting the candidate population according to the selection operator algorithm and the crowding distance information, and selecting the target optimized individuals to participate in the cycle until the maximum number of cycles is reached as the optimization scheme of the target decision function.