40 results about "Static optimization" patented technology

The invention discloses a thermal power plant whole-plant collaborative optimization system and method based on digital twin and AI algorithms, and belongs to the field of thermal power plant optimization control. The invention discloses a thermal power plant whole-plant collaborative optimization system and method based on digital twinborn and AI algorithms. The system comprises a data fusion processing module, a digital twinborn body construction module, a collaborative optimization and decision module, a strategy decomposition and execution module and an online learning and updating module. According to the method, the problems that the existing thermal power plant optimization control lacks global collaboration and is difficult to adapt to dynamic complex working conditions, and online self-evolution of a model and a strategy cannot be realized are solved; and a deep reinforcement learning algorithm is utilized to carry out multi-target collaborative optimization on the whole plant level, so that a global optimal control strategy which comprehensively considers the operation cost, the energy efficiency, the equipment service life and the environmental protection constraint can be dynamically generated, and the limitation of traditional decentralized control and static optimization is effectively overcome.

The invention discloses a real-time SLMP fuel cell hybrid electric vehicle energy management method. The method comprises the following steps: firstly, collecting vehicle operation data in real time, constructing an operation state vector containing kinetic parameters, and initializing a state transition matrix as agent action input; on the basis, the speed and the acceleration are predicted through a self-learning Markov predictor, and dynamic self-adaptive power distribution of the fuel cell and the power cell is achieved in combination with a multi-objective optimization function and power distribution constraints. The method passes hardware-in-the-loop (HIL) verification, aims at reducing hydrogen consumption and improving efficiency, and exits if two times of circulation are not passed. Compared with a traditional method based on rules or static optimization, the method innovatively embeds a self-learning mechanism, has the advantages of being high in adaptability, high in prediction precision and good in generalization performance, and is suitable for energy management optimization under complex working conditions.

The invention discloses a site-level CCUS dynamic source-sink matching optimization method for an oil field enterprise, relates to the technical field of large-scale deployment of carbon capture, utilization and storage of the oil field enterprise, and particularly relates to the site-level CCUS dynamic source-sink matching optimization method for the oil field enterprise. Comprising the following steps: integrating a carbon source end full life cycle technical economy evaluation system and a storage target area'geology-potential-economy 'three-dimensional grading model to form a dynamic database; on the basis of the dynamic database, a mixed integer linear programming model fusing source sink dynamic priority coefficients is constructed, three scenes of cost minimization, oil displacement income maximization and carbon sink subsidy excitation are set, and a constraint system is coupled; according to the method, carbon source technology economic evaluation and storage target area three-dimensional grading are fused, the specific injection-production cycle, policy incentive and pipe network constraint of an oil field are converted into time-varying weight coefficients, traditional static optimization limitation is broken through, and site-level source-sink dynamic accurate matching is achieved.

The invention provides a heat supply digital twin modeling method and system based on a U3D engine and a medium. The method comprises the steps that a historical static data set and a historical dynamic data set are obtained for preprocessing, a historical static optimized data set and a historical dynamic optimized data set are obtained, a U3D engine is combined with a preset parameterized component library to conduct heat supply digital twinning model construction, initialization is conducted based on the historical static data set, and a heat supply digital twinning model is obtained. Obtaining an initial heat supply digital twinborn model, generating an LOD model set with different precisions through an LOD rendering method, inputting the historical dynamic optimization data set into the initial heat supply digital twinborn model for processing, obtaining a simulation data set and equipment operation state evaluation data, and evaluating the qualified state of the model through threshold comparison; according to the method, data security fusion is realized through the block chain, the hydraulic and thermal coupling model is constructed by using the U3D engine, and predictive maintenance and multi-objective optimization are realized through an intelligent algorithm, so that intelligent construction of the heat supply word twinborn model is realized.

The invention provides a reinforcement learning dynamic pricing method based on game feedback, and relates to the technical field of online data market dynamic pricing. Through combination of leader-follower strategy interaction of the Stackelberg game and the exploration-utilization balance principle of the dobby machine, the problems of price adjustment lag, large income fluctuation and insufficient strategy stability in a non-stable environment are solved. By introducing a data freshness evaluation mechanism, data weight is dynamically adjusted by integrating data timeliness attenuation and market correlation analysis, it is ensured that data input into a model always has high real-time performance and strong correlation, and the problem of decision lag caused by static data input in a comparison scheme is avoided; the exploration-utilization balance principle and the reinforcement learning model of the dobby machine are fused, the exploration rate is dynamically adjusted, a historical optimal strategy can be fully utilized in a non-stationary environment, a potential better scheme can be explored, and the static optimization bottleneck that a game model is limited to a preset objective function in a comparison scheme is broken through.

The invention discloses a power industry cue word optimization method and system, and the method comprises the steps: carrying out the static structured optimization of a non-standardized cue word inputted by a user, obtaining a structured cue word which accords with the AI model understanding logic and the power industry specification, and storing a data record generated by the static structured optimization into a static optimization result temporary storage library; constructing a reinforcement learning model, carrying out offline training and online iteration, carrying out secondary optimization on the structured cue word by utilizing the trained reinforcement learning model, and inputting an optimization result into an AI model; and storing an output result of the AI model, user feedback, a data record generated by static structured optimization of the cue word and data generated by secondary optimization structured cue word of the reinforcement learning model into a cue word optimization case library. According to the method, the accuracy, the reliability and the adaptivity of the cue word generation result are improved, the application of a large model in the power industry is further promoted, and powerful technical support is provided for intelligent transformation of the power industry.

The invention belongs to the field of wireless communication, and particularly relates to a wireless signal multi-dimensional array coding method and system based on dynamic rank optimization. The method comprises the following steps: detecting state information of a wireless channel, predicting state change of the channel by using a pre-trained deep learning model, and generating a dynamic optimization multiplier; adjusting a preset static optimization multiplier by using the dynamic optimization multiplier, constructing a target function for jointly optimizing the energy efficiency and the spectrum efficiency, and determining an optimal transmission rank by solving the target function; comparing the optimal transmission rank with the current transmission rank to determine a rank-up or rank-down operation; and reconstructing a transmission signal of the wireless channel according to a result of the rank increasing or decreasing operation. According to the method, the optimization target is dynamically adjusted by predicting the channel change, and the asymmetric ascending and descending rank algorithm is adopted, so that the adaptive transmission rank optimization in a complex time-varying channel environment is realized, and the communication performance is remarkably improved.

The invention belongs to the technical field of artificial intelligence, and relates to a dynamic controllability management method and system for long-term evolution of a large model, and the method comprises the steps: S1, obtaining the current state information of the large model; s2, calculating a controllability score of the large model based on the output distribution entropy, the distribution variable quantity and the logits drift of the large model, judging whether the current state of the large model deviates from a controllability critical zone or not based on the controllability score, if the current state deviates from the controllability critical zone, entering S3, and otherwise, directly entering S4; s3, actively correcting the offset behavior of the large model in the training process through a controllable calibration head, so that the current state of the large model does not deviate from the controllable critical zone; and S4, dynamically adjusting a training strategy of the large model based on the controllability score and the task category. According to the method, the large model has the capabilities of long-term plasticity maintenance, continuous performance improvement and rapid adaptation to new tasks, and the fundamental upgrade from a static optimization model to a self-evolution model system is realized.

The invention provides an aggregation subject market transaction matching method for multi-dimensional target constraint, and relates to the technical field of resource allocation optimization, and the method comprises the steps: collecting transaction participation data of a plurality of aggregation subjects, carrying out the feature extraction of each transaction participation data, and obtaining an aggregation subject feature set; constructing a multi-dimensional target optimization model by taking cost, income and stability as multiple targets based on the aggregated main body feature set, and introducing output constraints, time sequence constraints and market rule constraints to form a target constraint matrix; and under the constraint of the target constraint matrix, carrying out iterative solution on the multi-dimensional target optimization model by adopting a multi-target optimization algorithm, and generating an optimal transaction matching scheme of each aggregation main body. Through the method and the device, the technical problem of poor feasibility of a transaction matching scheme caused by difficulty in processing complex multi-target conflicts due to adoption of a simplified static optimization model in the prior art is solved, and the feasibility of the aggregation subject market transaction matching scheme is improved through multi-dimensional target optimization.

The invention discloses a spanning frame structure size optimization method and system based on a firefly algorithm, and relates to the technical field of electric power engineering construction, and the method comprises the steps: firstly building a parameterized finite element model, implementing normalized hierarchical loading, obtaining a tangent stiffness matrix evolved along with a load, calculating the modal participation degree of a component, and constructing a dynamic participation degree track; and according to the quantitative trajectory smoothing index, the modal dominance and the key component suppression index, constructing a brightness function with coupled rigidity and trajectory features. The firefly attraction degree is corrected by means of trajectory similarity based on modal energy fingerprints, and nonlinear search is executed through a characteristic resonance mechanism. The method solves the problem that traditional static optimization neglects geometric nonlinear evolution to cause modal abrupt change, and the dynamic stability and evolution robustness of the spanning frame in the full stress process are remarkably improved while the structural light weight is achieved.

This invention discloses a method and system for optimizing the relocation and allocation of large gantry cranes based on SPMT (Specialized Spatial Management Machine), relating to the field of SPMT vehicle allocation technology. The method includes: establishing an iterative optimization framework connecting upper, middle, and lower layers; feeding back the dynamic risk value output by the lower-layer risk assessment model to the upper-layer optimization model to generate a new candidate configuration set; subsequently, performing simulation prediction by the middle-layer model and risk assessment by the lower-layer model on the new candidate configuration set again, until the generated candidate configuration set meets the preset convergence conditions. This achieves integrated dynamic and static optimization of the SPMT grouping scheme, overcoming the shortcomings of traditional empirical methods that rely on static estimation and cannot assess dynamic risks. It can significantly reduce vehicle configuration redundancy and operating costs while ensuring absolute safety, ultimately outputting a safe, reliable, and economically optimal relocation and allocation scheme.

The invention relates to the technical field of artificial intelligence, in particular to an application program performance optimization method and device, equipment, a medium and a product. The method comprises the steps that source codes of an application program are obtained, and a plurality of optimization strategies are determined; obtaining a static optimization factor based on the optimization effect of the optimization strategy; analyzing the running log of the application program to obtain corresponding system index information; comparing the system index information with a preset index to obtain a dynamic optimization factor; utilizing a deep learning model to confirm a performance score vector of the application program; and determining a performance optimization scheme based on the performance score vector. The static performance of the application program can be analyzed in real time by performing exception analysis on the source code; by analyzing the running log during running of the application program, the suspicious behavior during running of the application program and the influence degree on the performance can be analyzed, the comprehensive performance conditions of program code development and real-time running are considered, and the method is more flexible and intelligent.

The present application relates to the field of prefabricated component transportation scheduling of assembly type building, and particularly discloses a dynamic transportation scheduling optimization method for prefabricated components with interference of assembly type building, which designs an improved NSGA-II algorithm combining a simulated annealing mechanism and an adaptive strategy; for the multi-objective optimization problem of dynamic transportation scheduling of prefabricated components with interference, the present application combs three types of interference events, i.e., emergency orders, order modifications and vehicle transportation time changes, which occur frequently in the transportation process, and formulates dynamic rescheduling strategies for different disturbance types; in combination with a rolling horizon optimization method, the continuous dynamic scheduling problem is converted into a series of static sub-problems, and then static optimization schemes are adopted in each static process to realize dynamic optimization.

The application discloses a large language model reasoning optimization method and device, electronic equipment and a storage medium, and is used for solving the problem that the huge parameter quantity in the prior art leads to large model reasoning demand for computing and storage resources and how to reduce the huge resources required during large model reasoning. The method comprises the following steps: acquiring configuration parameter data and computing resource data of a large language model during model reasoning in real time; performing static optimization analysis on the configuration parameter data and outputting configuration analysis results; performing automatic tuning of the configuration parameters based on reinforcement learning in combination with the configuration parameter data and the configuration analysis results to obtain optimal parameter configuration; performing dynamic optimization processing on the computing resource data based on an unloading strategy to obtain optimal resource configuration; and dynamically optimizing parameter configuration and resource allocation of the large language model during model reasoning according to the optimal parameter configuration and the optimal resource configuration.

The application discloses an automatic theorem proving method based on neural network guidance and quantum optimization, comprising the following steps: extracting the logical structure features of a propositional logic task, and predicting the applicability weight of natural deduction rules by using a preconfigured neural network; performing a forward reasoning iteration process, dynamically identifying potential intermediate conclusions to determine a variable space, and constructing a quadratic unconstrained binary optimization (QUBO) model according to the variable space; in the construction process, the applicability weight is used to adjust the penalty coefficient of the rule constraint term, and the energy topography of the solution space is reshaped; according to the problem size, the computing resources are adaptively scheduled, the model is mapped to a coherent Ising machine or a classical simulation backend for solving, and the proof path is reconstructed through double verification. The application effectively solves the problems that the traditional method is blind in rule selection and the static optimization model is difficult to handle dynamic multi-step reasoning, and the efficiency and scalability of the proof are improved.

The invention discloses a parameterized compilation optimization method and system based on multi-stage intermediate representation, belongs to the technical field of computer compilation, and aims to solve the technical problem of how to unify parameterized optimization strategy logic and program core representation to solve the separation of static optimization and dynamic adaptation. Comprising the steps that a user-defined MMIR dialect is defined, and the dialect comprises a strategy.space operation, a strategy.path operation and a strategy.guard operation; the target operation is moved into an area of the newly created strategy.space operation, and a plurality of strategy.path operations are generated; the method comprises the following steps of: searching strategy.guard, evaluating a region of each strategy.guard, generating a reasoning result, and carrying out IR transformation based on the reasoning result.

This invention discloses a method for optimizing the operation of water-cooled walls in pulverized coal boilers, belonging to the field of thermal power generation equipment control technology. The method includes: performing a four-level hierarchical selection of boiler operating parameters (thermal inertia time-scale elimination, steady-state sensitivity coarse screening, XGBoost-SHAP fine screening, and controllability verification), dividing the boiler into a predicted input set and a control suggestion set; constructing an encoder-decoder deep network based on the predicted input set to generate multi-region future wall temperature prediction sequences; searching a historical database of excellent operating conditions with the current load as a hard constraint, selecting template operating conditions based on wall temperature performance in risk areas, and extracting their controllable parameters as optimization guidance. This invention solves the problem of mismatch between static optimization parameters and the actual execution capabilities of power plants in existing technologies by reusing historical cases to replace global mathematical optimization, achieving water-cooled wall operation optimization that conforms to operating experience and can be safely executed.

The application discloses a kind of thermal power plant double-layer cost decision optimization method and device, belong to thermal power plant cost optimization field.Therein method includes: establishing auxiliary database;According to auxiliary database design minimum coal purchase / stock cost function, calculate the lowest standard unit price of entering furnace, construct double-layer cost decision optimization model;According to the characteristics of double-layer cost decision optimization model, by McCormick-KKT is converted into single-layer model, by MATLAB is solved, obtains the optimal cost of base model;According to the optimal cost of base model, determine the expected cost, build IGDT model into different risk attitudes, to obtain risk decision optimization and robustness decision optimization;According to risk decision optimization and robustness decision optimization, calculate risk robustness-cost curve.The application overcomes the problems existing in the prior art, such as the cost optimization technology being difficult to perceive future coal spot market fluctuations, being mostly local static optimization, and having a narrow decision-making perspective.

The invention discloses a power system annual balance boundary optimization method and system, and belongs to the technical field of power system planning and operation optimization. Aiming at the problems of insufficient static optimization, insufficient risk identification and lack of dynamic feedback in the prior art, the method comprises the following steps: constructing an initial boundary set and an input scene set; the EENS and the EID are simulated and quantified through random production, and regional-month risk distribution is formed; calculating a supply-demand index and a resource adjustment matching item, and outputting a feedback factor; a feedback factor is introduced into the MILP framework to jointly optimize the three types of boundary plans, convergence is judged through a sliding window method, and a final scheme is output. According to the method, dynamic adaptive optimization of the annual balance boundary is realized, the system operation reliability, the resource allocation rationality and the complex working condition adaptability are remarkably improved, and the method is suitable for power system annual planning under high-proportion new energy access.

The invention discloses a building envelope reinforcement learning optimization method fusing multi-modal data, and belongs to the technical field of building energy-saving design, and the method comprises the steps: collecting the multi-modal data, carrying out the preprocessing, constructing a multi-modal data set, carrying out the feature extraction of the multi-modal data, obtaining multi-modal features, constructing a multi-modal feature library, and carrying out the optimization of building envelope reinforcement learning. And obtaining a dynamic multi-modal feature library by utilizing a time sequence sliding window mechanism based on the multi-modal feature library, constructing a reinforcement learning intelligent optimization model, and obtaining an optimal building envelope parameter combination by utilizing the reinforcement learning intelligent optimization model based on multi-modal data. According to the method, static optimization is effectively upgraded to a dynamic adaptive scene, manual trial calculation is converted to data-driven autonomous optimization, universal optimization across building types and climate zones is achieved, and the problems that an existing building envelope optimization technology is single in data dimension, static in optimization mode, depends on artificial experience and is insufficient in universality are solved.

The application discloses a cell culture multi-parameter collaborative AI dynamic optimization system, and relates to the technical field of cell culture. The system comprises a collector configured to acquire growth cycle data of multiple groups of historical cell cultures; and a processor configured to acquire a data feature group corresponding to each group of cells based on physiological parameters of each group of historical cell cultures. The application breaks through the limitation of traditional static optimization methods that cannot adapt to the dynamic time-varying process of cells by precisely anchoring cell metabolic stress, co-stimulation coupling and functional balance state. At the same time, based on eigenvalue multidimensional clustering, the law of high-quality cell populations is mined, and the particle swarm algorithm is improved to obtain a fitness value capable of representing the pros and cons of the cell culture state, realize dynamic coupling analysis of culture parameters and cell metabolism and phenotype state, and overcome the regulation lag problem caused by the fact that traditional optimization methods cannot analyze the dynamic coupling of metabolism and culture parameters and are difficult to track the cell demand within the window.

The invention provides a Transform pipeline collaborative acceleration architecture and an acceleration method based on adaptive correlation. The acceleration architecture comprises a top logic controller, a core processing module cluster, a nonlinear arithmetic logic module and a storage interaction module. The invention aims to solve the problems of insufficient fine granularity exploration of structural redundancy, mismatching between dynamic redundancy and static optimization, mismatching between calculation granularity and a hardware execution model and the like in the reasoning process of the conventional Transform accelerator architecture. And the calculation load is compressed as much as possible by utilizing the reasoning correlation so as to reduce the calculation load and memory occupation as much as possible, and efficient acceleration of a large-scale model is realized.

The application provides a big data driven dust-free drag chain antistatic optimization design method, relates to the technical field of computer aided design, and realizes self-power supply of an electrostatic dissipation system by utilizing mechanical vibration energy of the drag chain itself, avoids dependence on an external power supply, and improves independence and flexibility of the system; based on closed loop control of real-time monitoring data, dynamic adjustment of antistatic performance is realized, the industrial environment is adapted, and electrostatic dissipation efficiency is improved; energy collection, storage and consumption are optimized by an energy storage management unit, and energy utilization efficiency is improved.

The invention provides a path retrieval system and method for vehicle recovery, and relates to the technical field of intelligent logistics, and the method comprises the steps: constructing a recovery network topology, building a path cost evaluation function, carrying out the heuristic path retrieval of the recovery network topology, obtaining a candidate path set, and carrying out the dynamic optimization of the non-cooperative game weight of the candidate path set. An equilibrium weight ratio is obtained; cost weight feedback updating is carried out on the path cost evaluation function based on an equilibrium weight ratio, path evolution is carried out on the candidate path set, and an evolution path set is obtained; performing node state exploration and cost updating decision on the evolution candidate path set to obtain the disposal cost of each path in the evolution candidate path set; and determining the recovery path of the target vehicle from the evolution path set according to the disposal cost, thereby realizing game equilibrium optimization based on multi-target weight and exploration decision of uncertain node state, and further performing global static optimization and local dynamic adaptation on the vehicle recovery path.

The invention belongs to the technical field of aerospace, underwater explosion and topological optimization, and particularly relates to an anti-explosion impact structure optimization method considering material damage, which comprises the following steps of: firstly, acquiring a dynamic response field of a structure through accurate nonlinear dynamic response analysis, judging a failure block and failure time of the structure according to the dynamic response field, and finishing displacement field repair; decoupling the response of the nonlinear dynamics in each time step into a series of linear statics responses; structural sensitivity calculation is carried out in a linear statics framework, and optimization design is realized through flexibility minimization under a statics equivalent working condition; and through repeated iteration and execution of accurate nonlinear dynamic response analysis and the equivalent statics optimization process, the optimization design of the anti-explosion impact structure is finally completed. According to the invention, a user can be helped to quickly explore and obtain a novel structure with high rigidity, light weight and excellent impact resistance in a wide design space.

The invention discloses a dynamic scheduling and decision optimization system for feeding and discharging of a machine tool, and belongs to the technical field of production scheduling management, and the system comprises a solution set fingerprint construction module which is used for constructing a solution set fingerprint for an obtained Pareto solution set; the path prediction model training module is used for training a solution set structure path prediction model based on the historical solution set fingerprint sequence; the scheduling anchoring solution determining module is used for determining a scheduling anchoring solution of the current period according to the current Pareto solution set and a future solution set form output by the prediction model; and the anti-disturbance test module is used for forming a resource allocation cluster based on the scheduling anchoring solution. According to the method, continuous static optimization of dynamic scheduling can be converted into smooth evolution decision by constructing a solution set fingerprint and training a solution set structure path prediction model, resource waste caused by frequent disturbance recalculation of a traditional algorithm is avoided, meanwhile, production line oscillation and pause caused by failure or waiting of an old scheme in a recalculation window period are eliminated, and the production efficiency is improved. And the scheduling continuity and stability in a dynamic environment are improved.

The present application relates to the technical field of industrial internet data processing, in particular to an industrial equipment data analysis parameter adaptive configuration method based on operating condition characteristics; the method models the optimization process as a multi-stage state sequence decision process; the core is to construct a data analysis model processing state characteristic vector, and input the same into a parameter configuration module to calculate and select the candidate configuration parameter with the greatest value; after the parameter configuration is executed, the system dynamically corrects the parameter configuration module according to the deviation of the actual and expected processing state characteristics; the present application converts discrete static optimization into continuous dynamic decision sequence, so that the data processing control parameter can be self-adaptively adjusted according to the real-time state of the model, and the problem of fixed processing mode is solved.

The invention relates to the technical field of industrial internet data processing, in particular to an industrial equipment data analysis parameter self-adaptive configuration method based on operation condition characteristics. According to the method, an optimization process is modeled as a multi-stage state sequence decision process; the core of the method is to construct a processing state feature vector of a data analysis model and input the processing state feature vector into a parameter configuration module to calculate and select a candidate configuration parameter with the maximum value; after parameter configuration is executed, the system dynamically corrects the parameter configuration module according to the deviation between actual and expected processing state characteristics; according to the method, discrete static optimization is converted into a continuous dynamic decision sequence, so that data processing control parameters can be adaptively adjusted according to the real-time state of the model, and the problem that the processing mode is fixed is solved.

This invention belongs to the field of smart water conservancy and emergency management technology, and particularly relates to a flood control emergency decision-making method, system, and storage medium for flood storage and detention areas. The emergency decision-making method includes the following steps: constructing a static optimization model for flood control material deployment that integrates multi-source data; establishing a dynamic response mechanism for generating flood control material allocation schemes; establishing a scientific deployment scheme for flood control materials that considers supply and demand; and outputting dynamic allocation schemes for flood control materials under different flood conditions, as well as dynamic allocation schemes for flood control materials corresponding to each second line of defense scheme, providing flood control material deployment data support for the formulation of deployment schemes for each second line of defense in flood storage and detention areas. This invention innovatively proposes a flood control emergency decision-making system and method for flood storage and detention areas based on multi-source data fusion and artificial intelligence. Through planning, simulation, static optimization → dynamic response, efficient emergency rescue → scientific scheduling of deployment schemes, it constructs a full-process intelligent decision-making system of static optimization, dynamic response, and supply and demand matching.

The invention discloses a three-phase load optimization and adjustment method, system and device based on load space-time characteristics and a medium, and the method comprises the steps: collecting transformer area load data, and carrying out the preprocessing of the data, and forming a first data set; constructing a load space-time characteristic model according to the first data set, dynamically setting a target threshold according to a time period weight, and dividing user clusters; a multi-target collaborative optimization model is constructed, and optimization boundaries are determined by taking weighted comprehensive benefit maximization as a target and combining power grid topology constraints; based on the multi-objective collaborative optimization model, iteratively solving an optimal adjustment scheme by adopting a first optimization algorithm; and carrying out simulation verification on the optimal adjustment scheme, triggering update at regular time, and carrying out early warning on a failure risk. The method solves the problems of poor static optimization adaptability, non-quantitative decision of multi-target conflict and low calculation efficiency in the prior art, can realize efficient collaborative optimization of the three-phase load of the transformer district, and is suitable for the scenes of urban and rural power distribution networks, industrial parks and the like.