226 results about "Combinatorial optimization" patented technology

The invention relates to the field of computer industry, and discloses an integrated computer software and hardware combined sales optimization system and method, and the method comprises the steps: extracting software and hardware sales records, customer transaction behaviors and version updating tracks, and constructing a fusion data set of heterogeneous sales data; performing association weight analysis on the fused data set, and extracting strategy sensitive points in software and hardware linkage transaction in combination with a customer behavior intention model and product life cycle characteristics; constructing a product combination feasible region in a specific sales window by adopting a combination optimization graph search algorithm based on the tag nested structure; mapping the candidate combined product list to a historical sales behavior map, and extracting dynamic feedback modes of a combined strategy under different sales paths by introducing a price elastic response model and market disturbance fluctuation characteristics; and carrying out iterative adjustment and node intervention on the strategy path based on the high-sensitivity area. The method has the advantage of improving the scientificity of sales strategy formulation.

The invention relates to the field of combinatorial optimization and artificial intelligence, and discloses a group logistics transportation scheduling method and system based on a role interaction graph neural network, and the method comprises the following steps: S1, dividing agent nodes and position nodes, and generating initial features; s2, iteratively updating node embedding by using a multi-channel attention mechanism of a graph neural network; s3, generating a delivery point distribution probability based on node embedding, and determining an initial distribution scheme; s4, local redistribution optimization is performed on the delivery points with low confidence distribution; and S5, performing parallel path planning on the optimal scheme, and outputting a result for reinforcement learning feedback. In the invention, through modeling of a graph neural network multi-channel attention mechanism on a complex interaction relationship and a synergistic effect of local redistribution optimization and parallel path planning, a second-level generation of a high-quality scheduling scheme is realized, cross-scale scene migration of the model is achieved, and group logistics transportation scheduling efficiency and robustness are improved.

The invention relates to the technical field of biological breeding, in particular to a rice hybrid combination optimization method based on an adaptive algorithm, and aims to solve the problems that existing rice breeding is poor in adaptability, multi-objective optimization is difficult and artificial experience is limited. The method is characterized in that a data integration module, a dynamic target module, a parent library module, a genetic prediction module, a self-adaptive optimization module (including a genetic algorithm and reinforcement learning), a simulation evaluation module and a recommendation feedback module are constructed, and precise prediction and dynamic optimization of hybrid combination are achieved. By adopting the scheme, the limitation of artificial experience is effectively overcome, the breeding decision is changed from static experience to dynamic data drive, and the breeding efficiency, accuracy and adaptability are remarkably improved.

The embodiment of the invention discloses a quantum computer, a method and a related device for solving a combinatorial optimization problem, and the quantum computer comprises a measurement and control all-in-one machine which is configured to generate a control signal based on a constraint condition, the constraint condition is used for representing the constraint when a plurality of elements are combined; the quantum chip comprises at least one group of quantum bits, and the number of the quantum bits is the same as that of the elements; and the interface is connected with the measurement and control all-in-one machine and the quantum chip and is configured to control the quantum chip based on the control signal so as to generate an optimal combination of elements represented by the ground state of the group of quantum bits. By adopting the embodiment of the invention, the requirement of quantum bits can be effectively reduced, the method is suitable for a real quantum computer, and a practical and feasible quantum circuit can be operated to solve a combinatorial optimization problem.

The invention discloses a quantum Isin model construction method for a security constraint unit commitment optimization problem, and relates to the field of quantum computation.The quantum Isin model construction method comprises the steps that a security constraint unit commitment optimization model is constructed, and parameters of a mixed integer programming problem are obtained; using a Benders decomposition method to decompose a mixed integer programming problem into a main problem and a sub-problem; substituting the optimal binary solution to solve the sub-problem to obtain a new cut plane and expand a cut plane set; constructing a compact high-dimensional quadratic function fitting cutting plane set; solving a positive semidefinite programming problem to obtain a high-dimensional quadratic function parameter; converting a quadratic unconstrained binary optimization model constructed based on a high-dimensional quadratic function into an Isin model; solving the Isin model to obtain a quantum bit state, and solving an optimal binary solution; and substituting the optimal binary solution into the above steps for iterative solution. According to the invention, the problem of huge consumption of quantum bit resources in the prior art is solved, especially the problem of difficulty in processing NP with complex constraints and more variables is solved.

The invention relates to the technical field of industrial compressed air system intelligent control, in particular to a digital air compression station intelligent control method and system based on AI self-learning, and the system comprises a data collection layer, an AI prediction layer, an optimization control layer and an execution layer. The data acquisition layer realizes accurate acquisition and preprocessing of multi-dimensional data through a three-stage high-precision sensor network and an edge computing node; the AI prediction layer adopts an XGBoost and LSTM mixed model to be combined with an online learning mechanism to realize accurate prediction of the gas consumption demand and the energy efficiency ratio within 1-24 hours; the optimization control layer completes air compressor operation combination optimization and pressure dynamic control based on an improved NSGA-II algorithm and self-adaptive PID adjustment; the execution layer realizes control instruction landing through frequency converter and cluster cooperative scheduling. Meanwhile, an equipment health degree monitoring module, a multi-source data fusion correction module and a waste heat-gas consumption collaborative optimization module are innovatively introduced, and the problems that a traditional air compression station is low in energy efficiency, poor in self-adaption, insufficient in prediction precision and lack of equipment management are solved.

A computing system including one or more classical binary computers coupled to one or more quantum computers. The computing system is configured to process the one or more computing tasks including at least one combinatorial optimization task using a Filtering Variational Quantum Eigensolver (F-VQE) algorithm implemented by using one or more Ansätze circuits and a cost function arrangement to generate one or more quantum circuits in the quantum computer. The computing system iteratively applies a filtering operator to a cost function arrangement to generate a corresponding filtered cost function arrangement that excludes energy states that exceed an energy threshold and uses the filtered cost function arrangement in the one or more quantum circuits to generate output results.

The invention discloses a magnesium alloy forging and pressing process parameter optimization method and system based on finite element analysis, and the method comprises the steps: collecting forging and pressing process parameters and test data of preset magnesium alloy, and carrying out the preprocessing of the forging and pressing process parameters and the test data; a forging and pressing temperature interval is obtained through the test data, a magnesium alloy finite element model is constructed based on the temperature-strain rate coupling effect, self-adaptive grids are divided, and boundary conditions are set; the forging and pressing process parameters are adjusted in the forging and pressing temperature interval, the magnesium alloy finite element model is adopted for analog simulation to obtain simulation data, and a quality evaluation function is constructed according to customer requirements and the simulation data; and a magnesium alloy forging and pressing process parameter optimization model is constructed according to the quality evaluation function, a magnesium alloy deformation mechanism is adopted to optimize the magnesium alloy forging and pressing process parameter optimization model, and a multi-objective optimization algorithm is adopted to carry out combination optimization on the second process parameters to obtain optimized combination parameters.

The invention discloses a rivet neglected loading detection method and system based on CAD digital-analog point cloud mapping, and the method comprises the steps: 1, carrying out the collection and preprocessing of an aircraft panel rivet image, and constructing a data set; 2, inputting an aircraft panel rivet image in the data set into the target detection model to obtain a rivet centroid coordinate; 3, performing coarse positioning and fine positioning on the rivet; 4, sending the fine positioning area to a detection head of the target detection model to obtain a predicted rivet neglected loading result, constructing a total loss function by using the real rivet neglected loading result and the predicted rivet neglected loading result, circulating the steps 2 to 4, and minimizing the total loss function until the total loss function is converged; and 5, detecting rivet neglected loading by using the target detection model and the semantic segmentation model on the equipment end to obtain a detection result. According to the method, deep fusion is carried out on point cloud geometric feature analysis and a combinatorial optimization method, a closed-loop coarse registration process without manual intervention is formed, and the workload of manual reference point selection is reduced.

The invention relates to an electric sanitation vehicle intelligent scheduling method and device and electronic equipment, and relates to the technical field of electric vehicle scheduling, abnormal data is eliminated through a multi-source data credibility verification mechanism, and a four-dimensional space-time tensor model fused with sanitation parameters is constructed to realize accurate extraction of dynamic features; breaking through the bottleneck of large-scale path combinatorial optimization based on a quantum-classical hybrid solver, and dynamically correcting an execution deviation by combining model prediction rolling optimization; and finally, synchronous scheduling of the path instruction and the charging resource is realized through vehicle-station-cloud cooperative control. Through dynamic weight calculation and a Byzantine fault-tolerant arbitration mechanism, abnormal data of the sensor are effectively eliminated, and multi-source information fusion is realized. According to the technology, the problem of path planning deviation caused by data distortion of a traditional scheduling system is solved, it is ensured that the input data of the space-time modeling and optimization model has high credibility, and therefore the reliability of the whole scheduling scheme is improved.

Provided is a quantum computing method for obtaining an optimal solution of a problem with multiple discrete variables, wherein the problem is represented by a cost function, the method comprising:—generating a graph structure from the cost function,—dividing the graph structure into at least two disjunct subgraph structures, wherein each subgraph structure comprises a subset of the multiple variables,—mapping each subgraph structure to a local cost function represented as local cost Hamiltonian,—determining, for each local cost Hamiltonian, all eigenstates corresponding to an energy below a predetermined cut off energy using a quantum processing device, wherein each variable of the subset of multiple variables is represented by a qubit of the quantum processing device,—recombining the determined eigenstates, and-approximating a ground state from the recombined eigenstates, wherein the ground state represents the optimal solution.

The invention provides an air compressor scheduling and energy consumption optimization method and system based on a cloud platform, and a medium, and the method comprises the steps: collecting and uploading the operation parameter information of an air compressor through a sensor disposed at an air compressor terminal, obtaining a load rate through the combination of a real-time predicted target load, and obtaining the energy consumption of the air compressor through the combination of a mapping relation between the load rate and a mode; determining an operation control mode of the frequency converter so as to achieve the effects of single-machine energy saving and operation stability improvement; an air compressor health index is output based on a preset health assessment model, a load task is dynamically allocated under a constraint condition by adopting a combinatorial optimization algorithm, the equipment wear condition is balanced and controlled, and the service life of key parts is prolonged; the actual energy efficiency is evaluated through the energy-saving achievement rate, incremental learning and parameter iteration of the load prediction model and the health evaluation model are triggered, closed-loop self-optimization is achieved, and the preset accuracy is improved.

The invention discloses an AI business process automatic generation method based on AI model combinatorial optimization, and the method comprises the following steps: constructing a multi-level AI capability decoupling and reconstruction module, and carrying out the bottom-up hierarchical modeling and top-down modular decoupling, dynamic mapping of an AI atomic power layer, an AI modular production capacity layer, an AI general capability layer and an AI application business layer is realized; an elastic AI capability combinatorial optimization module is constructed, AI capability combinatorial optimization oriented to three dimensions of data, features and models is carried out based on service quality requirements, and multiplexing, combination and arrangement of AI capabilities are realized through evolutionary computation optimization driven by an agent model; and an AI business process automatic generation module is constructed, an AI service containerization deployment scheme is generated and optimized based on data-driven process mining and a hyper-heuristic algorithm, and AI business process automatic generation is realized. According to the invention, the adaptability and execution efficiency of the AI technology in a complex scene can be improved.

The invention relates to a method for achieving feeding and discharging of special-shaped parts of a coating production line and a robot system, and belongs to the technical field of industrial robots, and the method for achieving feeding and discharging of the special-shaped parts of the coating production line comprises the steps that scene point cloud of the coated special-shaped parts in three-dimensional point cloud of a material box is obtained; converting the constructed three-dimensional model of the coated special-shaped part to obtain a model point cloud of the coated special-shaped part; performing coarse registration and fine registration on the scene point cloud and the model point cloud in sequence, planning a feeding and discharging motion track of the robot by adopting a 3-5-3 combined piecewise polynomial interpolation function so as to construct a robot joint space track model, and taking motion time as an optimization variable so as to construct a robot joint space track model; the comprehensive optimization of the loading and unloading time, impact and dexterity of the robot is taken as a target function, and the particle swarm whale combinatorial optimization algorithm is adopted to optimize the trajectory parameters of the robot joint space trajectory model so as to obtain the optimal loading and unloading trajectory of the robot, so that the loading and unloading takt time of the robot is shortened, and the production efficiency is improved.

The invention discloses a rock-fill dam deformation digital twin construction method based on generative AI, and the core is that a conditional denoising diffusion probability model is adopted, a conditional sampling mechanism is constructed through classifier-free guidance and continuous conditional vectors, and finite element simulation data, monitoring data and operation data are efficiently fused. And further combining a residual neural network ResNet-18 and a K-means clustering method to carry out finite element data unsupervised classification, introducing combinatorial optimization, and identifying a finite element data category which is most matched with monitoring data, so that the condition-guided deformation field is generated. The framework is applied and verified on the highest two-estuary rockfill dam (303 meters) in the current built world. The result shows that the rockfill dam deformation digital twinborn body construction method based on the generative AI can efficiently reconstruct rockfill dam deformation, has high precision and real-time performance, remarkably improves the global deformation thorough sensing ability of the rockfill dam, and provides key technical support for safe operation of the rockfill dam.

The invention provides a unit commitment optimization method based on solution space expansion and space-time diagram modeling. A space enhancement strategy and a diagram neural network modeling technology are innovatively combined. According to the method, disturbance optimization and diversity constraints are introduced, a training data set containing rich approximate optimal solutions is constructed, and the generalization ability of the model is remarkably improved. Meanwhile, the unit commitment problem is converted into a space-time dependency graph structure, the sequential relation and structural constraint between variables are effectively extracted through a graph neural network, and deep modeling and prediction of the optimization problem are achieved. Furthermore, by designing a double-order constraint repair mechanism, feasibility correction and fine adjustment are carried out on a prediction result, and the feasibility and scheduling precision of a solution are guaranteed. The finally constructed scheduling framework has good real-time performance and deployability, and can provide efficient and accurate combined scheduling decision support for a large-scale power system.

This invention discloses a reinforcement learning-based optimization method for the construction process of cable-stayed tension structures, comprising the following steps: S1, randomly sorting the forming process sequence of the cable-stayed structure multiple times to generate a sample space matrix; S2, constructing a combined optimization model for the forming process of the cable-stayed structure based on reinforcement learning, and inputting the sample space matrix into the combined optimization model; S3, determining the range of various hyperparameters in the training of the combined optimization model, and finding the optimal training hyperparameters using the controlled variable method; S4, solving the sample space matrix using the model with the optimal training hyperparameters, and outputting the optimal forming process sequence. This invention, based on the Q-learning algorithm, explores the trade-off between the steel brace installation sequence and key mechanical performance characteristics during the forming process of the cable-stayed structure. The optimized steel brace installation sequence can reduce the maximum steel brace stress and the maximum cable lifting force, providing convenience for the selection of tooling in the actual forming process.

The invention relates to the technical field of power system auxiliary services, in particular to a multi-agent participated user group dynamic combination and economic optimization method in power auxiliary services, which comprises the following steps of: constructing a multi-agent collaborative model of'upper-layer scheduling-middle-layer aggregation-lower-layer execution ', formulating an incentive price through a Stackelberg game between upper-layer ISO (International Standard Organization) and LAs (Local Assisted Services), and establishing a multi-agent collaborative model of'upper-layer scheduling-middle-layer aggregation-lower-layer execution'; the evolutionary game between the lower-layer LAs and the user realizes user strategy interaction; introducing maximum profit power reduction and power transfer potential indexes, and performing user group preliminary screening in combination with a multi-energy profit map; and outputting an optimal user group combination scheme through iterative solution and joint uncertainty analysis. The invention further provides an alternative scheme, and dynamic combination optimization is realized by utilizing the user potential portrait and a reinforcement learning mechanism. The method can effectively improve the economy, flexibility and robustness of the power auxiliary service, and gives consideration to the comfort of the user and the adjustment demands of the system.

This invention proposes a demand response resource combination optimization method that considers response reliability and main transformer load balancing. By constructing a demand response resource combination optimization model, the reliability of resource response and the load balancing of multiple main transformers are incorporated into the evaluation. A heuristic optimization algorithm is used to solve the problem, resulting in a demand response resource combination. The demand response resource combination optimization model includes: an objective function that minimizes the overall network demand response resource response cost; response reliability constraints of participating resources; load balancing constraints after the demand response resource is connected to the main transformer; and logical constraints representing whether the demand response resource is included. The heuristic optimization algorithm, combined with the objective function that minimizes the demand response cost, sequentially judges the satisfaction of response reliability constraints and main transformer load balancing constraints after a certain demand response resource is included in the response, and adjusts the combination of demand response resources to provide a feasible solution to the problem.

This application relates to a real-time power load forecasting method and system based on a hybrid model. The method includes: acquiring current cycle load data and combining it with historical load data to form a load sequence, and acquiring corresponding weather data; performing timestamp alignment, anomaly processing, and normalization on the load sequence and weather data to obtain a standardized input sequence; determining the order parameters of the linear forecasting model and the set of hyperparameters to be optimized, consisting of the network structure and training parameters of the nonlinear forecasting model, and optimizing them through a combined optimization algorithm to update the training configuration of the two models; outputting the first and second forecast sequences for the next cycle in the current cycle, respectively, and using the second forecast sequence as a trend term to compensate the first forecast sequence to obtain a fused forecast sequence; acquiring the fused forecast sequence for the current cycle from the previous cycle, constructing a residual sequence with the current cycle load data and determining the deviation term, calibrating the current cycle fused forecast sequence online, and outputting the calibrated load forecast result.

The invention relates to the field of quantum calculation and combinatorial optimization solution, and provides a measurement resource adaptive allocation and risk control recursive dimension reduction method and system based on a parameterized quantum circuit, and a medium. The classical controller issues a circuit control and read-out instruction to the quantum processing unit, executes calculation base projection read-out sampling, counts and estimates diagonal Pauli observable measurement and a tensor product term thereof, and generates point fixation and edge contraction candidate actions. Constructing a lower confidence boundary based on observation uncertainty and dynamically distributing sampling times; forming classical confidence in combination with the flipping cost, performing weighted fusion on the classical confidence and the quantum confidence according to uncertainty to obtain a score, and calculating the risk; selecting low-risk actions in batches under conflict constraints, performing elimination, updating coupling and an external field, and executing amplitude cutting, re-calibration and rarefaction; and iterating to a scale solvable solution and backfilling an output solution.

The invention provides an asset combination optimization method which can be applied to the field of artificial intelligence. The asset combinatorial optimization method comprises the steps of obtaining a to-be-processed asset set, wherein each asset corresponds to a group of structured attribute data; receiving an optimization target and a business rule constraint defined by a user; constructing a mixed integer linear programming model according to the attribute data, the optimization target and the business rule constraint; wherein the optimization target is converted into a target optimization function of the model, and the business rule constraint is used as a linear constraint condition of the model; selecting a target engine from at least two mutually independent and pluggable optimization solution engines, transmitting the mixed integer linear programming model to the target engine for solution operation, and obtaining a final assignment result of the binary decision variable; and based on the final assignment result, screening out asset subsets conforming to business rule constraints, and forming an optimized asset combination. The invention further provides an asset combinatorial optimization device and equipment, a storage medium and a program product.

This invention relates to an optimization method for the minimum operating combination of thermal power units considering static voltage stability and safety constraints, belonging to the field of power system security analysis and optimization scheduling technology. The method includes: acquiring a power grid model and pre-condition boundary conditions; constructing a minimum operating combination model of thermal power units considering safety constraints with the objective of minimizing the operating capacity of thermal power units; performing redundant constraint filtering on the N-1 safety constraints to reduce the computational scale of the optimization problem; solving the minimum operating combination model with safety constraints to obtain the initial minimum unit combination; performing a voltage stability margin scan to calculate the voltage stability margin for fault-free conditions and various anticipated fault conditions; for anticipated faults where the voltage stability margin does not meet the requirements, constructing a set of proposed operating units, and using the constraint that at least one unit in the proposed operating unit set must be operating, constructing and solving the minimum unit combination model considering static voltage stability and safety constraints, determining the minimum unit combination, until the requirements are met.

The invention relates to the technical field of discrete manufacturing workshops, in particular to a discrete manufacturing unmanned intelligent workshop AGV number optimization method and a related device. The method comprises the following steps: constructing a mathematical combination optimization model for the discrete manufacturing unmanned intelligent workshop according to a preset constraint condition and a preset objective function; the process information of the discrete manufacturing unmanned intelligent workshop is determined, the process information comprises a plurality of processes, and each process corresponds to single demand information; and solving the demand information corresponding to each process based on the mathematical combination optimization model to obtain the number of AGVs required in each process. According to the invention, the number of AGVs in the discrete manufacturing unmanned intelligent workshop can be optimized, and the operation cost is reduced.

The invention provides the system, the device and the method for determining the optimal bidding strategy of which the performance is better than that of the bidding adjustment mode in a scene of repeatedly participating in the first-level price auction. The invention provides a method for predicting inventory. According to the method, an online real-time repeated auction scene can be converted into an offline optimization problem; specifically, auction is summarized through a small number of parameters, and joint distribution of the parameters is predicted based on historical data. Meanwhile, the invention further develops a method named as strategy search, and the method adopts a combined optimization means to search a bidding strategy capable of realizing the optimal performance of the advertisement activity on the premise of meeting specific constraint conditions.

The present application relates to the technical field of intelligent manufacturing production scheduling and combination optimization, in particular to a distributed flexible job shop scheduling method considering process dependency, comprising: initializing algorithm parameters, alternately using a heuristic method and a random method to generate an initial population; selecting a parent solution from the current population through an adaptive adjustment strategy, sequentially executing a crossover operator, a mutation operator and a local search on the parent solution, updating the population; calculating the current optimal solution of the current population, judging whether the termination time is reached, if yes, terminating evolution, outputting the current optimal solution and the maximum completion time, otherwise, continuing iteration. The present application solves the problems of existing solving methods, such as significant time consumption increase and search into local optimum when the scale of the distributed flexible job shop scheduling problem considering process dependency is expanded, and achieves the positive effects of reducing the maximum completion time and improving the search efficiency and solution quality of large-scale instances.

The application provides a real-time adjustable capacity calculation method for power quality regulation resource combination, and belongs to the field of power regulation, and comprises the following steps: step 1: acquiring and solving m single regulation resources required for real-time power parameter calculation, based on a power decomposition method, each type of compensation capacity of each single regulation resource real-time output is calculated by decomposition; step 2: calculating the real-time compensated output capacity of each single regulation resource and the real-time adjustable capacity of each single regulation resource; step 3: based on the calculation formula of the real-time adjustable capacity of each single regulation resource, the real-time adjustable capacity of the power quality regulation resource combination is integrated. The application helps to realize the combined optimization configuration of a large number of heterogeneous power quality regulation resources in a power grid.

The application discloses a processing method and device for a combination optimization problem. The method comprises the following steps: running a segmented quantum circuit obtained by splitting an original quantum circuit, and performing error suppression on the measurement result of each segmented quantum circuit to obtain a first measurement result corresponding to each segmented quantum circuit after error suppression; recombining the obtained first measurement result to generate a second measurement result; obtaining an average energy expectation of the original quantum circuit based on the generated second measurement result; and when the execution of the segmented quantum circuit satisfies a specified condition, generating a target combination corresponding to the target combination optimization problem based on the average energy expectation. By using the embodiment of the application, the operation of a combination optimization algorithm with a large number of bits on quantum hardware is realized through the operation and recombination of the split quantum circuit.

The invention relates to the technical field of production line machining, in particular to a brake disc automatic production line process parameter optimization control method and system, and the method comprises the steps: constructing a multi-objective optimization model which comprises objective models for calculating production time, surface roughness, tool wear rate and machining energy consumption; acquiring sensor data and process parameters in the production process to form a multi-dimensional data set; generating multiple groups of target model combinations based on the data set and the optimization model; performing combination optimization processing to obtain an optimal sensor data set; an optimal process parameter combination is determined based on the optimal sensor data set. Through multi-objective collaborative optimization and data-driven parameter decision, dynamic and accurate adjustment of process parameters is achieved, the machining efficiency and quality can be improved at the same time, the service life of a tool is prolonged, energy consumption is reduced, and the method is suitable for intelligent control over the automatic production line of the brake disc.

The invention discloses an electric power spot minimum electricity purchase cost function fitting method adaptive to quantum computing, and belongs to the crossing field of electric power system optimization and quantum computing, and the method comprises the steps: building a security constraint unit commitment optimization model based on electric power system parameters; based on the security constraint unit commitment optimization model, constructing a training sample set of a unit startup state and the minimum power purchase cost; based on the training sample set, fitting an explicit function relationship between the minimum power purchase cost and the unit starting state by adopting a kernel function enhanced elastic network regression algorithm; and converting the explicit function relationship into a quadratic unconstrained binary optimization model adaptive to quantum calculation, and solving the quadratic unconstrained binary optimization model to realize power spot minimum power purchase cost function fitting adaptive to quantum calculation. According to the method, the calculation efficiency bottleneck in clearing of the electric power spot market and the objective function dominant expression problem in quantum calculation adaptation of an existing method are solved.