382 results about "Combinatorial optimization" patented technology

The invention discloses an integer programming automatic modeling method based on a large language model, and the method comprises the steps: obtaining an integer programming problem data set, carrying out the processing through employing a data enhancement and dynamic complexity balance method, and obtaining a balance data set of multi-modal representation; processing by adopting a model architecture search and combinatorial optimization method to obtain an optimized neural network model structure; processing by adopting a dynamic sparse training and self-adaptive difficulty course learning method to obtain a model subjected to first-stage training; processing by adopting hierarchical reinforcement learning and a multi-target Pareto frontier exploration method to obtain a final model subjected to second-stage training; processing by adopting a dynamic neural symbol inference method to obtain an integer programming model with enhanced logic consistency; and processing by adopting a self-adaptive antagonism test generation method to obtain a comprehensive evaluation result. According to the method, the modeling efficiency, accuracy and adaptability of the integer programming problem are improved.

The invention relates to the field of underground powerhouse construction risk identification, and provides an underground powerhouse construction risk identification and disposal method, system, device and medium, and the method comprises the steps: collecting multi-source heterogeneous data in real time, obtaining historical risk case data, and carrying out the preprocessing to obtain structured time-space correlation data; constructing a multi-dimensional analysis model based on a parallel computing algorithm, and performing multi-scale risk analysis on the structured time-space associated data to obtain multi-level risk feature data; performing risk feature recognition through a multi-modal machine learning model to obtain risk quantitative indexes, and performing recognition based on a fuzzy comprehensive evaluation algorithm to obtain construction risk levels; and matching emergency strategies of construction risk levels, carrying out parameter expansion through a combinatorial optimization algorithm, generating a plurality of candidate disposal schemes, carrying out weight calculation and sorting on the candidate disposal schemes by adopting a multi-criterion evaluation model, and outputting an optimal disposal scheme. According to the invention, efficient identification and accurate emergency decision-making of the construction risk of the underground powerhouse are realized.

The invention discloses a traffic infrastructure full life cycle carbon emission assessment method based on a knowledge graph. The method comprises the steps that knowledge is acquired through multi-source data acquisition, entities, attributes and relations related to carbon emission are defined, and knowledge graph ontology design and graph construction are carried out; establishing a carbon emission evaluation model based on an artificial neural network on a framework of the knowledge graph through data preprocessing, multi-layer perceptron network structure design and model training; and in combination with the carbon emission evaluation result and the optimization knowledge in the knowledge graph, generating a specific optimization strategy, and performing combinatorial optimization of the optimization strategy to formulate a recommendable optimization scheme for execution of an optimization decision. According to the scheme provided by the invention, carbon emission can be accurately quantified, a scientific and reasonable optimization strategy is generated, dynamic adjustment is supported, and a systematic and intelligent solution is provided for green and low-carbon development of traffic infrastructures and other engineering projects.

The invention relates to a multi-domain knowledge hinge-oriented proposition combination optimization multi-hop retrieval method and device. The method comprises the following steps: acquiring a cross-domain problem and a non-structural document related to the domain; performing proposition extraction on each non-structural document to obtain a set containing atomic propositions; constructing a proposition network according to an entity co-occurrence relationship among the atomic propositions; initializing a plurality of candidate proposition combinations, selecting neighbor propositions of each atomic proposition in each candidate proposition combination in the proposition network through a pruning method, and obtaining a plurality of new candidate proposition combinations according to each candidate proposition combination and the corresponding neighbor propositions; iteratively updating each candidate proposition combination, and outputting an optimal proposition combination set when iteration is stopped; and inputting the optimal proposition combination set into a pre-trained large language model to generate answers corresponding to the cross-domain questions. By adopting the method, the reliability of outputting the answer content when the large language model faces multi-field questions can be improved.

The invention discloses a two-stage distribution robust unit combination optimization method considering inertia and wind power dual uncertainty. The method comprises the following steps: acquiring load prediction data of each synchronous generator set in a power system and a wind power plant power prediction value; considering inertia and wind power dual uncertainty, selecting an inertia prediction error probability density function and a wind power prediction error probability distribution function, and obtaining wind power and inertia multi-discrete scenes and corresponding scene probabilities by using Latin extraction scene generation and K-means scene clustering; deducing a dynamic frequency constraint containing an inertia error of the system in a frequency response process; building a two-stage distribution robust unit combinatorial optimization model containing dynamic frequency constraint; an auxiliary variable is introduced to linearize absolute value constraints in the confidence set, and Camp is adopted; the CG algorithm divides the optimization model into a main problem and a sub-problem for iterative solution. According to the method, the uncertainty of inertia prediction and the uncertainty of wind power are considered, so that the unit combination model has higher frequency safety and economical efficiency.

The invention provides a TSP path combinatorial optimization method and system based on reinforcement learning, and the method comprises the steps: constructing an anisotropic graph neural network as a backbone network of a diffusion model through the combination of solution distribution learning and combinatorial optimization target learning, capturing a complex relation in graph structure data through the representation capability of the network, and carrying out the optimization of a TSP path. And probability distribution is modeled by using a diffusion model in a single-to-Markov forward process. In addition, two kinds of accelerated sampling methods DDIM and DPM-solver of diffusion models are introduced, the sampling process of denoising is accelerated, and the training efficiency is improved. According to the method, direct preference optimization can be introduced into a diffusion model to further provide preference-guided combinatorial optimization (PGCO), so that the generalization ability and adaptability of a traveling salesman problem (TSP) solving model are improved, and a more efficient and high-quality model for solving large-scale combinatorial optimization is provided.

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.

A system and method include reformulating an optimization program as a Quadratic Unconstrained Binary Optimization (QUBO) model and in a single iteration, solving the optimization program by inputting the QUBO model into a quantum computing solver, instructing the quantum computing solver to generate a plurality of solutions to the optimization program based on the QUBO model, receiving the plurality of solutions from the quantum computing solver, inputting each of the plurality of solutions into a nonquantum computing solver, wherein the nonquantum computing solver uses each of the plurality of solutions as a starting point to continue solving the optimization program, and outputting an optimal solution to the optimization program from the nonquantum computing solver.

The invention discloses a multi-target killing chain optimization method and device based on combinatorial optimization, and the method comprises the steps: constructing a combat network according to the interaction relation between combat units, and determining an edge attribute as a combat index according to a network node parameter; according to the number of targets attacked in the current combat scene and the number of combat indexes required for optimization, whether a static killing chain or a dynamic killing chain is generated is judged; if the number of the attacking targets in the current combat scene is single, carrying out monocular single-index killing chain optimization, or carrying out monocular multi-index killing chain optimization to generate a static killing chain; and if the number of the combat targets in the current combat scene is multiple and the number of the combat indexes is multiple, performing multi-view and multi-index killing chain optimization by combining and optimizing different killing chain paths, and generating a dynamic killing chain. According to the invention, a commanding mechanism can be assisted to select a static killing chain or a dynamic killing chain which most meets requirements from a plurality of killing chains based on the four combat indexes.

The invention discloses a power transmission and distribution power grid robust optimization unit scheduling method based on the whole frequency response process, and relates to the technical field of power systems. The method comprises the following steps: constructing a whole-process frequency security constraint around three links of inertial response, primary frequency modulation and secondary frequency modulation, establishing a power transmission and distribution network two-stage robust unit combinatorial optimization model considering the whole frequency response process by taking min-max-min as a structure, and minimizing the start-stop cost, the power generation cost and the standby cost of the primary frequency modulation and the secondary frequency modulation as a target; and converting the two-stage robust unit combinatorial optimization model of the power transmission and distribution network into a mixed integer second-order cone programming model by adopting a column sum constraint generation algorithm and a strong duality theory, and carrying out iterative solution. According to the method, the frequency regulation capability of the power transmission and distribution network can be effectively improved, the operation cost is reduced, the robustness and efficiency of a scheduling scheme are improved, and safe, stable and economical operation of the power transmission and distribution network under the output uncertainty condition of new energy such as wind and light is ensured.

The invention relates to the technical field of unmanned aerial vehicle signal transmission, and discloses an unmanned aerial vehicle signal transmission rate detection method and system.The method comprises the steps that a compressed sampling matrix is constructed through quantum random projection to reduce the signal dimension, and a quantum entanglement gate is driven based on attitude parameters to generate a dynamic sparse basis matrix; a federated learning framework is adopted to be fused with multi-node data optimization base parameters, rate detection is converted into combinatorial optimization solution in combination with a quantum annealing algorithm, and finally real-time adjustment of a modulation mode and transmitting power is achieved through closed-loop control. The innovation is embodied in a quantum compression sampling-federated optimization-annealing detection cross-domain cooperation mechanism, the defects of a traditional method in dynamic adaptability, calculation efficiency and anti-interference performance are overcome, and the method has the technical advantages of sparse representation space dynamic adaptation, quantum parallel acceleration optimization, multi-target parameter balance and the like. And the communication rate detection precision and link reliability of the unmanned aerial vehicle in a complex electromagnetic environment are remarkably improved.

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 provides a GPU-based parallel tetrahedral mesh optimization method, system and device and a medium, and relates to the technical field of numerical simulation. Comprising the steps of generating a triangular mesh covering a surface for a three-dimensional geometric model; generating an initial tetrahedral mesh based on the triangular mesh; performing a round of point smoothing operation on each tetrahedron by using a plurality of GPU threads; carrying out iterative circulation of the combination optimization operation, and simultaneously carrying out a round of point smoothing, edge deletion and multi-surface deletion operation on a plurality of initial tetrahedrons by utilizing a plurality of GPU threads; and when the edge deletion operation and the multi-surface deletion operation cannot further improve the grid quality, simultaneously performing a round of edge contraction and edge segmentation operation on the plurality of initial tetrahedrons by utilizing the plurality of GPU threads. According to the method, the large-scale grid with reliable quality is quickly generated based on the low-quality input grid, and the grid optimization rate is improved, so that the requirement of large-scale unstructured grid optimization in a three-dimensional space is met.

A neuromorphic network may solve combinatorial optimization problems. The neuromorphic network may include variable neurons, a solution monitoring neuron, and one or more readout neurons. The variable neurons may each represent one binary variable in a combinatorial optimization problem. An internal state of a variable neuron may change as the variable flips. The internal state may be stored in a memory of the variable neuron. The variable neuron may spike when its internal state changes. One or more other variable neurons receiving the spike may determine whether to change their internal states based on the spike. The variable neurons may send their internal states to the solution monitoring neuron to compute a cost of the QUBO problem and determine whether a solution is found. A readout neuron may receive variable assignments resulting in the solution from at least some variable neurons and integrate the variable assignments into one message.

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.

The invention discloses a dynamic traffic distribution and signal control combined optimization method, and belongs to the field of intelligent traffic. The method specifically comprises the steps that firstly, a road section impedance function is constructed, node impedance is introduced to obtain an intersection impedance combination function, and the intersection impedance combination function is optimized through the traffic flow, density and speed relation based on dynamic adjustment parameters, vehicle type differences, weather conditions and traffic flow speed to obtain a combination optimization traffic impedance model; and solving the combinatorial optimization traffic impedance model by using a particle swarm algorithm to output a traffic distribution scheme. According to the method, dynamic traffic distribution and adaptive signal control are integrated, the road network emergency response capability is improved, traffic prediction and signal regulation and control synchronization is realized, and an accurate impedance model is introduced to dynamically adapt to driver preferences, so that congestion is relieved, and travel time and resource allocation are optimized.

The invention relates to a security constraint unit commitment problem processing method and device based on time decoupling, computer equipment and a storage medium. Relates to the technical field of power systems. The method comprises the steps of determining a plurality of target coupling time points in a to-be-optimized time period of a power system through a pre-constructed comprehensive evaluation model; dividing the to-be-optimized time period into a plurality of sub time periods based on the plurality of target coupling time points; decomposing a long-time-scale security constraint unit commitment problem to obtain a sub-problem optimization model corresponding to each sub-time period; and according to each sub-problem optimization model, solving to obtain a unit commitment optimization result of the power system. By adopting the method, the constraint calculation efficiency of the unit combination in the large-scale power system can be improved.

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 process optimization method, device, equipment and system, and relates to the field of process optimization. The method comprises the following steps: acquiring input data of a user, wherein the input data comprises process boundary conditions; generating an orthogonal test scheme through a preset orthogonal test design method based on the input data, wherein the orthogonal test scheme comprises a plurality of orthogonal test samples; determining an alternative parameter combination based on the plurality of orthogonal test samples and a prediction model, wherein the prediction model is constructed based on a random forest regression model; and determining the alternative parameter combination as an initial solution of a simulated annealing algorithm, and obtaining an optimized parameter combination through the simulated annealing algorithm, wherein the optimized parameter combination is used for process optimization. According to the method, the optimized parameter combination can be obtained and used for process optimization.

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 discloses an intelligent discharging method and device based on array combinatorial optimization and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: analyzing a target graphic file to extract cutting piece information in the target graphic file; classifying the cutting piece information so as to classify the outline of the flexible material and sub cutting pieces in the flexible material; constructing an initial typesetting scheme according to the flexible material contour and the sub-cutting pieces; selecting a reference typesetting scheme from the initial typesetting schemes according to a preset fitness function; performing cross processing on the reference typesetting scheme; performing variation processing on the part of the reference typesetting schemes after the cross processing; carrying out correction processing on the reference typesetting scheme which is subjected to cross processing and is not subjected to variation processing and the reference typesetting scheme which is subjected to variation processing; and taking the reference typesetting scheme with the highest fitness as a target typesetting scheme. Typesetting of the sub-cutting pieces is regarded as a combinatorial optimization problem, and the material utilization rate is improved.

The invention discloses a parallel computing method for a unit commitment optimization problem, which relates to the technical field of power systems and comprises the following steps: acquiring day-ahead optimization scheduling input data of a power system; decomposing the scheduling time period set with the high time resolution into a reference scheduling time period subset and a coordinated scheduling time period subset; building mutually independent unit commitment sub-optimization models in parallel and solving the mutually independent unit commitment sub-optimization models; according to a preset data restoration rule and the first unit optimal start-stop plan matrix of each unit combination sub-optimization model, restoring to obtain a second unit optimal start-stop plan matrix; determining a global set of units needing to be started and stopped and a set of remaining units; determining the start-up time period of each unit in the remaining unit set in a rolling identification form; and a unit commitment optimization model is constructed and solved. The method is used for solving the problem of unit commitment optimization, especially the problem of unit commitment optimization of a power system with high time resolution and the day-ahead clearing calculation of a power spot market.

The invention discloses an NRO-QMIX-based urban environment multi-unmanned aerial vehicle cooperative target search method, which comprises the following steps: constructing a multi-unmanned aerial vehicle search environment architecture based on a to-be-searched unknown region, designing an unmanned aerial vehicle search target detection rule and a reward function, establishing a region information credible graph for mapping the unknown region, and searching the unknown region according to the region information credible graph. According to the method, the optimization problem of fusing target search and information collection and realizing maximized updating of a regional credible graph is proposed, an improved NRO-QMIX algorithm is designed to solve the optimization problem, the optimal solution of the execution action of each unmanned aerial vehicle is obtained, and cooperative target search and information collection of multiple unmanned aerial vehicles in the urban complex environment are completed. According to the method, the problems that combined optimization tasks of target search and information collection are difficult to perform at the same time in a complex urban environment and the intelligent agent and environment exploration efficiency is low due to the fact that an existing reinforcement learning method uses a greedy strategy to select actions are solved, and multi-unmanned aerial vehicle cooperative target search and information collection in the urban environment are realized.

The invention discloses a motor multi-objective optimization method and device based on a combined proxy model. The method comprises the following steps: training each meta-proxy model by adopting a one-leaving cross validation method; determining a weight factor of each element agent model based on the error correlation matrix, and combining the element agent models into an optimized combination model according to the weight factors of the element agent models; during optimization, processing the extracted data of each to-be-optimized variable by using the optimization combination model to obtain optimization target data, and performing Sobol sensitivity analysis on each to-be-optimized variable by using the optimization target data to obtain a first-order influence index corresponding to each to-be-optimized variable and each total effect index; on the basis of the first-order influence index and the total effect index, hierarchically dividing each to-be-optimized variable; optimizing each to-be-optimized variable layer by layer by using the combinatorial optimization model; and selecting a final optimization result of each to-be-optimized variable of the target motor from the optimization result of the last layer.

The invention provides a rapid detection method and device for adulteration of cod surimi based on a hyperspectral imaging technology, and the method comprises the steps: preparing a to-be-detected cod surimi sample containing different component adulteration concentration proportions, collecting hyperspectral image information of the to-be-detected cod surimi sample, and carrying out black and white correction processing; extracting an ROI (Region of Interest) of the hyperspectral image after black and white correction; dividing all the obtained spectral data into a training set and a prediction set, and constructing a BP-ANN classification model; preprocessing the spectral data set, constructing a PLS regression model, and determining an optimal preprocessing mode; two variable screening algorithms, namely a self-adaptive reweighting algorithm CARS and an interval combinatorial optimization algorithm ICO, are adopted to simplify the model, the robustness of the model is improved, and the optimal model is selected. By adopting the hyperspectral imaging technology and combining the traditional chemometrics and deep learning, the method has the advantages of no damage, no pollution, automation, rapidness and high efficiency in the detection of the adulteration of the cod surimi.

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.