105results about How to "Quick solve" patented technology

The invention discloses a robot full-coverage path planning method based on cattle farming movement. The method aims at improving the path coverage efficiency of a robot in a complex environment. According to the method, a two-dimensional plane map of a real environment is obtained and converted into a binary image, and a grid map suitable for the size of a current robot is generated through rasterization processing. And dynamically adjusting a path planning direction according to a main direction and regional characteristics of a map / map region by adopting a motion direction priority judgment method of cattle farming type motion. And when the robot encounters an obstacle and falls into a dead zone, replanning a path by using a backtracking point set construction rule in combination with an A * algorithm, optimizing path selection of the robot for escaping from the dead zone, and ensuring that the robot can cover all reachable areas. The method effectively reduces path overlapping and calculation amount, improves efficiency and coverage rate of path planning, and is especially suitable for environments with complex boundaries and obstacles. The invention further discloses a system and application for implementing the path planning method, and the system and the application have wide application scenes.

The invention discloses a multi-level voltage collaborative optimization method for a power distribution network and a related device. The method comprises the following steps: acquiring day-ahead prediction data, intra-day multi-period rolling prediction data and a multi-target cross-layer voltage optimization model which takes electric energy quality and economic operation into consideration and comprises a transformer substation layer, a feeder layer and an equivalent transformer area layer; the day-ahead prediction data are input into a multi-target cross-layer voltage optimization model, a first regulation and control instruction used for regulating a transformer substation layer-feeder layer-equivalent transformer area voltage layer is output, and the first regulation and control instruction comprises a switch action control instruction and an active-reactive power control instruction; reducing decision variables and constraint conditions of the multi-target cross-layer voltage optimization model in a time domain by using the time scale of the intra-day multi-cycle rolling prediction data to obtain an intra-day multi-cycle rolling optimization model; the intra-day multi-cycle rolling optimization model is solved by adopting a distributed primitive dual gradient method, a second regulation and control instruction used for adjusting a transformer substation layer-feeder layer-equivalent transformer area voltage layer is obtained, and the second regulation and control instruction comprises an active power-reactive power control instruction; and performing power distribution network multi-level voltage collaborative optimization according to the first regulation and control instruction and / or the second regulation and control instruction. The method aims at giving consideration to the power quality and economic benefits of the power distribution network, avoiding voltage out-of-limit, better utilizing various adjusting resources to achieve the highest efficiency, reducing the calculation amount by adopting a distributed algorithm, and improving the calculation speed.

The invention discloses a data lossless fusion and aero-optical effect calculation method based on flow field characteristics, and belongs to the technical field of aero-optical effect calculation, and the method comprises the steps: constructing a coordinate system based on an optical window and optical calculation parameters; constructing a first layer of optical grid based on a window plane; coordinate transformation of a flow field and a first layer of optical grids; generating optical calculation grid points based on a linear equation and a plane equation; performing interpolation conversion on the flow field data to an optical computational grid; and completing ray tracing and optical function calculation. According to the method, the optical computational grid and data interpolation extraction can be quickly generated with high precision, so that the pneumatic optical effect can be calculated with high precision.

The invention discloses a production line configuration optimization method and device based on digital twinborn and dual-population genetic algorithm collaboration. The method comprises the following steps: constructing a PCB production line twinborn model; constructing a single-row layout model of the PCB production line, and constructing and forming a multi-target planning model by taking the maximum production efficiency, the maximum equipment utilization rate and the minimum logistics cost as targets; the method comprises the following steps: acquiring twinborn data in real time, acquiring a priority sequence of each process unit of a PCB production line according to the twinborn data, and encoding the priority sequence of each process unit and spacing distances among the process units by using a double-population genetic algorithm, carrying out iterative solution by using a fitness function constructed based on a multi-objective planning model to obtain an optimal configuration scheme; and mapping an optimal configuration scheme obtained by iterative solution to the twin model, and evaluating an optimization effect. According to the invention, an accurate and reliable PCB production line resource configuration optimization scheme can be efficiently generated, and the production line efficiency and the equipment utilization rate are improved.

The invention relates to the technical field of fatigue time-varying reliability analysis, in particular to a fatigue time-varying reliability calculation method and device, equipment, a storage medium and a product. The method comprises the following steps: performing crack propagation simulation on a three-dimensional plate shell structure based on a random propagation finite element method, and establishing a geometric model of a crack propagation path; based on the geometric model, calculating a stress intensity factor of the crack tip by adopting an interaction integral method, and determining change data of the stress intensity factor in the crack propagation process; based on the stress intensity factor of the crack tip and the change data of the stress intensity factor in the crack propagation process, performing numerical simulation on the crack propagation process by adopting an propagation finite element method; and based on a numerical simulation result, optimizing the crack propagation process by adopting an HLRF-BFGS optimization algorithm, and determining the reliability of fatigue crack propagation. According to the method, the fatigue reliability evaluation effect is improved.

The invention relates to the technical field of calculation, and discloses a continuous time digital Isin model hardware solver based on combinational logic, which comprises a plurality of neurons; the data initialization module is used for carrying out initialization assignment on corresponding coefficients of the hardware solver; after the control signal is used for carrying out initialization assignment on neurons, iterative calculation is started; the neuron updating module is used for carrying out matrix operation on an interaction relation weight matrix formed by the initialized weight coefficients and initialized neurons, and adding the matrix operation and bias to obtain the cumulative sum of each neuron in the current neuron state; the neuron activation module is used for adding nonlinear factors for iterative calculation; the high precision of a digital circuit is utilized, but the continuity of a physical system is also utilized at the same time, so that acceleration hardware which is lower in energy consumption and faster in digit solving is realized.

The invention relates to a novel noise reduction device aiming at the noise problem in a subway shield tunnel and an evaluation method of the novel noise reduction device. The device is a honeycomb-multilayer composite sound energy absorption device based on a honeycomb structure, the device is composed of a multilayer honeycomb energy absorption pad structure, the reflection frequency of noise entering the absorption pad is greatly increased, and the laying condition of shield tunnel bent duct pieces can be adapted through unitized honeycomb holes. And meanwhile, a vehicle-track-infrastructure vibration noise analysis model containing a fluid domain is established as an evaluation method, so that the noise reduction performance of the device can be evaluated and verified. According to the honeycomb-multilayer composite sound energy absorption device, the porous material with an excellent sound absorption effect is adopted, so that the honeycomb-multilayer composite sound energy absorption device has independent noise reduction capability in structure and material, the composite noise reduction capability is more excellent, the established evaluation method can be suitable for device performance modulation of various scenes, and the evaluation method is suitable for popularization and application. And the method has relatively high universality.

The invention discloses a parameter optimization method for a phase-shifting transformer with a double-core structure, which comprises the following steps of: firstly, determining performance parameters of the phase-shifting transformer with the double-core structure according to requirements of a user on a high-integration-level phase-shifting transformer and related standards, setting constraint conditions and optimizing a value range of parameter variables; secondly, constructing a multi-objective optimization function considering the high integration level of the phase-shifting transformer; further, solving an optimization problem by adopting an improved multi-target bionic grasshopper optimization algorithm; finally, the optimization result is corrected and verified, and a design result is output. According to the method, the multi-objective optimization function containing the high-integration index is constructed, and the improved multi-objective bionic grasshopper optimization algorithm is adopted, so that the high-integration rapid design of the phase-shifting transformer can be realized.

The invention discloses a flexible interconnected power distribution network scheduling method and system based on a Gaussian mixture model, and the method comprises the steps: building a source load power probability model of a flexible interconnected power distribution network, and determining an injection power random variable through an injection power prediction value and a sampling value of an injection power prediction error; taking the minimum operation loss cost of the flexible interconnected power distribution network as a target function, establishing a constraint condition of the target function, and determining an inverse function of a cumulative distribution function of an injection power random variable based on a source load power probability model; transforming the probability constraint condition that the node voltage does not exceed the limit and the probability constraint condition that the branch power does not exceed the limit by using the inverse function and the node injection power adjustment amount, performing relaxation processing on the power balance constraint condition of the AC / DC system, and forming a flexible interconnected power distribution network dispatching optimization model with the target function; and carrying out iterative solution on the flexible interconnection power distribution network scheduling optimization model to obtain an optimal value of the injection power adjustment amount of each node, taking the optimal value as a power value exchanged by the flexible interconnection device, and meeting the scheduling requirement of the medium-voltage flexible interconnection system.

The present invention relates to a method for characterizing aircraft stall and deviation characteristics, including a stall dimension characterization method and a deviation dimension characterization method; the stall dimension characterization method includes the establishment of a stall function model and a stall warning; the deviation dimension characterization method includes using the deviation angle of attack, deviation characteristics, and spin sensitivity obtained by static criteria to compare with similar models as evaluation indicators to evaluate the aircraft deviation characteristics and spin sensitivity, and using the working efficiency and reliability of the control law as evaluation indicators. The present invention covers multiple levels such as aerodynamic models, stall warnings, characteristic predictions, and control law design, and provides a good verification method for evaluating the aerodynamic characteristics and safety of high angles of attack during the aircraft design stage; it has rigorous logic and solution framework, low computational complexity, and can be quickly solved with low machine time on general performance computing devices.

The invention discloses an electromagnetic wave rain attenuation prediction method based on nonlinear regression, and belongs to the technical field of communication. The method is based on a classical ITU-R rain attenuation model, and comprises the following steps: constructing a training and testing data set for fitting model parameters k and b by collecting rainfall rate and attenuation data; a nonlinear regression method in deep learning is adopted, a model containing k and b parameters is designed and trained, a mean square error is used as a loss function, and parameter solving is carried out through a gradient descent optimization algorithm; and when the similarity between the fitting result on the test set and the real attenuation value reaches a specified threshold value, stopping training and outputting an optimal parameter. According to the method, automatic solving of the rain attenuation model parameters is realized, human intervention is reduced, the calculation precision and the generalization ability of the model are improved, and the method is suitable for electromagnetic wave propagation research in various rain attenuation scenes.

The application discloses a point spread function calculation method and device, electronic equipment and medium. The method can include: establishing an initial point spread function; setting a constraint parameter; obtaining a final point spread function according to the initial point spread function and the constraint parameter and calculating the solution. The application realizes fast solution of the point spread function based on a local neighborhood and a high-frequency approximation, better serves the properties of the Hessian matrix and the high-dimensional forward research of seismic data, provides a new technical idea for high-precision and efficient high-resolution processing of seismic data in the future, and provides strong technical support for actual production and application.

The invention discloses a driving distance measurement and calculation method, device and equipment and a storage medium, and relates to the technical field of distance measurement and calculation, and the method comprises the steps: obtaining a vehicle coordinate of a vehicle on a lane, and a lane coordinate point sequence of the lane; determining a plurality of lane segments of the lane based on a plurality of lane coordinate points in the lane coordinate point sequence; and determining the driving distance of the vehicle on the lane based on the vehicle coordinates and the plurality of lane segments, through the above method, the distance between the projection point of the vehicle on the lane and the starting point of the lane in different scenes can be rapidly solved, and the method has good performance in any complex scene such as a curve or a U-shaped lane. Therefore, the accuracy of calculating the distance between the projection point of the vehicle and the starting point of the lane in a complex lane scene is improved.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The application discloses a three-dimensional model visual line direction detection method and a terminal. The method comprises the following steps: acquiring basic information, then solving a corneal curvature radius based on the basic information; updating eye diagram information, and taking the corneal curvature radius as a constant to solve a corneal curvature center; taking the corneal curvature radius and the corneal curvature center as constants, solving a distance from the corneal curvature center to a pupil center based on the basic information and a pupil edge point coordinate; finally, calculating a Kappa angle of an eyeball; determining a primary and secondary eye, performing visual line fusion through the primary and secondary eye, and obtaining a final visual line direction. The binocular three-dimensional eyeball model is divided into two monocular eyeball models, and the solving parameters between each model only need to solve the human eye parameters related to the visual line, without mutual constraint of the parameters between the binoculars.

This invention discloses a method for eliminating moss growth on macadamia nut leaves, relating to the field of agricultural planting technology. The specific method involves spraying a moss-removing agent onto the macadamia nut leaves during the moss occurrence period, applying moss inhibitor I to the main stem, and applying moss inhibitor II to the soil surface around the main stem. The moss-removing agent comprises the following raw materials in parts by weight: 40-50 parts of allicin, 15-24 parts of urea, 5-18 parts of calcium propionate, 20-27 parts of calcium sulfate, 10-17 parts of calcium chloride, 20-35 parts of vegetable oil, 1-10 parts of titanium dioxide, 1-5 parts of glutathione, and 1-3 parts of dispersant. This invention solves the problems of slow effectiveness, short duration of action, and low plant safety in current macadamia nut leaf moss control methods, achieving highly efficient control.

This invention relates to the fields of automated packaging and logistics warehousing applications, and discloses an automated packing method based on reinforcement learning and dynamic search. This method addresses the difficulties and shortcomings of traditional algorithms when applied to automated online packing scenarios. It combines the advantages of reinforcement learning (which continuously adjusts its strategy based on environmental feedback to achieve automated learning) with the fast solution speed and strong adaptability of dynamic search algorithms. While ensuring maximum utilization of box space, it achieves the goal of quickly solving and optimizing the stacking actions of goods. Furthermore, during the stacking process, the algorithm can continuously adjust its stacking actions and positions based on real-time feedback from the box and the environment, achieving the effect of automatically learning the optimal stacking strategy and providing real-time online guidance for packing.

The invention relates to an improved butterfly optimization algorithm-based circular cooler bulk material collection vehicle scheduling method, which comprises the following steps of: starting from the angle of efficient and low-frequency operation of a circular cooler bulk material collection vehicle, taking the minimum comprehensive cost of the operation time, the total driving distance and the operation times of the bulk material collection vehicle as a target; according to the method, an annular cooler bulk cargo collection vehicle scheduling problem model is constructed, an improved butterfly optimization algorithm is adopted to help the algorithm jump out of local optimum, and a bulk cargo collection vehicle scheduling optimal scheme with higher precision is solved more quickly. Starting from the actual working condition of the circular cooler, the scheduling of the bulk material collecting vehicle is optimized in real time by adopting the improved butterfly optimization algorithm, and on the basis of shortening the running time, the running distance can be effectively saved, and the running efficiency of the bulk material collecting vehicle is improved.

This invention discloses a comprehensive optimization method and system for green travel routes in multi-agent decision-making. The method includes: constructing a travel-specific spatiotemporal environment, comprising a static environment describing the topological relationships of traffic infrastructure and individual travel plans; a dynamic environment describing the time-varying characteristics of traffic states and supporting dynamic interaction and feedback with agent decision results; and a user-perceived environment describing the probability distribution of individuals' perceptions of travel parameters such as carbon emissions, costs, and time consumption under different travel mode categories; performing comprehensive route optimization based on the environment, calculating the perception weight of a certain type of traveler to a certain topological edge at the current time according to the current moment, the current traveler's category, the attributes of the road segment to be evaluated, and the accumulated amount of travel states generated, and searching for the optimal path in the time-varying topological network with the objective of minimizing the sum of perception weights from the start point to the destination; and the agent moving according to the optimal path and updating the traffic state parameters in the dynamic environment. This invention makes the green travel route recommendation results more closely match the actual choice preferences and heterogeneous decision-making needs of different users.

The invention discloses a control method for accurately and quickly obtaining various elements based on a multi-network model, comprising the following steps: setting an element solving neural network model to obtain shooting elements in a windless state, wherein the shooting elements include a sight, a range and a ballistic height; setting a longitudinal wind neural network model to obtain the average longitudinal wind during the flight of a shell according to the sight and the ballistic height in the shooting elements in the windless state, as well as the actual wind speed and wind direction, as the average ballistic longitudinal wind; setting an element correction neural network model, taking the average ballistic longitudinal wind as input, outputting a sight correction amount, correcting the shooting elements in the windless state, and obtaining the final shooting elements; adjusting the launch platform according to the final shooting elements, and launching the shell. The control method for accurately and quickly obtaining various elements based on a multi-network model provided by the invention fully considers real-time meteorology, can improve the accuracy of element solving, has a small amount of calculation, is short in time consumption, does not require cumbersome firing table query, can be deployed in a fire control computer, and can effectively avoid missing the target.

This invention discloses an underwater electric field source localization method based on a determinant orthogonal propagation operator and adaptive hybrid-driven differential evolution, belonging to the field of electromagnetic positioning and detection. This invention acquires and receives array signals and calculates the covariance matrix. Based on the propagation operator, it constructs a projection operator matrix orthogonal to the array manifold to replace the noise subspace, thereby constructing a determinant spatial spectrum function and defining a cost function. Then, based on this cost function, it constructs an adaptive hybrid-driven differential evolution framework, fusing the alpha evolution algorithm and an adaptive differential evolution algorithm based on successful historical records to solve for the target position coordinates. This invention eliminates the need for eigenvalue decomposition, reducing computational complexity, and accelerates convergence through an adaptive hybrid-driven search strategy, enabling fast and high-precision electric field source localization in complex underwater environments.

The invention relates to the technical field of nuclear reactor thermal hydraulic power, in particular to a method and equipment for improving the parallel channel flow distribution iteration speed and a storage medium, and the method comprises the following steps: S1, constructing a channel flow initialization model, and initializing the flow of each channel inlet; s2, the pressure drop of each channel is calculated, an iterative algorithm model is constructed according to the direct proportion relation between the one-way flow pressure drop and the flow exponential function, and the channel flow after iteration is calculated; and S3, setting an error limit, carrying out iterative convergence judgment, if the iterative convergence is not greater than the error limit, ending iteration, and otherwise, returning to S2 to continue iterative calculation. The method aims at solving the problems that an existing parallel multi-channel flow distribution model is low in efficiency and cannot guarantee convergence of the trial and error process, and efficient and stable numerical solution of an existing nonlinear, large-scale and parallel multi-channel flow distribution theoretical model is achieved.

The invention relates to the technical field of robot autonomous navigation, in particular to a fusion cellular genetic algorithm-based path planning method, which comprises the following steps of: establishing an environment map and a path coding strategy; determining a fitness function; under continuous iteration of the initialized population evolution rule, all path individuals are initialized, and a cellular state matrix storing initial population information is obtained; selecting a solution with high fitness from the population to directly enter the next generation, and performing random pairwise pairing operation on the remaining individuals; iterative evolution is carried out on the cellular state matrix through crossover and variation evolution rules, and the population is updated through fitness ranking; and when starting from a certain generation, the optimal fitness individual in the population does not change for several consecutive generations, terminating the iteration process of the algorithm, and outputting the optimal solution in the population at the moment as a path planning result. The method is high in solving speed and high in path planning fitness.

The invention discloses a method and a device for determining two fluid parameters of a nuclear reactor, a medium and equipment. The method comprises the following steps: determining a preset constraint condition and a two-fluid control equation of a nuclear reactor according to a working condition variable of the nuclear reactor; constructing a target loss function according to a preset constraint condition and the two-fluid control equation; iteratively training by minimizing the target loss function to obtain a parameter prediction model; and inputting the target working condition parameters into the parameter prediction model to obtain characteristic parameters of the two-phase flow behavior of the nuclear reactor under the target working condition parameters. According to the method provided by the invention, the control equation is embedded into the network model, so that the complexity of a problem solution space is remarkably reduced, the requirement on training sample data is reduced, the quick solution of the two-fluid equation of the nuclear reactor in a complex and changeable environment is realized, and meanwhile, the prediction reliability and generalization ability of two-phase characteristic parameters are considered.

The present application belongs to the technical field of power system balance analysis and intelligent dispatching, and relates to a method and system for constructing a hydroelectric flexible interval considering new energy uncertainty, the method comprising: 1. obtaining basic technical data of a power system containing new energy; 2. constructing a day-ahead power balance analysis model and establishing a system hydroelectric flexible interval value function; 3. constructing a two-stage robust optimization model based on decision-dependent uncertainty set; dividing the problem into a main problem and a sub-problem for iterative solution; and 4. multi-objective automatic optimization based on mixed integer linear programming. The present application improves the response capability of the system to uncertain factors under the premise of ensuring that the time and space boundaries of hydroelectric resources are not exceeded, effectively solves the problem of imbalance between real-time clearing results and medium and long-term decomposition strategies, further reduces the complexity of the real-time clearing model, and improves the overall clearing efficiency.

The present application discloses a method, device, medium and product for joint peak dispatching of zones taking into account the day-ahead operation safety domain, and relates to the field of power dispatching technology. The method includes: solving the overall day-ahead dispatching model after dimensionality reduction to obtain the overall day-ahead dispatching plan; constructing the day-ahead operation safety domain based on the overall day-ahead dispatching plan and the local load of each zone, determining the upward adjustment capacity and downward adjustment capacity under different unit operation points in the day-ahead operation safety domain, and performing cluster screening to obtain the zone adjustable capacity; determining the zone day-ahead dispatching plan of each zone based on the overall day-ahead dispatching plan; constructing and solving the peak demand allocation model to obtain the zone peak demand of each zone; inputting the zone day-ahead dispatching plan and the zone peak demand into the zone plan correction model to obtain the zone day-ahead dispatching correction plan. The present application solves the problem that the day-ahead dispatching plan cannot be quickly corrected in the face of new peak demands.

The invention relates to the technical field of robot autonomous navigation, in particular to a cellular genetic path planning method based on multi-population cooperative adaptability, which comprises the following steps: one grid in a grid map corresponds to one cell; setting the number of sub-populations according to a path planning task; generating primary path individuals of a plurality of sub-populations in a cellular evolution mode; in each sub-population, a path individual with the maximum gene coding fitness is extracted, random pairwise pairing operation is carried out on other path individuals, and a cellular state matrix is adjusted based on crossing and variation rules to carry out iterative evolution; the individuals with the maximum fitness and the individuals obtained through evolution serve as sub-population representative individuals; calculating cooperative fitness according to the sub-population representative individuals of each sub-population; the population is updated according to collaborative fitness sorting; outputting an optimal solution; according to the method, the population cooperation relationship is taken as an optimization condition, and the evolution of the cellular automaton is utilized, so that the optimal path in a multi-task complex scene can be quickly generated.

The invention discloses an electricity-carbon coupled regional multi-energy complementary system carbon emission analysis method and system, and relates to the technical field of energy system optimization, and the method comprises the steps: converging energy data in a multi-energy complementary system; establishing a network load flow calculation model, and solving a load flow result; measuring and calculating the power carbon emission of the power generation side of the multi-energy complementary system; and constructing a carbon emission analysis model of the regional multi-energy complementary system. According to the regional multi-energy complementary power system data fusion method provided by the invention, the problem that the algorithm precision is influenced by inaccurate data caused by bad data is reduced, and then a network load flow calculation model of a multi-energy complementary power system containing renewable energy sources is established; according to the method, the power flow results of the power generation side, the network side and the power utilization side of the system can be solved quickly, and the power flow direction of renewable energy of the power generation side can be tracked.

The invention relates to a multi-channel modularized ultrasonic front-end system based on an FPGA (Field Programmable Gate Array), belongs to the field of ultrasonic equipment, and solves the problems of slow delay parameter calculation and low delay precision of the existing system. The system comprises an FPGA main control module, a high-voltage driving circuit, an ultrasonic probe and a receiving and conditioning circuit, wherein the FPGA main control module comprises a transmitting module and a receiving module; the transmitting module generates a control signal based on the delay parameter, triggers the high-voltage driving circuit to generate a high-voltage pulse, and excites the ultrasonic probe to transmit a wave beam; the ultrasonic probe converts the reflected wave beam into an electric signal, sends the electric signal as an echo signal to a receiving conditioning circuit for processing, and transmits the echo signal to a receiving module to form a focusing signal; the receiving module comprises a delay signal calculation module and a focusing signal calculation module, and the delay signal calculation module converts the processed echo signal into a delay signal by adopting a mode of combining coarse delay and fine delay based on a radix-4-CORDIC algorithm; the latter adopts a radix-4-CORDIC algorithm to calculate a focusing signal based on the delay signal. The ultrasonic imaging front-end system is high in speed and precision.

This invention discloses a two-tier decision-making method and system for collaborative electricity-carbon trading among building producers and consumers. The method constructs a two-tier optimization framework for collaborative electricity-carbon trading based on the functional responsibilities of upper-level aggregators and the carbon trading positioning of lower-level building producers and consumers. It categorizes individual buildings according to differentiated carbon emission compliance requirements and calculates CCER mutual recognition amounts based on the principle of emission reduction equivalence and the amount of green electricity consumed in each scheduling period. Through dynamic application and allocation by aggregators, a dynamic carbon emission reduction mutual recognition mechanism is constructed. Combining the framework and mutual recognition mechanism, a two-tier objective function and constraints including building thermal balance are constructed, forming a two-tier nonlinear optimization model. This model is then transformed into a single-tier mixed-integer linear programming model through Boolean variable constraint relaxation, Lagrangian function construction, KKT condition transformation, and strong duality principle and Big M method linearization to obtain the decision results. This invention solves the problems of missing differentiated carbon compliance paths for building producers and consumers and poor adaptability of the electricity-carbon collaborative model in existing technologies.