81 results about "Second-order cone programming" patented technology

A power grid planning method and system adapted to multiple uncertainties and multi-objective requirements, which relate to the technical field of power grid planning. The method comprises: constructing a deterministic multi-objective coordinated planning model with the objectives of minimizing economic cost, reliability risks and environmental impact; on the basis of an IGDT, constructing a multi-uncertainty model considering wind power, photovoltaic power and load; converting a model into a second-order cone programming model by using variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation methods; and solving the second-order cone programming model, in order to obtain a power grid planning scheme that adapts to uncertainties in wind and photovoltaic power output and load demand growth and meets the requirements for the economy, reliability and environmental friendliness of power grid operation. Thus, economic benefits of power grid planning are improved, carbon emissions from power grid operation are reduced, the reliability of a power grid is enhanced, and an obtained planning scheme has high robustness within expected objectives of planners.

The invention provides an active power distribution network multi-time scale rolling optimization method driven by multi-agent graph reinforcement learning. The method comprises the following steps: dividing slow time scale equipment and fast time scale equipment; the slow time scale is optimized and modeled into a multi-agent Markov game, and a global state space and a combined action space corresponding to switch actions, reactive compensation equipment gears and voltage regulation equipment gears are defined; switching action candidate vectors are generated based on a basic loop of the power distribution network, and action space dimensions are compressed through a scheme of excluding conflicting actions of the same common branch; fitting an action value function of each agent by adopting a graph neural network, generating a dimensionality-reduced discrete action strategy according to the global state space, and carrying out collaborative optimization through a distributed decision and a global reward mechanism; and on the basis of the slow time scale optimization result, the fast time scale optimization problem is converted into a mixed integer second-order cone programming model, and an instruction is solved in a rolling manner.

The invention relates to the field of power distribution network energy storage, in particular to an optimal configuration method for a power distribution network energy storage system containing wind and light. According to the method, a double-layer optimization modeling framework is adopted, an outer-layer model is used for determining site selection and capacity configuration of the energy storage system, the aim is to comprehensively optimize energy storage investment cost and operation performance indexes of the power distribution network, and configuration benefits are maximized on the basis of meeting operation constraints. An energy storage optimization benefit balance index is introduced into the outer layer model to serve as an evaluation index, and an adaptive variation particle swarm optimization algorithm is adopted to solve the configuration position and capacity of the energy storage system. And the inner layer model is constructed based on a second-order cone programming method and is used for optimizing a charging and discharging plan of the energy storage system under a given energy storage configuration condition. And the active power loss and the voltage deviation of the system are comprehensively considered by the inner-layer objective function so as to realize the optimal operation benefit. Through double-layer collaborative optimization, combined design of configuration and scheduling of the energy storage system is realized, and the operation stability and economy of the power distribution network under the access of renewable energy sources are improved.

The invention discloses a distributed power grid dispatching management and optimization method, and relates to the technical field of power grid automation, and the method comprises the steps: collecting multi-dimensional power grid data in a system, carrying out the data preprocessing of the multi-dimensional power grid data, carrying out the electrical load prediction of the initial multi-dimensional power grid data through an LSTM neural network, and carrying out the optimization of the electrical load. Performing energy storage SOC state real-time evaluation on the electrical load data based on a DTW dynamic time warping load identification model, performing state judgment on power grid equipment according to an evaluation result, constructing a multi-target optimization model by using mixed integer second-order cone programming, performing power grid constraint adjustment by using the multi-target optimization model, and performing power grid constraint adjustment on the basis of a power grid constraint result. And performing real-time power generation plan adjustment by using an improved particle swarm algorithm to obtain target electric power output of the power grid, and performing power grid energy storage control optimization according to the target electric power output. And the prediction accuracy of the new energy output and the load demand is obviously improved.

A multi-energy coupling aggregator feasible region rapid identification method relates to the technical field of comprehensive energy system operation optimization, and comprises the following steps: firstly, establishing an optimization operation model which aims at minimizing the total operation cost and covers electricity-gas-heat multi-energy coupling and equipment operation constraints; secondly, on the basis of the model, defining an interactive energy feasible region (IEFR) and a flexible climbing feasible region (FRFR) to be identified; then, a max-min robust optimization model used for depicting a feasible region boundary is constructed, and the max-min robust optimization model is converted into a mixed integer second-order cone programming MISOCP model capable of being efficiently solved through a strong dual theory and a KKT condition; and finally, carrying out iterative solution by adopting a polyhedral projection algorithm based on a dichotomy and a plane cutting mechanism, and rapidly obtaining an accurate boundary of the IEFR and the FRFR.

The present invention relates to a method and system for generating a real-time penetration trajectory of an aircraft. The present invention solves the penetration trajectory optimization problem by using a second-order cone programming method to obtain the penetration trajectory under different initial conditions, and after generating a data set, uses the generated data set to train a deep neural network to obtain a trained deep neural network model, and then uses the trained deep neural network model as a real-time controller of the angle of attack and the roll angle to quickly generate a penetration trajectory based on the current state parameters of the hypersonic gliding aircraft. In the actual application process, the penetration trajectory can be obtained by simply inputting the obtained current state parameters, so as to improve the solution efficiency of the convex optimization problem, thereby meeting the real-time requirements of the highly dynamic environment of the HGV.

The invention provides a power distribution network comprehensive planning method and system in electric power engineering design. The method comprises the following steps: firstly, acquiring geographic space data and historical operation data in a planning area and cable vibration spectrum data monitored by an optical fiber sensing network; carrying out distributed storage and correlation analysis on the geographic space data and the cable vibration spectrum data through a mixed integer programming parallel acceleration algorithm, and generating a cable aging risk distribution diagram based on spatial topology constraints; performing multi-target collaborative optimization based on a second-order cone programming model, and generating a high-voltage distribution network line capacity increasing scheme and a cable replacement priority sequence; and finally, in combination with a partial discharge frequency domain characteristic change trend in the cable vibration spectrum data, correcting a line corridor extension parameter, and outputting a dynamic planning map including a cable life prediction node and a capacity increasing path. According to the technical scheme provided by the invention, precise planning and optimization of the high-voltage power distribution network are realized, and the accuracy and efficiency of cable life prediction and capacity increasing path planning are improved.

The invention discloses an energy storage type intelligent soft switch double-layer optimization site selection method based on improved whale algorithm and second-order cone programming cooperation, and belongs to the technical field of power distribution networking and optimization operation, and the method comprises the steps: collecting and preprocessing the basic data of a power distribution network; a double-layer optimization model with the minimum daily operation cost of the system as the target is established, the upper layer takes the installation position of the energy storage type intelligent soft switch as a decision variable, and the lower layer takes the three-port power transmission value as a decision variable and constructs constraints based on the power flow equation after second-order cone relaxation and the energy storage operation limitation; an upper layer site selection problem is solved by adopting an improved whale optimization algorithm introducing a self-adaptive inertia weight, a lower layer operation problem is solved by adopting a second-order cone programming method, and collaborative optimization is realized through double-layer interaction. According to the method, global collaborative optimization of planning and operation is realized, the solving precision and efficiency of the site selection problem are improved, and the system operation cost and the network loss are effectively reduced.

The invention discloses a probabilistic optimal power flow calculation method based on a second-order cone and unscented transformation, belongs to the field of power system optimization, and efficiently processes the uncertainty of new energy output and load by coupling the probabilistic sampling capability of unscented transformation (UT) and a convex optimization framework of second-order cone programming (SOCP). According to the method, unscented transformation efficient sampling is utilized, a covariance matrix is directly embedded to simplify correlation processing, and accurate probability distribution of network loss, voltage and line power can be output. Test results show that the calculation efficiency is improved by dozens of times compared with Monte Carlo simulation, the error is lower than 3%, and the method is suitable for risk assessment and operation optimization of the power system.

The invention provides a power distribution network reactive power optimization method and device based on deep reinforcement learning, and the method comprises the steps: building a mixed integer second-order cone programming model according to the state parameters of low-speed reactive power regulation equipment in a power distribution network, solving the mixed integer second-order cone programming model, and obtaining the day-ahead optimal scheduling result of each piece of low-speed reactive power regulation equipment; based on the day-ahead optimal scheduling result and the operation characteristics of the current power distribution network, establishing a multi-agent reinforcement learning model with the goal of minimizing the current operation cost of the power distribution network; a multi-agent depth deterministic strategy gradient algorithm is adopted to train the multi-agent reinforcement learning model, and an intra-day multi-agent real-time scheduling strategy is generated; and fusing the day-ahead optimal scheduling result with the intra-day multi-agent real-time scheduling strategy, and outputting a multi-time-scale coordinated power distribution network reactive power optimization control instruction. According to the technical scheme, a multi-time-scale coordinated reactive power optimization mechanism considering day-ahead economical efficiency and intra-day real-time performance is realized.

The invention provides an energy storage optimization configuration method and system, and the method comprises the steps: building a layered optimization architecture which comprises a dynamic strategy generator and a distributed solver; the dynamic strategy generator drives the Actor-Critic network to generate an energy storage charging and discharging strategy through a constraint-oriented confrontation exploration mechanism; a distributed solver decomposes the global optimization problem into sub-region optimization modules, generates a region decision variable and converts the region decision variable into a global coordination variable; performing relaxation processing on the global coordination variable based on second-order cone programming to generate a coordinator residual error and a relaxation gap parameter; the hierarchical optimization architecture corrects constraint boundaries according to the parameter feedback, and generates corrected power grid constraints and energy storage configuration parameters; and iteratively executing until the coordinator residual error meets a preset threshold value, and outputting an optimal configuration scheme. According to the method, the deep reinforcement learning dynamic decision and distributed convex optimization calculation reliability are combined, and online self-adaptive energy storage configuration is achieved.

The invention relates to the technical field of optimized operation of a comprehensive energy system, and discloses a P2G two-stage modeling gas-electricity-hydrogen coupling distribution network distribution robust low-carbon operation method. The method comprises the following steps: establishing a P2G refinement model in consideration of two-stage processes of water electrolysis hydrogen production and hydrogen methanation, constructing a target function which takes the minimum operation cost of a gas-electricity-hydrogen coupling distribution network system as a target function, and converting a low-carbon operation optimization model into a mixed integer second-order cone programming model for solving; uncertain scenes of wind power output and load fluctuation are obtained, an uncertain parameter distribution probability fuzzy set is constructed based on KL divergence, and a gas-electricity-hydrogen coupling distribution network distribution robust low-carbon operation model considering source load uncertainty is established; according to the method, the flexibility and the environmental protection property of system scheduling can be improved, and the optimal operation decision for balancing the economical efficiency and the safety is obtained on the premise of guaranteeing the safe operation of the system.

This invention discloses a distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication, belonging to the technical field of radar resource allocation. It aims to solve the performance balance problem under multi-task coordination in distributed MIMO radar air combat scenarios. An integrated reconnaissance, interception, and communication adversarial scenario is constructed, using the lower bound of position estimation error (SPEB), missile detection probability, and inter-node communication capacity as performance evaluation indicators for reconnaissance, interception, and communication, respectively. Then, with the goal of minimizing the total system transmit power, a model is constructed by combining relevant performance constraints and node task rules. Finally, the model is transformed into a mixed-integer second-order cone programming problem, and the non-convex terms are convexified using the McCormick envelope method. A numerical solution is obtained based on the CVX solver in MATLAB, achieving joint optimization of node task allocation and power allocation. This invention reduces the total transmit power while satisfying three types of performance requirements, achieving efficient waveform resource scheduling and improving the practicality and robustness of radar countermeasures.

This invention discloses an intelligent control system for a space-type atomic layer deposition equipment, belonging to the field of intelligent control technology. The system of this invention includes: an acquisition module that acquires the pressure difference and flow rate values ​​of the gas curtain pipeline and performs low-pass filtering to obtain a pure signal matrix; a feature extraction module that calculates the flow field impedance and impedance residual based on this matrix and converts them into an empirical probability distribution vector; a model building module that constructs a degenerate distribution uncertainty set and a scheduling objective cost function based on this vector, and converts them into a mixed integer second-order cone programming model; an offline solution module that discretizes and classifies the dynamic distribution divergence tolerance and solves the model, and pre-generates a scheduling strategy library; and an online control module that looks up tables in the strategy library according to the real-time divergence tolerance and outputs substrate transmission speed control and channel switching commands. This invention transforms the minimization-maximization probability model into a convex optimization problem for offline solution, breaking the computing power bottleneck and balancing control response speed and scheduling decision-making through efficient table lookup.

The present invention discloses a reentry tracking guidance method for an aircraft based on a reinforcement learning algorithm, which is used to solve the problems of high dependence on the system model, poor adaptability and guidance performance in the prior art. The implementation scheme is as follows: establishing the continuous optimal control problem of aircraft reentry in the semi-speed coordinate system; transforming the continuous optimal control problem of aircraft reentry into a sequential convex optimal control problem; transforming the sequential convex optimal control problem into a sequential second-order cone programming problem; solving the sequential second-order cone programming problem to obtain the optimal bank angle of the aircraft; sampling the optimal bank angle trajectory to obtain a reference trajectory training data set; constructing a neural network and a reward function; using the training data set to perform offline training on the neural network until the cumulative reward converges to the maximum value to obtain a guidance network; obtaining the reentry guidance command of the aircraft online through the guidance network and tracking the target altitude in real time. The present invention has strong adaptability and high guidance accuracy, and can be used for rocket recovery.

The present invention discloses a positioning method for a wireless sensor network based on hybrid RSS / AOA with unknown transmission power. Based on the RSS measurement model, the AOA azimuth measurement model, and the AOA elevation angle measurement model, an original non-convex objective optimization problem is constructed according to the least squares criterion; the semi-definite relaxation technique and the second-order cone relaxation technique are used to relax the variables or constraint conditions in the equivalent description after introducing variables in the original non-convex objective optimization problem constructed according to the least squares criterion, so as to obtain the final convex objective optimization problem; the hybrid semi-definite / second-order cone programming technique is used to solve the final convex objective optimization problem, and the position and transmission power of the target node are jointly estimated; the advantage is that it can ensure that the optimization of unknown variables can obtain the global optimal solution and is not affected by local convergence, thereby effectively improving the positioning accuracy and effectively suppressing the influence of measurement noise errors.

The invention provides an off-grid ReP2A integrated system topology capacity collaborative planning method, and relates to the technical field of renewable energy source hydrogen production and synthesis ammonia, and the method comprises the steps: constructing a mixed integer second-order cone programming model covering a power network, energy storage, hydrogen production, hydrogen storage, hydrogen transmission and synthesis ammonia full-chain coupling constraint; wherein the mixed integer second-order cone programming model takes the minimum annual average comprehensive cost of the system as a target to collaboratively optimize equipment capacity configuration, power transmission and hydrogen transmission network topology and a system operation strategy; and solving the mixed integer second-order cone programming model by adopting a branch and bound Benders decomposition algorithm, and outputting a programming construction scheme. The scheme can solve the problems of high comprehensive cost and low operation efficiency of the system.

The invention discloses an active power distribution network two-stage risk scheduling method and device, a medium and a product, and relates to the technical field of power distribution network scheduling, and the method comprises the steps: constructing a source-load typical scene set considering prediction error space-time correlation; constructing an active power distribution network two-stage risk scheduling model based on the source-load typical scene set; the active power distribution network two-stage risk scheduling model comprises a day-ahead scheduling stage model and an intra-day scheduling stage model; and converting the two-stage risk scheduling model of the active power distribution network from a mixed integer nonlinear programming problem to a mixed integer second-order cone programming problem through a second-order cone relaxation method, and solving the two-stage risk scheduling model of the active power distribution network to obtain a scheduling scheme of the active power distribution network. The method can effectively cooperate with the source-grid-load storage to flexibly adjust the resources so as to reduce the power grid dispatching operation risk caused by the source-load uncertainty.

The invention discloses a power distribution network new energy bearing capacity determination method and device, a medium and electronic equipment, and relates to the technical field of new energy access, and the method comprises the steps: obtaining a plurality of transformer area topology networks of a target power distribution network and operation parameters of multilevel new energy bearing capacity evaluation indexes corresponding to the transformer area topology networks; performing constraint condition construction by adopting the operation parameters corresponding to the topological networks to obtain constraint conditions corresponding to the topological networks; based on each constraint condition and a target function taking maximization of the photovoltaic access capacity of the target power distribution network as a target, a mixed integer second-order cone programming model is adopted to carry out model construction, and an optimization model of the target power distribution network is obtained; and solving the optimization model by adopting a branch and bound algorithm to obtain the new energy bearing capacity of the target power distribution network. The new energy bearing capacity of the power distribution network determined by the method is more accurate.

The present invention discloses a method for optimizing the trajectory of an aircraft during reentry based on a neural network, which mainly solves the problems of poor real-time performance and adaptability in the prior art. The implementation scheme is as follows: establishing the continuous optimal control problem of aircraft reentry in the semi-speed coordinate system; transforming the continuous optimal control problem of aircraft reentry into a sequential convex optimal control problem; transforming the sequential convex optimal control problem into a sequential second-order cone programming problem; solving the sequential second-order cone programming problem; sampling from the solution results to obtain a state quantity data set and a control quantity data set; constructing a neural network and a loss function; using part of the state quantity data set as a training data set to perform offline training on the neural network until the loss function converges to a minimum value, obtaining a trained trajectory network; and using the trajectory network to online obtain the trajectory optimization result of the aircraft during reentry. The present invention has strong adaptability and good real-time performance, reduces the influence of parameter changes on the aircraft, and can be used for rocket recovery.

The invention relates to a train energy-saving optimization method and device and a storage medium. The method comprises the following steps: acquiring train operation line data and train parameters; a train energy-saving convex optimization model is determined, the train energy-saving convex optimization model comprises an objective function and constraint conditions, the constraint conditions comprise constraint conditions for convex relaxation auxiliary variables, and the convex relaxation auxiliary variables are used for replacing non-convex terms in the train energy-saving optimization model; and based on the line data and the train parameters, solving the train energy-saving convex optimization model by taking the minimum train operation energy consumption as an optimization target, and determining train operation state information. According to the embodiment of the invention, an energy-saving optimization problem of the train can be converted into a second-order cone programming problem (namely a convex optimization problem), the effect of stable and rapid solution can be realized, a globally optimal solution can be obtained, various complex route constraint conditions can be processed, the calculation efficiency is improved, and calculation resources are saved.

The invention provides a hybrid optimization algorithm for a double-layer planning model of a rotary power flow controller and an energy router. According to the invention, a hybrid optimization algorithm combining an improved particle swarm optimization algorithm and second-order cone programming is formed for a double-layer programming model of a rotation power flow controller and an energy router based on space-time characteristics. The algorithm comprises the following steps of: solving an upper model by adopting a time-varying coefficient improved particle swarm optimization (IPSO) algorithm; and second-order cone relaxation processing is adopted for the lower-layer model. And outputting an optimal operation result of the RPFC and the ER through a hybrid optimization algorithm according to the framework of the double-layer coordinated planning model and the solving process of the model in combination with the objective function and the constraint condition of the double-layer model. Finally, through the joint access of the RPFC and the ER, the voltage spatial and temporal distribution of the power distribution network can be effectively improved, and the annual investment and operation cost is reduced.

The invention discloses an MPC-based urban power system multi-state-multi-time scale optimization scheduling method, and belongs to the technical field of power system optimization scheduling. The method comprises the following steps: S1, dividing a system operation state based on a main and distribution network connection state and a node voltage out-of-limit condition; s2, establishing corresponding optimization scheduling models for different states; s3, based on a model prediction control framework, adaptively adjusting a prediction time domain and a control time domain according to the state emergency degree, and introducing a state switching hysteresis mechanism to suppress frequent switching; s4, constructing a multi-state-multi-time scale collaborative optimization scheduling model; and S5, solving by adopting a mixed integer second-order cone programming method to obtain an optimal scheduling plan of each distributed power supply, energy storage and load. According to the method, the voltage safety, the economical efficiency and the operation stability of the urban power system containing the high-proportion renewable energy sources in various operation states can be effectively improved, the problem of short view of energy storage is solved, and fine scheduling is achieved.

The invention provides a voltage out-of-limit suppression type power distribution network dynamic reconstruction method and system, and relates to the technical field of power distribution network dynamic reconstruction, and the method comprises the steps: obtaining basic data of a power distribution network, including a topological structure, system impedance, DG output and load parameters; establishing a radial operation constraint of the power distribution network based on the quantitative relationship among the branch number, the node number and the root node of the power distribution network; based on the single commodity flow thought, constructing a power distribution network dynamic reconstruction constraint based on the single commodity flow thought; the method comprises the following steps: introducing a branch state variable on the basis of a traditional Distflow power flow model, converting the branch state variable into a power distribution network power flow model in a mixed integer nonlinear programming form, and converting the model into a mixed integer second-order cone programming model by adopting a second-order cone relaxation and Big-M method to form a complete voltage out-of-limit suppression type power distribution network dynamic reconstruction model; and solving the dynamic reconstruction model, generating a switching operation sequence meeting voltage constraints, and completing topological optimization of the power distribution network.

The invention relates to the technical field of power grid optimization scheduling, and discloses a power distribution network distributed photovoltaic consumption capability improvement method, system and device and a medium, and the method comprises the steps: obtaining the operation side data of a power distribution network to construct a power distribution network distributed photovoltaic consumption capability improvement model, converting the model into a mixed integer second-order cone programming model, and carrying out the optimization scheduling of the power distribution network; a KSCH-Homotopy algorithm formed by a Homotopy algorithm introducing a KSCH smoothing function is adopted for solving, so that when the cone relaxation deviation of the obtained solving result does not meet the preset precision requirement, the power distribution network cut plane constraint introducing a dynamic adjustment multiplier is added in the mixed integer second-order cone programming model; and based on the updated mixed integer second-order cone programming model, iteratively executing the model construction step and the model solving step until the cone relaxation deviation formed by solving meets the preset precision requirement, and executing the solving result as a consumption capability improvement strategy, thereby effectively improving the distributed photovoltaic consumption capability.

The invention relates to the technical field of reactive power optimization of a power system, and discloses a low-voltage power distribution network reconstruction method, system and device based on demand side response, and a medium, and the method comprises the steps: enabling a demand side response strategy to be embedded into a power distribution network reconstruction model, and enabling the adjustment effect of demand side resources to be fully exerted, and randomness and volatility caused by access of high-proportion clean energy are effectively balanced. In the optimization solution process, second-order cone relaxation and reasonable variable setting are adopted, a complex non-convex problem is converted into a mixed integer second-order cone programming problem capable of being efficiently solved, and the defect that a traditional method is prone to falling into a local optimal solution is overcome. In the reconstruction process of the power distribution network, strict topology constraint verification and switch operation frequency limitation ensure the safety and reliability of the power distribution network after reconstruction.

The application discloses a distributed decoupling optimization method for a comprehensive energy system considering carbon trading, and comprises the following steps: establishing a centralized optimization framework of an electrically coupled system, initializing global data and information of a power system and a gas system; constructing a centralized model of a regional comprehensive energy system, wherein an objective function of the model comprises the power system, the gas system and carbon emission cost, and corresponding operation constraints are added to the objective function; converting an original non-convex problem into a mixed integer second-order cone programming based on SOCP relaxation, and performing reactive power optimization by using an ADMM consistent distributed algorithm; introducing consensus variables to represent the synergistic effect between the power and natural gas networks based on the ADMM method of consistent variables, so as to solve a distributed optimization problem of the regional comprehensive energy system; and finally, a case analysis of a regional electric and gas comprehensive energy integrated system is performed, and the distributed decoupling optimization of the system is realized by using the ADMM method of consistency. The application also comprises a distributed decoupling optimization system for a comprehensive energy system considering carbon trading.

The present application belongs to the technical field of trajectory optimization of morphing aircraft, and specifically discloses a reentry trajectory optimization method of morphing aircraft, comprising: establishing a reentry dynamics model and a multi-constraint condition model of the morphing aircraft; constructing a minimum drag coefficient configuration-Mach number profile and a reentry height flight corridor, and converting the trajectory optimization problem of the morphing aircraft into a trajectory optimization problem with a fixed aerodynamic shape; performing dimensionless, linearization and discretization processing on the reentry dynamics model, and introducing a control quantity transformation, so as to convert the trajectory optimization problem into a standard second-order cone programming problem; using a primal-dual interior point method to discretely solve the second-order cone programming problem, so as to obtain an optimal reentry trajectory satisfying all constraint conditions, and complete the reentry trajectory optimization method of the morphing aircraft. The present application avoids calculating the configuration parameter derivative in the trajectory optimization, and solves the problem that the derivative information of the configuration parameter cannot be obtained from the aerodynamic data.

The invention provides a slope overall process analysis method based on a mathematical optimization particle finite element method, which comprises the following steps of: establishing a control equation of quasi-static and dynamic elastic-plastic problems under a plane strain condition in a slope region, and converting the control equation into a minimum-maximum optimization problem according to a Hellinger-Reissner variation principle, therefore, a complex motion form in the slope area is converted into a simple minimum-maximum optimization problem; finite element discrete analysis is carried out on the minimum-maximum optimization problem, then a standard second-order cone programming problem is constructed, and then an optimal solution of the second-order cone programming problem can be rapidly obtained through an interior point method, namely, a state variable is obtained; and according to the state variables and in combination with a particle finite element method, calculating the motion state before and after slope instability, thereby obtaining the whole-process operation state of the slope. The analysis method provided by the invention not only can judge the safety and stability of the slope in the static state, but also can simulate and analyze the movement process after the slope is damaged.

The invention provides a quasi-regular double six-discontinuous triangulation network construction method for potential sliding surface search of a side slope. The method comprises the following steps: iteratively generating a quasi-regular initial triangulation network adapted to the boundary of a side slope model; dividing the local part into six grids, and encrypting to generate a six-direction array arrangement topological grid; constructing a six-node triangular unit and a high-order discontinuous line data format; and solving and searching the potential sliding surface of the slope by a second-order cone programming model. The method not only adapts to the geometric boundary of a slope discrete model, but also can meet the special requirements of a quasi-regular double six-discontinuous triangulation network, and a six-direction through discontinuous grid structure in any tiny area is formed; meanwhile, local partitions can be further preset through the double six-discontinuous grids, resource consumption of grid units in non-critical areas is reduced, the double six-discontinuous grids are applied to limit analysis of upper limit finite elements, high-precision search of the potential sliding surface of the side slope is achieved through rapid solving of a second-order cone programming problem, and the search precision can be guaranteed while the calculation burden can be reduced.