64 results about "Global optimization problem" patented technology

The invention discloses an agricultural product cold chain carriage temperature control method based on space equalization, and relates to the technical field of intelligent cold chain logistics, and the method comprises the steps: collecting carriage three-dimensional point cloud data and agricultural product electronic tag information, building a carriage loading three-dimensional model, carrying out the space grid division, and generating a partition topological feature vector; collecting temperature, humidity and carbon dioxide concentration data, calculating average temperature and average gas concentration observation values, and generating a breathing heat estimation value in combination with the partition topological feature vector; inputting the breathing heat estimation value into a dynamic prediction model, generating a subarea cold load curve and a priority weight by combining the subarea temperature deviation, and outputting a subarea cold load sequence; and according to the partition cold load sequence, generating a local execution proposal, constructing a global optimization problem taking the space temperature balance degree as an optimization target, and generating a global control plan. According to the method, global temperature equalization and energy consumption collaborative optimization are realized through network reasoning and dynamic optimization of the meta-reinforcement learning strategy.

The invention relates to the technical field of optical signal processing and analysis, and particularly provides a seabed crustal activity monitoring method based on a differential phase sensitive optical time domain reflectometer, which comprises the following steps: carrying out differential processing on an echo optical pulse signal to obtain a first differential echo matrix; and setting a dynamic window constraint to obtain a second differential echo matrix. The second differential echo matrix includes a low rank term, a sparse term, and a noise term. And constructing a global optimization problem according to the second differential echo matrix, and solving a sparse item estimation matrix corresponding to the sparse item. And separating irrelevant activities in the sparse item estimation matrix to obtain a target activity matrix, and then obtaining a monitoring result. According to the method, non-low-rank trend features such as slow-varying interference are converted into low-rank features through differential processing, periodic and regular components in received signals are clearer, periodic events and sudden events can be separated through a low-rank sparse decomposition model, human-related activities are further separated, and the accuracy of the received signals is improved. Accurate monitoring of the sudden movement of the seabed crust is realized.

The invention belongs to the field of power distribution network operation control, and discloses a power distribution network global optimization regulation and control method, system and device and a medium, and the method comprises the steps: obtaining multi-dimensional indexes including regional modularity, regional reactive power balance degree and regional active power balance degree in a power distribution network; based on a comprehensive evaluation mechanism of a multi-dimensional index, an optimal power distribution network region division result is obtained by utilizing iterative calculation of a heuristic search algorithm; a power distribution network global optimization model with cost optimization as a target is constructed so as to realize control of the comprehensive cost of the power distribution network; on the basis of a distributed optimization algorithm, a global optimization problem of the power distribution network is decomposed into a cloud main problem and a side terminal problem, and global optimality of resource allocation among different power distribution network regions is realized by solving the cloud main problem; and the optimal control strategy in the power distribution network area is realized by using the control result of the comprehensive cost of the power distribution network and the solution of the edge terminal problem. And the coordination control of the global massive controllable resources of the power distribution network is realized.

The invention discloses a distributed energy resource intelligent scheduling method based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: collecting operation data of distributed energy equipment, carrying out the preprocessing of the operation data, obtaining an equipment operation data set, carrying out the group intelligent initialization of the equipment operation data set, and generating an initial pheromone matrix; carrying out local optimization on the equipment operation data set and the initial pheromone matrix to generate an updated pheromone matrix and an equipment candidate scheduling scheme, and carrying out screening and verification to generate a local optimal scheduling scheme; receiving a local optimal scheduling scheme by adopting a quantum computing node, taking the local optimal scheduling scheme as an initial solution, constructing a global optimization problem, outputting a global optimal scheduling scheme, and converting the global optimal scheduling scheme into a global scheduling instruction; according to the method, by formulating a global optimization problem, equipment interaction, economic factors and multi-dimensional constraints in a network range are captured, coordination and constraint compliance among equipment are realized, and the problem of instability of a power grid is prevented.

The invention discloses a multi-vehicle-queue cooperative control method based on distributed model predictive control, and the method comprises the following steps: constructing a multi-vehicle-queue system and a dynamic model, building a one-way communication topological structure, defining the coupling constraint of vehicles and queues based on the communication topological structure, and carrying out the prediction of the multi-vehicle-queue cooperative control. Constructing a distributed model predictive control optimization problem and a cost function, wherein the distributed model predictive control optimization problem comprises a vehicle node local optimization problem, a queue global optimization problem and a multi-target optimization problem; and solving an optimization control problem for each vehicle node, and adjusting the control input and the motion state of each vehicle. According to the technical scheme, dynamic synchronization and stable control between vehicles and between queues are achieved by means of one-way communication topology design, introduction of coupling constraints inside the queues and between the queues, construction of a local optimization problem and combination of a global coordination mechanism, and the cooperative control performance of a multi-vehicle queue system in a complex dynamic environment can be effectively improved.

The invention discloses an electric vehicle collaborative optimization scheduling method based on a'network-station-vehicle 'layered architecture. The method comprises the following steps: constructing a double-layer distributed scheduling model; in the upper-layer model, modeling is carried out on aggregation flexibility of the charging stations, and an aggregation charging and discharging plan of each charging station is formulated by taking maximization of the total benefit of the system as a target; in the lower-layer model, the charging and discharging behaviors of the individual electric vehicle are scheduled by following an upper-layer plan instruction and taking the comprehensive satisfaction degree of a vehicle owner as a target; a second-order cone programming relaxation technology is adopted to convert a non-convex power flow constraint equation in the upper-layer model into convex constraint; decomposing the global optimization problem into a plurality of independent sub-problems which can be solved by each charging station in parallel by adopting an improved Lagrange relaxation dual method; and iteratively updating the Lagrangian multiplier through a sectional self-adaptive step length strategy until the algorithm is converged and a global optimal scheduling plan is generated. According to the method, the scheduling effect close to centralized optimization can be obtained while the privacy of the user is protected; and the problem of curse of dimensionality is effectively solved.

The invention discloses a power distribution network voltage coordination control method based on an event trigger mechanism, and belongs to the technical field of power distribution control. Dividing the power distribution network into a plurality of sub-regions, establishing a coupling constraint of each region, and decomposing a global optimization problem into optimization problems of each region; a pre-designed ETM-DAL-ADMM algorithm is adopted, a Lagrange function of an optimization problem of each region of a penalty coefficient is linearly processed and dynamically adjusted, local decision variables including boundary variables of each region are updated, inter-region information exchange is carried out according to a preset event triggering mechanism, and a final local decision variable is iteratively solved; and according to the final local decision variable, adjusting the reactive power output of the distributed generator in each region, so that the voltage of the power distribution network conforms to the constraint and the branch loss is minimized. The problem that the stability of the power distribution system cannot be ensured when the communication capability is insufficient in the existing control method can be solved.

The invention discloses a magnetic source target positioning method, which belongs to the technical field of geophysical exploration, is used for positioning a magnetic source target, and comprises the following steps: collecting and preprocessing plane grid magnetic anomaly data, constructing a simplified Euler deconvolution model under the constraint of derivative features, segmenting observation data by using a local sliding window, and resolving to generate an Euler solution point cloud. The method comprises the following steps: converting a selection problem of an equivalent scale factor into a one-dimensional global optimization problem, adaptively searching an optimal solution through a Bayesian optimization method, identifying a magnetic source high-probability solution point and a noise solution point by using a density-based spatial clustering method, introducing a density penalty term to strengthen a high-probability solution constraint, and constructing a final positioning target function. And outputting the optimal position solution of the magnetic source. According to the method, a traditional Euler deconvolution model is simplified, noise interference is effectively suppressed, the stability and robustness of the inversion process are improved, uncertainty caused by manual parameter selection is avoided, and the positioning precision is improved.

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 discloses an electricity-carbon point-to-point distributed transaction method, system, device and medium, and belongs to the technical field of electricity-carbon transactions, and the method comprises the steps: obtaining a power grid structure and carbon emission data, and constructing a network constraint model; based on the network constraint model, decomposing a complex global optimization problem into a plurality of sub-problems through an alternating direction multiplier method to obtain an optimal transaction matching scheme; based on the optimal transaction matching scheme, the data is stored to a plurality of nodes in a scattered mode through the block chain technology, and a safe transaction environment is obtained; based on a safe transaction environment, an electricity-carbon point-to-point transaction mechanism is adopted, and accurate distribution of carbon quotas is realized through transaction carbon emission reduction service. According to the invention, the transaction security and economy are improved, and the method is suitable for a smart power grid scene with high-proportion renewable energy access.

The embodiment of the invention provides a data-driven resource allocation method, which belongs to the technical field of communication, and specifically comprises the following steps: forming an air-space-ground network; performing three-layer clustering on the monitoring area; merging and splitting the cluster and the group according to a preset standard; designing a three-hop transmission process; designing a ground-sky transmission model according to beam allocation and a rendezvous point rotation mechanism; designing a space-air transmission model according to a non-orthogonal frequency division multiple access technology; constructing a global optimization problem according to the ground-sky transmission model and the sky-air transmission model, and splitting the global optimization problem into a first optimization problem corresponding to the ground-sky transmission model and a second optimization problem corresponding to the sky-air transmission model; setting a solution scheme of the first optimization problem; setting a solution scheme of a second optimization problem; designing an ISS-PPO algorithm to solve the first optimization problem and the second optimization problem; and designing a global optimization problem solution. Through the scheme disclosed by the invention, the data transmission timeliness and the frequency spectrum energy efficiency are ensured.

This invention discloses an optimization control method for deep purification and high-value copper and arsenic recovery in a heavy metal wastewater treatment process. The heavy metal wastewater treatment process utilizes a cascade of multiple unit-level equipment, with process state variables of adjacent unit-level equipment coupled to each other. The optimization control method at each time step includes: first, substituting current data into a global optimization problem model and solving the optimization problem to obtain the current setpoint concentration of each heavy metal ion for each unit-level equipment; the global optimization objective is to achieve the heavy metal ion removal rate and economic benefit of heavy metal recovery for the entire heavy metal wastewater treatment process; then, a decentralized control scheme is proposed, whereby each unit-level equipment uses a model predictive control strategy based on its current setpoint concentration of heavy metal ions to obtain the current control input for each unit-level equipment. This invention can improve the purity of sulfide slag and the economic benefits of the process.

The invention provides a rapid trajectory planning and guidance method for a rocket landing section based on flight airspace grid division. The method mainly comprises the steps of establishment of a rocket landing column coordinate system, flight airspace grid division, a sequence convex optimization model for solving an absolute optimal trajectory, online interpolation reduction of the absolute optimal trajectory, online sequence convex optimization trajectory optimization and polynomial guidance. The invention aims to solve the problem of high-precision fixed-point landing of a reusable carrier rocket power landing section, three-dimensional motion of a rocket landing process is decomposed into two-dimensional motion and tangential motion of a longitudinal plane by using a landing column coordinate system, the complexity of an optimization model is reduced, an interpolation table is established by using a flight airspace grid division method, and the reliability of the model is improved. According to the method, the absolute optimal trajectory is rapidly obtained on line, the global optimization problem is converted into the local optimization problem, the solving scale of an optimization model is reduced, and the real-time performance of online optimization is improved while various constraints and fuel optimization are met.

The application relates to the field of intelligent control and information technology, and provides a natural gas generator set power device multi-working condition prediction control method. The application divides different intervals based on torque, designs optimal control strategies in each interval, converts a global optimization problem into a local optimization problem, and the algorithm idea is to guide a local minimum value optimization algorithm through the global optimization, and finally, better results can be achieved in control.

The application provides a kind of R&D resource visual scheduling method and system, method includes: based on original data, including several task entities and several resource entities are constructed, the matching degree between task entity and resource entity is obtained;The static business value score corresponding to the task entity, the time urgency factor, the task dependence coupling strength are obtained, and then the priority coefficient is obtained;Based on task entity and resource entity, decision variable is constructed, and final multi-objective optimization function is constructed, resource scheduling scheme is obtained based on final multi-objective optimization function, and resource scheduling scheme is displayed through visual board.The matching degree and priority coefficient are combined, the final multi-objective optimization function is constructed, the subjective choice of weighing between multiple optimization targets in traditional method is converted into global optimization problem, and the automatic and intelligent resource scheduling strategy weighing is realized.

The invention belongs to the technical field of traffic transportation, and discloses a fuel cell vehicle power system SOC track rapid planning method, system and device, and a medium, and the method comprises the steps: obtaining the current starting and ending point coordinate data of a vehicle and the real-time traffic flow data of each road section; inputting the current starting and ending point coordinate data and the real-time traffic flow data of each road section into a driving power prediction model for classified prediction, and outputting the average driving power of each road section; according to the distribution of the average driving power of each road section, selecting the average power of the fuel cell system of each road section as a decision variable, and constructing a global optimization problem by taking the minimum fuel consumption as an optimization target; and solving the global optimization problem, and calculating the SOC variation of each road section corresponding to the average power of the fuel cell system of each road section to obtain a current global reference SOC trajectory. The SOC track rapid planning method does not depend on excessive urban traffic characteristics, and the SOC track rapid planning method is small in calculation amount and high in real-time performance.

The application discloses a power distribution network voltage coordination control method based on an event triggering mechanism and belongs to the technical field of power distribution control. The method comprises the following steps: a global optimization model is constructed; the power distribution network is divided into multiple sub-regions, coupling constraints of each region are established, and the global optimization problem is decomposed into regional optimization problems; a pre-designed ETM-DAL-ADMM algorithm is adopted, a Lagrange function of the regional optimization problem is linearly processed and dynamically adjusted in a penalty coefficient, local decision variables of each region including boundary variables are updated, information exchange between regions is carried out according to a pre-set event triggering mechanism, and final local decision variables are iteratively solved; and reactive power outputs of distributed generators in each region are adjusted according to the final local decision variables, so that the voltage of the power distribution network meets the constraints and branch loss is minimized. The application can solve the problem that the existing control method cannot guarantee the stability of the power distribution system when the communication capability is insufficient.

The invention discloses a high-fidelity identification method and system for a pump and a pipe network system based on active resistance modulation, and the method comprises the steps: firstly, manually setting at least two pipeline resistance states with obvious differences through adjusting devices such as valves, carrying out the frequency conversion operation of a pump in each state, and collecting a plurality of groups of pressure and frequency data; then, an integrated system physical model is constructed, and the model forcibly uses uniquely shared pump performance parameters and pipe network static lift parameters to synchronously fit all data clusters in different resistance states; and finally, by solving the global optimization problem of the model, identifying a real performance curve of the pump, the static lift of the pipe network and the independent resistance coefficient in each state at one time with high precision. According to the method, a flow sensor is not needed, the ill-conditioned problem of a passive data analysis method is solved, on-site in-situ calibration comparable with laboratory precision is achieved, and a reliable basis is provided for construction of a digital twin model and a virtual flowmeter and system health assessment.

The invention provides a hierarchical collaborative optimization and settlement method for large-scale industrial demand response, and relates to the technical field of power systems, and the method comprises the steps: dividing large-scale industrial demand response resources into a plurality of hierarchies, and constructing an optimization model; disassembling a global optimization problem into local sub-problems, defining a consistency variable to constrain global consistency, and generating a scheduling instruction through iterative calculation; acquiring actual response data in response to the scheduling instruction, and performing hash operation and digital signature processing on the actual response data to generate a signature abstract; verifying the signature abstract by using an intelligent contract, executing real-time settlement after verification is passed to obtain real-time settlement data, and summarizing to obtain periodic settlement data; and calculating a performance index according to the scheduling instruction and the actual response data, and performing numerical correction on the real-time settlement data and the periodic settlement data by using the performance index to obtain adjusted settlement data. According to the invention, rapid scheduling and convergence of large-scale resources are realized, and the enthusiasm and fairness of users participating in scheduling are effectively improved.

The invention relates to the technical field of energy internet and electricity-hydrogen coupling, in particular to an electricity-hydrogen coupling intelligent management and control platform. The platform comprises an energy input module, an electricity-hydrogen conversion module, a hybrid energy storage module and an intelligent management and control center. The distributed optimization method is adopted, the global optimization problem is decomposed into a plurality of micro-grid sub-problems, interaction is carried out only through boundary information, the calculation complexity is remarkably reduced while privacy of all micro-grids is protected, an electrolytic cell serves as a flexible load to be connected into the micro-grids, the operation power is improved in the low-electricity-price period, the hydrogen energy yield is increased, and the energy efficiency is improved. The operation state index is monitored in real time, the power distribution of the electrolytic cell is dynamically adjusted, that is, the operation power is automatically reduced and the equipment overload risk is prevented when abnormal fluctuation of the power is detected, and the method is suitable for an electrolytic cell control strategy of a wind and light power supply with strong fluctuation; the service life of the electrolytic bath is prolonged, and the hydrogen production cost of the system is reduced.

This invention discloses a multi-agent collaborative optimization scheduling method for medium- and low-voltage distribution networks under extreme scenarios, relating to the field of energy dispatching technology. The method includes: S1, unified modeling based on probability distribution, which constructs random state vectors and uses probabilistic statistical methods to uniformly characterize the random characteristics of different types of resources; S2, extreme scenario generation based on conditional variational autoencoders, which generates a set of scenario samples satisfying conditional distributions by learning the mapping relationship between historical meteorological data and operating states; S3, collaborative optimization based on Lagrange price signals, which achieves multi-agent collaborative decision-making driven by constructing a revenue function coupled with the scenario; S4, multi-agent coupled modeling based on graph neural networks, which constructs a distribution network topology graph and uses graph convolution operations to aggregate features of node states and extract the interaction relationship between electrical distance and power; and S5, a distributed optimization solution method based on the alternating direction multiplier method, which decomposes the global optimization problem into sub-problems of each agent.

The invention discloses a high-precision hand-eye calibration algorithm based on nonlinear optimization. The algorithm comprises the following steps: S1, data acquisition; s2, constructing a rotation matrix error model and performing precision analysis; s3, formulating an optimal calibration track rule according to the error model; s4, quaternion parameterization is used, and a global optimization problem is established; s5, solving a global optimization problem through a convex relaxation technology; and S6, outputting and verifying a calibration result. The invention relates to the technical field of robot vision and automatic control, and has the beneficial effects that a rotation matrix error analysis model is constructed (step S2), and the internal mathematical relationship between the hand-eye calibration precision and the motion track parameters (such as the rotation angle and the rotation axis included angle) of the mechanical arm is quantitatively revealed for the first time.

The invention relates to the technical field of virtual power plants, in particular to a virtual power plant energy scheduling method and system based on distributed optimization, and the method comprises the steps: constructing a distributed energy node state sensing network, collecting the operation state data and load prediction data of each distributed energy unit in real time, and transmitting the data to a distributed optimization scheduling center; based on an improved distributed model predictive control algorithm, establishing a multi-target energy scheduling optimization model, and decomposing a global optimization problem into local sub-problems of each node; parallel iterative solution of each sub-problem is realized through a distributed collaborative solution mechanism, and a scheduling scheme is dynamically corrected; and based on the real-time operation deviation and the external environment change, adaptively adjusting a scheduling strategy and issuing the scheduling strategy to each energy unit for execution. According to the invention, distributed energy resources are integrated through a distributed optimization architecture, efficient collaborative scheduling of virtual power plant energy is realized, the energy utilization rate is improved, the operation cost is reduced, and the system operation stability and flexibility are enhanced.

The present application belongs to one-dimensional signal processing technical field, and particularly relates to a Duffing oscillator weak signal enhancement method with automatic parameter optimization. The present application takes a double-stage parallel Duffing random resonance system as a mixed signal processing model, uses a quantum particle swarm algorithm with outstanding performance in optimization solution as a parameter optimization algorithm, converts the double-stage parallel random resonance parameter adjustment problem into a multi-parameter global optimization problem, and performs signal enhancement processing on the signal to be enhanced input into the system according to the best parameters, and outputs the enhanced signal. The present application uses parameter self-adaptive optimization to quickly obtain the best parameters of the double-stage parallel Duffing random resonance system, greatly improves the efficiency and precision of the system best parameter setting, and can quickly and high-quality output the enhanced useful signal.

The present invention discloses a multi-period empty container transfer system and method. To address the problem of more efficient container utilization in transportation systems and reduced transportation and storage costs, the present invention addresses the dynamic and real-time nature of empty container transfer, constructing an optimization model for the coordinated transfer of empty containers between railway container freight stations based on Lyapunov optimization. The model is simplified using the Lyapunov optimization method, transforming the global optimization problem into multiple single-period problems. The resulting empty container transfer schemes between container freight stations for each period within the planned cycle can address various unexpected changes in actual operations during empty container transfers between railway container freight stations, avoiding untimely empty container transfers or long, circuitous empty container transfer routes, and substantially reducing transfer costs.

The invention discloses a cooperative positioning method based on bargaining game and improved bidding auction, and belongs to the field of wireless positioning, and the method comprises the following steps: constructing an air-ground integrated cooperative positioning network model composed of three types of nodes of a base station, a user and an unmanned aerial vehicle; constructing a global optimization problem of the cooperative positioning network model under the resource constraint, and decoupling the global optimization problem into two sub-problems; adopting bargaining game and improved bidding auction to obtain cooperative positioning schemes of different cooperative modes; a Lagrangian multiplier method is adopted to solve a proxy node cooperation sub-problem, an improved bidding auction algorithm is adopted to solve a proxy node and unmanned aerial vehicle node cooperation sub-problem, and finally cooperative positioning is realized. According to the method, the game theory, the Lagrangian multiplier method and the improved bidding auction algorithm are combined, the cooperative positioning problem of agent nodes in an air-ground integrated scene with limited resources is solved, and effective configuration of the resources is realized while the positioning precision is improved.

This invention provides a rapid trajectory planning and guidance method for rocket landing phase based on flight airspace grid partitioning. It mainly includes establishing a rocket landing column coordinate system, flight airspace grid partitioning, a sequential convex optimization model for solving the absolute optimal trajectory, online interpolation to restore the absolute optimal trajectory, online sequential convex optimization trajectory optimization, and polynomial guidance. This invention aims to solve the problem of high-precision fixed-point landing of reusable launch vehicles' powered landing phase. By utilizing the landing column coordinate system, the three-dimensional motion of the rocket landing process is decomposed into two-dimensional motion in the longitudinal plane and tangential motion, reducing the complexity of the optimization model. An interpolation table is established using the flight airspace grid partitioning method, enabling rapid online acquisition of the absolute optimal trajectory. This transforms the global optimization problem into a local optimization problem, reducing the solution scale of the optimization model and improving the real-time performance of online optimization while satisfying various constraints and fuel optimization.

The invention discloses a rural electricity-gas-heat integrated energy system distributed collaborative optimization scheduling method under a weak communication condition, and relates to the technical field of integrated energy system scheduling, and the method comprises the steps: collecting energy parameters and data in a rural integrated energy system, carrying out the function division, and dividing the energy parameters and data into subsystems; modeling is carried out on the divided subsystems, constraint conditions are established at the same time, and a global optimization objective function is established; decomposing a global optimization problem into independent sub-problems of subsystems based on the constructed subsystem model and constraint conditions; establishing a distributed collaborative optimization scheduling model by taking the operation cost minimization of each subsystem as a target and considering the coupling constraint with other subsystems at the same time; and performing distributed iterative solution based on the distributed collaborative optimization scheduling model. According to the method, the overall optimization problem is divided into a plurality of sub-problems through the alternating direction multiplier method, each sub-system can independently complete calculation locally, only limited information interaction is needed, and therefore the adaptability of the system is remarkably improved.

The present invention relates to the technical field of optical signal processing and analysis, and specifically provides a method for monitoring seabed crust activity based on a differential phase-sensitive optical time domain reflectometer, comprising: differentially processing an echo optical pulse signal to obtain a first differential echo matrix. Setting a dynamic window constraint to obtain a second differential echo matrix. The second differential echo matrix includes low-rank terms, sparse terms, and noise terms. A global optimization problem is constructed based on the second differential echo matrix, and the sparse term estimation matrix corresponding to the sparse terms is solved. The irrelevant activities in the sparse term estimation matrix are separated to obtain a target activity matrix, and then the monitoring results are obtained. The above method converts non-low-rank trend features such as slowly varying interference into low-rank features through differential processing, so that the periodic and regular components in the received signal are clearer, and can separate periodic events and sudden events through a low-rank sparse decomposition model, and further separate irrelevant human activities, thereby realizing accurate monitoring of sudden activities in the seabed crust.

The invention discloses a virtual power plant control method and system based on a consistency principle, and the method comprises the steps: configuring an intelligent agent for each virtual power plant, and calculating the adjustment cost and adjustment time based on the consistency principle through each intelligent agent; the adjustment cost comprises power change cost and scheduling instruction cost; constructing a multi-objective optimization function containing adjustment cost and adjustment time, and solving a control strategy of each agent; and each intelligent agent is adjusted according to the control strategy. According to the method, a complicated global optimization problem is decomposed into a plurality of local optimization sub-problems based on a hierarchical control model of a multi-agent architecture, so that the solving difficulty is remarkably reduced; a distributed optimization algorithm is designed based on a consistency theory, and a global optimization target is achieved through local information interaction between agents.