55 results about "Static optimization" patented technology

The invention discloses a thermal power plant whole-plant collaborative optimization system and method based on digital twin and AI algorithms, and belongs to the field of thermal power plant optimization control. The invention discloses a thermal power plant whole-plant collaborative optimization system and method based on digital twinborn and AI algorithms. The system comprises a data fusion processing module, a digital twinborn body construction module, a collaborative optimization and decision module, a strategy decomposition and execution module and an online learning and updating module. According to the method, the problems that the existing thermal power plant optimization control lacks global collaboration and is difficult to adapt to dynamic complex working conditions, and online self-evolution of a model and a strategy cannot be realized are solved; and a deep reinforcement learning algorithm is utilized to carry out multi-target collaborative optimization on the whole plant level, so that a global optimal control strategy which comprehensively considers the operation cost, the energy efficiency, the equipment service life and the environmental protection constraint can be dynamically generated, and the limitation of traditional decentralized control and static optimization is effectively overcome.

The invention discloses an air conditioning-storage-charging micro-grid and power distribution network collaborative optimization control method, which comprises the steps of constructing a multi-target optimization model with low carbon, economical efficiency and low load as targets, and realizing dynamic prediction and rolling optimization of a system state based on model prediction control; introducing an online decision-making tool, and dynamically adjusting an energy exchange strategy of the micro-grid and the power distribution network according to the air quality and the operation state; and the control effect is verified through system simulation. Compared with an existing static optimization method, the method has the advantages that dynamic cooperative mutual aid control under multiple targets is achieved, higher real-time performance, higher adaptive capacity and higher operation efficiency are achieved, and the stability and the overall performance of a building energy system in a complex environment are remarkably improved.

The invention discloses a real-time SLMP fuel cell hybrid electric vehicle energy management method. The method comprises the following steps: firstly, collecting vehicle operation data in real time, constructing an operation state vector containing kinetic parameters, and initializing a state transition matrix as agent action input; on the basis, the speed and the acceleration are predicted through a self-learning Markov predictor, and dynamic self-adaptive power distribution of the fuel cell and the power cell is achieved in combination with a multi-objective optimization function and power distribution constraints. The method passes hardware-in-the-loop (HIL) verification, aims at reducing hydrogen consumption and improving efficiency, and exits if two times of circulation are not passed. Compared with a traditional method based on rules or static optimization, the method innovatively embeds a self-learning mechanism, has the advantages of being high in adaptability, high in prediction precision and good in generalization performance, and is suitable for energy management optimization under complex working conditions.

The invention discloses a site-level CCUS dynamic source-sink matching optimization method for an oil field enterprise, relates to the technical field of large-scale deployment of carbon capture, utilization and storage of the oil field enterprise, and particularly relates to the site-level CCUS dynamic source-sink matching optimization method for the oil field enterprise. Comprising the following steps: integrating a carbon source end full life cycle technical economy evaluation system and a storage target area'geology-potential-economy 'three-dimensional grading model to form a dynamic database; on the basis of the dynamic database, a mixed integer linear programming model fusing source sink dynamic priority coefficients is constructed, three scenes of cost minimization, oil displacement income maximization and carbon sink subsidy excitation are set, and a constraint system is coupled; according to the method, carbon source technology economic evaluation and storage target area three-dimensional grading are fused, the specific injection-production cycle, policy incentive and pipe network constraint of an oil field are converted into time-varying weight coefficients, traditional static optimization limitation is broken through, and site-level source-sink dynamic accurate matching is achieved.

The invention discloses a dynamic optimization method and system for a service function chain of a 5G core network, and relates to the technical field related to mobile communication, and the method comprises the steps: collecting network service data in real time through a three-dimensional perception module; decomposing a service function chain optimization problem into a multi-stage decision model; decomposing into a long-term UPF deployment main problem and a short-term traffic scheduling sub-problem, and solving to obtain a global optimal solution; dynamically determining UPF deployment positions and number; dynamically selecting a traffic forwarding path meeting the service level; and constructing a dual-node deployment framework at the core layer and the edge layer, and executing a differentiated service function link control strategy. The technical problems of low resource utilization rate, high energy consumption and poor service adaptability caused by lack of dynamic collaborative optimization and service function chain static optimization of UPF deployment and traffic scheduling in the prior art are solved, and the technical effects of improving the resource utilization rate of the edge server, reducing the network energy consumption, optimizing the service quality and guaranteeing the user experience are achieved.

The invention provides a heat supply digital twin modeling method and system based on a U3D engine and a medium. The method comprises the steps that a historical static data set and a historical dynamic data set are obtained for preprocessing, a historical static optimized data set and a historical dynamic optimized data set are obtained, a U3D engine is combined with a preset parameterized component library to conduct heat supply digital twinning model construction, initialization is conducted based on the historical static data set, and a heat supply digital twinning model is obtained. Obtaining an initial heat supply digital twinborn model, generating an LOD model set with different precisions through an LOD rendering method, inputting the historical dynamic optimization data set into the initial heat supply digital twinborn model for processing, obtaining a simulation data set and equipment operation state evaluation data, and evaluating the qualified state of the model through threshold comparison; according to the method, data security fusion is realized through the block chain, the hydraulic and thermal coupling model is constructed by using the U3D engine, and predictive maintenance and multi-objective optimization are realized through an intelligent algorithm, so that intelligent construction of the heat supply word twinborn model is realized.

The invention provides a hierarchical multi-agent bridge and tunnel group maintenance decision-making method and device based on GA-RL, and relates to the technical field of civil engineering. The method comprises the steps that an expert strategy library is constructed offline according to an expert strategy and a genetic algorithm, and an optimal strategy is selected online according to a network-level reinforcement learning agent; a near-end strategy optimization algorithm is adopted to train an exclusive bridge and tunnel level reinforcement learning agent for each bridge and tunnel type, and a maintenance application of the to-be-maintained bridge and tunnel is generated; and converting the optimal strategy into a computable priority evaluation model, generating a bridge and tunnel maintenance priority sequence, optimizing a budget allocation proportion according to a differential evolution algorithm, and generating a bridge and tunnel group network-level maintenance scheme. A hierarchical multi-agent decision framework based on a GA-RL hybrid algorithm is provided, the defect that static optimization lacks environmental adaptability is overcome, the calculation bottleneck of dynamic planning in a large-scale scene is broken through, and a more efficient and adaptive solution is provided for complex bridge and tunnel group network maintenance decision.

The invention provides a reinforcement learning dynamic pricing method based on game feedback, and relates to the technical field of online data market dynamic pricing. Through combination of leader-follower strategy interaction of the Stackelberg game and the exploration-utilization balance principle of the dobby machine, the problems of price adjustment lag, large income fluctuation and insufficient strategy stability in a non-stable environment are solved. By introducing a data freshness evaluation mechanism, data weight is dynamically adjusted by integrating data timeliness attenuation and market correlation analysis, it is ensured that data input into a model always has high real-time performance and strong correlation, and the problem of decision lag caused by static data input in a comparison scheme is avoided; the exploration-utilization balance principle and the reinforcement learning model of the dobby machine are fused, the exploration rate is dynamically adjusted, a historical optimal strategy can be fully utilized in a non-stationary environment, a potential better scheme can be explored, and the static optimization bottleneck that a game model is limited to a preset objective function in a comparison scheme is broken through.

The invention discloses a power industry cue word optimization method and system, and the method comprises the steps: carrying out the static structured optimization of a non-standardized cue word inputted by a user, obtaining a structured cue word which accords with the AI model understanding logic and the power industry specification, and storing a data record generated by the static structured optimization into a static optimization result temporary storage library; constructing a reinforcement learning model, carrying out offline training and online iteration, carrying out secondary optimization on the structured cue word by utilizing the trained reinforcement learning model, and inputting an optimization result into an AI model; and storing an output result of the AI model, user feedback, a data record generated by static structured optimization of the cue word and data generated by secondary optimization structured cue word of the reinforcement learning model into a cue word optimization case library. According to the method, the accuracy, the reliability and the adaptivity of the cue word generation result are improved, the application of a large model in the power industry is further promoted, and powerful technical support is provided for intelligent transformation of the power industry.

The invention relates to the technical field of data processing, and discloses a crop culture medium production management method and system based on planting and breeding circulation, which constructs a whole-process intelligent management system from raw material proportioning, conveying scheduling to production dynamic regulation and control through multi-source data fusion, multi-objective optimization and deep reinforcement learning. Based on matrix demands and nutritional demands in demand prediction, a dynamic and static combined optimization decision is realized through multi-objective optimization and reinforcement learning. Therefore, the initial production strategy is planned through multi-objective optimization, scientific configuration and intelligent management of multi-source matrix production raw materials are achieved, then the initial production strategy is dynamically optimized through reinforcement learning, the method can adapt to the influence of different actual factors on the production strategy, the problems of raw material heterogeneity processing and supply uncertainty are solved, and the production efficiency is improved. Cost and matrix adaptability are balanced, a crop cultivation matrix production management scheme aiming at the organic vinegar residues and the silkworm excrement is provided, and intelligent agricultural planting and breeding circulation development is promoted.

The invention belongs to the field of wireless communication, and particularly relates to a wireless signal multi-dimensional array coding method and system based on dynamic rank optimization. The method comprises the following steps: detecting state information of a wireless channel, predicting state change of the channel by using a pre-trained deep learning model, and generating a dynamic optimization multiplier; adjusting a preset static optimization multiplier by using the dynamic optimization multiplier, constructing a target function for jointly optimizing the energy efficiency and the spectrum efficiency, and determining an optimal transmission rank by solving the target function; comparing the optimal transmission rank with the current transmission rank to determine a rank-up or rank-down operation; and reconstructing a transmission signal of the wireless channel according to a result of the rank increasing or decreasing operation. According to the method, the optimization target is dynamically adjusted by predicting the channel change, and the asymmetric ascending and descending rank algorithm is adopted, so that the adaptive transmission rank optimization in a complex time-varying channel environment is realized, and the communication performance is remarkably improved.

The invention belongs to the technical field of artificial intelligence, and relates to a dynamic controllability management method and system for long-term evolution of a large model, and the method comprises the steps: S1, obtaining the current state information of the large model; s2, calculating a controllability score of the large model based on the output distribution entropy, the distribution variable quantity and the logits drift of the large model, judging whether the current state of the large model deviates from a controllability critical zone or not based on the controllability score, if the current state deviates from the controllability critical zone, entering S3, and otherwise, directly entering S4; s3, actively correcting the offset behavior of the large model in the training process through a controllable calibration head, so that the current state of the large model does not deviate from the controllable critical zone; and S4, dynamically adjusting a training strategy of the large model based on the controllability score and the task category. According to the method, the large model has the capabilities of long-term plasticity maintenance, continuous performance improvement and rapid adaptation to new tasks, and the fundamental upgrade from a static optimization model to a self-evolution model system is realized.

The invention relates to a compiler design and code optimization technology, in particular to a compiler infrastructure based on multi-order intermediate representation and an implementation method of the compiler infrastructure. A traditional compiler adopts single intermediate representation, so that the coupling degree of the optimization process is high; the method is closely related to high-level / low-level languages, so that the compiler is poor in universality. In order to solve the problems, the invention provides the compiler infrastructure based on the multi-level intermediate representation and the implementation method of the compiler infrastructure, through modular design and hierarchical processing, the relevance of high-level / low-level languages is stripped, and the multi-level intermediate representation is designed, so that the problem that the compiler infrastructure cannot be used in the process of converting the high-level languages into the low-level languages is solved; the processing complexity of the control flow and the data flow is high, the universality is poor, and the subsequent maintenance difficulty is high. The method is suitable for real-time compiling, heterogeneous hardware adaptation and multi-language mixed programming and static optimization scenes, and the development and maintenance cost of the compiler can be greatly reduced.

The invention discloses a reservoir group medium-and-long-term power generation optimization scheduling method based on gallery iteration static optimization, and the method comprises the steps: firstly calibrating convex hull parameters based on a planar convex hull linear approximation method, constructing a scheduling model, and solving an initial solution (including the storage capacity and the output flow of each time period); dividing the whole output feasible region into small-scale regions in a high-density manner, and respectively calibrating convex hull parameters of each region; comparing the initial solution with the small-scale region boundary period by period, determining the region to which the initial solution belongs, and updating the convex hull parameter of the corresponding period; constructing a time period corridor by taking the initial solution as a reference, and constructing a new model in combination with the updated parameters to solve a current iterative solution; and if the target function value change is smaller than the threshold value, converging and outputting the optimal solution, otherwise, updating the scale parameter and repeating iteration. According to the method, the optimal solution is approximated through static calibration-parameter selection, linear errors are corrected, efficient solution can be achieved, operation of the cascade hydropower stations can be guided, and coordinated utilization of water resources is optimized.

The application belongs to the technical field of artificial intelligence, is suitable for medical field and financial field, and discloses a dynamic optimization method of resource configuration, which comprises the following steps: acquiring market data and target information, identifying market trend and potential risk through a pre-trained machine learning model, and generating market state labels for representing current market state; adopting a multi-objective optimization algorithm to dynamically adjust a resource configuration scheme, and generating multi-objective optimization configuration results; screening factors having a significant influence on yield by using the machine learning model, generating an optimal factor combination based on optimized factor weights; and adjusting the optimization configuration results according to the optimal factor combination, and outputting a final resource configuration scheme. The application solves the technical problems that static optimization cannot adapt to changes, single-target optimization ignores multi-target balance, and factor optimization lacks dynamic adjustment capability in the prior art.

The invention provides an aggregation subject market transaction matching method for multi-dimensional target constraint, and relates to the technical field of resource allocation optimization, and the method comprises the steps: collecting transaction participation data of a plurality of aggregation subjects, carrying out the feature extraction of each transaction participation data, and obtaining an aggregation subject feature set; constructing a multi-dimensional target optimization model by taking cost, income and stability as multiple targets based on the aggregated main body feature set, and introducing output constraints, time sequence constraints and market rule constraints to form a target constraint matrix; and under the constraint of the target constraint matrix, carrying out iterative solution on the multi-dimensional target optimization model by adopting a multi-target optimization algorithm, and generating an optimal transaction matching scheme of each aggregation main body. Through the method and the device, the technical problem of poor feasibility of a transaction matching scheme caused by difficulty in processing complex multi-target conflicts due to adoption of a simplified static optimization model in the prior art is solved, and the feasibility of the aggregation subject market transaction matching scheme is improved through multi-dimensional target optimization.

The invention discloses a spanning frame structure size optimization method and system based on a firefly algorithm, and relates to the technical field of electric power engineering construction, and the method comprises the steps: firstly building a parameterized finite element model, implementing normalized hierarchical loading, obtaining a tangent stiffness matrix evolved along with a load, calculating the modal participation degree of a component, and constructing a dynamic participation degree track; and according to the quantitative trajectory smoothing index, the modal dominance and the key component suppression index, constructing a brightness function with coupled rigidity and trajectory features. The firefly attraction degree is corrected by means of trajectory similarity based on modal energy fingerprints, and nonlinear search is executed through a characteristic resonance mechanism. The method solves the problem that traditional static optimization neglects geometric nonlinear evolution to cause modal abrupt change, and the dynamic stability and evolution robustness of the spanning frame in the full stress process are remarkably improved while the structural light weight is achieved.

This invention discloses a method and system for optimizing the relocation and allocation of large gantry cranes based on SPMT (Specialized Spatial Management Machine), relating to the field of SPMT vehicle allocation technology. The method includes: establishing an iterative optimization framework connecting upper, middle, and lower layers; feeding back the dynamic risk value output by the lower-layer risk assessment model to the upper-layer optimization model to generate a new candidate configuration set; subsequently, performing simulation prediction by the middle-layer model and risk assessment by the lower-layer model on the new candidate configuration set again, until the generated candidate configuration set meets the preset convergence conditions. This achieves integrated dynamic and static optimization of the SPMT grouping scheme, overcoming the shortcomings of traditional empirical methods that rely on static estimation and cannot assess dynamic risks. It can significantly reduce vehicle configuration redundancy and operating costs while ensuring absolute safety, ultimately outputting a safe, reliable, and economically optimal relocation and allocation scheme.

The invention relates to the technical field of artificial intelligence, in particular to an application program performance optimization method and device, equipment, a medium and a product. The method comprises the steps that source codes of an application program are obtained, and a plurality of optimization strategies are determined; obtaining a static optimization factor based on the optimization effect of the optimization strategy; analyzing the running log of the application program to obtain corresponding system index information; comparing the system index information with a preset index to obtain a dynamic optimization factor; utilizing a deep learning model to confirm a performance score vector of the application program; and determining a performance optimization scheme based on the performance score vector. The static performance of the application program can be analyzed in real time by performing exception analysis on the source code; by analyzing the running log during running of the application program, the suspicious behavior during running of the application program and the influence degree on the performance can be analyzed, the comprehensive performance conditions of program code development and real-time running are considered, and the method is more flexible and intelligent.

The present application relates to the field of prefabricated component transportation scheduling of assembly type building, and particularly discloses a dynamic transportation scheduling optimization method for prefabricated components with interference of assembly type building, which designs an improved NSGA-II algorithm combining a simulated annealing mechanism and an adaptive strategy; for the multi-objective optimization problem of dynamic transportation scheduling of prefabricated components with interference, the present application combs three types of interference events, i.e., emergency orders, order modifications and vehicle transportation time changes, which occur frequently in the transportation process, and formulates dynamic rescheduling strategies for different disturbance types; in combination with a rolling horizon optimization method, the continuous dynamic scheduling problem is converted into a series of static sub-problems, and then static optimization schemes are adopted in each static process to realize dynamic optimization.

The application discloses a large language model reasoning optimization method and device, electronic equipment and a storage medium, and is used for solving the problem that the huge parameter quantity in the prior art leads to large model reasoning demand for computing and storage resources and how to reduce the huge resources required during large model reasoning. The method comprises the following steps: acquiring configuration parameter data and computing resource data of a large language model during model reasoning in real time; performing static optimization analysis on the configuration parameter data and outputting configuration analysis results; performing automatic tuning of the configuration parameters based on reinforcement learning in combination with the configuration parameter data and the configuration analysis results to obtain optimal parameter configuration; performing dynamic optimization processing on the computing resource data based on an unloading strategy to obtain optimal resource configuration; and dynamically optimizing parameter configuration and resource allocation of the large language model during model reasoning according to the optimal parameter configuration and the optimal resource configuration.

The application discloses an automatic theorem proving method based on neural network guidance and quantum optimization, comprising the following steps: extracting the logical structure features of a propositional logic task, and predicting the applicability weight of natural deduction rules by using a preconfigured neural network; performing a forward reasoning iteration process, dynamically identifying potential intermediate conclusions to determine a variable space, and constructing a quadratic unconstrained binary optimization (QUBO) model according to the variable space; in the construction process, the applicability weight is used to adjust the penalty coefficient of the rule constraint term, and the energy topography of the solution space is reshaped; according to the problem size, the computing resources are adaptively scheduled, the model is mapped to a coherent Ising machine or a classical simulation backend for solving, and the proof path is reconstructed through double verification. The application effectively solves the problems that the traditional method is blind in rule selection and the static optimization model is difficult to handle dynamic multi-step reasoning, and the efficiency and scalability of the proof are improved.

The application discloses a kind of 5G core network service function chain dynamic optimization method and system, it is related to mobile communication relevant technical field, the method includes: by three-dimensional perception module, real-time collection network service data;Service function chain optimization problem is decomposed into multi-stage decision model;Decomposed into long-term UPF deployment main problem, short-term traffic scheduling sub-problem, obtain global optimal solution by solving;Dynamically determine UPF deployment location and quantity;Dynamically select the traffic forwarding path that satisfies service level;In core layer and edge layer, construct double node deployment framework, and execute differentiated service function chain link control strategy.The technical problems that UPF deployment and traffic scheduling lack dynamic collaborative optimization in prior art, service function chain static optimization leads to low resource utilization, high energy consumption, poor service adaptability, to improve edge server resource utilization, reduce network energy consumption, optimize service quality and guarantee the technical effects of user experience.

The invention discloses a parameterized compilation optimization method and system based on multi-stage intermediate representation, belongs to the technical field of computer compilation, and aims to solve the technical problem of how to unify parameterized optimization strategy logic and program core representation to solve the separation of static optimization and dynamic adaptation. Comprising the steps that a user-defined MMIR dialect is defined, and the dialect comprises a strategy.space operation, a strategy.path operation and a strategy.guard operation; the target operation is moved into an area of the newly created strategy.space operation, and a plurality of strategy.path operations are generated; the method comprises the following steps of: searching strategy.guard, evaluating a region of each strategy.guard, generating a reasoning result, and carrying out IR transformation based on the reasoning result.

This invention discloses a method for optimizing the operation of water-cooled walls in pulverized coal boilers, belonging to the field of thermal power generation equipment control technology. The method includes: performing a four-level hierarchical selection of boiler operating parameters (thermal inertia time-scale elimination, steady-state sensitivity coarse screening, XGBoost-SHAP fine screening, and controllability verification), dividing the boiler into a predicted input set and a control suggestion set; constructing an encoder-decoder deep network based on the predicted input set to generate multi-region future wall temperature prediction sequences; searching a historical database of excellent operating conditions with the current load as a hard constraint, selecting template operating conditions based on wall temperature performance in risk areas, and extracting their controllable parameters as optimization guidance. This invention solves the problem of mismatch between static optimization parameters and the actual execution capabilities of power plants in existing technologies by reusing historical cases to replace global mathematical optimization, achieving water-cooled wall operation optimization that conforms to operating experience and can be safely executed.

The application discloses a kind of thermal power plant double-layer cost decision optimization method and device, belong to thermal power plant cost optimization field.Therein method includes: establishing auxiliary database;According to auxiliary database design minimum coal purchase / stock cost function, calculate the lowest standard unit price of entering furnace, construct double-layer cost decision optimization model;According to the characteristics of double-layer cost decision optimization model, by McCormick-KKT is converted into single-layer model, by MATLAB is solved, obtains the optimal cost of base model;According to the optimal cost of base model, determine the expected cost, build IGDT model into different risk attitudes, to obtain risk decision optimization and robustness decision optimization;According to risk decision optimization and robustness decision optimization, calculate risk robustness-cost curve.The application overcomes the problems existing in the prior art, such as the cost optimization technology being difficult to perceive future coal spot market fluctuations, being mostly local static optimization, and having a narrow decision-making perspective.

The invention discloses a power system annual balance boundary optimization method and system, and belongs to the technical field of power system planning and operation optimization. Aiming at the problems of insufficient static optimization, insufficient risk identification and lack of dynamic feedback in the prior art, the method comprises the following steps: constructing an initial boundary set and an input scene set; the EENS and the EID are simulated and quantified through random production, and regional-month risk distribution is formed; calculating a supply-demand index and a resource adjustment matching item, and outputting a feedback factor; a feedback factor is introduced into the MILP framework to jointly optimize the three types of boundary plans, convergence is judged through a sliding window method, and a final scheme is output. According to the method, dynamic adaptive optimization of the annual balance boundary is realized, the system operation reliability, the resource allocation rationality and the complex working condition adaptability are remarkably improved, and the method is suitable for power system annual planning under high-proportion new energy access.

The invention discloses a building envelope reinforcement learning optimization method fusing multi-modal data, and belongs to the technical field of building energy-saving design, and the method comprises the steps: collecting the multi-modal data, carrying out the preprocessing, constructing a multi-modal data set, carrying out the feature extraction of the multi-modal data, obtaining multi-modal features, constructing a multi-modal feature library, and carrying out the optimization of building envelope reinforcement learning. And obtaining a dynamic multi-modal feature library by utilizing a time sequence sliding window mechanism based on the multi-modal feature library, constructing a reinforcement learning intelligent optimization model, and obtaining an optimal building envelope parameter combination by utilizing the reinforcement learning intelligent optimization model based on multi-modal data. According to the method, static optimization is effectively upgraded to a dynamic adaptive scene, manual trial calculation is converted to data-driven autonomous optimization, universal optimization across building types and climate zones is achieved, and the problems that an existing building envelope optimization technology is single in data dimension, static in optimization mode, depends on artificial experience and is insufficient in universality are solved.

The invention discloses a component optimization method and device, equipment and a storage medium, and the method comprises the steps: analyzing a target component to separate a component template, a component style and a component method of the target component; performing static optimization on the target component by utilizing the component template, the component style and the component method to obtain a first component; obtaining the network type and bandwidth information of the front end in real time, and detecting the current network data of the front end; dynamically adjusting a network loading strategy of the front end in real time according to the network data, the network type and the bandwidth information; and dynamically optimizing the loading behavior of the first component based on the network loading strategy. Static optimization is achieved by separating the component template, the component style and the component method, meanwhile, the network loading strategy of the front end is adjusted in real time, dynamic optimization is carried out, high-precision optimization is comprehensively achieved in the mode of'static optimization + dynamic optimization ', and normal proceeding of the development process can be guaranteed.

The application discloses a cell culture multi-parameter collaborative AI dynamic optimization system, and relates to the technical field of cell culture. The system comprises a collector configured to acquire growth cycle data of multiple groups of historical cell cultures; and a processor configured to acquire a data feature group corresponding to each group of cells based on physiological parameters of each group of historical cell cultures. The application breaks through the limitation of traditional static optimization methods that cannot adapt to the dynamic time-varying process of cells by precisely anchoring cell metabolic stress, co-stimulation coupling and functional balance state. At the same time, based on eigenvalue multidimensional clustering, the law of high-quality cell populations is mined, and the particle swarm algorithm is improved to obtain a fitness value capable of representing the pros and cons of the cell culture state, realize dynamic coupling analysis of culture parameters and cell metabolism and phenotype state, and overcome the regulation lag problem caused by the fact that traditional optimization methods cannot analyze the dynamic coupling of metabolism and culture parameters and are difficult to track the cell demand within the window.