800 results about "System parameters" patented technology

The invention relates to the technical field of power system monitoring, and discloses a wire and cable fault early warning system based on intelligent monitoring, which comprises a data sensing module, a multi-mode fusion module, a characteristic evolution module, an abnormal early warning module and a dynamic optimization module. The data sensing module collects multi-source heterogeneous data, the multi-modal fusion module processes the data to generate a spatial-temporal feature matrix, the feature evolution module extracts cable degradation features, the abnormity early warning module performs fault early warning based on the cable degradation features, and the dynamic optimization module optimizes system parameters by using federal learning. In addition, the system also comprises a digital twin mapping and topology analysis module for assisting decision making and enhancing positioning. According to the invention, real-time monitoring, accurate fault early warning and system performance optimization of the operation state of the wire and cable are realized, the stability and reliability of power transmission are improved, and the system has the advantages of comprehensive multi-source data acquisition, efficient data processing, accurate early warning, data privacy protection and the like.

The invention discloses a heterogeneous sensing early warning system based on decoupling perception and adversarial robust learning, and the system comprises a feature extraction module which processes heterogeneous sensor original data collected in real time through a multi-layer decoupling encoder, separates target related features and environment interference features, and suppresses noise pollution from the source; the multi-dimensional collaborative fusion module adopts a cross-domain adversarial robustness learning framework to carry out space-time sequence alignment and deep fusion on decoupling features to generate high-robustness joint representation, and a data missing problem is processed through a cross-modal generative feature completion mechanism; and the cognitive enhancement closed-loop decision module constructs a cognitive heuristic confidence evaluation model based on joint representation, realizes graded early warning by combining real-time quality scoring and behavior prediction, and dynamically optimizes system parameters through a feedback mechanism. According to the method, the problems of poor target detection robustness, high delay and low accuracy in a complex dynamic environment are solved, the detection precision is remarkably improved, the false alarm rate is reduced, and the all-weather adaptive capacity is enhanced.

The invention discloses a multi-modal data processing method and system, computer equipment and a readable storage medium, which can realize deep association and complementarity mining of multi-modal information and improve the accuracy and robustness of multi-modal understanding. The method comprises the following steps: an environment sensing module adjusts an environment sensing strategy according to feedback information transmitted by a self-adaptive decision module, and acquires multi-modal data according to the environment sensing strategy; the multi-modal encoding module encodes the multi-modal data into multi-modal feature vectors of the same dimension; a cross-modal fusion module fuses the multi-modal feature vectors to obtain fusion features; the self-adaptive decision-making module selects a decision-making network matched with the task type from a predefined network library according to the task type of the current decision-making task, inputs the fusion features into the decision-making network, and generates feedback information according to the decision-making process of the decision-making network; and the meta-learning controller evaluates the system performance of the current multi-modal data processing system and adjusts system parameters according to an evaluation result.

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention relates to the field of heat energy engineering and automatic control, and discloses a multi-mode optimization system for combustion efficiency of a thermal power boiler. The system comprises a multi-modal data perception and space-time alignment module, a tensor manifold modeling and physical constraint feature extraction module, a space-time coupling dynamic prediction and uncertainty quantification module, a quantum optimization decision and DCS cooperative control module and a combustion state derivative early warning and optimization feedback module. Through multi-modal data space-time alignment, five-order tensor physical constraint modeling, PDE deep network prediction, quantum optimization decision and a closed-loop feedback mechanism, space-time unified fusion and physical feature extraction of combustion data are realized, the reliability of combustion state prediction is improved, an optimal control instruction is efficiently solved, system parameters are dynamically corrected, and the reliability of combustion state prediction is improved. The problems that in the prior art, data fusion is difficult, modeling physical constraints are lacked, optimization real-time performance is poor, and adaptivity is weak are solved, and the combustion efficiency and the intelligent control level of the thermal power boiler are remarkably improved.

The systems and methods disclosed herein enable dynamic selection of a routing model for generation of an output in response to a provided input (e.g., a prompt for a large-language model). Based on the selected routing model, the data generation platform can evaluate the input and / or other suitable system parameters (e.g., system resource usage) to determine a suitable model for processing the provided input. For example, the routing model can determine a technical application associated with the input and dynamically determine to modify the input prior to generation of the output based on system resource measurement values and / or other suitable information, thereby conferring efficiency, security, and accuracy benefits while preserving system resilience.

According to the tray cycle scheduling system and the application method, historical data and a production plan are fused, an improved algorithm is adopted to predict tray requirements, and an attention mechanism is introduced to improve precision; the method comprises the following steps: acquiring multi-dimensional state information of a tray through a multi-modal sensor, eliminating noise by using a data fusion algorithm, and constructing a digital twin model to realize state synchronization; a double-layer optimization architecture is constructed, an upper layer solves a global scheme by combining an improved particle swarm and a simulated annealing algorithm, and a lower layer dynamically adjusts a path through reinforcement learning; an instruction is generated based on a digital twin model, an event triggering mechanism is adopted to reduce communication load, and virtual-real interaction closed-loop control is realized; a real-time evaluation index system is established, a meta-learning algorithm is utilized to quickly adapt to a new environment, and system parameters are continuously optimized. Multi-module collaborative innovation is achieved, the tray scheduling efficiency and the intelligent level are remarkably improved, production logistics whole-process collaborative optimization is achieved, and core support is provided for cost reduction and efficiency improvement of enterprises.

The invention discloses a multi-agent collaborative decision-making system and method for neural symbol fusion, and relates to the technical field of artificial intelligence, and the system comprises a neural symbol fusion engine which constructs a knowledge double-layer representation architecture, and achieves the organic fusion of symbol reasoning accuracy and neural learning adaptability; the intelligent agent coordination optimizer quantifies the intelligent agent difference through cognitive state mapping, constructs a consensus feasible region, carries out hybrid verification and constraint optimization, and selects an optimal decision scheme; and the adaptive interpretation system constructs a decision evidence chain and realizes continuous optimization of system parameters through feedback learning. The technical challenges of symbol reasoning and neural learning fusion, multi-agent cognitive difference coordination, decision reliability and interpretability and the like are effectively solved, and the method is suitable for complex decision scenes of medical treatment, finance, intelligent manufacturing and the like.

The invention discloses a data collaborative directory management method and system, and relates to the technical field of government affair informatization, and the method comprises the steps: generating a directory snapshot containing a global hash value; calculating a directory node hash value of each edge directory node based on the directory snapshot identifier; eliminating clock drift interference through time sequence alignment and dynamic tolerance filtering; inputting the Hash difference time sequence into an isolated forest model to judge a substantial change node; based on the difference entry number and the historical calling weight, combining a dual-threshold rule and an online dichotomy model to hierarchically synchronize requirements; according to a grading result, matching an incremental push mode or a full pull mode, and constructing a synchronous transaction context containing an exponential backoff retry mechanism; synchronous operation is executed through the two-stage state model, and compensation rollback is triggered when the synchronous operation fails; and calculating a health index of the substantially changed node, dynamically selecting a self-healing action and optimizing system parameters. The problem of misjudgment caused by time sequence drift is effectively solved, the synchronization efficiency is improved, and the consistency of directory versions is guaranteed.

The invention relates to the technical field of artificial intelligence application, and discloses an information technology auxiliary consultation system based on artificial intelligence. The system comprises a data acquisition module, a knowledge graph construction module, an intention analysis module, a decision engine module, a strategy optimization module and a feedback correction module. The data acquisition module acquires multi-dimensional data such as a semantic type, an intention label and a historical interaction record of a user consultation request in real time; the knowledge graph construction module dynamically generates a hierarchically associated domain knowledge graph according to the domain database; and the intention analysis module completes user intention classification and analysis through a multi-level attention mechanism. The decision engine module combines the analysis result and the knowledge graph to generate candidate strategies, and the strategy optimization module screens out target strategies meeting real-time response requirements through an adaptive weighting algorithm. The feedback correction module utilizes user interaction data to update system parameters, improves service precision, and is suitable for various information technology consultation scenes.

The invention relates to the technical field of encoders, in particular to an incremental optical encoder signal error compensation system and method.The incremental optical encoder signal error compensation system comprises a signal acquisition module, a preprocessing module, an error analysis module, a compensation operation module, a data storage module and an output control module; the preprocessing module preprocesses the original signal; the error analysis module analyzes and identifies the error type and characteristics of the processing signal in a multi-dimensional manner, and calculates compensation values of the scribing error, the subdivision error and the eccentric error through the compensation operation module; a compensation value is superposed with an original signal, a compensated high-precision signal is obtained, the output control module outputs the high-precision signal to a subsequent device, meanwhile, system parameters are adjusted according to an external instruction, a high-precision error model is constructed by the system, and the high-precision error model is obtained by combining an advanced digital signal processing technology and an intelligent algorithm. And the scribing error, the subdivision error, the eccentric error and the like of the encoder are effectively compensated.

The invention discloses a multi-dimensional monitoring and early warning system and method for displacement, axial force and water level in a deep foundation pit. The system comprises a data acquisition module, a data transmission module, a data processing module, a risk assessment module, an early warning issuing module and a user interaction module. The data acquisition module is composed of a displacement monitoring sub-module, an axial force monitoring sub-module and a water level monitoring sub-module and is used for respectively acquiring displacement, axial force and water level data in the foundation pit; the data transmission module is responsible for transmitting data to the data processing module; the data processing module cleans, fuses, analyzes and processes the data; the risk assessment module performs risk assessment on the processed data according to a preset standard; the early warning issuing module issues early warning information according to the risk assessment result; the user interaction module is used for displaying information and configuring system parameters. The method has the advantages that real-time, continuous and multi-dimensional monitoring of the construction process of the deep foundation pit is achieved, high-risk points can be accurately recognized, early warning can be conducted in time, and the construction safety of the foundation pit is guaranteed.

The invention relates to the technical field of power system monitoring, in particular to a power system parameter dynamic verification method, system and device based on digital twinning and a storage medium. Constructing a digital twinborn model of the power system, establishing a state mapping relation between a physical system and a digital model, realizing bidirectional dynamic mapping between the physical system and the digital model through a digital mapping mechanism, and obtaining system operation characteristics; constructing a parameter verification model based on the operation characteristics, obtaining a verification objective function by fusing physical constraints and data driving, and dynamically adjusting parameter values to generate parameter correction values by evaluating model errors in real time by adopting an adaptive parameter correction algorithm; based on a dynamic optimization mechanism of multi-objective optimization, an optimization strategy is adaptively adjusted according to the running state, and the verification process is continuously optimized; and performing multi-dimensional evaluation on the verification result through the verification precision evaluation system to generate a parameter verification result. The technical problems that a traditional method is inaccurate in modeling, low in response speed and poor in adaptability are effectively solved.

The invention belongs to the technical field of multi-body dynamics analysis, and discloses a wind driven generator transmission chain rigid-flexible coupling multi-body dynamics analysis method based on dynamic mode decomposition, and the method comprises the steps: firstly, enabling multi-degree-of-freedom time series data to be non-linearly embedded into a high-dimensional feature space through an encoder neural network; extracting a dominant mode by utilizing intrinsic orthogonal decomposition (POD), and constructing a low-dimensional feature space; parameterized dynamic mode decomposition and radial basis function regression are adopted, a mapping relation between system parameters and Koopman operators is established, and accurate prediction of dynamic characteristics under variable working conditions is achieved; and finally, reconstructing a physical response through a decoder, and optimizing model parameters in combination with an error driving mechanism. The problems that a traditional method is low in calculation efficiency, poor in nonlinear adaptability and difficult in multi-parameter coupling prediction are effectively solved, the efficiency and precision of transmission chain dynamic analysis are remarkably improved, and reliable technical support is provided for state monitoring and service life prediction of the wind turbine generator.

The invention relates to the technical field of industrial automatic monitoring, and provides a mining equipment remote monitoring method and system and a storage medium. The method comprises the following steps: dividing mining equipment into core equipment and general equipment based on equipment operation parameters; performing fault risk assessment on the core equipment by adopting a dynamic threshold value adaptively generated based on historical data, and performing deviation detection on the general equipment by adopting a fixed threshold value; fusing the fault risk assessment result of the core equipment and the deviation detection result of the general equipment to generate global maintenance decision information; optimizing a field maintenance path according to the geographic position and the emergency degree of the maintenance task; and continuously calibrating system parameters through a closed-loop feedback mechanism. According to the invention, through hierarchical monitoring and adaptive optimization, the problems of uneven distribution of monitoring resources, rigid threshold setting and isolated maintenance decision in the prior art are solved, and accurate configuration of mining equipment monitoring resources and remarkable improvement of fault early warning capability are realized.

The invention belongs to the technical field of parameter identification, and provides a thermal power generating unit electro-hydraulic servo system PID parameter identification method and system, and the method comprises the steps: obtaining the historical operation data of a steam turbine, carrying out the preliminary screening of the data, and constructing a parameter identification data set; according to the mean square error between the actual value and the calculated value of the opening degree of the valve, an objective function of PID parameter identification of the DEH electro-hydraulic servo system is established; setting population initial parameters, and generating a chaotic initial population; based on a parameter identification data set and a target function, introducing an initial population value of the generated chaotic initial population into an improved Bayesian optimization model, and obtaining a preliminary optimization result through Gaussian modeling and kernel function prediction; and taking the preliminary optimization result as input, and utilizing a whale optimization algorithm introducing a Levy flight disturbance mechanism to carry out iterative search for multiple times to obtain a final optimization result. According to the invention, the parameter identification precision and robustness are improved.

The invention belongs to the technical field of analog simulation, and particularly relates to an intelligent simulation system with an autonomous decision-making capability, which comprises simulation management used for task scene initialization, system parameter configuration and simulation process control and operation. The display system is used for 2D / 3D visual display and playback of simulation results; the model implementation system is connected with the simulation management and display system and is used for implementing a target model and a flight mode model according to configuration parameters; the intelligent target generation system is used for automatically generating a maneuvering combination strategy, a task load control strategy and a radar control strategy according to the simulation situation information generated by the model implementation system, and feeding back the maneuvering combination strategy, the task load control strategy and the radar control strategy to the model implementation system to drive simulation deduction; wherein the system can execute a plurality of simulation modes including large sample space random task simulation, task simulation without autonomous capability level and task simulation with certain autonomous capability level according to simulation types set by the simulation management and display system.

The invention discloses a city management AI dispatch algorithm and system based on historical mining and responsibility matching, and the method comprises the steps: building and dynamically updating a city management element evolution graph through obtaining the multi-mode description information of a city management case and the real-time state data of disposal resources; calculating potential disposal effects of different candidate dispatching schemes by using a causal inference engine, and generating a comprehensive efficiency estimation vector; on the basis, a multi-target reinforcement learning strategy is adopted to generate an optimal dispatch instruction, and system parameters are continuously optimized through online element learning during execution; cooperative processing network analysis is activated for sudden complex events, and responsibility atlas reconstruction is triggered when the matching efficiency is low. According to the method, the accuracy and efficiency of case disposal are remarkably improved, disposal timeliness optimization, resource load balancing and improvement of the first solution rate are realized, and meanwhile, the adaptive capacity and continuous optimization efficiency of the system to complex scenes are enhanced.

The invention discloses a wind-solar-water-storage complementary system short-term risk scheduling method considering uncertainty, and the method comprises the steps: converging historical physical operation data and multi-subject behavior data, and constructing a training data set and a system parameter set; based on the training data set, constructing a combined robust radius and wind-solar combined scene containing behavior risk quantification; based on the system parameter set and the training data set, constructing a dynamic risk scheduling unit fusing multi-dimensional risks; solving a candidate short-term scheduling scheme by combining a joint robust radius and a dynamic risk scheduling unit and adopting a behavior risk-oriented Bayesian optimization method; and performing multi-subject consensus evaluation on the candidate short-term scheduling scheme, determining a final execution short-term scheduling scheme, and performing uplink execution. According to the method, the problem of separation of physical risks and behavior risks is solved, and the behavior acceptability of the scheme is improved.

The invention provides a K8S-based satellite image recognition resource dynamic elastic scheduling system. The system comprises a task perception module, a resource evaluation module, an elastic scheduling module, a fault tolerance module and a learning optimization module. The task sensing module collects satellite image recognition task metadata and transfers the metadata to a priority label library; the resource evaluation module monitors node resources, quantifies the health degree and screens healthy nodes into a candidate pool; the elastic scheduling module executes Pod dynamic capacity expansion and contraction and other decisions according to tasks and resources; the fault-tolerant module identifies the fault Pod, reconstructs and distributes a new Pod according to an anti-affinity rule; the learning optimization module optimizes the system parameters according to the historical data. The invention also provides a dynamic elastic scheduling method. Therefore, the resource utilization rate and the system stability of the satellite image recognition task are remarkably improved.

The invention discloses a large language model training-oriented FP8 gradient adaptive optimization method and device, and relates to the technical field of computer machine learning. The method comprises the steps that in the learning iteration process of a large language model for data input by a user, a processor obtains an original high-precision gradient tensor of a current iteration step and historical information which is stored in a storage and contains historical gradients, and a scaling factor and a gradient value are quantized according to a target FP8 format to obtain quantized input data; after the quantized input data stored in the memory is filtered and denoised, the state of the optimizer is updated and stored in the memory, and the processor obtains the update quantity of the system parameters by utilizing a learning mechanism corresponding to the optimizer according to the basic learning rate and the system parameters. And optimizing the large language model according to the system parameter update quantity and a preset evaluation strategy to obtain a trained large language model. By adopting the method, the optimal balance of computer storage precision, efficiency and system resource consumption under large language model low-precision training is realized.

An equipment system optimization method driven by deep reinforcement learning comprises the following steps: sequentially constructing an equipment system architecture model and a combat scene simulation model based on a DoDAF framework, then carrying out parameter space sampling in the equipment system architecture model by using a uniform design method, and according to capability items and equipment elements defined by the equipment system architecture model, carrying out parameter space sampling on the equipment system architecture model; constructing a combat effectiveness evaluation model and a system cost evaluation model for evaluating the system; constructing a parameter-modulated deep reinforcement learning (PM-DRL) model to explicitly embed system parameters into an agent state space, and performing data collection and evaluation through the trained PM-DRL model to obtain lt; system parameter-evaluation result gt; a data set; and finally, constructing an optimization model by taking the agent model as a target function, and determining an optimal solution, namely system parameter configuration, by combining the solved Pareto frontier with the preference of a decision maker. According to the method, under the same combat effectiveness requirement, system parameter configuration with lower construction cost can be obtained through optimization.

The invention provides an anti-quantum identification signature method based on an on-lattice SIS problem. The method comprises the following steps: selecting system safety parameters and other related system parameters; generating a system public parameter and a master key by using the system security parameter and other related system parameters; generating a corresponding public and private key pair by using the user identity ID; the user generates a signature of the message by using the private key; and the verifier verifies the validity of the signature by using the system parameters and the user public key. According to the method, a lattice-based digital signature scheme is constructed by utilizing an SIS difficulty problem on a middle lattice, and quantum attack resistance security is provided for an information system for deploying the algorithm scheme; personal identity information of the user is used for registering and generating public and private keys, the key escrow problem participated by a third party is relieved, and the privacy security of the user is improved.

The invention discloses a deep neural network probabilistic load flow calculation method and system, and belongs to the field of power system analysis. The method comprises the following steps: acquiring power system parameters and confidence levels, and determining state variables and orders; establishing and linearizing a nonlinear power flow equation to obtain a Jacobian matrix; a node voltage vector is used as input, branch admittance is used as physical information to be embedded into a deep neural network model based on a multi-head self-attention mechanism and semi-invariant guidance, a Jacobian matrix is solved, and a sensitivity matrix is calculated; calculating and aggregating semi-invariants, and transmitting the semi-invariants to a state variable; and after standardization, obtaining a distribution function by using a six-order Cornish-Fisher series, and calculating a fluctuation interval in combination with a confidence level under opportunity constraint to realize quantitative evaluation. According to the method, the probability load flow calculation complexity is reduced, the limitation of a traditional method in processing the network topology complexity problem is solved, and quantitative evaluation of the probability load flow is realized.

The invention relates to the field of hydraulic engineering, and provides a one-way tower theoretical scheme optimization method and system based on an intelligent algorithm, a terminal and a medium, and the method comprises the following steps: collecting pipeline system parameters, and deducing a one-way tower theoretical protection scheme based on a water hammer theory; all theoretical values in the unidirectional tower theoretical protection scheme are extracted, corresponding data of pipeline characteristic parameters and design parameters are generated, a training sample library is constructed, a prediction model is trained through a neural network algorithm, and nonlinear mapping prediction from the pipeline characteristic parameters to the design parameters is achieved; and a target function is constructed based on the output of the prediction model, global optimization is carried out by using the prediction model in combination with an optimization algorithm, and a one-way tower volume and water supplementing amount global collaborative optimization scheme which not only meets the pipeline pressure bearing standard but also minimizes an original target item is obtained. The method can solve the problems of lack of accurate optimization and easy falling into a local optimal solution in the prior art.

The invention relates to the technical field of optics and the like, in particular to a coupled system parameter prediction method based on a physical-data co-driven deep neural network CMT-NN. The method comprises the following steps: taking optical response spectral lines of a double-micro-ring coupling system under different physical parameters as the input of a CMT-NN model, and taking the output as the physical parameters of a coupling resonance system; the step of constructing the trained CMT-NN model specifically comprises the following steps: S1, constructing a physical model of a target coupling system; s2, constructing a CMT-NN model, bringing physical constraints and physical parameters of the coupling model into a loss function of CMT-NN at the same time, calculating physical loss in a back propagation process through the loss function through iterative training and back propagation, and if a preset condition is met, completing training to obtain the CMT-NN model for predicting the physical parameters of the coupling resonance system; and S3, verification of the CMT-NN model is completed, and a trained CMT-NN model is obtained.

The invention discloses a power distribution network short-circuit parameter real-time measurement method, system and device based on PMU cooperation and a medium, and belongs to the technical field of power systems and automation, and the method comprises the steps: collecting PMU real-time measurement data, loading preset parameters, and constructing a PMU cooperation measurement network; carrying out a synchronous sampling algorithm, carrying out comprehensive preprocessing on real-time measurement data, and carrying out state reconstruction and inhibiting the influence of abnormal data according to a state estimator; establishing a short-circuit parameter identification model, and executing short-circuit parameter identification; performing multi-dimensional evaluation on the identification result, and evaluating the real-time performance of the algorithm; and based on the evaluation result, updating the key parameters, and executing adaptive adjustment of the system parameters. According to the invention, through high-precision synchronous acquisition, adaptive signal processing and closed-loop optimization mechanisms, comprehensive improvement of the real-time measurement precision, reliability and adaptability of the short-circuit parameters of the power distribution network is realized.

The invention discloses a self-adaptive optical system parameter adjusting method and device combining offline pre-training and online reinforcement learning, and belongs to the field of intelligent control combining self-adaptive optics and reinforcement learning. Multi-modal state modeling, off-line strategy pre-training and an online strategy enhancement mechanism guided by behavior advantages are introduced, and key control parameters in the AO system are dynamically optimized by using a reinforcement learning algorithm. According to the method, multi-modal heterogeneous data can be effectively integrated, the dynamic modeling and prediction capability of the system is improved, and stable, efficient and robust control strategy updating is realized.

The invention discloses an industrial internet collaborative manufacturing resource dynamic scheduling system and method based on edge computing, and relates to the technical field of resource scheduling, and the system comprises a resource sensing module which generates a manufacturing resource state data set; the edge collaboration module distributes the manufacturing resource state data set to edge nodes for localization processing, and constructs an edge side resource scheduling candidate set; the scheduling decision module is used for establishing an intelligent dynamic scheduling model based on the edge side resource scheduling candidate set and the production task priority, and generating a manufacturing resource dynamic scheduling scheme based on the output of the intelligent dynamic scheduling model; and the dynamic updating module is used for dynamically adjusting a resource scheduling strategy and updating system parameters according to the real-time state change of the manufacturing resources and scheduling scheme execution feedback. According to the method, production scheduling is optimized through real-time data acquisition and edge calculation, the scheduling precision is improved by combining a genetic algorithm and fuzzy control, dynamic updating is realized through historical feedback, and efficient and stable operation of the system is ensured.

The invention discloses a scanning control method and system for a magnetic resonance spectrometer, and the method comprises the following steps: an upper computer analyzes and loads sequence file parameters meeting the Pulseq standard, and integrates the sequence file parameters and pre-configured system parameters into a scanning parameter set; the upper computer generates an executable spectrometer hardware control instruction based on the scanning parameter set, and writes the spectrometer hardware control instruction into a register of a PCIE board card through a PCIE driving module; the PCIE board card generates a hardware control signal according to the content written in the register, controls the spectrometer hardware module to execute corresponding operation, and generates magnetic resonance original data; and the upper computer reads the magnetic resonance original data and reconstructs the magnetic resonance original data into magnetic resonance image data based on analysis of the magnetic resonance original data. According to the invention, the compatibility and flexibility of the system are significantly improved, different upper computers can communicate and cooperate with the spectrometer hardware module, sequence sharing and use among different devices are facilitated, and the integration and maintenance costs of the system are reduced.