4236 results about "Computational model" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

The present invention discloses a method for dynamically assessing slope safety, and the method comprises the following steps: S1, carrying out geologic model generalization to the slope according to slope type, surface elevation, slope structure, stratum characteristics and a deformation failure mode to obtain a slope geologic model, creating a slope geometric model according to the said slope geologic model, carrying out the subdivision of computational grid, and selecting a reasonable numerical simulation method, mechanical constitutive and initial boundary value conditions to form a computational model; and S2, adjusting stratum parameters, structural plane parameters and activating factor strength based on the said computational model, carrying out a large amount of numerical simulation, summarizing results of the said numerical simulation, normalizing input quantities and output quantities to establish machine learning samples.

The invention provides a curtain wall deformation condition safety detection method and device based on laser point clouds, and relates to the technical field of constructional engineering, and the method comprises the steps: 1, carrying out the coordinate system unification processing of a three-dimensional point cloud data set covering the whole surface of a curtain wall, generating a curtain wall surface digital twin model, and carrying out the coordinate system unification processing of a three-dimensional point cloud data set covering the whole surface of the curtain wall in the digital twin model; defining a first detection target point and a second detection target point according to geometrical characteristics and material joint characteristics of the curtain wall sheet; 2, according to the digital twinborn model, surface normal vector distribution characteristics are extracted, actually-measured wind pressure load distribution data and temperature field parameters are fused, a dynamic flexural deformation coupling calculation model is constructed, and global deformation parameters and local deformation parameters are output. According to the invention, the digital twinborn model is constructed through point cloud data processing, deformation and displacement parameters are extracted, the safety coefficient is evaluated, and high-precision, omnibearing and dynamic detection and safety risk early warning of the flatness of the building outer wall are realized.

The invention discloses an industrial equipment fault prediction method based on multi-modal data, and belongs to the technical field of specific calculation models, and the method comprises the steps: carrying out the preprocessing according to the collected multi-modal data of the operation of industrial equipment, so as to unify the format of the multi-modal data, and obtaining the structural data; extracting features of the structured data one by one according to data categories, and obtaining a multi-modal fusion feature through a dynamic fusion mechanism; according to the multi-modal fusion features, a fault prediction classification score is obtained through a deep neural network model to perform fault prediction; and when the drift parameter of the multi-modal data is greater than a preset threshold value, performing incremental training on the deep neural network model through the multi-modal data collected in real time to update parameters of the deep neural network model. Through multi-modal data unified processing, dynamic feature fusion, deep neural network modeling and an online learning mechanism, the problems of insufficient multi-modal data fusion, prediction uncertainty quantization deficiency, poor model adaptability and the like are solved.

The invention relates to an intelligent operation and maintenance question-answering system for cable manufacturing equipment, and belongs to the technical field of computer systems based on specific calculation models. The system comprises an edge data acquisition module, a predictive map construction module, a semantic perception module, a question-oriented reasoning module and a question and answer generation module. According to the system, multi-dimensional real-time data in the operation process of equipment is collected and structurally processed, a process knowledge graph is constructed in combination with industry knowledge, and the causal relationship and reasoning parameters in the graph are dynamically updated according to the data trend. A semantic perception module is used for recognizing the problem intention of a user, a semantic weight vector is formed to guide the reasoning process, and a problem-oriented reasoning module is made to execute joint reasoning on the basis of combining real-time data and a knowledge graph and generate an explanatory conclusion. Finally, operation and maintenance suggestions with high readability are output through a question and answer generation module, and the targets of equipment fault intelligent diagnosis, process optimization and man-machine efficient interaction are achieved.

The invention belongs to the technical field of cable service life evaluation, and particularly discloses a cable defect multi-parameter aging characteristic and service life evaluation method and system, and the method comprises the steps: obtaining a plurality of first parameters which can affect the aging of a cable, and a second parameter which changes when each first parameter affects the aging characterization of the cable; constructing a calculation model containing a first incidence relation between each second parameter and the corresponding first parameter; constructing a prediction model for calculating the service life of the cable according to the calculation model, and configuring a correction factor for representing the degree of influence on the defect of the cable on each calculation model; determining the numerical value of each correction factor by adopting an optimization algorithm, and updating the correction factors according to the numerical values to optimize the prediction model; calculating the service life of the cable through the optimized prediction model; the method has the advantages that cable aging is comprehensively analyzed, modeling is clear, influence factor relations are defined, an algorithm is optimized to adjust correction factors, and prediction accuracy is improved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic signal edge computing resource dynamic allocation method and system.The method comprises the steps that firstly, real-time and historical traffic data of multiple intersections are obtained, a traffic prediction model is constructed, and future flow changes are pre-judged; thirdly, forming an edge computing resource demand matrix according to a prediction result and computing resource demands, and dynamically adjusting resource allocation to generate a resource slicing scheme; then, the flow gradient between intersections is analyzed, an edge node cooperative calculation model is established, and calculation task distribution and migration are optimized; and finally, in combination with system operation and traffic control effects, forming closed-loop feedback, continuously optimizing resource allocation and a cooperative strategy, and through traffic flow prediction driven resource demand modeling, realizing active pre-allocation of computing resources, converting resource allocation from passive response to active pre-adaptation, and improving the system resource utilization rate by 40%-60%.

The invention provides a learning performance evaluation-driven teaching management method constructed based on a capability graph. The method comprises the following steps: S1, constructing a multi-dimensional capability graph; s2, a step of operating a multi-modal data acquisition system; s3, performing a dynamic capability value calculation model; s4, a step of constructing a real-time capability early warning system; s5, generating a self-adaptive teaching strategy; s6, a step of carrying out interdisciplinary ability association analysis; s7, generating a personalized learning path; s8, dynamically evaluating the teaching effect; and S9, dynamically optimizing the teaching resources. According to the method, the core problems of data lag, inaccuracy in intervention and one-sided evaluation in traditional education management are solved by constructing a dynamic capability evaluation model, and a quantifiable decision support system is provided for precise teaching. The innovation of the method is that differential equation modeling and reinforcement learning are combined, and continuous optimization and adaptive adjustment of the education process are realized.

The invention discloses an industrial system automatic fault diagnosis method based on a large language model. According to the method, a three-layer mapping system of industrial data, natural language description and knowledge reasoning is constructed, field multi-source sensor data are subjected to semantic conversion, and a quantitative calculation model based on a large language model is constructed based on historical data and logs. And a fault case is matched in real time with the help of a retrieval-enhancement generation technology to serve as a reference, the fault case and abnormal information are input into a knowledge reasoning model based on a large language model together, a structured logical reasoning chain is generated, and a diagnosis conclusion containing candidate faults, cause analysis and disposal suggestions is further output. Meanwhile, through user feedback and a reinforcement learning mechanism, the model and the knowledge base are adaptively updated, the defects of traditional static rules and expert experience are effectively overcome, the accuracy, interpretability and robustness of fault detection are remarkably improved, and the method adapts to complex and changeable working condition requirements.

The invention provides a heat input regulation and control method and system for electric arc additive continuous forming, and relates to the technical field of additive manufacturing, and the method comprises the following steps: collecting process parameters in real time, inputting pretreatment process data into a self-adaptive heat input quantity calculation model, and constructing and calculating to obtain a real-time heat input quantity based on a thermodynamic principle and a heat transfer theory; starting the multispectral imaging equipment, performing real-time temperature field scanning, recording temperature field scanning data, extracting temperature gradient and cooling rate information, performing correlation calculation with real-time heat input quantity, and determining an influence relation evaluation result; generating a heat input sequence according to the influence relation evaluation result; and inputting the heat input sequence into a self-adaptive control system constructed based on a reinforcement learning algorithm, calculating an optimal parameter scheme according to a preset target sedimentary layer performance index and a current temperature field state, adjusting corresponding spatial-temporal distribution of control energy input according to the optimal parameter scheme, and performing control heat input on different positions and different layers of the sedimentary layer.

The invention discloses an attention state recognition neural network modeling and reasoning method based on an electroencephalogram sequence. The core is to construct and train a deep neural network model suitable for electroencephalogram signals so as to realize intelligent recognition and classification. Firstly, wavelet transformation and time-frequency feature extraction are carried out on electroencephalogram time sequence signals, and a multi-dimensional input tensor is generated in combination with channel position information; and inputting the feature into a deep network fusing spatial convolution, gating circulation and a residual connection structure, and extracting spatio-temporal joint features. A cross-time-step attention mechanism and a dynamic loss adjustment strategy are introduced in a training stage, so that the discrimination capability of the model on an alertness state transition region is improved. The final model can conduct reasoning on electroencephalogram data of any length, and a classification label and a confidence score are output and used for measuring classification reliability. The method focuses on construction and optimization of a specific calculation model, reflects application characteristics of an intelligent algorithm in cognitive state recognition, and belongs to an intelligent calculation method with a neural network as a core.

Accurate prompting improves the operation, efficiency, and output of computer models, such as language models. The disclosed systems and methods improve interaction with computer models by facilitating the generation of accurate prompts for targeted interactions with computer models. For example, disclosed systems can be configured to store prompts and responses generated with computer models in session memories. The system can use information in session memories to generate combined prompts when receiving prompts that refer to previous prompts or answers. The system improves prompt accuracy by generating combined prompts—formed by combining the context from the stored information (e.g., in context sub-prompts) and instructions in the new prompt (e.g., in instructions sub-prompts). The system can generate responses based on the combined prompts allowing the computer models to leverage the context in previous interactions, without burdensome or complicated prompts, for more meaningful or accurate responses.

The invention relates to the technical field of social information, in particular to a social network influence prediction method and system. According to the method, brand-related users, content and interaction data are obtained from multiple platforms, a multi-dimensional influence scoring model is constructed, and initial influence scores of the users are calculated. And in combination with a social network cascade propagation theory, constructing a propagation probability calculation model, and predicting an information propagation range and user influence changes. And finally, generating an influence prediction report containing KOL ranking, hot topic analysis and optimal intervention opportunity suggestions. According to the method and system, through organic combination of a plurality of innovation points, the accuracy, comprehensiveness and practicability of prediction are remarkably improved, the existing technical problems are effectively solved, and a powerful support tool is provided for a brand to formulate a precision marketing strategy in a complex social media environment.

A method for predicting remote sensing image time series based on reservoir computing utilizes temporal spectral information of remote sensing images to study prediction methods for high-dimensional short-term sequences. Based on the theoretical model of a nonlinear dynamical system and using the spatiotemporal transformation equation and its conjugate equation, a multi-layer reservoir computing model is constructed to achieve multi-step prediction on a time dimension. Only a small number of short-term samples are required to predict dynamic temporal information. Compared with the traditional neural network training process, the method is low-cost training. Different from traditional reservoir computing, the method converts the observed high-dimensional data into reservoir computing and uses intrinsic dynamics of the observation / target system to analyze remote sensing time series data. With only a few samples, by learning the spatiotemporal transformation equation, effective prediction results can be generated and applied to feature processing of remote sensing images with small samples.

The invention discloses a partial discharge signal identification method and system based on deep learning and a storage medium, and relates to the field of computer systems based on a specific calculation model, and the method comprises the steps: dividing multi-source monitoring data into a high-frequency signal group and a low-frequency signal group; respectively inputting corresponding feature information to the first deep neural network and the second deep neural network to obtain a high-frequency feature vector and a low-frequency feature vector; generating a signal attenuation compensation factor based on the low-frequency feature vector, and performing distortion compensation on the high-frequency feature vector to obtain a high-frequency correction vector; calculating the feature correlation degree of the high-frequency correction vector and the low-frequency feature vector, and when the feature correlation degree is lower than a preset correlation threshold value, extracting transient feature information of the high-frequency correction vector, and fusing the low-frequency feature vector to obtain a low-frequency correction vector; and inputting the high-frequency correction vector and the low-frequency correction vector to a partial discharge identification model to obtain a partial discharge type and identification confidence. According to the invention, the identification accuracy of the partial discharge signal can be improved.

The invention relates to the technical field of power grid energy storage systems, in particular to a battery intelligent fault diagnosis method and system suitable for a power grid energy storage system. According to the method, multi-source monitoring data including battery electrical parameters, power grid features, environmental conditions and historical operation records are obtained, unified time step alignment and feature extraction processing are carried out on the data, and statistical features, time sequence features and frequency domain features are obtained. A double-layer diagnosis model structure is adopted, a random forest model is used for diagnosing main fault modes such as over-charging and over-discharging, and a convolutional neural network model is used for diagnosing complex fault modes such as short circuit and temperature out-of-control. The system has a dynamic updating mechanism, and online optimization is realized by calculating incremental updating values of model parameters; meanwhile, a model evaluation mechanism is established, and when an evaluation index is lower than a preset standard, a retraining process is triggered. According to the invention, high-precision and real-time identification and early warning of the battery fault are realized, and the operation reliability and safety of the energy storage system are significantly improved.

The invention belongs to the field of computer systems based on calculation models, and discloses a neural network model lithium ion battery state prediction method based on convolution and bidirectional long and short time memory, comprising the following steps: S1, data acquisition and data preprocessing; the method comprises the following steps: S1, constructing battery discharge data under a plurality of temperatures and different driving cycles by adopting a sliding window technology, S3, constructing a neural network model, namely a CNN-BiLSTM model, of a convolution and bidirectional long-short term memory network, S4, carrying out model migration, and S5, carrying out model migration according to an obtained CNN-BiLSTM-TL network model of a target domain. The SOC and the SOE can be accurately estimated, the charge and discharge control strategy of the battery can be optimized, the service life of the battery is prolonged, the efficiency of the battery is improved, and meanwhile the method is a key for guaranteeing efficient and stable operation of a battery system.

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The invention relates to the technical field of electric power monitoring analysis, and discloses an intelligent analysis system for electric power monitoring data based on a mutual inductor. The system comprises a mutual inductor data acquisition and structuring module which acquires a current waveform, a voltage waveform and a harmonic component in real time, and generates a standardized monitoring data unit through preprocessing, field mapping and association identification establishment; the theoretical monitoring value calculation module constructs a dynamic calculation model according to historical power data, and an input standard data unit outputs a theoretical value; the rule conformity verification module is used for matching the equipment type with the operation rule and generating single compliance judgment; a multi-dimensional difference analysis module compares a theoretical value with a measured value from time domain deviation, frequency domain deviation and waveform distortion, and generates an equipment-level difference coefficient matrix; the association network construction module is used for constructing a multi-monitoring-point association map according to the equipment identifier and the position information; and the anomaly positioning and strategy generation module combines the map, compliance judgment and a difference matrix, calculates a risk score, generates an anomaly probability distribution map, positions anomaly and matches a monitoring strategy.

The invention discloses a scene topology understanding method and device, a storage medium and a program product, and relates to the field of computer systems based on a specific calculation model, and the method comprises the steps: inputting a multi-view environment image set into a backbone network, and generating bird's-eye view features corresponding to the environment image set; calculating a spatial transformation matrix of the aerial view features at the current moment and the aerial view features of the previous K frames, and performing space-time alignment on the obtained K + 1 frames of aerial view features to obtain multi-frame fusion features; inputting the multi-frame fusion features into a map prior model to obtain aerial view correction features; decoding the aerial view correction features based on a topological decoder, and generating a lane topological graph; comparing the lane topological graph with the annotation data, calculating an error loss function, and optimizing network parameters based on the error loss function; and generating an optimized topological graph, and determining a scene topology result based on the optimized topological graph. By implementing the method, the environment topology understanding capability in a complex scene can be improved, and the generation precision of the lane topological graph is optimized.

The invention provides a spectral value calculation model training method and device, a medium and equipment, and the method comprises the steps: determining a target spectral dimension, obtaining a first structure parameter set as a training sample, and taking a corresponding spectral value as an annotation; and constructing a to-be-trained spectrum value calculation model according to the target spectrum dimension, inputting a training sample into the spectrum value calculation model, and training the spectrum value calculation model by taking the minimum difference between output and annotation as an optimization target. And according to the determined target dimension number, constructing the to-be-trained spectrum value calculation model, so that the trained spectrum value calculation model outputs the spectrum values of the target dimension number, and the efficiency of constructing the native database is improved. In addition, the size of the spectral value calculation model is far smaller than that of the native database, so that when optical critical dimension measurement is carried out on different devices, the spectral value calculation model can be migrated in the different devices to replace migration of the native database, the migration speed is increased, and storage resources of the devices are saved.

The invention relates to a transformer multi-physics field coupling numerical simulation method and device and a storage medium. The method comprises the following steps: performing coupling calculation on a target working condition by using an electromagnetic, heat, flow and force coupling calculation model of a transformer to obtain transformer loss data and temperature rise data; mapping the temperature rise data to a transformer electromagnetic and force coupling calculation model by using a grid node data mapping method to serve as an initial value, and performing coupling calculation on a target working condition to obtain dynamic data of an internal structural member of the transformer; performing fast Fourier transform on the dynamic data in a preset frequency range to obtain dynamic data under each frequency; and mapping the temperature rise data and the dynamic data under each frequency to a transformer sound and force coupling calculation model as initial values, and carrying out coupling calculation on a target working condition to obtain transformer oil tank vibration and external noise data. Compared with the prior art, the method has the advantages that the calculation amount is effectively reduced, and the accuracy of predicting the internal state of the transformer is improved.

The invention provides an optical critical dimension measurement method and device, a medium and equipment. The method comprises the following steps: acquiring a structure parameter set of a grating model and a corresponding first theoretical spectral value; sampling the structural parameter set, and determining a training sample of the spectral value calculation model; and training the spectral value calculation model by taking the output of the spectral value calculation model as a training spectral value and taking the minimum difference between the training spectral value and the corresponding first theoretical spectral value as an optimization target. The trained spectral value calculation model is used as the native database, so that the efficiency of constructing the native database is improved, the size of the native database is reduced, the migration difficulty of the native database is reduced, all structure parameter sets and corresponding spectral values do not need to be loaded when optical critical dimension measurement is carried out, and the measurement efficiency is improved. And the efficiency of optical critical dimension measurement is improved. In the training process of the spectral value calculation model, the acquired grating structure is sampled, so that the time of model training is shortened.

Various approaches to efficiently allocating and utilizing hardware resources in data centers while maintaining compliance with a service level objective (SLO) specified for a computational workload is translated into a hardware-level SLO to facilitate direct enforcement by the hardware processor, e.g., using a feedback control loop or model-based mapping of the hardware-level SLO to allocations of microarchitecture resources of the processor. In some embodiments, a computational model of the hardware behavior under resource contention is used to predict the application performance (e.g., as measured in terms of the hardware-level SLO) to be expected under certain contention scenarios. Scheduling of workloads among the compute nodes within the data center may be based on such predictions. In further embodiments, configurations of microservices are optimized to minimize hardware resources while meeting a specified performance goal.

Disclosed in the present invention are a method and system for simulation based on identification of a rock mass structural plane and prediction of a mechanics parameter. The method comprises: obtaining rock mass multi-scale structural plane data and a rock mass physical mechanics parameter at a tunnel construction site; according to the rock mass multi-scale structural plane data, constructing a tunnel construction numerical calculation model; inputting the rock mass physical mechanics parameter into the tunnel construction numerical calculation model, and performing tunnel excavation numerical simulation; during simulation, obtaining a real-time tunneling parameter of construction machinery; according to the real-time tunneling parameter of the construction machinery and a correlation model between tunneling parameters and rock mass physical mechanics parameters, obtaining a predicted physical mechanics parameter of a rock mass in front of a tunneling point; and updating the tunnel construction numerical calculation model by means of the predicted physical mechanics parameter of the rock mass in front of the tunneling point. The accurate simulation of excavation of a tunnel is implemented.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a blood oxygen change monitoring algorithm fused with dynamic Bayesian modeling, which comprises the following steps: quantizing signal uncertainty by calculating entropy of pulse waveform harmonic energy distribution; and on the basis of the entropy value, driving a dynamic Bayesian network to carry out confidence coefficient evaluation and weighting processing on the blood oxygen estimation model, and finally outputting a decision pair containing a blood oxygen estimation value and the confidence coefficient thereof. According to the method, the signal uncertainty is converted into a computable entropy index, so that the system can autonomously distinguish real physiological changes and measurement noise, the problem of misjudgment caused by motion interference in traditional blood oxygen monitoring is avoided, meanwhile, non-inductive personalized calibration is achieved by utilizing the continuous learning ability of the Bayesian network, and the accuracy of the system is improved. And the reliability and the practicability of the medical wearable equipment are remarkably improved.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

Systems and methods are disclosed for simulation of occluded arteries and / or optimization of occlusion-based treatments. One method includes obtaining a patient-specific anatomic model of a patient's vasculature; obtaining an initial computational model of blood flow through the patient's vasculature based on the patient-specific anatomic model; obtaining a post-treatment computational model by modifying portions of the initial computational model based on an occlusion-based treatment; generating a pre-treatment blood flow characteristic using the initial computational model or computing a post-treatment blood flow using the post-treatment computational model; and outputting a representation of the pre-treatment blood flow characteristic or the post-treatment blood flow characteristic.

Systems, computer program products, and methods are described herein for application anomaly detection using advanced computational AI machine learning modeling. In this way, performance metric data is extracted and the variances are derived by comparing the performance metrics of the application workload for the current time period against the same for a previous period. The AI model is trained at regular intervals by using the derived performance metrics data to identify only the candidate workloads which are degrading or underperforming at an early state, while avoiding reporting workloads that are not causing impact to performance stability of applications across an entity database network. The system monitors application workload across a relational database of an entity for degraded application performance and identifies changes in application workload performance and applies the anomaly detection artificial intelligence machine learning model.

The invention discloses a gobi photovoltaic base ecological restoration carbon sequestration benefit evaluation method and system, and particularly relates to the technical field of ecological environment governance, and the method comprises the following steps: dividing a photovoltaic base region into a photovoltaic panel lower region, a photovoltaic panel inter-row channel region and a photovoltaic field peripheral region, building corresponding ecological simulation models for different regions, and constructing a photovoltaic field simulation model; simulation data are obtained and substituted into the first common calculation model and the second common calculation model respectively, calculation results of all the areas are summarized and substituted into a pre-trained machine learning model, and an output result is an ecological restoration carbon sequestration benefit value used for representing the final score of overall ecological restoration of the photovoltaic base; according to the method, the vegetation restoration and soil carbon storage process can be dynamically simulated, compared with a traditional static monitoring method, the future ecological restoration trend can be predicted, scientific support is provided for long-term ecological management, a targeted ecological optimization scheme can be provided based on regionalized simulation, and the ecological value of renewable energy sources is improved.