186 results about "Self-organizing map" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention provides a deep learning-based rapid ore sorting method under adaptive illumination intensity, and relates to the technical field of ore sorting, the deep learning-based rapid ore sorting method comprises the following steps: obtaining illumination intensity distribution information and ore three-dimensional point cloud data, and calculating ore space orientation parameters based on quaternion attitude estimation; inputting the obtained data into a layered illumination compensation network, and obtaining an illumination normalized enhanced image through adaptive brightness mapping and local light spot correction in combination with reflectivity recovery and non-local mean filtering; inputting the illumination normalized enhanced image and the ore three-dimensional point cloud data into a multi-branch feature extraction network, constructing feature high-order association, and adopting adaptive feature reconstruction and conditional random field optimization features; and inputting the reconstructed features into a classification model based on active learning, optimizing a feature manifold space by using a self-organizing mapping network, carrying out sample labeling in combination with active sampling of adaptive entropy threshold screening and uncertainty perception, and realizing ore classification and sorting through random forest integration.

The invention provides a self-organizing closed-loop feedback driven iterative optimization power exception processing method and system, and relates to the technical field of power exception processing. Comprising the steps of inputting modal data into a hybrid expert model, and obtaining comprehensive features of the multi-modal data through a weighted fusion method; based on the comprehensive characteristics, detecting the abnormality of the power system; adopting a reinforcement learning method to generate a plurality of processing suggestions for the power system abnormity; obtaining feedback information of a user on various processing suggestions, inputting the feedback information into the self-organizing mapping model for clustering, and dynamically adjusting the weight of each modal data in the hybrid expert model based on a clustering result; the feedback information serves as a reward for generating multiple processing suggestions in reinforcement learning, and generation of the multiple processing suggestions in the next round is optimized; and iteration is carried out until an optimal processing suggestion is obtained. According to the method, the accuracy, the real-time performance and the individuation degree of anomaly detection and processing can be effectively improved.

The invention discloses an intelligent prediction method for fracture pressure of a compact conglomerate reservoir based on an improved Transform architecture, and relates to the technical field of combination of deep learning and oil extraction. Comprising the following steps: acquiring fracture pressure data of fractured wells in the same block and logging parameters related to the fracture pressure, matching the fracture pressure with the logging parameters, and preprocessing the data to obtain initial data; based on a CAE-SOM self-organizing mapping neural network, clustering the initial data to obtain sample categories under different geological conditions; based on a generative adversarial network, sample categories under any geological condition are expanded to obtain a training set; and in combination with an attention mechanism, training the Transform model, and predicting the fracture pressure of the block reservoir by using the trained Transform prediction model. According to the method, the limited fracture pressure data can be accurately utilized for prediction, so that a more efficient method is provided for providing powerful support for fracture design and segmented clustering optimization.

The invention relates to an integrated management system for diabetic patients, which comprises the following steps of: constructing a high-dimensional metabolic data model, performing multi-modal fusion on genome information, metabolome data and life behavior data, and mining a potential mode of metabolic characteristics of the patients; carrying out patient typing by utilizing a clustering algorithm comprising a self-organizing mapping network, and identifying different metabolism types comprising an insulin resistance type and an insulin secretion insufficiency type; developing a hierarchical management strategy based on a typing result, wherein the hierarchical management strategy comprises diet, exercise and medicine intervention schemes for different types; collecting dynamic quantitative indexes of the metabolic load, and integrating the blood glucose fluctuation amplitude, insulin sensitivity and daily nutrition intake of the patient; constructing a metabolic load prediction model, and combining a time sequence deep learning algorithm with real-time data, including blood glucose, diet and exercise, of a patient to measure a future metabolic load; and establishing a causal relationship model between patient behaviors and metabolic reactions, and analyzing an interaction mode of diet, exercise and metabolic loads by adopting a causal inference technology.

The invention discloses a hybrid clustering and stacking integrated deep learning-based photovoltaic output prediction method and system, and relates to the technical field of artificial intelligence and new energy output prediction, and the method comprises the steps: carrying out the data preprocessing, carrying out the cleaning, filling and standardization of collected data, and carrying out the marking and data set division; then constructing a mixed weather clustering model based on self-organizing mapping (SOM) and a K-means clustering method; secondly, constructing a stacked integrated deep learning model which comprises a basic model of a first layer and a meta-model of a second layer; and finally, constructing a photovoltaic output prediction model based on the constructed hybrid clustering model and the stack integration deep learning model, designing a training and evaluation framework of the model, and realizing high-precision photovoltaic power generation capacity prediction by performing clustering classification on weather data and performing stack integration by using multiple neural network models. According to the method, the accuracy of photovoltaic output prediction is improved, the robustness and adaptability of the model are enhanced, and the stable operation of a power system is optimized.

The invention discloses a fault prediction model training method and a photovoltaic equipment inspection method, which are applied to the field of artificial intelligence, and comprise the following steps: obtaining historical data and an operation state of photovoltaic equipment in a distributed photovoltaic field station; training the self-organizing mapping model by using historical data to obtain a trained self-organizing mapping model and output data; the output data is a feature identification vector corresponding to each piece of historical data; training a radial basis function neural network model by using the output data, the historical data and the operation state to obtain a trained radial basis function neural network model; the trained self-organizing mapping model and the trained radial basis function neural network model jointly construct a fault prediction model, and the output of the trained self-organizing mapping model serves as the input of the trained radial basis function neural network. According to the method, the complexity and the calculation amount are reduced, the accuracy of fault identification can be improved, the inspection efficiency and accuracy are further improved, the operation and maintenance cost is reduced, and the safety is enhanced.

The invention discloses a dynamic DEM spatial interpolation method which comprises the following steps: performing depression filling, flow direction analysis and confluence cumulant calculation on DEM data, and extracting a natural sub-basin unit by adopting a minimum catchment area threshold method; constructing a topographic feature matrix, performing refined second-level classification on the first-level drainage basin through an improved self-organizing mapping network, generating a hydrological response unit through boundary processing, and establishing a hydrological attribute library; fusing multi-source data, supplementing attribute interpolation such as underlying surface and rainfall, and constructing an interpolation auxiliary parameter system; a drainage basin is divided into regular grids as neurons, a dynamic neural network containing dynamic states and static attributes is constructed, and nonlinear mapping of DEM correction parameters is achieved through optimization of a dynamic activation function and a loss function. The method overcomes the defects that a traditional interpolation algorithm does not consider hydrological boundary constraints, a neural network model topological structure is fixed and the like, and the DEM interpolation precision and the hydrological simulation effect of the complex terrain area are improved.

The invention discloses an unmanned aerial vehicle three-dimensional flight path planning method for wireless sensor network data collection and energy supplementation, and belongs to the technical field of Internet of Things. A network is divided into a plurality of clusters through a density peak clustering algorithm, and a dynamic cluster head selection mechanism based on a routing protocol is designed to optimize the positions of cluster heads. The shortest flight path is determined by adopting a self-organizing mapping network introducing a penetration mechanism. Through a depth deterministic strategy gradient algorithm, the flight height of the unmanned aerial vehicle is dynamically adjusted, and meanwhile, the flight route of the unmanned aerial vehicle is planned, so that the unmanned aerial vehicle can efficiently collect data and supplement energy to sensor nodes in a charging range. According to the method, the distance of the unmanned aerial vehicle for charging the sensor and collecting data is obviously optimized, the energy utilization efficiency is improved, the energy supplement and data collection efficiency of the network is improved, and the network survival time is prolonged.

The invention discloses a classification aggregation identification method based on adjustable load response performance, and the method comprises the steps: constructing index order-based classification according to a integrating degree index and an access standard, and dividing adjustable load resources managed by a virtual power plant into a main energy load resource set, a frequency modulation load resource set, a standby load resource set and a comprehensive load resource set; and aggregating the classified resources by using a self-organizing mapping (SOM) clustering method based on feature vectors, and performing external characteristic identification analysis on an aggregate based on maximum flexibility. According to the method, the adaptation degree of the adjustable load resources and the power transaction products is fully considered, corresponding integrating degree indexes are set, classification is carried out in a bit order sorting mode, and the adjustable load resources in different categories are aggregated by adopting a self-organizing mapping neural network (SOM) clustering method based on feature vectors, so that the power transaction products are classified. And finally, evaluating the external characteristics of the polymer by adopting an external characteristic identification mode with maximized flexibility.

The invention discloses a health assessment and life estimation method for a sludge dewatering machine, and belongs to the field of equipment health assessment and prediction. Comprising the following steps: acquiring operating data such as temperature and vibration of each key part of equipment and working data such as cumulative processing amount from a sludge dewatering machine monitoring system, and extracting a total feature set of the operating data after preprocessing by a one-dimensional convolutional neural network and a stacked denoising auto-encoder; and then inputting a scoring model formed by two self-organizing mapping neural networks to carry out health scoring. And merging the total feature set and the working data, inputting the merged total feature set and working data into a feed-forward neural network life estimation model based on genetic algorithm optimization, and finally carrying out residual life estimation by adopting an FNN (Feedforward Neural Network) of an optimal hyper-parameter combination. The influence of multiple parameters on the health state of the sludge dewatering machine is considered, and the evaluation accuracy is improved; the intelligent level of equipment management of a current sewage treatment plant can be effectively improved, the problems of under-maintenance, over-maintenance and the like are avoided, and the purposes of reducing cost and improving efficiency are achieved.

The invention provides a photovoltaic power station output power prediction method, a terminal and a medium. The method comprises the steps that real-time meteorological data are collected and preprocessed; a self-organizing mapping network is constructed, different environment modes are identified and classified under the unsupervised condition, and meanwhile photovoltaic power generation power is predicted; designing a region model of a cross-region knowledge migration algorithm based on a meta-learning model, and carrying out knowledge migration among different regions; predicting the category of the current meteorological environment according to new meteorological data recollected in real time, then calculating the prediction error of each regional model under the new environmental data, and selecting the regional model with the minimum error to carry out power prediction; and constructing a personalized recommendation strategy model according to the predicted power to perform real-time feedback and optimization on power prediction. According to the method, strong adaptive capacity and generalization capacity are provided, and more accurate decision support is provided for intelligent scheduling and power grid management of photovoltaic power generation.

The invention relates to the technical field of resource allocation, in particular to an edge computing resource allocation method and system, and the method comprises the following steps: collecting network node information, recognizing key nodes through embedded learning, and analyzing a communication mode. A graph convolutional network and a self-organizing mapping algorithm are utilized to analyze and encode node connectivity and resource states, and a network structure is iteratively optimized. And monitoring and updating a network state, adjusting a resource allocation strategy, and generating a dynamic resource allocation model. According to the method, the data processing task is performed on the near side of the data source, so that the data transmission distance and delay are remarkably reduced, the processing speed is increased, and the application performance is directly improved. Simulation mapping and dynamic adjustment of resource states and demands enable resource allocation to be more suitable for actual demands, and resource waste is reduced. Self-adaptive adjustment of resource allocation is carried out according to network real-time data, and real-time performance and accuracy of resource allocation are guaranteed. The instant response and prediction capability optimizes the operation of the whole network.

The invention discloses a waypoint file-oriented unmanned aerial vehicle flight route generation method, which comprises the following steps of: acquiring a waypoint file comprising a waypoint to be flied of an unmanned aerial vehicle, analyzing the waypoint file to obtain latitude and longitude information and flight height information of the waypoint to be flied, and generating a flight route of the unmanned aerial vehicle; the latitude and longitude information and the flight height information of the waypoint to be flied are converted into three-dimensional space information under a Cartesian coordinate system, the three-dimensional space information of the waypoint to be flied is obtained, a waypoint sequence is generated by using a self-organizing mapping neural network model or in a Q learning mode, and a flight route file of the unmanned aerial vehicle is generated based on the waypoint sequence. According to the invention, automation of unmanned aerial vehicle flight route generation is realized.

The invention relates to the technical field of optical fiber sensing, in particular to an optical fiber temperature measurement method and system, and the method comprises the steps: injecting a multi-scale dynamic modulation coding laser signal into a to-be-measured optical fiber and a reference optical fiber respectively, collecting two paths of optical signals, and generating frequency domain data containing modulation coding characteristics through fast Fourier transform; constructing a virtual reference channel, fusing pre-stored historical data through digital matched filtering and self-organizing mapping, and realizing stable output of a reference signal; adaptive calibration is performed on data to be measured by using extended Kalman filtering in combination with fuzzy logic, and calibrated phase data accurately reflecting temperature change is output; and finally, outputting high-precision temperature distribution data based on the temperature inversion model and multi-dimensional correlation analysis. The temperature measurement precision and the system robustness are remarkably improved, and the method is suitable for the fields of industrial and environmental monitoring and the like.

The invention discloses a micro spring washer production method based on genetic algorithm optimization, and the method comprises the following steps: S1, initializing a technological parameter set, and generating a preliminary optimization scheme through a morphological optimization control model; s2, mapping high-dimensional parameters to a low-dimensional morphological feature space through self-organizing mapping, and implementing adaptive variation adjustment; s3, integrating the processed morphological characteristics and technological parameters into chromosome codes, and performing multi-generation iterative optimization; s4, combining an evolutionary game model and fuzzy logic control, and dynamically adjusting a process parameter combination; s5, cooperatively regulating and controlling technological parameters through crossover and mutation operations; s6, performing fitness evaluation, and adjusting genetic algorithm parameters; and S7, after a convergence condition is reached, outputting an optimal process parameter, and applying the optimal process parameter to actual production. The method can significantly improve the production efficiency and the product quality, and is suitable for the precision manufacturing field of complex process optimization.

The invention relates to a user power consumption mode identification method and device considering multi-factor association and a medium. The method comprises the steps of obtaining historical load data and historical meteorological data and performing preprocessing; processing the historical load data and the historical meteorological data under different time resolutions, and constructing a load data set and a meteorological data set under different resolutions; constructing a load curve of each day based on the load data set, performing coarse clustering on the load curves by using a self-organizing mapping neural network, extracting a power utilization mode of the user, and identifying whether the user has an energy storage device or not; calculating the relevance between the meteorological factors and the load through correlation analysis, and selecting key factors; and based on the selected key factors, performing fine-grained analysis on the power consumption mode of the user by adopting a mode of combining a time convolutional network and a pyramid attention mechanism, and accurately identifying the power consumption mode. Compared with the prior art, the method provided by the invention can realize the identification of the power consumption mode of the user with coarse and fine granularity under the multi-resolution condition and multi-factor association.

The invention provides a medical image deep learning auxiliary diagnosis system. The system comprises an image preprocessing module; the feature extraction module is connected with the image preprocessing module; the target detection / segmentation module is connected with the feature extraction module; the classification diagnosis module is connected with the feature extraction module and the target detection / segmentation module; and the result fusion module is connected with the target detection / segmentation module and the classification diagnosis module. According to the invention, a deep learning algorithm, self-organizing mapping, a mixed density network and a clustering algorithm are deeply fused in a feature extraction module, and feature extraction and analysis are carried out on an image from multiple angles. The algorithms cooperate and complement each other, and the limitation of a single algorithm is effectively avoided, so that the system can extract lesion features more comprehensively and accurately, and the disease diagnosis accuracy is improved.

The embodiment of the invention provides a landslide susceptibility prediction method, system and device and a computer readable storage medium. The method comprises the steps of obtaining a to-be-divided topographic image; wherein the topographic image is a slope direction and mountain shadow map extracted based on the digital topographic image; performing region division on the topographic image through a multi-scale algorithm to obtain slope units; wherein the multi-scale algorithm is used for carrying out slope unit division on the terrain; based on the slope unit, environment factor extraction is carried out, and multiple evaluation factors are determined; performing landslide susceptibility prediction on the slope unit based on the plurality of evaluation factors through a landslide susceptibility prediction model to obtain a landslide susceptibility prediction result corresponding to the slope unit; wherein the landslide susceptibility prediction model comprises a self-organizing mapping neural network module, an information amount module and a support vector machine module; the landslide susceptibility prediction model is a model which is trained by adopting historical landslide data and is used for carrying out landslide prediction on the slope unit. According to the scheme, the accuracy of landslide susceptibility prediction can be improved.

The invention discloses an agricultural multi-source knowledge migration method based on a graph model, and belongs to the technical field of agricultural information, and the method comprises the steps: carrying out the reduction, feature selection and feature fusion of agricultural literature data; the data processing efficiency and accuracy are improved by utilizing agricultural data reduction based on self-organizing mapping and feature selection and fusion based on spatial mapping; constructing a knowledge migration network based on a graph model, and performing knowledge semantic extraction and knowledge migration relationship mining; agricultural multi-carrier meta-knowledge acquisition and information feedback are adjusted, and meta-knowledge acquisition and network weight adjustment are optimized. According to the method, large-scale and heterogeneous agricultural data can be effectively processed, effective migration of knowledge is realized, the performance of agricultural learning tasks is greatly improved, and the method is of great significance to improvement of agricultural production efficiency.

The invention discloses a method for identifying a fault mode of a battery pack, which comprises the following steps of: 1) defining n fault modes according to fault characteristics, and acquiring a fault data set under each fault mode; 2) screening the fault data set by using the box diagram to obtain a fault optimal data set; 3) building a battery pack fault mode diagnosis model by using a self-organizing mapping-feedforward neural network algorithm; 4) training the battery pack fault mode diagnosis model by using the fault optimization data set to obtain a trained battery pack fault mode recognition model; 5) monitoring a vehicle operation signal, and transmitting the vehicle operation signal to the battery pack fault mode recognition model to obtain a battery pack fault recognition result; the problem that the fault mode cannot be recognized and early warning cannot be provided when the battery pack system breaks down in the long-term use process is solved.

The invention relates to the technical field of land resource management, in particular to a land stock index management method based on data analysis. The method comprises the following steps: establishing a cross-data-source data sharing mechanism through a block chain technology, and obtaining multi-source land data; performing natural resource, planning and building information fusion on the multi-source land data based on a knowledge graph to obtain a geographic space semantic network; performing multi-scale space-time clustering on the geographic space semantic network through a self-organizing mapping algorithm, and performing key feature capture of land utilization change based on an attention mechanism to obtain land utilization change feature data; and performing land utilization mode identification according to the land utilization change characteristic data to obtain a land utilization dynamic characteristic spectrum. Through a multi-agent system and a hierarchical reinforcement learning algorithm, a complex land utilization decision process can be simulated, and an optimal decision scheme is realized.

The invention discloses a deep learning-based drainage basin abnormal rainfall event identification monitoring and weather behavior analysis method and system, and the method comprises the steps: obtaining the summer rainfall original data of a drainage basin, and carrying out the preprocessing; the abnormal rainfall event is divided into different subspace modes through a self-organizing mapping machine learning method; qualitatively analyzing a space-time evolution rule and an interaction mechanism of water vapor conveying characteristics and atmospheric circulation configuration in abnormal rainfall according to each subspace mode; evaluating the influence of different meteorological factors on extreme weather conditions through a Bayesian method, and quantitatively exploring abnormal rainfall event change factors; the system comprises a data preprocessing module, a self-organizing mapping mode classification module, a water vapor conveying and atmospheric circulation influence mechanism module and a Bayesian method meteorological factor influence analysis module. According to the method, accurate identification and monitoring of the drainage basin abnormal rainfall event are realized, the internal characteristics of rainfall data can be effectively learned, and the identification capability of the abnormal rainfall mode is enhanced.

The invention provides a coastal erosion rate prediction method, and belongs to the technical field of coastal erosion, and the method comprises the steps: building a three-dimensional seabed grid model, solving a hydrodynamic field through depth average simplification and GPU parallel calculation, and calculating sediment flux distribution based on shear stress discrimination and a high-order windward format. Outputting an erosion mode category and a local erosion strength coefficient by using a coastline erosion feature recognition model comprising a Josephh ring screening layer and a self-organizing mapping projection layer, starting adaptive grid encryption when the local erosion strength exceeds a threshold value, and calling a corresponding parameter set according to an erosion mode to calculate a seabed elevation change and a coastline erosion rate; and a wave energy spectrum reconstruction algorithm is adopted to generate future wave sequence cycle prediction, so that the technical problem that calculation precision and calculation efficiency are difficult to consider in coast erosion rate prediction under a complex wave power condition is solved.

The invention discloses a logistics unmanned aerial vehicle urban transportation network planning method based on transfer point site selection, and belongs to the field of unmanned aerial vehicle transportation network planning, and the method comprises the steps: carrying out the three-dimensional discretization modeling of a logistics unmanned aerial vehicle operation environment through employing a grid method based on ground space layout and urban airspace characteristics, and recognizing the potential obstacles of an airspace; designing a logistics unmanned aerial vehicle transportation network system architecture containing one network, two layers and three nodes; establishing a double-layer multi-target planning model including transfer point site selection and network construction by considering the number of transfer points, the operation cost and the balance of distribution network service pressure; and establishing a double-layer solving framework based on the self-organizing mapping neural network and a hybrid simulated annealing algorithm, and outputting a logistics unmanned aerial vehicle three-dimensional transportation network topological structure. According to the method, the logistics unmanned aerial vehicle three-dimensional transportation network is constructed for urban dense distribution requirements, the urban large-scale logistics unmanned aerial vehicles can be guided to fly orderly, and optimal configuration of low-altitude airspace resources is realized.

The invention provides a power grid constant value detection method and system, equipment and a storage medium, and belongs to the technical field of power grid detection, and the method comprises the steps: obtaining to-be-detected test constant value data; determining a target neuron matched with the test constant value data from the self-organizing mapping model, and determining a test error between the test constant value data and a weight vector of the target neuron; based on the test error and an error threshold value, determining whether the test constant value data is abnormal or not; wherein the error threshold value is determined by the following steps: acquiring each historical constant value data of the power grid; determining first neurons respectively matched with the historical constant value data from a self-organizing mapping model; and determining an error threshold value based on the weight vector corresponding to each first neuron and the historical constant value data corresponding to each first neuron. According to the power grid constant value detection method and system, the equipment and the storage medium provided by the invention, the recognition precision of the abnormal constant value can be improved.

The invention discloses a distributed photovoltaic cluster division method considering frequency supporting capacity, and belongs to the field of novel power system frequency stabilization and cluster dynamic division. According to the method, a comprehensive cluster division index is formed by combining a system power flow operation result and comprehensively considering multi-dimensional indexes such as node sensitivity, frequency modulation response intensity and frequency modulation response depth. On the basis, a self-organizing mapping neural network is adopted to carry out optimization division on a photovoltaic cluster, the characteristic relation in the cluster is automatically learned, and a global optimal division result is ensured. In addition, the clustering effect is evaluated through quantization errors, neural network parameters are further optimized, and the cluster division precision and the system frequency support reliability are improved. According to the method, the defects of randomness, frequency fluctuation influence and classification precision of an existing photovoltaic cluster division method are effectively overcome, and a powerful guarantee is provided for the safety and the operation efficiency of a power system.