114 results about "Fuzzy clusters" patented technology

The invention discloses a territorial space layout adjustment decision method based on planning conflict identification, and belongs to the technical field of territorial space planning and management. Comprising the following steps: constructing a space-time conflict feature vector library containing multi-dimensional attributes; generating a semantic knowledge graph containing a causal relationship, a sequential relationship, a spatial relationship and an affiliation relationship; accurate prediction of influence intensity and duration of the conflict state along with time lapse is realized; trend analysis and risk level evaluation of a cross-scale conflict propagation path are realized; automatically identifying dominant conflicts in the current layout and potential conflicts based on propagation prediction, and intelligently classifying composite conflicts by using fuzzy clustering; a staged differentiation conflict intervention strategy recommendation scheme is automatically generated; solving a territorial space layout adjustment scheme; judging whether the layout adjustment scheme meets a preset conflict relieving standard or not;

The invention relates to the technical field of data analysis, and discloses a data analysis method based on a chain operation benefit supervision system, and the method comprises the steps: carrying out the collection and preprocessing of the multi-dimensional data of each store of a chain operation enterprise, and obtaining the multi-dimensional time series data; performing multi-dimensional similarity calculation and fuzzy clustering to obtain a store clustering result and a fuzzy membership degree; constructing a decision tree model and performing store operation state evaluation to obtain a store operation state evaluation result; time sequence feature extraction is carried out, a semantic segmentation network is constructed, and a store business prediction model is obtained; performing deep prediction on the multi-store business data to obtain a store business prediction result; and based on the store operation state evaluation result, the store business prediction result and the fuzzy membership degree, comprehensive anomaly prediction and resource optimization configuration are carried out to obtain an anomaly prediction report and an optimization scheme, so that the accuracy of operation state evaluation is improved.

The invention provides an image noise mark feature selection method and system, a storage medium and a computer. The method comprises the steps of obtaining a to-be-processed image noise mark data set; embedding a sample set in the image noise mark data set into a multi-granularity fuzzy cluster to construct a dynamic fuzzy membership evaluation matrix; dynamically evolving a multi-level high-precision granular ball cluster; obtaining mark distribution with high identification degree; constructing a rough perception feature evaluation framework based on granular ball topology driving, extracting decision equivalence classes by combining rough set upper and lower approximation and extended positive domain models, and determining and measuring the contribution degree of each feature to a decision system by fusing multi-granular-ball decision boundary information based on a dependency degree quantitative model; a particle and ball structure consistency verification mechanism is introduced, and multi-level evaluation is carried out on the importance of the features through dependency and consistency. According to the method, the optimal feature subset with strong anti-noise performance and high discrimination capability is obtained, and stable and efficient input support is provided for a subsequent image noise mark learning model.

The invention relates to the technical field of lithium battery energy storage and temperature control, in particular to an electronic radiator intelligent temperature control system based on a lithium battery energy storage cabinet. The system comprises an edge temperature sensing module, a cloud thermal management strategy module, a cloud clustering analysis module, a thermal field topological optimization module, a multi-source thermal feature fusion module, a dynamic temperature control strategy generation module and a grading strategy issuing execution module. An edge temperature sensing module collects real-time temperature data of multiple monitoring points and an edge side preliminary temperature control scene judgment result; the cloud heat management strategy module receives battery pack topology connection data; the cloud clustering analysis module carries out fuzzy clustering on the data to obtain a temperature state and scene discrimination clustering center; the thermal field topological optimization module optimizes a temperature state clustering center in combination with topological data; the multi-source thermal feature fusion module fuses the two clustering centers to form multi-source fusion feature representation; the dynamic temperature control strategy generation module determines a temperature control strategy identifier accordingly, and the grading strategy issuing execution module matches and distributes a gradient temperature control strategy to a heat dissipation actuator.

The invention discloses a GMM-L2 fault diagnosis method, device and equipment based on a DBGSA algorithm and a medium, and relates to the technical field of engine fault detection, and the method comprises the steps: obtaining the original data of a wind power engine; processing the original data of the wind power engine to obtain second data; then calculating a first clustering center in the second data for generating a first Gaussian mixture model for calculating a health degree; setting an alarm limit according to the health degree, and if the second data is smaller than the alarm limit, determining that the second data is in an abnormal state; processing the second data in the abnormal state to obtain third data; updating the clustering center of the third data by adopting a DBGSA algorithm of fuzzy clustering to obtain a third clustering center; and setting a membership threshold value of the fault type, and calculating the membership probability of the fault type to which each cluster belongs to obtain the fault type to which the third data belongs. According to the method, missing detection caused by classifying only one data point into one fault can be avoided.

The invention discloses a transformer oil chromatographic analysis method based on an improved fuzzy clustering algorithm and an LSTM (Long Short Term Memory) algorithm. The method comprises the following steps: acquiring sample data; s2, randomly dividing the sample data into a plurality of cluster sets; s3, obtaining an initial clustering center of the clustering cluster set; s4, acquiring a comprehensive distance between the initial clustering center of each clustering cluster set and the sample data about the Euclidean distance and the cosine distance; s5, the membership matrix is updated according to the comprehensive distance, the steps S3 to S4 are repeatedly and iteratively executed until the iteration convergence condition is met, and the final clustering center of the clustering set is obtained; and according to the final clustering center, determining a clustering result of the transformer fault type. S6, dividing the clustering result into a data training set and a data test set according to a preset proportion; s7, establishing an LSTM model and taking the LSTM model as a prediction model for transformer fault analysis; and obtaining an optimal prediction model based on the data training set and the data verification set. The problems that a large amount of manpower and financial resources are consumed in a traditional regular overhaul mode, particularly in remote areas with inconvenient traffic, overhaul work faces many challenges, and in addition, human errors possibly occur in the overhaul process, and equipment is damaged are solved.

The invention relates to the technical field of aero-engine fault prediction, in particular to an aero-engine residual life prediction method based on fuzzy modal regression SCN, and the method comprises the steps: obtaining multi-source monitoring data of an aero-engine under different working conditions; performing fuzzy clustering on the training sample set based on a fuzzy C-means algorithm; training the SCN structure according to the subspace training sample; optimizing the local prediction sub-model based on a modal regression model; according to a generalized maximum correlation entropy loss function, performing robustness optimization on the local prediction sub-model after modal regression in combination with a semi-quadratic optimization strategy and a sparse regular term; predicting a to-be-tested aero-engine sample according to the local aero-engine residual life prediction model; and fuzzy weighted fusion is carried out on the local residual life prediction value of the aero-engine. According to the method, the prediction information of each modal subspace can be integrated, the multi-modal degradation behavior is comprehensively represented, and the accuracy of prediction of the residual life of the aero-engine is remarkably improved.

The invention provides an industrial air conditioner and waste heat recovery collaborative intelligent optimization control method and related equipment, and is applied to the technical field of data processing. Industrial air conditioner and waste heat recovery system data are fused through the Internet of Things and edge calculation, a standardized parameter sequence is generated through wavelet noise reduction and is converted into an energy flow network atlas, high-energy-consumption working conditions are subdivided through fuzzy clustering, and a dynamic energy efficiency coupling model is constructed. And based on the model, constructing an optimization equation by using an improved particle swarm algorithm, and obtaining the energy consumption of the key node in combination with an energy gradient utilization matrix. And an energy-saving potential coefficient is generated through the multi-dimensional feature matrix, a production plan grouping operation mode is combined, a self-adaptive optimization control model is constructed by using deep reinforcement learning, and finally a real-time regulation and control instruction and an energy efficiency scheme are generated to realize system collaborative optimization.

The invention provides a driving anti-slip optimization control method based on vehicle-road cooperation road fusion estimation, and the method comprises the following steps: S1, fusing vehicle-mounted and roadside visual recognition information, and obtaining a vehicle-road cooperation road state recognition result through employing a weighted voting method in combination with road invariant hypothesis and historical information assistance judgment; s2, adaptively adjusting parameters of a dynamic estimator by using a pavement state recognition result, establishing a sample set based on a pavement fusion estimation result, introducing a fuzzy clustering algorithm of particle swarm optimization to realize self-evolution optimization of the structure and the parameters of the estimator, and outputting a fused attachment coefficient estimation result; and S3, designing a slip rate and wheel acceleration combined control strategy to deal with potential slip in advance, and outputting a control torque result of a slip rate controller. Compared with the prior art, the method integrates visual perception and dynamic information to realize accurate estimation of the pavement state, supports advanced intervention of the slip rate controller before slip, and improves the driving stability of the whole vehicle.

The present application provides a multi-objective material distribution center site selection and path planning method, which obtains material distribution data; the material distribution data includes geographic information data, traffic condition data, material demand data, meteorological data and socio-economic data; the material distribution data is processed by a missing data processing algorithm based on fuzzy clustering to obtain processed material distribution data; the processed material distribution data is analyzed by a deep learning model to evaluate the material demand characteristics and predict the material demand change trend; a path planning model based on multi-objective optimization is constructed according to the material demand characteristics and material demand change trend; the multi-objective function in the path planning model based on multi-objective optimization is solved based on a microhabitat genetic algorithm optimized by a marine predator algorithm to obtain a globally optimized distribution center site selection and planning path, thereby solving the problem of inaccurate material distribution center site selection and planning path.

The invention provides a multi-modal data security analysis technology based on a depth fuzzy wavelet learning model. A deep fuzzy wavelet learning model is widely applied to the field of security analysis due to the self-adaptive capability of the deep fuzzy wavelet learning model in a complex data environment. According to the method, a deep fuzzy wavelet learning framework is improved and is applied to multi-modal data security analysis. According to the model, firstly, multi-modal data (texts, voices, videos and images) are subjected to feature decomposition and noise reduction processing by using a wavelet transform and fuzzy clustering combined method, key semantic, time sequence and spatial features are extracted, and high-dimensional information expression is provided for subsequent safety analysis. Secondly, constructing a deep fuzzy wavelet neural network, inputting the extracted features into an adaptive fuzzy decision layer, and capturing space-time correlation features in combination with a wavelet learning model, thereby improving the perception ability of the model to a complex attack mode; and finally, a security analysis result is enhanced by adopting a robust and spatial search optimization algorithm, so that malicious samples are effectively detected, backdoor attacks are identified, the attack defense capability is improved, and finally accurate multi-modal data security assessment is realized.

The invention discloses a transformer fault diagnosis method based on an improved neural network, and provides a fault diagnosis technology based on an improved algorithm aiming at the problems of low efficiency and strong diagnosis result subjectivity of traditional transformer fault diagnosis. The method comprises the following steps: firstly, preprocessing voiceprint data of a transformer, further processing preprocessed voiceprint features through a fuzzy clustering C-means clustering algorithm, and labeling the voiceprint features to obtain a data set; and then constructing a neural network model optimized based on the improved He-Map algorithm, optimizing parameters of the neural network through the He-Map algorithm, and improving search and convergence performance of the model. According to the method, the efficiency and accuracy of fault diagnosis are improved, the generalization ability of the model is enhanced, and powerful technical support is provided for maintenance and fault prevention of the transformer.

The invention discloses an electric vehicle charging load prediction method based on an optimized fuzzy neural network, and the method comprises the steps: obtaining a plurality of historical load data of an electric vehicle, and processing all historical load data to generate a multi-dimensional feature data set; dividing an input feature space for the multi-dimensional feature data set based on an improved fuzzy clustering algorithm to obtain a clustering result and an activation intensity-contribution degree two-dimensional evaluation index; iteratively optimizing FNN parameters based on an improved adaptive differential evolution algorithm; constructing a rule contribution degree analysis model based on an orthogonal experimental design, setting a quantitative truncation threshold with the cumulative interpretation degree greater than or equal to 85%, and realizing self-adaptive compression of the scale of the rule base; an activation intensity threshold value dynamic calculation method is used, low-efficiency rules are automatically identified through sliding window statistics, and rule pruning is achieved; and outputting a load prediction result, and applying non-negativity and peak constraint to a prediction value.

The invention discloses an elevator maintenance quality evaluation method based on an SVR-NSGA-I algorithm. The method comprises a data acquisition stage, a data preprocessing stage, an SVR model training stage, an NSGA-I optimization stage and an evaluation system construction stage. Compared with the prior art, the method has the advantages that PSO optimized SVR and adaptive NSGA-I are deeply fused, the multi-target optimization problem of elevator maintenance is solved, an isolated forest-KNN combined preprocessing method is provided, high-dimensional, nonlinear and noisy maintenance data are effectively processed, a time decay factor and fuzzy clustering are introduced, and the maintenance efficiency is improved. The real-time dynamic adjustment of the evaluation standard is realized, and the model transparency and practicability are improved in combination with SHAP value analysis and visual decision support.

The invention relates to the technical field of data processing, and particularly discloses a shadow set fuzzy clustering method and system based on decision weight driving, and the method comprises the steps: carrying out the clustering initialization; different decision weight matrixes are generated by calculating a membership matrix and constructing a decision weight function so as to accurately measure the influence weights of different samples in different areas on the cluster center; constructing a fuzzy cluster shadow set: establishing an optimization principle, calculating a partition threshold value of each fuzzy cluster through a particle swarm optimization algorithm, and dividing each fuzzy cluster into a core region, a shadow region and an exclusion region; updating the clustering center: updating the clustering center based on the contribution difference of each region sample to ensure that the contribution of the sample of the core region to the updating is maximum, and meanwhile, excluding the region sample is brought into calculation to prevent the clustering center from falling into local optimum; iterating and stopping when a stopping condition is met. According to the method, the advantages of the decision weighting function conforming to fuzzy cluster distribution and the shadow set are effectively combined, and the accuracy and robustness of fuzzy clustering are improved.

The invention provides a rotational symmetry point cloud registration method and device based on fuzzy clustering and a storage medium, and the method comprises the steps: extracting inner points through employing an algorithm combining the minimum median square and random sampling consistency, and estimating a rotating main axis through principal component analysis; realizing point cloud global coarse registration based on a rotating main shaft; fuzzy clustering is carried out on the fixed point cloud to construct local geometric description, a progressive constraint mechanism and a directional search strategy based on a main shaft are introduced, and fine registration is completed in combination with branch and bound and gradient descent algorithms. According to the embodiment of the invention, the registration precision and efficiency of point clouds with rotational symmetry, such as a computer-aided design (CAD) model, can be effectively improved.

The invention provides a distributed photovoltaic cluster division method and device, and the method comprises the steps: obtaining the modularity, power balance degree and line impedance distance of a distributed photovoltaic system according to the information of each photovoltaic power generation node in the distributed photovoltaic system; and dividing the distributed photovoltaic system into a plurality of clusters by using an improved fuzzy clustering algorithm based on feature weighting according to the modularity, the power balance degree and the line impedance distance. According to the method, multiple indexes are used for cluster division, so that electrical connection between nodes in the clusters is close, connection between the clusters is loose, and operation management of the clusters is facilitated; the improved fuzzy clustering algorithm based on feature weighting is adopted to quantify each clustering index, the weight of each index is obtained, cluster division is achieved through iteration, and the accuracy of distributed photovoltaic cluster division is improved.

The invention provides an early warning method and system for mine slope monitoring, and relates to the technical field of geographic information analysis systems. The method comprises the following steps: acquiring a real-time slope monitoring data set based on slope monitoring equipment deployed in a plurality of mine slope key areas; determining multi-attribute features of the monitoring data, and obtaining a plurality of slope section clusters by using a multi-attribute fuzzy clustering algorithm based on mixed data; screening a target risk slope section cluster according to the historical disaster recall rate of each slope section cluster; constructing slope section monitoring time sequence data according to the real-time slope monitoring data corresponding to each target risk cluster and the corresponding historical slope monitoring data set; acquiring a meteorological parameter sequence of the key area of the mine slope; and inputting the monitoring time sequence data of each slope section and the meteorological parameter sequence into a slope risk monitoring model to obtain a slope risk identification result, and further performing early warning. Therefore, multi-dimensional sensing equipment and an advanced data processing algorithm are comprehensively utilized, and real-time monitoring and early warning for the stability of the mine slope are achieved.

The invention provides a cartridge case precision forming process abnormal working condition monitoring and early warning method. Diffusion distance fuzzy clustering (DDFCM) and dynamic internal principal component analysis (DiPCA) technologies are fused. The method comprises the following steps: firstly, performing fuzzy clustering on process data based on a diffusion distance by adopting a DDFCM method, automatically identifying key sample groups under different working conditions, and constructing a representative local training data set; thirdly, for each clustering sample group, constructing a dynamic monitoring model by using a DiPCA technology, effectively extracting potential variables containing time lag features, and capturing a dynamic change mode of a process; in a new data acquisition stage, firstly, the membership degree of a new sample in each cluster is calculated through DDFCM, and the new sample is distributed to a corresponding local DiPCA model for state discrimination according to a maximum membership degree principle. According to the method, the time sequence dynamic change in the process can be fully captured, the local characteristics in different production states are also considered, and a powerful guarantee is provided for the production stability and the product quality of the cartridge case coating, rolling and cutting process.

The invention provides an industrial load adjustable potential assessment method and device considering security constraints, and relates to the technical field of potential assessment. The method comprises the following steps: constructing a high-energy-consumption industrial user side load model and establishing a load curve; clustering the load curve by using a black hole entropy fuzzy clustering algorithm and determining a load interruption interval and a transferable interval; corresponding indexes are obtained according to the corresponding intervals, and multiple indexes such as safety consideration are introduced; an improved G1 method based on Spearman rank correlation is used for carrying out weighting on each index and determining the weight, and then the adjustable potential is evaluated. In this way, data support is provided for power supply enterprises to reasonably dispatch user side resources. The device comprises a first construction module, a second construction module, a weight value determination module and a calculation module, all the modules cooperate with one another to achieve the industrial load adjustable potential evaluation method considering the security constraints, and quantitative evaluation of the adjustable capability of industrial users is greatly achieved.

The invention discloses a sintering fuel proportion optimization method and system based on data mining and a memory. According to the method, a fuzzy clustering algorithm is adopted to classify operation modes, air permeability states and heat levels in the sintering process, and an association rule base of different operation modes, air permeability states and heat levels and fuel ratios is constructed through a fuzzy association rule mining algorithm; then, according to key operation parameters or online detection parameters in the actual production process, the current operation mode, the air permeability state and the heat level are recognized, fuel proportion optimization regulation and control rules are matched from the rule base through fuzzy logic reasoning, and optimization setting of the sintering fuel proportion is achieved. According to the optimization setting system containing the computer program, provided based on the method, the technical problem of optimization setting of the sintering fuel proportion is effectively solved, intelligent regulation and control of the fuel proportion in the sintering process can be achieved, and the optimization setting system has practical significance for improving the stability of the sintering process and reducing production energy consumption.

The present invention discloses a method and system for planning agricultural machinery operations based on unmanned aerial vehicles, which relates to the technical field of agricultural operation planning. Based on the acquired geographic image frame, a complete farmland map is formed. This step helps to identify and collect the crop distribution set in the complete terrace map, and provides accurate spatial information for farmland management and operation; and a fuzzy clustering algorithm is used to classify the terrain of the terrace area, analyze the influence of different gradient terrains on the speed of agricultural machinery operation, and construct the corresponding influence coefficient Yxxs. This process provides an operation speed optimization strategy based on actual terrain conditions, thereby improving operation efficiency and utilization of farmland resources. Based on field status data information, a condition analysis model is created and trained, and the terrain complexity coefficient Dfxs in different gradient terrain areas is analyzed and obtained to grasp the conditions in different areas.

The invention provides a Chinese resident food consumption carbon emission classification method and system based on fuzzy clustering, and belongs to the field of data analysis and application statistical analysis. The invention aims to solve the problem that the existing K-means clustering algorithm depends on the load distance of each region on each principal component factor to carry out region division, is restrained by the fact that the region belongs to a certain index, and is low in practical significance. The method comprises the following steps: firstly, establishing a related index system, performing dimension reduction processing by utilizing principal component analysis, introducing an improved fuzzy C-means algorithm, taking a principal component factor score after dimension reduction as original data of the improved fuzzy C-means algorithm, and introducing a fuzzy clustering effectiveness index to obtain a fuzzy clustering result; the maximum intra-class similarity, the maximum inter-class dissimilarity and the minimum inter-class overlapping degree can be met to the maximum extent, and the optimal classification is determined according to the effectiveness index. The degree of influence of different indexes on each region is well displayed through principal component analysis, various clustering parameters are fully considered, and optimal principle classification is carried out.

A computing entity obtains product information corresponding to a product and defines a multi-region metric space vector based on the product information. The multi-region metric space vector is a vector within a multi-region metric space that comprises a first region corresponding to supply chain / component information, a second region corresponding to assembly / fabrication information, and a third region corresponding to usage / usage environment information. Each of the first, second, and third regions are multi-dimensional. The computing entity processes the multi-region metric space vector using a component, assembly, and usage perspicacity model configured to define fuzzy clusters within the multi-region metric space; determines a parameter corresponding to the product based on at least one fuzzy cluster to which the perspicacity model assigned the multi-region space vector; and provides or causes providing of (a) a visual / audible representation of the parameter or (b) a machine-readable representation of the parameter as input to an application / program.

The present invention relates to the technical field of economic resource management, and discloses an economic resource management optimization method based on intelligent decision-making. The method first collects multi-source heterogeneous data of the economic system, including resource stocks, demand fluctuations, etc. Then, based on multi-dimensional feature analysis, the dynamic resource pool is divided, and a nonlinear optimization model is constructed to predict changes in resource supply and demand to generate an allocation plan. Then, a multi-stage decision tree is used to optimize the allocation path, and the model parameters are updated through an adaptive learning mechanism according to market feedback and changes in constraints, and the allocation plan is corrected in real time. In addition, key technical details such as the elastic quota adjustment formula and the fuzzy clustering algorithm membership function are also given. The present invention can effectively integrate complex data, scientifically dispatch resources, accurately predict supply and demand, optimize allocation paths, adapt to environmental changes, significantly improve the efficiency and benefits of economic resource management, and provide scientific and reasonable resource management decision support for economic entities.

This invention discloses a robust fusion method for large models based on semantically aligned fuzzy clustering. The method includes: first, obtaining a sequence of probability distribution vectors from the outputs of multiple heterogeneous large-scale pre-trained models for the samples to be processed; then, introducing non-negative reliability weights to weight this sequence to obtain an aggregated probability matrix, and constructing a graph Laplacian matrix accordingly; second, approximating the aggregated probability matrix into a fuzzy membership matrix and a semantic prototype matrix, constructing a cost objective function by combining the graph Laplacian matrix, and iteratively updating each matrix and weight until convergence; finally, solving for the maximum value of the converged fuzzy membership matrix to obtain the sample prediction category result. This invention can effectively suppress the influence of inferior models in unsupervised environments, significantly improving the accuracy and robustness of fusion prediction.

This application discloses a method for determining the threshold of watershed ecological environment quality based on the PSR model, belonging to the field of watershed ecological assessment technology; it includes: determining the evaluation index items of the target watershed; arranging and combining all possible score values ​​of each index to obtain all score combinations; calculating the comprehensive score of each combination to generate a full sample space; performing fuzzy clustering on the full sample space, iteratively determining the membership degree and cluster center value of each sample to multiple preset cluster categories; sorting the cluster center values ​​of each cluster category in ascending order to form multiple health levels; classifying each sample to the cluster category with the largest membership degree based on the principle of maximum membership degree to obtain the sample set corresponding to each health level; fitting continuous distribution density curves to the sample sets of each health level, and determining the intersection of the distribution density curves of adjacent health levels as the critical threshold between adjacent health levels; and determining the health level of the target watershed based on each critical threshold.

The invention relates to the technical field of road maintenance, in particular to a four-new road surface maintenance technology post-evaluation method for improving clustering iteration and preference, which comprises the following steps: step 1, assuming that w four-new technology project samples form a set, and the four-new technology samples comprise u index characteristic values; performing normalization processing on each sample index value in the w sample matrixes so as to eliminate the influence of dimensions among the indexes, and obtaining an index characteristic value normalization matrix; according to the method, an augmented Lagrangian multiplier meeting the preference constraint of a decision maker is added to a target function of a fuzzy clustering iteration model, and a relaxation factor is introduced to obtain an iteration formula of an optimal fuzzy clustering center, an optimal fuzzy membership matrix and an optimal index weight which meet the preference of the decision maker; the problem that the secondary index weight is too large when a traditional fuzzy clustering iteration model is used for clustering is solved.

This invention proposes a mobile swarm intelligence sensing task allocation method and system based on fuzzy clustering and dynamic scheduling. First, fuzzy clustering is used to mine the latent spatial structure of tasks, and tasks are classified into fuzzy clusters with membership attributes through soft partitioning. After obtaining the spatiotemporal distribution characteristics of tasks, a benefit matrix including spatiotemporal feasibility and time cost is constructed, and the Hungarian algorithm is used to achieve a globally optimal match between worker and task clusters. For the online execution phase, a dynamic scheduling mechanism including a primary task pool and a collaborative task pool is designed. Workers select tasks based on a hybrid priority index combining time urgency, spatial proximity, and cluster membership. This invention solves the problem of balancing global efficiency and local flexibility in large-scale task allocation through a hierarchical mechanism of "macro-partitioning-meso-matching-micro-scheduling," significantly improving the overall task completion rate while effectively reducing the system's time resource cost.

The invention discloses a small sample hyperspectral image classification method and device based on fuzzy contrast graph convolution. The method comprises the following steps: determining membership information, fuzzy node initial feature vectors and similarity of each initial graph node corresponding to each fuzzy cluster based on a hyperspectral image; generating a fusion weight, a fusion feature and a target feature according to convolution features output by convolution of different layers of fuzzy images; performing linear transformation operation and probability distribution operation on the target features in sequence to obtain the probability that the corresponding initialized graph node belongs to each category; and performing argmax operation on the probability to obtain a category index corresponding to the hyperspectral image. The uncertainty of pixels is effectively processed through fuzzy learning, interference of neighborhood pixels is weakened, the uncertainty between the pixels is better processed, and meanwhile richer and more expressive feature representation is obtained. The method has higher classification effect and robustness, and plays an important role in multiple fields of mineral exploration, environment monitoring, forest management, precision agriculture and the like.