122 results about "Indicator matrix" patented technology

The invention relates to the technical field of intelligent garbage sorting, and discloses a garbage sorting method based on artificial intelligence. According to the method, heterogeneous data fusion is performed on a multi-source garbage feature data set through a semantic alignment module, feature dimensions are unified, redundant items are eliminated, and a standardized feature set is output. And extracting a spatial position identifier and a physical state identifier of the junk entity, and dynamically dividing time slices by adopting a transfer learning model to generate a periodic junk flow data set. The coordinates of the sorting starting point and the sorting ending point are converted into a three-dimensional space grid to construct a migration track topological graph, flow data and physical state identifiers are combined, sorting paths are clustered and analyzed according to material categories, the material flow intensity in a specific period of a target area is quantified, and a dynamic index matrix is generated. According to the method, space-time correlation analysis of multi-modal junk data is realized, and the adaptability of sorting path planning in a complex environment is improved.

The invention provides a Beidou intelligent early warning analysis method and system applied to a drainage basin-level reservoir dam group, and the method comprises the steps: obtaining a dam body deformation time sequence record and a corresponding hydrological environment time sequence record which are collected by a monitoring system of the drainage basin-level reservoir dam group through a Beidou satellite positioning terminal; dam body deformation response mode self-encoding processing is carried out on the associated time sequence data set, a deformation response feature element set representing the dam body structure state is generated, the deformation response feature element set is input into a pre-constructed basin reservoir dam group topological relation graph, abnormal state propagation calculation is carried out through the state dependency relation between graph nodes, and the abnormal state propagation calculation is carried out. And obtaining a chain risk transmission path set, performing risk evolution trend deduction on dam nodes in each path based on the chain risk transmission path set, generating a risk evolution index matrix, and generating a threatened risk identification sequence for key dam nodes. According to the method, the structuralization and response operability of the early warning information can be improved, and the scientificity and practicability of early warning analysis of the basin-level reservoir dam group are comprehensively improved.

The invention relates to the technical field of multi-view clustering, in particular to a multi-view clustering method and device, and can solve the problem that the overall effect of an existing method in a large-scale clustering task is limited due to the fact that the existing method has problems in the aspects of calculation efficiency, robustness and multi-view information integration to a certain extent. The method comprises the following steps: dynamically learning an anchor matrix and a projection matrix for each view, and constructing a bipartite graph to generate a similarity matrix; calculating a graph Laplacian matrix based on the similarity matrix of each view, and extracting spectrum embedding; the spectrums of multiple views are embedded and stacked into a third-order tensor, and cross-view shared information is extracted by using a low-rank tensor constraint; multi-view atlas embedding is aligned through a spectrum rotation technology, and a discrete clustering indication matrix is directly output.

The invention discloses a data acquisition method and system based on frequency adaptive adjustment, and relates to the field of data acquisition, and the method comprises the steps: obtaining a monitoring index acquisition value set; extracting local time sequence data of the monitoring index acquisition value set under various sliding windows, and calculating a multi-scale difference entropy value; extracting a real-time multi-dimensional monitoring index matrix corresponding to an acquisition moment closest to the current time from the monitoring index acquisition value set, and calculating a mahalanobis distance between the real-time multi-dimensional monitoring index matrix and the historical multi-dimensional monitoring index mean matrix; constructing an input state of a reinforcement learning model according to the first acquisition frequency, the mahalanobis distance and the multi-scale difference entropy, so as to determine a target action in a reinforcement learning mode; and determining a second acquisition frequency according to the first acquisition frequency and acquisition frequency adjustment information indicated by the target action. Therefore, multi-scale time sequence characteristics and real-time deviation calculation are fused, and intelligent self-adaptive adjustment of the sampling frequency is realized by using a reinforcement learning model.

The invention provides an active defense-based multi-source log data fusion sub-tree traceability graph attack detection method, which comprises the following steps of: constructing an attack behavior graph according to CTI (Computer Telephony Integration) data, fusing multi-source log data and constructing a traceability graph; constructing a node alignment rule, and performing node alignment on the attack behavior graph and the traceability graph; taking the attack behavior graph after node alignment as a query graph, taking the traceability graph after node alignment as a target graph, and respectively performing subtree construction on the query graph and the target graph; performing subtree isomorphic calculation and recursive updating on the constructed query graph subtree and target graph subtree to obtain a subtree isomorphic matrix, and calculating a final subtree isomorphic index according to the subtree isomorphic matrix; performing similarity calculation on the query graph and the target graph to obtain a generalized index matrix; and attack detection is carried out based on the generalized index matrix and the subtree isomorphic indexes. According to the method, the low time complexity can be guaranteed, meanwhile, the high recognition rate is maintained, and the APT attack detection efficiency and accuracy are remarkably improved.

An example apparatus includes audience metrics collecting circuitry to access first audience metrics from a server, and access second audience metrics from the server, matrix building circuitry to build a matrix of the first audience metrics and the second audience metrics, missing values of the matrix corresponding to the second audience metrics, data transforming circuitry to transform the first audience metrics and the second audience metrics in the matrix, missing value calculating circuitry to determine imputed transformed values of the missing values using a recommender system, and the data transforming circuitry to recover imputed values of the missing values based on the imputed transformed values.

The invention relates to the technical field of industrial tablet computer calculation optimization control, and discloses an industrial tablet computer power consumption optimization control method and system based on edge AI.The industrial tablet computer power consumption optimization control method based on the edge AI comprises the steps that a task dependency graph of an industrial application program is constructed, task node hotspot scores are calculated, and the task node hotspot scores are calculated; identifying a high-energy-consumption calculation hotspot; training a calculation path heat generation model, predicting the temperature change trend of the hot task and classifying the temperature change trend; constructing a heterogeneous computing unit power consumption performance characteristic database, and computing energy efficiency ratio index matrixes of tasks on different execution units; the task execution sequence is optimized, and targeted heat dissipation measures are activated in advance; optimizing a data access path of a non-migratable task, and continuously optimizing a prediction model and a scheduling strategy through online learning; according to the invention, stability improvement, temperature peak value reduction and power consumption optimization of the industrial panel under a high-load condition are realized, and the technical defects of traditional resource scheduling in the aspect of power consumption management are overcome.

The invention relates to the technical field of laser welding, in particular to a foamed ceramic plate performance analysis method and device.The foamed ceramic plate performance analysis method comprises the steps that surface scanning is conducted on a foamed ceramic plate sample to obtain collected data and a stereoscopic image sequence, and a three-dimensional pore model is constructed according to a surface feature matrix and the stereoscopic image sequence; carrying out topology and geometric feature extraction processing on the three-dimensional pore model to obtain a pore feature vector, and carrying out thermal performance parameter derivation processing on the pore feature vector to generate a thermal conductivity prediction formula; according to the pore feature vector and a thermal conductivity prediction formula, iteratively calculating influence data of pores on stress distribution, and generating a mechanical property prediction formula; and constructing a performance index matrix according to the thermal conductivity prediction formula and the mechanical property prediction formula, and performing optimization processing on the performance index matrix to obtain a comprehensive performance score table. According to the method, through combination of microstructure analysis and macroscopic performance prediction, the accuracy and comprehensiveness of comprehensive performance evaluation of the foamed ceramic plate are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle network security, and discloses an unmanned aerial vehicle network security verification effect evaluation method, system and device, and a medium, and the method comprises the steps: carrying out the preprocessing of the index data related to the network security of an unmanned aerial vehicle, and constructing an index matrix; performing dimension reduction processing on the index matrix based on a nonlinear excitation learning model, and extracting weight information based on a spatial information dynamic path iteration model to obtain an evaluation threshold vector of unmanned aerial vehicle network security and a weight vector thereof; calculating a normalized comprehensive threshold based on the evaluation threshold vector and the weight vector thereof; and calculating an evaluation vector of each node of the unmanned aerial vehicle network, and comparing the evaluation vector with the normalized comprehensive threshold to obtain an unmanned aerial vehicle network security evaluation result. The method is suitable for a complex network protocol operation mechanism, more accurate and objective analysis is realized by fully combining a model hierarchical structure, a more reliable and comprehensive evaluation effect is achieved, and the method is closer to an actual application scene of an unmanned aerial vehicle network.

The invention discloses an asymmetric cross-modal hash learning method for potential structure information, and relates to the technical field of cross-modal retrieval. Analyzing a sample clustering structure, sample similarity information and semantic similarity information among the multi-modal samples to obtain a projection matrix of different modals, a clustering center of a projection space, a clustering indication matrix, a mapping matrix, a discrete hash code and a calculation formula of an auxiliary variable; updating the projection matrix of each modal, the clustering center of the projection space, the clustering indication matrix, the mapping matrix, the discrete hash code and the auxiliary variable by using an iteration strategy; determining a hash function of each modal according to the discrete hash code obtained by the last iteration and the multi-modal training sample; the hash function is used for performing similarity retrieval on the query sample in the multi-modal data. According to the method, the Hash code with higher discrimination can be obtained, so that the retrieval performance of cross-modal retrieval is improved.

The invention discloses a traffic flow prediction method and device based on a space-time diagram convolutional network, and the method comprises the steps: collecting road traffic flow data, and constructing a traffic congestion index matrix model; calculating a road traffic congestion index based on the collected road traffic flow data, and constructing a traffic flow adjacency matrix by using the road traffic congestion index; performing fast time convolution processing on the traffic flow adjacency matrix to generate a traffic flow data sequence containing spatio-temporal information; inputting the traffic flow data sequence into a space-time synchronization graph convolutional network model STSGCN, further learning a spatial topological structure, and deeply extracting space-time features by stacking multiple layers of space-time synchronization graph convolutional layers; and inputting the spatial-temporal characteristics into a wave loss function-based random vector function link network model Wave-RVFL optimized by an index and trigonometric function optimization algorithm, and predicting the traffic flow. The method can improve the precision and efficiency of traffic flow prediction.

The invention provides a feedback control method and system for semiconductor polishing, and relates to the technical field of semiconductor polishing machining.The method comprises the steps that an intelligent feedback device is constructed, and the intelligent feedback device comprises a dot matrix evolution unit, a feedback decision unit and a fuzzy analysis unit; determining a target verification dot matrix in combination with a dot matrix evolution unit; performing polishing control based on the cooperative control scheme, synchronously executing sensing monitoring based on the target verification dot matrix, and determining monitoring feedback data; index matrix group extraction and fuzzy feedback decision making are carried out on the monitoring feedback data, and a feedback control strategy is determined; and receiving a subjective feedback instruction, and executing feedback polishing regulation and control in combination with the feedback control strategy. The technical problems that due to the fact that the processing amount of monitoring data is large and targeted monitoring cannot be achieved, timeliness and accuracy of monitoring feedback are poor, and the polishing quality of the semiconductor is poor can be solved, the pertinence and purposiveness of semiconductor polishing monitoring can be improved, and the technical effect of improving the polishing quality of the semiconductor is achieved.

A production green quality comprehensive evaluation method for a distribution transformer comprises the steps of collecting data, forming a quality detection index matrix, a production energy consumption index matrix and a carbon footprint characteristic index matrix, screening procedures which have great influence on energy consumption by using a K-means clustering algorithm, constructing a production energy consumption index set, updating the production energy consumption index matrix, and evaluating the production green quality of the distribution transformer. And performing normalization processing on various index matrixes to form a normalized index data matrix. On the basis, respectively calculating the weight of each index by adopting a CRITIC method and an entropy weight method to obtain an overall CRITIC weight vector and an overall entropy weight vector; according to a minimum identification information principle, solving an optimal combination weight through a Lagrange multiplier method; and calculating a comprehensive evaluation score and a corresponding grade. The multi-dimensional data fusion mode can reflect the green production quality of the distribution transformer more comprehensively and accurately.

The invention discloses a business flow site selection optimization method based on multi-source data fusion, and relates to the technical field of big data processing and multi-source data fusion, and the method comprises the following steps: obtaining multi-source data of a to-be-detected region, constructing multi-source features, carrying out the cross-modal fusion of the multi-source features through an adaptive weight distribution mechanism, and generating a comprehensive feature representation; based on comprehensive feature representation, passenger flow trends and causal contributions of the candidate points in different scenes are output through a causal inference method; taking the passenger flow trend and the causal contribution as input, establishing a multi-objective optimization model, performing solving, and outputting a comprehensive score and a sorting result of the candidate points; constructing an explanatory index matrix according to the comprehensive score and the sorting result in combination with a causal contribution result, and outputting a site selection scheme through a visual platform; according to the method, the problems of single data source and incomplete candidate point description in the prior art are solved by fusing the spatial geographic data, the space-time traffic data and the consumption behavior data.

The invention discloses an underwater hyperspectral clustering method based on a bipartite graph, and relates to the technical field of underwater image processing. The method comprises the following steps: acquiring spectral images of a to-be-processed image in different spectral intervals, taking the spectral image of each spectral interval as a view, and constructing hyperspectral data based on the acquired views; constructing a hyperspectral clustering model based on subspace clustering learning, and processing the hyperspectral image based on the hyperspectral clustering model; the hyperspectral clustering model comprises an adaptive dynamic anchor point selection module, an anchor point centroid learning module and a bipartite graph decomposition module; and iterating the hyperspectral clustering model based on an alternative update variable strategy until an iteration stop condition is met, and outputting a clustering result based on the clustering indication index matrix. According to the method, a model based on subspace clustering learning is constructed, uniform self-adaptive anchor points are dynamically learned in all pixel points, and the optimal cluster with accurate pixel division can be directly obtained from bipartite graph decomposition.

The embodiment of the invention provides a public opinion evaluation method based on a multi-dimensional knowledge graph and related equipment, and belongs to the technical field of public opinion evaluation. The method comprises the steps that multi-source heterogeneous public opinion data are obtained and preprocessed, a multi-dimensional public opinion knowledge graph is obtained, and the multi-dimensional public opinion knowledge graph comprises entity nodes and association relations of users, topics, events, spaces and figures; based on the multi-dimensional public opinion knowledge graph, determining a hierarchical index system comprising a target layer, a middle layer and an index layer to obtain multi-dimensional public opinion index data; performing matrix standardization processing on the multi-dimensional public opinion index data to obtain a multi-dimensional public opinion index matrix; and inputting the multi-dimensional public opinion index matrix into a pre-trained convolutional neural network model for feature extraction, quantitative analysis and weight distribution to obtain a public opinion dynamic evaluation result. According to the embodiment of the invention, objectivity, accuracy and dynamic response capability of public opinion risk assessment can be improved.

The invention discloses a management method and system for Internet resources, and relates to the field of data management. The method comprises: receiving resource data indicators and obtaining a resource data indicator matrix; calculating the dynamic entropy weight of each resource indicator based on the matrix; constructing an adaptive entropy weight hybrid optimization algorithm based on the dynamic entropy weight to obtain a resource allocation scheme; performing real-time simulation of the resource allocation scheme through digital twin technology and obtaining simulation results, and optimizing the parameters of the adaptive entropy weight hybrid optimization algorithm based on the simulation results; obtaining the state prediction of Internet resources, and inputting the state prediction into the optimized adaptive entropy weight hybrid optimization algorithm to realize dynamic adjustment of resource allocation strategy. By calculating the dynamic entropy weight of resource data indicators, constructing an adaptive entropy weight hybrid optimization algorithm and generating a resource allocation scheme, and performing real-time simulation and feedback optimization of the allocation scheme through digital twin technology, and combining resource state prediction at the same time, dynamic adjustment of resource allocation is realized.

The invention discloses a coal bed gas typical reservoir gas content evaluation method based on well logging information, and relates to the technical field of coal bed gas evaluation. According to the coal bed gas typical reservoir gas content evaluation method based on the logging information, logging data and core data of a set area are obtained and preprocessed; performing screening processing based on the logging data to obtain a screening set of the set area; based on the core data, constructing a lithology indication matrix and physical property constraint characteristics of the set area; constructing a constrained multiple linear regression model of the set area based on the screening set of the set area, the lithology indication matrix and the physical property constraint characteristics; according to the method, the target logging data of the to-be-evaluated target layer section are obtained, analysis is carried out in combination with the logging coefficient set, and the coal seam gas content value of the to-be-evaluated target layer section is obtained, so that the gas content of coal seam gas is evaluated more accurately; and the accuracy and reliability of the evaluation result are improved.

The invention provides a bridge state monitoring method, an evaluation method and equipment based on cloud edge cooperation and multi-index fusion, and a storage medium. The monitoring method comprises the following steps: acquiring a plurality of monitoring physical quantities corresponding to each monitoring point of a target bridge in a monitoring period according to the position of the monitoring point; performing Kalman filtering processing on a plurality of monitoring values of each monitoring physical quantity of each monitoring point; constructing an index matrix taking an average value of all the filtered monitoring values as an element; obtaining a health assessment score of each monitoring point based on the index matrix and the weight matrix; obtaining a Sharpley value of each monitoring physical quantity of the abnormal monitoring point; and obtaining the average change rate of the monitoring physical quantity corresponding to the maximum Sharpley value and the center distance between the abnormal monitoring point and the corresponding camera, and re-determining and adjusting the sampling frequency of the sensor of the abnormal monitoring point and the sampling visual angle of the camera. By adopting the monitoring method provided by the invention, more efficient and more accurate monitoring of the target bridge can be realized.

The invention relates to the technical field of geotechnical engineering monitoring, in particular to a coal mine soft rock roadway surrounding rock argillization degree multi-dimensional index analysis method. The method comprises the following steps: extracting a plurality of analysis index data from a rock sample, listing an index matrix based on each analysis index data, then carrying out quantitative processing on all the rock sample index data according to the matrix, determining objective weights of all the rock sample index data through a CRITIC method, and finally determining the objective weights of all the rock sample index data according to the objective weights of all the rock sample index data. And according to the quantified roadway surrounding rock index data and the corresponding weight, calculating the evaluation score of the argillization degree of the surrounding rock, then establishing a specific grading table of the judgment value Q of the argillization degree of the roadway surrounding rock, and according to the comprehensive evaluation score of the argillization degree of the roadway surrounding rock, analyzing the grade of the argillization degree of the rock sample, and establishing a comprehensive and multi-dimensional analysis framework. Comprehensive data support can be provided for analysis of the argillization degree of the roadway surrounding rock, and multidimensional analysis of the argillization degree is guaranteed.

The invention discloses an Internet resource management method and system, and relates to the field of data management, and the method comprises the steps: receiving a resource data index, and obtaining a resource data index matrix; calculating the dynamic entropy weight of each resource index based on the matrix; constructing an adaptive entropy weight hybrid optimization algorithm based on the dynamic entropy weight to obtain a resource allocation scheme; performing real-time simulation on the resource allocation scheme through a digital twinborn technology, obtaining a simulation result, and optimizing parameters of an adaptive entropy weight hybrid optimization algorithm based on the simulation result; and obtaining state prediction of the Internet resources, and inputting the state prediction into the optimized adaptive entropy weight hybrid optimization algorithm to realize dynamic adjustment of a resource allocation strategy. Dynamic adjustment of resource allocation is realized by calculating the dynamic entropy weight of a resource data index, constructing a self-adaptive entropy weight hybrid optimization algorithm and generating a resource allocation scheme, performing real-time simulation and feedback optimization on the allocation scheme through a digital twin technology, and combining with resource state prediction.

The invention relates to an elastic balance area multi-objective planning method considering source-network load interaction, and the method comprises the steps: analyzing the real-time operation data of a power distribution network and the topological graph data of the power distribution network, and obtaining a source load feature matrix, an interaction mode matrix, and a final constraint set; constructing an optimization objective function set and a coordination mechanism set according to the source load feature matrix, the interaction mode matrix and the final constraint set; and establishing a comprehensive optimization model according to the optimization objective function set, the coordination mechanism set and the final constraint set to obtain a complete optimization model and a hierarchical structure matrix, and solving based on the complete optimization model and the hierarchical structure matrix to obtain a final optimization solution and a performance index matrix. According to the method, the uncertain influence caused by the random fluctuation power supply in the active power distribution network can be better processed.

The invention discloses a forest seedling raising period management optimization method based on an adaptive algorithm. The method comprises the following steps: S1, collecting multi-source environment data and seedling growth state data in a forest seedling raising process; s2, the original index data set is processed, a standardized index matrix is obtained, and a stage index evaluation input set is constructed; s3, constructing a multi-target periodic scheduling model based on the stage index evaluation input set; s4, introducing an entropy weight adaptive grey wolf optimization algorithm to solve the multi-target scheduling function set, and outputting a periodic scheduling optimal solution; s5, generating a seedling culture management execution strategy based on the period scheduling optimal solution; s6, forming a feedback monitoring data set by the environment feedback data collected in real time and the seedling feedback data, and generating an updated seedling management execution strategy; and S7, synchronizing the updated seedling culture management execution strategy to the seedling culture management platform. According to the invention, intelligent scheduling and adaptive management optimization of the whole forest seedling raising process are realized.

The invention relates to the technical field of territorial space planning, and provides a territorial space planning risk assessment method which comprises the following steps: step 1, based on a space planning target of a target area, dividing multiple levels of assessment units, and constructing a three-dimensional risk assessment index system comprising a risk source index, a risk bearing body index and a risk conduction path index; 2, collecting multi-source heterogeneous data including remote sensing images, geological and geographic data and social and economic data, and generating a spatial database through data fusion; and step 3, classifying the data by adopting a Jenks natural breakpoint method, calculating an index weight in combination with an empowerment method, and constructing a dynamic risk assessment index matrix. 12 types of indexes are integrated through a risk source-bearing body-conduction path three-dimensional model, and the assessment coverage rate is increased by 40%; multi-scale trend distribution point correction enables the remote sensing image registration error to be reduced to 1.2 pixels, and the disaster identification false alarm rate is reduced by 35%.

The invention relates to the technical field of data analysis, and discloses an intelligent physical examination data dynamic analysis method, which is characterized by comprising the following steps of: fixing a centroid matrix F (v) of vth test index data, a common clustering index matrix G and a weight factor alpha (v) of the vth test index data, and updating a diagonal matrix D (v) corresponding to a vth view; fixing G, D (v) and alpha (v) and updating the cluster centroid of each view F (v); fixing F (v), D (v) and alpha (v) and updating G; f (v), G and D (v) are fixed, alpha (v) is updated, potential models of data are mined from features of known training data, an effective model is formed by clustering each data to different clusters, then unknown new data are divided by using the model, physical state clustering and evaluated multi-view clustering of a tested person are carried out, and the optimal physical state of the tested person is obtained. And physical examination test indexes are taken as responses of physical conditions from different data perspectives.

This invention belongs to the field of target detection technology, specifically relating to a method, system, and device for target area identification in complex electromagnetic environments. The method constructs a two-dimensional plane to form a planar coordinate system, using the center of the target area as the origin. The target area is discretized into a uniform grid, covering a square region centered at the origin. The inscribed circular region within the grid is considered the outer region, generating an outer region indicator matrix. This outer region indicator matrix is ​​then used to generate an arrival angle sector region indicator matrix, and further, an intersection region indicator matrix is ​​generated. Finally, the proportion of the arrival angle sector intersection region within the target area is calculated using the intersection region indicator matrix, and a judgment threshold is set. If the proportion is greater than or equal to the threshold, the target is determined to be within the target area; otherwise, the target is determined to be outside the target area. The system and device are used to implement this method. This invention significantly improves the accuracy, robustness, and computational efficiency of target area identification.

The invention provides a transformer operation and maintenance management method and system based on digital twinning, and relates to the technical field of power equipment operation and maintenance, and the method comprises the steps: carrying out the multi-source integration of transformer sampling data, and obtaining traceable state monitoring data through combining with a distributed block chain technology; adaptively decomposing the monitoring data according to frequency bands and extracting time-varying features, and introducing a cross-frequency domain feature fusion device based on mutual information maximization to analyze relevance of different frequency bands to obtain a state index matrix; constructing a state evolution diagram of the transformer parts, analyzing state propagation characteristics among the parts by adopting dynamic causal reasoning, extracting multi-scale time sequence characteristics, and performing layered interactive coupling to realize space-time conjoint analysis; and calculating the reliability index of each component based on probability distribution, tracing degradation time nodes and influence factors, and formulating a maintenance strategy. According to the invention, omnibearing monitoring and accurate prediction of the state of the transformer are realized, and the operation and maintenance efficiency and the equipment reliability are improved.

The invention discloses a forest stand attribute analysis method and system based on unmanned aerial vehicle remote sensing data feedback, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-analyzed forest region data, querying the historical route data of an unmanned aerial vehicle based on position data, and determining remote sensing data collection equipment information and corresponding multi-source environment information; determining a forest stand attribute influence first sub-value of the to-be-analyzed forest; constructing an attribute analysis space coordinate system, determining to-be-analyzed remote sensing data corresponding to the coordinate points, and obtaining a to-be-analyzed forest stand attribute influence second sub-value; analyzing the remote sensing data to obtain a forest stand attribute index matrix; and based on the matrix and the to-be-analyzed forest stand attribute influence second sub-value, obtaining a forest stand attribute standard matrix and drawing up a forest management strategy. The system comprises an acquisition module and an unmanned aerial vehicle data and environment information association module. According to the method, systematic integration of multi-source data is realized, the forest stand attribute analysis precision is improved, and a scientific basis is provided for forest management.

The invention belongs to the field of metal object oxidation data judgment methods in the electroplating process, and particularly relates to an information judgment method in the metal object oxidation process in the electroplating process. The method comprises the steps of firstly collecting oxidation process information and identifying an oxidation abnormity type; then selecting a key reference index, and obtaining feature data; and arranging the data by using the performance index matrix, and carrying out normalization processing through a minimum-maximum standardization method. And further setting a standard performance index matrix, and calculating the cosine similarity between the target matrix and the standard matrix to identify the oxidation anomaly. Through systematic data analysis, automatic monitoring of the oxidation process is achieved, and the production efficiency and the reliability of metal objects in the electroplating process are improved.

An improved information entropy-based dynamic peak capacity analysis method and system for generator sets, characterized in that the method comprises the following steps: collecting performance indicator data of each generator set at each moment within a region, so as to form a performance indicator data matrix; normalizing the performance indicator data at a set moment and an offset value of the performance indicator data between adjacent moments, so as to respectively obtain a normalized performance indicator matrix and a normalized offset performance indicator matrix; calculating static and dynamic analysis peak capacities of each performance indicator of the generator set; and on the basis of the obtained peak capacities of the performance indicators and offset performance indicators, calculating an integrated peak capacity of each performance indicator of the generator set. By means of dynamically analyzing peak capacities at different moments, the present invention provides effective analysis rules for the power peak of generator sets, thereby determining the peak capacities of the generator sets at the current moment and different moments.