323 results about "Distance matrix" patented technology

Disclosed herein are systems and methods for graph-based dynamic information retrieval and synthesis, particularly for enhancing the response generation of chatbot applications using large language models (LLMs). The methods involve receiving a user query, generating a query embedding, and accessing a knowledge graph comprising nodes representing documents with associated document embeddings. A set of documents is retrieved based on similarity scores, and a distance matrix is constructed to calculate shortest-path distances between nodes. Documents are clustered to identify a largest cluster from which content is retrieved to formulate a response. The disclosed embodiments improve upon retrieval augmented generation (RAG) by leveraging the hierarchical relationships of a knowledge graph and node interconnectedness to provide contextually coherent information and mitigate the inclusion of information from irrelevant or superficially related documents in LLM responses.

The invention relates to a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

The invention provides an electric energy quality device power smooth flexible capacity expansion method for a new energy access scene. The method comprises the following steps: acquiring three-phase voltage, current and power signals, obtaining standardized three-dimensional tensor data through multi-dimensional tensor spectrum projection decomposition, calculating a characteristic value and a characteristic tensor base, and generating a dynamic characteristic vector and a weight coefficient set; a multi-layer probability graph matrix is constructed, a feature mapping matrix and a hierarchical contribution coefficient are obtained through iterative calculation, a fluctuation feature tensor is constructed, and a multi-time scale prediction model is established; constructing a control manifold space and a measurement tensor, calculating a topological connection matrix, designing a multi-dimensional control law and performing stability optimization; and constructing an embedded feature matrix and an inter-module distance matrix, calculating a capacity expansion weight and a power distribution scheme, and performing spectral domain equalization control and grid-connected coordination control. According to the invention, the power smoothing effect is obviously improved, and the system capacity expansion flexibility is enhanced.

The invention relates to the technical field of image analysis, in particular to a dispensing track optimization control system based on visual template conversion, which comprises a visual template analysis module, a contour point position correction module, a track vector generation module, a multi-parameter linkage feedback module and an optimal path planning module. According to the method, a boundary is extracted through clustering pixel superposition brightness and channel weight, a line segment frequency screening track is counted, feature stability and precision are enhanced through datum line construction, a deviation value is calculated through gradient direction segmentation boundary, a matching degree is improved through tangent point interpolation correction contour, and a weight factor is generated through included angle change and tool parameter normalization. A reference point is optimized through superposition increment, a path fusion tool attribute dynamic adaptation parameter is adopted, a multi-dimensional data fusion visual offset and floating weight real-time adjustment track is acquired, an execution point and an adjustment vector are superposed to calculate a distance matrix, an optimal path is screened through minimum distance and energy consumption, efficiency and consumption are both considered, and the system robustness and execution economical efficiency are remarkably improved.

The invention provides a land planning method based on data analysis, and the method comprises the steps: obtaining an initial distance matrix between land parcels in a farmland land parcel set around a town, calculating the direct connectivity between the land parcels and other land parcels based on a distance threshold value, and constructing an initial farmland ecological network topological structure diagram; the overall network connectivity, the node centrality and the average path length of the initial farmland ecological network topological structure diagram are calculated through a graph theory algorithm, so that the structural integrity of the ecological network is evaluated, and an ecological network structural evaluation index initial value is obtained; according to the adjusted distance matrix between the land parcels, recalculating the connectivity between the land parcels, reconstructing a farmland ecological network topology structure, and calculating various indexes of network structure integrity to assess an ecological network structure; and combining the plot connectivity and the land utilization efficiency to evaluate the feasibility and cost effectiveness of plough layer stripping and reutilization, and determining an optimal adjustment scheme.

The invention discloses a data dynamic partition storage method and system based on adaptive clustering, and relates to the field of data processing. The method comprises the following steps: S1, extracting multi-scale geometric features of a high-dimensional data set, calculating a local curvature and generating a curvature feature matrix; s2, constructing a feature distance matrix and a similar matrix based on the curvature feature matrix, and generating a low-dimensional embedding matrix; s3, executing self-optimization clustering according to the low-dimensional embedded matrix, determining a cluster number through singular value distribution, and generating an initial cluster; s4, calculating a stability factor of each cluster, and triggering cluster splitting or merging operation according to a threshold value to form updated cluster division; and S5, performing incremental processing on newly added data points, obtaining a new point local curvature through local curvature gradient correction, mapping the new point local curvature to a low-dimensional space, dynamically deciding affiliation based on a cluster radius, and updating partitions. Through multi-scale feature extraction, self-optimization clustering and incremental updating mechanisms, high-dimensional data clustering precision, robustness and calculation efficiency are improved.

The invention discloses a power load curve clustering method, system and equipment based on an improved density peak value clustering algorithm and a storage medium, and belongs to the field of intelligent power distribution networks, and the method comprises the steps: carrying out the sampling segmentation of power load data, constructing a daily load curve set, carrying out the standardization processing of each daily load curve, and generating a standardized sample set; based on the standardized sample set, calculating a sample distance relation of the daily load curve set, and constructing a corresponding distance matrix; establishing a local density estimation model according to the distance matrix, and calculating a density estimation value of each sample curve; according to the local density estimation value of each sample curve and the relative distance between each sample curve and other samples in the distance matrix, constructing a clustering decision value set, and selecting a plurality of sample curves with clustering decision values higher than a threshold value as clustering centers; and by taking the clustering center as a reference, sequentially distributing the residual sample curves to the class cluster to which the corresponding clustering center belongs according to the density estimation value and the distance relationship between the residual sample curves and each clustering center, and completing sample clustering division. According to the method, the problem of misclassification when the density difference of the power load curve set is too large is solved, the sample density difference when the data density difference is too large is accurately represented, the clustering precision of the load curve is effectively improved, reliable power consumer social attribute identification is provided for a power supply side, and an energy planning strategy is better implemented.

The invention discloses a Beidou navigation unmanned aerial vehicle inspection path planning method based on a simulated annealing algorithm. The method comprises the following steps: S1, reading latitude and longitude data: reading latitude and longitude data of an inspection target point and a base from a file or a database; s2, calculating a distance matrix: calculating an actual distance between any two points according to the longitude and latitude data, and constructing the distance matrix; s3, constructing a solution space: constructing all path sets; s4, selecting an initial solution: selecting an initial path from the path set as a current solution; s5, setting an initial temperature: setting the initial temperature of a simulated annealing algorithm; s6, loop iteration: performing an iteration process of a simulated annealing algorithm; and S7, generating a new solution: generating a new path as the new solution by using a transformation method 2 or a transformation method 3 according to the current solution. The method is used for solving the problem that a genetic algorithm and a particle swarm optimization algorithm are relatively weak in theoretical convergence guarantee, and particularly are possibly difficult to meet theoretical conditions in practical application.

The invention relates to the technical field of acceleration signal recognition, in particular to an acceleration signal recognition method and device, and the method comprises the following steps: carrying out the first-order numerical differential operation of a three-axis acceleration signal based on a collected original three-axis acceleration signal, and obtaining a movement speed sequence of the signal; according to the method, first-order and second-order numerical differential operation is carried out on an original three-axis acceleration signal, and the speed change and the impact change of the signal are extracted at the same time and combined to form a kinematic feature set, so that the dynamic features of the motion state can be fully expressed in the two dimensions of speed and impact. The Euclidean distance between the to-be-identified signal and the motion template is calculated based on the kinematic feature set to form the basic distance matrix, and the constraint cost matrix is constructed in combination with the speed sequence and the impact signal, so that the constraint on the kinematic consistency in the signal comparison process is enhanced, and the matching path is more reasonable.

The invention relates to a training method for generating a picture description model, a picture description generation method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring a sample, wherein the sample comprises a sample picture and a text corresponding to the sample picture; inputting the sample picture and the text into a picture description model to be trained and generated to obtain text combined image features; inputting the text into a to-be-trained generated picture description model to obtain text features; based on the text, combining image features with the text features to obtain a distance matrix; and generating a loss function based on the distance matrix, and training the to-be-trained generated picture description model through the loss function to obtain a target generated picture description model. By adopting the method, the accuracy can be improved.

The invention discloses an impact load time history reconstruction and positioning method and device, a medium and a product, and relates to the field of impact load recognition, and the method comprises the steps: obtaining an acceleration signal matrix and a dynamic graph structure of an impact load acting object; according to the acceleration signal matrix and the dynamic graph structure, utilizing a time history reconstruction model to obtain a reconstructed impact load time history of the impact load acting object; the time history reconstruction model is obtained by training a graph neural network model by using a first training data set and a dynamic graph structure of an impact load acting object for training; according to the coding distance matrix of the acceleration signal matrix, utilizing an impact positioning model to determine the impact position of the impact load acting object; the impact positioning model is obtained by training the full-connection neural network by using the second training data set. According to the invention, the impact load time history and the impact position when the load action position is unknown can be accurately identified.

The invention discloses a data optimization storage method and system for smart agriculture, and relates to the technical field of smart agriculture, and the method comprises the steps: collecting agricultural field data, remote sensing data and management data, and carrying out the preprocessing of the data, and constructing a multi-source heterogeneous data matrix; the method comprises the steps of calculating fuzzy similarity of a multi-source heterogeneous data matrix, constructing a time sequence distance matrix, calculating weight vectors of different data types, constructing a weighted distance matrix according to distribution weights of different data types, calculating a weighted fuzzy similarity matrix, constructing a Boolean similarity matrix, distinguishing redundant nodes and representative nodes, and determining difference vectors. According to the method, redundant data are accurately identified by using the weighted fuzzy similarity matrix, only the difference vector is stored, the storage space occupation is greatly reduced, the consumption of cloud or chain end storage resources is effectively reduced, the actually transmitted data volume is obviously reduced through storage compression and differential storage, and the data transmission efficiency is improved. And the data transmission burden of the edge device or the cloud node is reduced.

The invention relates to the technical field of point cloud semantic segmentation, and discloses a point cloud segmentation method and system based on point cloud serialization and a Mama network. Specifically, a geometric distance matrix and a semantic distance matrix are calculated for a plurality of representative point units obtained by sampling on an original point cloud, and a path sequence is generated based on a fused comprehensive distance matrix. And traversing the path sequence by adopting a bidirectional scanning strategy to generate a forward sequence and a reverse sequence, extracting features from the forward sequence and the reverse sequence by utilizing a Mama network, cascading or fusing the extracted features, and finally performing classification based on the fused features. According to the method, better long-range dependency and local context modeling effects can be obtained, the comprehensive expression capability of detail features and global context in sparse and irregular scenes is enhanced, and the calculation and memory overhead required for global dependency capture of large-scale point clouds is greatly reduced.

The invention provides a computing power sensing method and system with integrated computing network. The method comprises the following steps: constructing and analyzing a multi-level NUMA hierarchical structure diagram to obtain an inter-node distance matrix and a resource distribution condition; obtaining an optimized resource mapping scheme based on the inter-node distance matrix and the resource distribution condition; obtaining a node subset passing verification based on the resource mapping scheme and the information of the plurality of computing nodes; performing asynchronous Byzantine consensus processing by adopting a directed acyclic graph structure based on the request of the node subset passing the verification to obtain a consensus result of consistency of nodes of the whole network; based on the consensus result and the data feature distribution information of the plurality of computing nodes, obtaining a model knowledge transmission result between adjacent nodes; and based on a model knowledge transmission result, obtaining an optimization result of an end, edge and cloud three-layer model. According to the invention, a unified perception mechanism of end-edge-cloud cooperation is realized, the system response speed in a distributed environment is improved, and the computing power prediction accuracy is improved.

The invention discloses a multi-target path optimization method, equipment and a medium, and relates to the technical field of path planning. The method comprises the following steps: constructing a weighted graph model of a to-be-planned area and verifying an adjacent matrix to obtain verified weighted graph data; shortest path distances among all node pairs are calculated to form an all-source shortest path distance matrix and a precursor record set, and connectivity detection and feasible region pruning processing are carried out on unreachable node pairs; generating an optimized access sequence covering all target nodes based on the processed full-source shortest path metric data and a nearest neighbor greedy strategy, accumulating composite path cost, and performing feasibility check of time windows, capacity and risk constraints to generate an initial path sequence and accumulated cost data; and expanding adjacent node pairs in the initial path sequence into a specific executable path on the original graph according to the precursor record set, performing neighborhood optimization on the executable path by adopting a local search operator, and outputting an optimized final path scheme and an accumulated cost report.

The invention provides a global structure sensing vector quantization method based on optimal transmission, which comprises the following steps of: mapping input data to a continuous hidden variable space through an encoder to generate continuous representation; constructing a distance matrix between the continuous representation and the codebook, and solving a distribution matrix by using an optimal transmission problem; carrying out normalization processing on the distance matrix, and generating an initial value of a distribution matrix by adopting a special initialization method; performing row and column normalization iteration on the distribution matrix through a Sinkhorn-Knopp algorithm until the distribution matrix is converged; and after iteration is finished, quantitative characteristics are determined based on the maximum value of the distribution matrix, the quantitative characteristics are mapped back to an original data space through a decoder, and a reconstructed data result is obtained. Based on the scheme provided by the invention, a codebook utilization rate close to 100% and an excellent data reconstruction result can be ensured, and a powerful tool is provided for discrete compression scene reconstruction of continuous data such as images and videos.

The invention discloses a computer information screening system and method based on artificial intelligence, and particularly relates to the technical field of information screening. Analyzing the multi-modal feature vector of the to-be-screened information, and evaluating the distribution density in the high-dimensional feature space through the feature projection network; dimensionality reduction mapping is carried out on the multi-modal feature vectors, a distance matrix between feature clusters after dimensionality reduction is calculated, and the local folding degree of the feature space is evaluated; then, based on the feature distribution density and the local folding degree, whether the distortion degree of the feature space exceeds a preset threshold value or not is judged; when the distortion degree exceeds a threshold value, the feature loss degree is evaluated by calculating mutual information between the original features and the reconstructed features; according to the method, the distribution density, the local folding degree and the feature loss degree of the multi-modal features are integrated, the overall reliability of the information screening result is evaluated, whether the screening strategy needs to be adjusted or not is judged, the accuracy and stability of complex data screening are effectively improved, and the method is suitable for large-scale data analysis and anomaly detection scenes.

The invention discloses an underwater robot multi-task-point path planning method considering a marine environment. Firstly, map construction is performed according to a grid method, and an ocean current function is constructed according to ocean current conditions; determining an evaluation system of path planning, and constructing a fitness function; then solving the shortest distance between any two points by adopting an A * algorithm, and transmitting a distance matrix to an improved ant colony algorithm; initializing related parameters of an ant algorithm, and setting the number of ants and the maximum number of iterations; an ant colony algorithm is improved by adopting a self-adaptive pheromone factor, a heuristic function factor and a volatile element factor; moving the positions of the ants by adopting a roulette method until the ants reach a target point, and updating pheromones after all the ants finish one iteration process; and repeating the iteration process until all ants reach the maximum number of iterations, and outputting an optimal path planning result. According to the improved ant colony algorithm provided by the invention, the convergence time is reduced, and different optimization results are obtained according to different requirements.

The invention discloses a perception-assisted unmanned aerial vehicle track and power distribution joint optimization method and system, and the method comprises the steps: firstly, extracting environment related features from high-dimensional CSI data, constructing a weighted graph, and calculating a shortest path distance matrix; and the CSI is mapped to a two-dimensional space through a twin neural network to maintain distance consistency, and user distribution is obtained by combining with known position correction. On the basis, an optimization model taking the minimization of the energy consumption of the unmanned aerial vehicle as a target and the user rate demand as a constraint is established, the optimization model is decomposed into a power distribution and trajectory optimization sub-problem, and the Lyapunov optimization and successive convex approximation are used for alternate solution. According to the method, high-precision positioning is realized through the channel map under the scene that the user position is unknown, the energy consumption of the unmanned aerial vehicle system is reduced by 27% or above through joint optimization, and meanwhile, 98% of user communication requirements are guaranteed.

The invention discloses a short-term power prediction method and system based on a wind power plant cluster. The method comprises the following steps: constructing a similarity distance matrix of a key meteorological element time-varying rule between different stations; calculating a Copula entropy between the historical power samples of each station, and constructing a spatial similarity matrix of station power according to each Copula entropy; performing normalization processing on the similarity distance matrix and the spatial similarity matrix, and summing the similarity distance matrix and the spatial similarity matrix after normalization processing to obtain a space-time similarity distance matrix; clustering the similarity distance matrix by adopting a hierarchical clustering algorithm, determining a partitioning scheme in combination with the geographic distribution of the new energy stations, and dividing each wind power plant in the target region into each region; and outputting a total power prediction result of the obtained region according to the LSTM time sequence prediction model. The overall precision and robustness of power prediction are improved, and a more reliable scheduling basis is provided for large-scale access of a new energy station to a power grid.

The invention discloses a gesture recognition method, system and device based on fine-grained clustering and a medium, and the method comprises the steps: inputting an obtained gesture image into a teacher model, extracting a high-dimensional feature embedding vector of a specific layer, decomposing the high-dimensional feature embedding vector into a plurality of fine-grained sub-blocks, carrying out the clustering of the sub-blocks through a distance matrix between the sub-blocks, and carrying out the recognition of the fine-grained sub-blocks. According to the similarity between each clustering set and the original gesture image, distributing a weight for each clustering set, and carrying out dot multiplication on the weights and the label matrix to generate a weight matrix for subsequent knowledge distillation; the student model performs weighted distillation learning by utilizing the teacher features extracted by the teacher model and the weight matrix through a dynamic weight regulation and control mechanism; through a hierarchical feature distillation mechanism, automatically extracting fine-grained features of gestures from deep features and shallow features of the teacher model and the student model through clustering, and accurately describing the features of the gestures; knowledge of a complex model is migrated to a lightweight model, and efficient operation is ensured.

The invention provides a multi-feature factor matching and interrupt processing method for storm tracking. The method comprises the following steps: calculating storm features required by tracking; carrying out discretization processing and coding on the storm features, and constructing a sparse matrix; constructing an auto-encoder model, performing data reconstruction on the sparse matrix based on the auto-encoder model, and calculating main features of the single storm; on the basis of the main features, calculating the feature vector distance between the previous and later time secondary storms, and constructing a feature space distance matrix; a bipartite graph algorithm is adopted, the incidence relation of the previous and later time-order storms is obtained based on the feature space distance matrix, and a storm matching result is constructed; identifying an interrupted storm based on a storm matching result and carrying out secondary matching; and constructing a storm trajectory based on a secondary matching result and removing a repeated storm trajectory. According to the method, the movement track of the storm can be accurately tracked from formation to extinction of the storm, reliable data support is provided for meteorological monitoring and forecasting, and the timeliness and accuracy of severe convective weather early warning are improved.

The invention discloses a federated learning method and device using high-order sub-models to assist training, and the method comprises the steps: enhancing the reasoning accuracy of a low-order sub-model through integrating the decision information of multiple high-order sub-models; the low-order sub-model can assist in training by means of decision information of the high-order sub-model and a quantization matrix of the highest-order sub-model based on the feature distance, and the training effect is improved; a low-order sub-model is optimized by reducing target loss (information entropy) of target category confidence, and the problem of information offset caused by re-fitting of target category confidence of a high-order sub-model in traditional self-distillation is avoided; unifying feature dimensions of all levels of sub-models by utilizing comparative learning, constructing a quantization matrix of the highest-order sub-model based on feature distance as supervision information, and guiding the low-order sub-model to approach the quantization matrix of the highest-order sub-model by minimizing the distribution difference between the low-order sub-model and the distance matrix of the highest-order sub-model, so as to realize the supervision of the highest-order sub-model. And a low-order sub-model is guided to mine semantic relationships in different images, so that the generalization ability of the model is improved.

The invention relates to the technical field of power grid analysis, and discloses a power grid topological structure extraction method, a power grid fault prediction method and a computer storage medium, and the method comprises the steps: firstly obtaining the data of each node in a power grid, constructing a power incidence matrix through the obtained data, determining the edge weight between the nodes through the power incidence matrix, and obtaining the edge weight of each node; constructing a vertical edge weight matrix, and establishing a diagonal angle matrix by using the edge weight matrix; constructing a weighting matrix based on the edge weight matrix and the diagonal angle matrix; and finally, constructing an objective function of a weighting matrix based on the power injection amount, solving the objective function through a gradient descent method to obtain an optimized weighting matrix, constructing a distance matrix by using the optimized weighting matrix, clustering each node by using the distance matrix, and extracting a power grid topological structure according to a clustering result. According to the extracted power grid topological structure, the accuracy of the connection relation between the nodes is improved, and the precision of the power grid topological structure is improved.

A system and method for detecting anomalies in a data stream is described. The system receives the data stream that comprises values of metrics derived from observations of operation of a computing entity over a time window. A model comprising variances of the data over the time window is formed. The model identifies operating thresholds for each metric based on the variances of the data for each metric in the data stream. The system computes a steady state distance matrix of the data stream. The system determines that the steady state distance matrix exceeds a steady state threshold. In response to determining that the steady state distance matrix exceeds the steady state threshold, the system computes a pattern distance matrix based on the steady state distance matrix. The anomaly in the data stream is detected based on the pattern distance matrix. The system generates an alert indicating the anomaly.

The invention belongs to the technical field related to data processing, and provides a brain network analysis method and system based on multi-network collaborative topology analysis in order to solve the problem that an analysis result is unstable due to threshold selection subjectivity in existing Alzheimer disease brain network analysis. Extracting brain regions corresponding to the default mode network, the significance network and the execution control network, calculating correlation coefficients among different brain regions to construct a function connection matrix, and converting the function connection matrix into a distance matrix meeting complex construction requirements; constructing a Vietories-Rips complex based on the distance matrix, calculating a coherence group under each scale of the filtering sequence, and extracting topological features of a 0-dimensional Betti number and a 1-dimensional Betti number; according to the method, the Betti number is extracted, a curve that the Betti number changes along with the threshold value is drawn, the difference of the two groups in topological characteristics is compared, the specific topological biomarker related to the Alzheimer's disease is identified, and richer biomarker information is provided for early diagnosis of the Alzheimer's disease.

The invention relates to the technical field of automatic driving, and provides a denoising optimization method and device for three-dimensional point cloud data, equipment, a medium and a product, and the denoising optimization method for the three-dimensional point cloud data comprises the steps: carrying out the fusion processing of a color image and the point cloud data, and obtaining a preliminary point cloud data set; performing simplification processing on the initial point cloud data set to obtain a target point cloud data set; in the target point cloud data set, constructing a graph structure according to the color information and the geometric structure, and determining weights of edges in the graph structure; and establishing a noise reduction model according to the graph structure, carrying out iteration on the noise reduction model, and alternately updating the feature distance matrix and the estimated value of the noise reduction point cloud until a convergence state is reached, so as to obtain denoised point cloud data. According to the method, multi-source data fusion, point cloud simplification, graph signal and graph structure construction and an iterative optimization algorithm are applied, point cloud data polluted by noise are efficiently processed and recovered, noise can be effectively suppressed, and key details and features in the point cloud data can be accurately reserved.

The invention relates to the technical field of urban road traffic state analysis and processing based on the Internet of Things, and discloses an urban road traffic state analysis and processing method based on the Internet of Things. A complete analysis chain from sensor data acquisition, error calibration and clock synchronization, road section length extraction by a high-precision electronic map, vehicle density calculation, graph structure modeling, distance matrix generation, attenuation parameter calculation, spectrum matrix construction and characteristic value solving, density prediction, anomaly detection, signal lamp adjustment and path optimization pushing is constructed. Through graph topology modeling and graph calculation, urban traffic is abstracted into a graph model with spatial characteristics and structure association, and the traffic density diffusion modeling and analysis capability is remarkably improved. A power iteration and residual error detection strategy is adopted to replace an empirical rule, so that a self-adaptive anomaly detection and signal regulation and control mechanism is realized, and the real-time performance and the intelligence of the system are enhanced.

The invention relates to an industrial pollution discharge equipment operation data analysis method and system, and the method comprises the steps: carrying out the normalization of each index of collected industrial pollution discharge equipment operation data, calculating the distance matrix of each index, obtaining a minimum spanning tree through the distance matrixes, calculating the distance average value in the minimum spanning tree of each index, and obtaining a minimum spanning tree of each index; setting a weight for each index according to the distance average value; calculating the distance between the operation data according to the weight, obtaining the local density and the high-density minimum distance of the operation data according to the truncation distance and the distance, determining the number of nearest neighbors according to the minimum spanning tree of the index, calculating the operation data and the standard deviation of the local density of the nearest neighbors of the number of the nearest neighbors of the operation data, and obtaining the standard deviation of the local density of the nearest neighbors of the operation data. Determining a cluster center by using the local density, the high-density minimum distance and the standard deviation; and clustering the industrial data according to the cluster center, and displaying a clustering result as a data analysis result in a chart form.

The invention discloses a taxi route recommendation method based on a FastDTW time sequence clustering algorithm. The method comprises the following steps: firstly, acquiring original GPS track data of a taxi from a public data platform, and preprocessing the data; thirdly, calculating the similarity between the trajectories by using a FastDTW algorithm, and constructing a distance matrix between every two trajectories; then, the distance matrix is applied to a hierarchical clustering algorithm, a high-frequency destination hot spot area is mined, and a high-probability passenger-carrying destination is obtained; and finally, in combination with factors such as the driving distance, the driving time and the driving cost, calling an Amod map # imgabs0 # to obtain detailed route information, and calculating and recommending an optimal route through comprehensive scores. According to the invention, based on hot destination prediction and multi-target route optimization of data mining, the optimal passenger searching route is recommended for the unloaded taxi, the unloaded rate of the taxi is reduced, the passenger loading probability of the unloaded taxi is improved, and time and resource consumption are reduced, so that the vehicle scheduling efficiency and the driver operation efficiency are improved.