194 results about "Data density" patented technology

The invention discloses an interactive large-screen visualization system and method based on multi-dimensional data real-time rendering, relates to the technical field of image rendering, and aims to solve the bottleneck problems of a traditional system in the aspects of data throughput efficiency, rendering delay and interactive response. The method comprises the following steps: dividing space blocks by adopting a non-uniform octree, dynamically adjusting the size of the blocks according to data density, and combining a ZSTD + Hoffman hybrid compression algorithm; a CPU-GPU three-level asynchronous processing assembly line is constructed, and task priorities are dynamically adjusted through a fixation point weight algorithm; the master node dynamically allocates LOD parameters, and the slave nodes achieve special-shaped large-screen sub-pixel-level splicing based on a spherical coordinate system model; a Q-learning algorithm is adopted to predict a user operation sequence, and dual-channel bandwidth adaptive allocation is realized in combination with an ASTP protocol. The system comprises a dynamic partitioning module, a pipeline scheduling module, a multi-node cooperation module, an intelligent preloading module and an exception handling module. Real-time interaction visualization of a large amount of data can be realized, the rendering efficiency is improved, and hardware resource occupation is reduced.

The invention relates to an optimization processing method for a streaming data dynamic window, and the method comprises the steps: taking streaming data as a dynamic system, and calculating the data disturbance energy at each moment on a time axis; constructing a window trigger point according to the potential energy curve; when the trigger potential locally reaches a peak value or the trend changes, adaptively dividing the window; the starting boundary and the ending boundary of each window are defined as elastic intervals; before the window slides or is closed, window boundary self-adjustment and complementation are carried out according to compensatory analysis of subsequent data streams; taking a large window as a container, and dividing sub-segments according to data density change; partial states are shared among the sub-windows, and fine-grained analysis including anomaly detection and instantaneous peak value recognition is executed; introducing a life cycle termination relationship driven by an entropy threshold: when the window activeness is lower than the entropy threshold, triggering state compression or early release; the accuracy and dynamics of window division are realized, the response capability of a system to data fluctuation and burst is enhanced, and the elasticity and fault-tolerant capability of window boundaries are improved.

The invention relates to the technical field of storage systems, in particular to an electrolytic cell test data cloud storage method, which comprises the following steps: performing timestamp marking and partition size calculation on electrolytic cell test data, aggregating the data into partitions with fixed sizes according to timestamps, calculating a data density value and a write-in rate value of each partition, obtaining a partition data set, and storing the partition data set into a database; and comparing the write-in rate value in the partition data set with a preset partition threshold value. According to the method, through a dynamic partition management and multi-path data flow processing mechanism, in the storage process of the test data of the electrolytic cell, the data is subjected to partition aggregation through timestamp marking, a data density value and a write-in rate value reflect the dynamic change of the partition, and the size of the partition is adjusted in real time to cope with the fluctuation of a data write-in load. Multi-channel data stream distribution and merge sorting improve concurrent processing performance and eliminate single-channel bottleneck restriction. A double-buffer-area framework is matched with data access frequency analysis to support intelligent migration of cold and hot data, and IO resource configuration is optimized.

The embodiment of the invention provides a recommendation model training method, an object recommendation method, a recommendation system and computing equipment. The recommendation model training method comprises the following steps: acquiring user data of a sample user for a sample object; performing confrontation pre-training on the initial recommendation model through a domain classifier of a user type based on coding features of user data output by the initial recommendation model to obtain a pre-trained recommendation model; dividing the user data into meta-learning task sets of a plurality of recommendation scenes, and training the pre-training recommendation model based on the meta-learning task sets to obtain a meta-parameter recommendation model; and based on the meta learning task set of the target recommendation scene, constructing an adaptive task set of the target recommendation scene, and based on the adaptive task set, training the meta parameter recommendation model to obtain an adaptive recommendation model of the target recommendation scene. Recommendation precision is guaranteed under low data density, balance between sparse data and high precision is achieved, and training efficiency is improved.

The invention discloses a volume grid construction method and a related device. The method comprises the following steps: creating initial volume data of a target shape, realizing rough shaping of volume grid construction, and improving the controllability of the volume shape during volume grid construction; for volume data in a target density range in the initial volume data, namely the volume data of which the data density of each voxel is greater than the sampling density, sparse sampling is performed to obtain a plurality of target sampling points, so that data selection of volume grid construction is realized, and the data volume of the volume data during volume grid construction is reduced; on the basis of a plurality of target sampling points with a small data volume, three inserts are constructed for each target sampling point to obtain a three-insert grid in a target shape, and three patches in each three insert of the three-insert grid are perpendicular to one another and intersect with one another, so that the three-insert grid is higher in construction speed and stronger in volume feeling, and the three-insert grid can be more accurately constructed. Grid construction of volume grid construction is achieved, the volume grid construction time is shortened, the resource consumption is reduced, and the volume grid construction efficiency is improved.

The invention relates to the technical field of data recognition, and discloses an abnormal data recognition method, device and equipment and a storage medium, and the method comprises the steps: extracting data distribution characteristics in each time window in a high-dimensional time series data stream, and carrying out the recognition of abnormal data based on the data distribution characteristics and an adjusted random projection matrix of the high-dimensional time series data stream; the method comprises the steps of generating dimension-reduced low-dimensional data, configuring local sensitive hash resolution parameters according to local volatility of the low-dimensional data, constructing a multi-resolution hash table, and identifying abnormal data points according to in-bucket data density of the multi-resolution hash table. According to the method, the dimension reduction process is adjusted by extracting the data distribution characteristics, the calculation complexity is reduced while key information is reserved, the multi-resolution hash table is constructed based on the local volatility of the dimension reduction data, adaptive analysis of different data areas is achieved, abnormal data points are identified according to the density of the data in the barrel, and the accuracy of the data in the barrel is improved. The dimension problem and the distribution change problem can be effectively solved, and the processing efficiency is improved while the detection precision is ensured.

The invention discloses a power grid data processing method based on dynamic mode decomposition and belongs to the technical field of power system data analysis. In order to solve the technical problem that the accuracy and the real-time performance of power grid data monitoring are poor due to the fact that data sources are diversified and data densities are different in an existing data monitoring system, so that the data are difficult to effectively compare and analyze in time sequence, the invention provides a new data processing method. By processing the power grid operation data from different data sources and different sampling frequencies, the time sequence registration of the data is realized, so that the accuracy and the real-time performance of power grid data monitoring are improved, the automation level and the intelligent degree of a power system are further improved, powerful support is provided for safe and stable operation of the power system, and the power grid data monitoring method is suitable for popularization and application. And the method plays an important role in guiding actual operation.

The invention discloses a multi-dimensional time series data dynamic evolution analysis method and device, a medium and electronic equipment. The method comprises the steps that time series data are obtained and subjected to structured preprocessing; performing multi-dimensional quantization and attribute endowing on each piece of time series data, and constructing a three-dimensional data node containing a space coordinate, an initial weight and a timestamp; based on the set of the three-dimensional data nodes in a specific time window, constructing a three-dimensional data density field capable of representing data hotspot distribution and intensity in the time window; in the three-dimensional data density field, identifying a density peak region as a core data cluster, calculating the cluster mass according to the density and the coverage range of the core data cluster, and calculating the association strength among different core data clusters; and serializing a plurality of continuous time window data field state snapshots to form a dynamic evolution view capable of showing data hot spot generation and extinguishing and data cluster fusion and splitting processes. According to the invention, abstract multi-dimensional data is converted into a visual three-dimensional density field.

The invention relates to the technical field of hardware size measurement and detection, in particular to a hardware size error detection method and system based on point cloud processing. The method comprises the following steps: firstly, acquiring point cloud data of hardware and carrying out preliminary density clustering on the point cloud data; further analyzing from a plurality of perspectives of distribution similarity characteristics of the target data point and other data points in the initial density clustering cluster to which the target data point belongs, repeated characteristics of curvature and similar characteristics of data density of the target data point to obtain a hardware membership degree of the target data point; further adjusting the distance between the target data point and the central point in the initial density clustering cluster according to the hardware membership degree, carrying out secondary density clustering, and obtaining de-noised point cloud data; and finally, carrying out size error detection on the hardware according to the de-noised point cloud data. According to the method, the possibility that the data points belong to the hardware area is analyzed from multiple angles, the density clustering process is optimized, noise interference in the point cloud data is accurately removed, and a reliable hardware processing product size error detection result is obtained.

The invention discloses a sensor data enhancement method and system, relates to the technical field of sensor data processing, and solves the problem that an existing sensor data enhancement scheme is based on a static, single and fixed processing mode. In order to solve the problems that data distribution characteristics of different scenes cannot be adapted, environmental interference or sensor inherent errors are not considered, and the influence of abnormal values is easily caused, the technical scheme is characterized in that sensor data enhancement is performed based on a dynamic adaptive grid and a multi-compensation mechanism, and dynamic grid division, sensor error compensation and abnormal value influence processing are realized; static interpolation is upgraded into a dynamic self-adaptive data interpolation engine, data values of unknown points in a whole research area are calculated through limited sampling point numerical values, and by means of the data processing and enhancing algorithm, the data density is improved, and the problems of data sparseness and data missing are solved.

The invention discloses a method, a system and equipment for dynamically adjusting an aggregation window of a database, and relates to the technical field of databases. The method comprises the following steps: acquiring current flow parameters, data density and load parameters of a database and query demand data of a user; calculating an adjustment factor of the aggregation window by using a preset formula based on the flow parameter, the data density and the load parameter, and adjusting the size of the initial aggregation window of the database based on the adjustment factor to obtain the size of a target aggregation window; determining a target aggregation granularity of the aggregation window based on the query demand data; and setting a new aggregation window based on the target aggregation window size and the target aggregation granularity, and performing aggregation query by using the new aggregation window. Therefore, by dynamically adjusting the window, the aggregation window is more suitable for the real-time change of the data, and the flexibility of aggregation query is improved.

The invention discloses an NUMA (Non Uniform Memory Access) architecture perceived high-scalability learning index construction method and system and an operation method. According to the construction method, a mixed node tree structure is adopted to organize data, the structure comprises internal nodes accurately searched by a linear model and leaf nodes for storing data, the leaf nodes are composed of ordered gap nodes and segment nodes, and the segment nodes support dynamic evolution from an ordered stage to a semi-ordered stage based on data density. Conflict data is managed using hierarchical benchmark buckets. According to the system, a self-adaptive node evolution mechanism based on an operation cost model is realized, index performance is optimized through node reconstruction triggered by foreground write operation and background hot and cold node compression, a multi-thread concurrency control strategy is adopted, and a memory management and thread scheduling framework perceived by NUMA is adopted. The operation method provides an efficient point query and point insertion operation process. The data layout can be dynamically optimized, the operation cost is reduced, and the method is suitable for high-performance database and memory management scenes.

The invention provides a polymer flooding reservoir model prediction method and device, equipment and a storage medium. Relates to the field of oil-gas field development engineering. The method comprises the steps that an injection-production system matrix is converted into a multi-channel field graph containing time codes, and a depth operator network is constructed in combination with three-dimensional physical field data output through reservoir numerical simulation; a branch network adopts a residual attention network to convert the injection-production system field graph into a parameter-dependent feature graph; the backbone network converts tensors of time coding into spatial modulation weights, and initial operators of a pressure field, an oil saturation field and a polymer concentration field are generated through dot product operation; dynamically enhancing the data density of the high-error region by using an adaptive sampling mechanism based on a Gaussian mixture model; and realizing transient multi-physics field synchronization high-precision prediction by combining geometric similarity matching multiplexing physical tags. The problems that a traditional numerical simulator is low in calculation efficiency and a conventional agent model is poor in generalization ability are solved.

The invention provides an operation and maintenance performance assessment data management system based on dynamic weight optimization, and relates to the technical field of operation and maintenance management, and the system comprises a quantification module which is used for obtaining an original operation and maintenance data sequence through a data collection interface, and mapping the operation and maintenance data sequence to a multi-dimensional feature space; in the multi-dimensional feature space, quantitative analysis is carried out on spatial distribution of data points, and data density distribution characteristics of all areas are determined; the calculation module is used for executing unsupervised clustering operation based on the density distribution characteristics and the proximity relation of the data points to obtain a group of data sets with internal consistency; core statistical features are calculated for each generated data set, and a specific calibration coefficient is derived for the data set based on the core statistical features. According to the invention, precision, scenario and high efficiency of operation and maintenance performance assessment are realized.

A hybrid recommendation system for generating personalized film recommendations, the system comprising the following: a processing unit for receiving metadata, configured to receive descriptive data related to films, the descriptive data including plot summaries, genre identifiers, cast lists and director names; a text preprocessing module that is communicatively coupled with the metadata ingestion processing unit and is configured to perform tokenization, stopword removal, stemming, and lemmatization on the descriptive data to obtain a processed corpus; a TF-IDF vectorization unit configured to encode the processed corpus into high-dimensional semantic feature vectors by calculating TF-IDF inverse document frequency values ​​over the entire film dataset, with the semantic feature vectors representing the contextual meaning of terms for individual films; a collaborative filter engine comprising a user-element interaction matrix, wherein the engine is configured to compute latent preference signals using one or more techniques selected from the group consisting of cosine similarity, k-nearest neighbor similarity, and matrix factorization; a score fusion controller coupled to both the TF-IDF vectorization unit and the collaborative filter engine, wherein the module is configured to normalize the semantic feature vectors and the collaborative preference scores and dynamically combine them according to an adaptive weighting coefficient, the coefficient being determined as a function of data density, interaction sparsity, and user history length; and a recommendation output module configured to evaluate candidate films for each user based on the combined score and generate a top-N recommendation list.

The invention discloses an offline reinforcement learning method based on a score model, and the method comprises the steps: building a score generation model, learning a gradient field of an offline data set through employing a denoising score matching technology, estimating a score value, introducing a score as a penalty term, and preventing an intelligent agent from exploring a region where the data density is rapidly reduced. In the training process, real data and synthetic data are combined, a soft actor commentator framework is used for optimizing a commentator network, a penalty term is introduced to limit the influence of the synthetic data on a strategy target, meanwhile, a Q value is maximized, an intelligent agent strategy is optimized in combination with an entropy term, the synthetic data quality is improved, and the robustness and generalization ability of the model are enhanced. In scenes such as robot control, the method can ensure safe operation of the robot in a complex environment, effectively utilizes the synthetic data to improve the strategy performance, and reduces the risk of online data acquisition. Particularly, when tasks such as robot navigation are processed, key information near a target point can be better handled, strategy failure caused by complexity of data distribution is avoided, and excellent practicability and adaptability are shown.

The invention relates to the technical field of supply chain traceability, in particular to a mother and baby product production traceability management method and system based on a block chain, and the method comprises the following steps: obtaining raw material and packaging chain segment data, extracting batch information and detection numbers, recognizing nodes and recording cross chains, counting the cross frequency of finished product segments, and constructing a path index map. And screening an optimal main path, generating a main path identifier, judging a filling data density grade, marking a chain segment needing to be adjusted, and generating a scheduling strategy parameter table. According to the method, field splicing is performed on the batch number, the raw material batch number and the detection report number to compare the segment identifier, so that node connection identification and cross structure establishment are realized, chain segment relevance is enhanced, cross node frequency is counted, high-frequency node pairs are extracted to construct the path, and the path identification accuracy is improved; and screening a path with the fewest skipping times and complete node coverage as a main path, enhancing the traceability stability, sensing the frequency based on the time interval and the chain block number, and realizing the dynamic scheduling of the chain segment state.

The present application relates to the technical field of storage, and discloses a data attribute-aware storage system and data management method for a key-value database. The method comprises two parts, i.e., data hotness attribute-based writing and data density attribute-based merging. The data hotness attribute-based writing comprises classifying key-value pairs into cold key-value pairs and hot key-value pairs, to realize separate storage of cold data and hot data by means of a data hotness attribute-based writing strategy. The data density attribute-based merging comprises: when key-value pairs in an SSTable are extracted during a merging operation and form a sorted key-value pair sequence, extracting the key-value pairs one by one, and calculating a binary difference between every two adjacent key-value pairs; and if the difference is greater than a set threshold, stopping filling the current SSTable, and creating a new SSTable. In the present invention, by analyzing data hotness and data density, data having different attributes are classified to different data partitions, thereby reducing I / O amplification caused by repeated reading and writing in a compression process.

The invention provides a data migration method and device, equipment and a medium. The method comprises the steps that a data density difference value in each sub-cluster in a federated cluster is determined, a first sub-cluster and a second sub-cluster in the federated cluster are determined according to the data density difference value of each sub-cluster in the federated cluster, the data density difference value of the first sub-cluster is larger than a first threshold value, and the data density difference value of the second sub-cluster is larger than a second threshold value. The data density difference value of the second sub-cluster is smaller than a second threshold, and the first threshold is larger than the second threshold; and migrating the data of the first sub-cluster to the second sub-cluster. According to the method, the data density difference value of each sub-cluster is compared with the first threshold value and the second threshold value, the state of the data density of each sub-cluster is confirmed in real time, and the data migration program is triggered according to the state of the data density of each sub-cluster, so that manual judgment is avoided, the storage data volume among the sub-clusters can be adjusted in time, and the data migration efficiency is improved. And the overall use effect of the federated cluster is improved.

The invention relates to the technical field of supply chain data storage, and discloses a supply chain performance data block chain storage method based on multi-party security computing. The method comprises the following steps: monitoring performance data states and relative interaction relationships of all participants of a supply chain in real time, and judging whether the data density in a target supply chain region exceeds a safety threshold value or not; when a threshold value is exceeded, analyzing the interaction influence of data of each participant through a multi-party security computing protocol, and evaluating the hidden danger of privacy disclosure caused by data sharing; analyzing and processing the spatial and temporal change of the historical performance data by adopting distributed feature learning and combining with a time sequence, and evaluating the potential interference degree of historical data fluctuation on the integrity of the current performance data; and based on the privacy leakage hidden danger and the potential interference degree of historical data fluctuation, whether overall block chain evidence storage optimization of the target supply chain region is carried out is judged. According to the method, the problems of supply chain data density and privacy protection can be solved in a targeted manner, and evidence storage optimization is realized by combining historical data spatial-temporal characteristics.

The invention relates to a space-time big data dynamic index construction method and device and a storage medium. The method comprises the following steps: inputting a spatio-temporal data stream into a distributed system to carry out fragmentation processing and index tree structure construction to obtain an initial index structure, and carrying out parallel feature extraction on the spatio-temporal data stream to obtain a spatio-temporal distribution feature vector; calculating a space-time overlapping degree, a data density similarity and a query frequency correlation degree of adjacent nodes based on the space-time distribution feature vectors to obtain a combined evaluation score of the node pair; performing multi-objective optimization based on the merging evaluation score to generate an optimal merging threshold value; and according to the optimal merging threshold, performing incremental merging operation on the node pairs, and performing data recombination and range boundary updating to obtain an optimized index tree structure. According to the method, smooth adjustment of the index structure is realized, the influence of structure adjustment on online query service is minimized, and the index structure can be continuously optimized and kept in an efficient state.

The invention discloses a geological mineral data intelligent modeling method and system based on multilevel space-time intrinsic coding, and the method comprises the steps: generating a 9-level adaptive grid through employing an improved HEALPix subdivision algorithm for the geological complexity and target data density of a to-be-detected region; based on a 9-level adaptive grid, encoding space time-varying grid units of existing geological mineral resource data to obtain a geological feature space matrix; constructing a multi-modal data register based on Transform, inputting the 9-level adaptive grid and the geologic feature space matrix into the multi-modal data register, and outputting an associated feature matrix; using a hybrid interpolation model to perform hybrid interpolation on the correlation feature matrix to obtain a full-grid correlation correlation interpolation matrix; and a CNN error correction module is used to optimize the full-grid correlation degree correlation interpolation matrix to obtain a geological mineral resource space-time intrinsic code, and geological mineral resource data intelligent modeling is realized based on the geological mineral resource space-time intrinsic code.

The present invention discloses a method for identifying a graphics rendering mechanism based on rendering pipeline alignment. The method pre-executes a benchmark test program in CPU mode and GPU mode respectively, completes pre-alignment of the key stage processing interval and the stable working interval in the two modes to form a first sampling interval to achieve alignment of the rendering pipeline. The second sampling interval is formed based on the pre-alignment of frame complexity. The target sampling interval is obtained by finding the intersection of the first sampling interval and the second sampling interval. During actual testing, CPU load data of each of the two modes is obtained, the CPU load data is sampled according to the target sampling interval, and the correlation between the two sampling data is calculated to determine the graphics rendering mechanism of the tested system. The unified rendering action ensures that the sampling data covers the complete rendering cycle. The reasonable distribution of data density under different load scenarios is ensured by analyzing the frame complexity. The combination of the two improves the representativeness and statistical effectiveness of the data, and effectively improves the accuracy of identifying the graphics rendering mechanism.

The invention relates to the technical field of underground resource drilling engineering, in particular to an oscillating shear valve and a continuous wave high-speed transmission method.The oscillating shear valve comprises a valve seat, a stator, a rotor and a driving assembly, and the inner side of the upper portion of the valve seat and the outer side of the upper end of the stator are fixedly installed together. The structure is reasonable and compact, the rotor is driven by the driving assembly to rotate, when the rotor rotates and slurry flows into the position below the valve element through the flow hole, due to the fact that the difference value between the third area and the second area changes along with rotation of the rotor, the pressure of the slurry changes, pulse signals are formed, and therefore the changed pressure is reserved when the slurry circulates to a wellhead; the difference value between the third area and the second area is the same as the difference value between the third area and the second area, the frequency can be changed instantly, and compared with a traditional oscillating shear valve, the buffer time occupied when the frequency is changed is saved, the transmission rate is increased, and higher data density can be generated.

The invention discloses a virtual space-time environment automatic completion method and system based on data density dynamic perception and a medium, and relates to the field of computer data processing. The method comprises the following steps: monitoring a data activeness index (such as spatio-temporal information entropy) of a spatio-temporal grid in real time in a distributed index database of a virtual spatio-temporal environment; when the index meets a preset data sparseness condition, generating a completion request; analyzing the space-time attribute tag of the target grid and acquiring associated multi-modal reference data from an external heterogeneous data source; carrying out feature recombination on the reference data by utilizing a generative neural network model, and generating discrete spatio-temporal data slices containing spatio-temporal anchor points and scene content attributes (such as environment illumination and material mapping); and finally, injecting the slices into the database to fill data holes. According to the method, a passive trigger mechanism and a structured reconstruction technology of unstructured data are introduced, so that the computing power cost of a large-scale virtual environment is effectively reduced, the problems of data sparseness and cold start of a long-tail region are solved, and the historical authenticity and time-space consistency of a virtual scene are ensured.

The invention discloses a genome variation cold and hot spot region prediction method and device, and the method comprises the steps: S1, carrying out the slicing of a target genome region according to a preset sliding window length, and constructing a multi-modal input tensor corresponding to a window; s2, inputting the multi-modal input tensor into a pre-trained deep learning prediction model, and outputting a cold and hot spot prediction score of each site in the window through a full connection layer; and S3, according to the cold and hot spot prediction scores, identifying a variation cold spot region and a variation hot spot region in the target genome region. According to the technical scheme provided by the invention, the dependence on the existing variation data density is eliminated, and non-blind area coverage in the whole exon group range is realized; meanwhile, the structured output based on the preset transcript coordinates can directly support clinical variation interpretation, a quantitative basis is provided for PM1 and cold spot evidence, and the proportion of unclear significance variation is effectively reduced.

The invention relates to the technical field of data storage, in particular to a distributed parallel data management method and system based on hierarchical storage, and the method comprises the steps: obtaining a distributed data set, numbering each data object in the distributed data set to obtain a first data object to an Nth data object, acquiring data type sequences, data size sequences and access frequency sequences of the first data object to the Nth data object, performing data density analysis to obtain first data density to Nth data density, and performing reference-dependency conjoint analysis on the first data object to the Nth data object to obtain a first weight matrix, the first correction density to the Nth correction density are obtained through density correction according to the first weight matrix, and hierarchical storage is performed according to the first correction density to the Nth correction density to obtain a data storage hierarchy allocation result, so that more accurate and efficient storage management of the distributed parallel data is realized.

The invention discloses a method for identifying a public charging infrastructure boundary in combination with Internet of Vehicles data and an artificial intelligence algorithm. The method comprises the steps of S1, data acquisition; s2, constructing a buffer area; s3, carrying out buffer region fusion; s4, decomposing and splitting a plurality of regions; s5, area calculation and small region elimination; s6, convex hull construction and softening treatment; and S7, determining the boundary, and determining the boundary of the charging infrastructure according to the softened boundary after the step S6. The technical scheme of the invention has the following advantages: (1) automatic identification is realized, and boundary information is extracted from actual behaviors without depending on manual delimitation or operator declaration; (2) the adaptability is high, the method can be suitable for different cities and different time periods, and different data densities and distributions can be flexibly adapted by adjusting parameters; (3) high-precision positioning is carried out, and real range identification can be realized by combining desensitized latitude and longitude data (4-bit precision) with a buffer zone strategy; and (5) the expandability is good, and boundary types and grades can be refined by combining other characteristics.

The invention relates to the technical field of data processing, in particular to a portable terminal with an electric power payment service intelligent management function, the system comprises a processor and a memory, and the processor executes a computer program of the memory to realize the following steps: obtaining an index combination for grouping users; clustering the users according to any index combination to obtain a cluster; obtaining an effect evaluation factor of any index combination according to the correlation of the historical data of the user behavior index of each user in each cluster, the data density in each cluster and the distance between every two clusters, obtaining an effective index combination according to the effect evaluation factor of each index combination, and obtaining the effective index combination according to the effective index combination. The users are grouped according to each effective index combination, and the user portraits are generated for providing personalized payment reminding services for each user, so that the accuracy of pushing the personalized payment reminding services for the users is improved.

The present invention relates to the field of data processing technology, and in particular to a data processing method for an electric power distribution device, comprising: constructing a data point set for reflecting the electric power load in the current area based on the collected electric power consumption data of users in the current area, clustering the data points in the data point set multiple times using the K-means clustering algorithm; obtaining multiple circular rings of each category in each clustering result, and determining the data density of each circular ring; sequentially determining the rationality of the intra-class density change, the rationality of the inter-class distribution, and the degree of preference of each clustering result; and determining the optimal clustering result of the K-means clustering algorithm based on the magnitude of the degree of preference, thereby realizing data processing of the electric power distribution device. The present invention deeply analyzes the distribution characteristics of the data points within the cluster category, taking into account the rationality of the intra-class density change and the rationality of the inter-class distribution, ensuring that the clustering results are more consistent with the spatial distribution and density differences of the actual electric power load, and more truly reflecting the electric power consumption behavior in different areas.