17 results about "Recursive partitioning" patented technology

The invention discloses a supply chain data-oriented enterprise upstream and downstream collaborative risk control prediction method and system, and relates to the technical field of supply chain financial risk management, and the method comprises the steps: extracting a payment time deviation value between enterprises to construct a payment behavior sequence, calculating a supply chain collaborative credit rating based on a fluctuation amplitude and a historical default record, and recognizing a risk source enterprise; adopting a recursive partition time window to extract a period index and a sudden change index of the payment behavior, and constructing a payment behavior portrait; identifying associated enterprises through the phase overlapping degree of the payment behavior track to form a risk conduction sequence; and based on the evolution law and the risk conduction sequence of the payment behavior portrait, predicting a risk triggering node, and generating a risk prevention and control scheme including a credit line regulation and control instruction and a behavior monitoring strategy. The method can effectively identify the supply chain risk source, predict the risk conduction path, and improve the financial risk control capability of the supply chain.

A system and method for anomaly detection. A method includes recursively partitioning a sample of device activity data including deterministic characteristics of a population of devices over iterations in order to create partitions. Each iteration includes determining a split density metric for a candidate subpopulation created by splitting a portion of the population with respect to a corresponding type of deterministic characteristic. The split density metric for the candidate subpopulation is determined based on a density value of the candidate subpopulation and a coverage value of the corresponding type of deterministic characteristic. The partitions include each candidate subpopulation meeting a split density metric threshold. A baseline for each of the partitions is established based on device activity for devices represented in device activity data of the partition. An anomaly is detected based on behavior of a device and the baseline established for a partition corresponding to the device.

This invention discloses a sparse method and related equipment for quadrant amplitude mapping arrays. The method, through recursive partitioning and a deterministic amplitude mapping mechanism, eliminates the randomness of traditional probabilistic methods, ensuring the uniqueness and stability of the output layout under the same input conditions, thereby significantly improving the repeatability and engineering reliability of the design results. The method in this embodiment achieves high-precision matching between amplitude distribution and cell position through sparse cell allocation based on recursive quadrant partitioning, significantly reducing sidelobe levels while lowering the standard deviation of performance fluctuations. Furthermore, the method in this embodiment is independent of specific array geometry and naturally supports rectangular, circular, and irregular arrays with internal obstacles through its recursive processing mechanism, demonstrating excellent versatility and flexibility, and can be widely applied in the field of data processing technology.

The present invention discloses an inventory management scheduling optimization system and method based on data prediction, which relates to the technical field of inventory management and is used to improve problems such as insufficient or excessive equipment inventory caused by changes in the equipment iteration speed. It includes identifying equipment models to determine equipment information, retrieving equipment based on the equipment information, obtaining historical data of the same type of equipment, using recursive partitioning to delimit equipment categories according to the historical data, setting an analysis time, obtaining the equipment transfer volume within the analysis time, setting the sensitive time level of the equipment based on the comprehensive equipment transfer volume and equipment category, formulating an inventory retrieval mode according to the sensitive time level of the equipment, calculating the change prediction coefficient of equipment movement according to the inventory retrieval mode, and simultaneously monitoring the release information of new equipment of the same type. By comprehensively using the release information of new equipment of the same type and the change prediction coefficient, the Robust Scaling algorithm and the decision function are used to change the inventory retrieval mode, thereby reducing the problems of insufficient or excessive inventory caused by the iteration rates of different equipment.

The invention relates to the technical field of territorial monitoring data processing, in particular to a method and system based on territorial monitoring data integration. The method comprises the following steps: acquiring heterogeneous territorial monitoring data; performing heterogeneous feature dimension interaction data analysis based on the heterogeneous territorial monitoring data to generate heterogeneous feature multi-dimensional interaction data; performing territorial integrated three-dimensional model analysis according to the heterogeneous feature multi-dimensional interaction data and the heterogeneous territorial monitoring data to generate a territorial integrated three-dimensional model; performing self-adaptive territorial space recursive partitioning processing on the territorial integrated three-dimensional model to generate self-adaptive partitioned territorial space data; marking a landmark index of the self-adaptive partitioned territorial space data to obtain index partitioned territorial space data; and analyzing index partition adjacency characteristic land change transfer trend data through index partition territorial space data, and executing territorial change monitoring intelligent feedback operation. According to the invention, through integration of heterogeneous territorial monitoring, efficient territorial monitoring data feedback is realized.

The invention belongs to the technical field of power equipment purchase, and provides a power equipment purchase analysis method and system based on the degree of adaptation, and the method comprises the steps: obtaining equipment parameters participating in bidding; obtaining an equipment adaptation degree according to the equipment parameters and a preset equipment adaptation degree evaluation model; all the adaptation degrees are arranged according to the sequence from high to low, the highest adaptation degree is a first-level tendency, and in the same way, purchasing scheme tendency levels of all the devices are obtained; a polynomial model is adopted as an equipment adaptation degree evaluation model, and a correction coefficient, a power index and an expression weight corresponding to each parameter in the polynomial model are determined through a recursive segmentation algorithm and an iterative optimization method, so that adaptation degree evaluation can be quickly carried out on various types of power equipment, and on the premise of ensuring accuracy and reliability, the equipment adaptation degree evaluation efficiency is improved. The limitation of dependence on artificial experience and complex mathematical modeling in a traditional method is avoided.

Techniques for generating a hierarchical representation of a set of monitoring events. In some cases, an example system may generate a tree structure based on a set of monitoring events. The tree structure may be generated by: (i) extracting a set of feature values from the monitoring events, where the feature values may correspond to a defined set of event features, (ii) computing a set of entropy measures, each associated with one of the event features over all of the monitoring events, (iii) selecting an event feature for partitioning the events based on the entropy measures, (iv) dividing the events into subsets based on values of the selected feature, and (v) recursively partitioning the subsets based on entropy-selected features until a tree structure is formed.

The application relates to an automatic recursive partitioning method and device for a non-structure grid field special language, and the method proposes an automatic recursive partitioning and data conflict resolution software architecture scheme for a shared memory multi-core CPU platform in a non-structure grid field special language and a converter framework, a grid topology and an indirect access mode are uniformly described at a DSL level, a task dependency structure for conflict resolution is automatically constructed, and a global renumbering and data rearrangement mechanism is combined, so that the parallel efficiency, data locality and performance portability of the non-structure grid application on the multi-core CPU platform are improved under the premise of ensuring numerical correctness, and the application development and maintenance cost is reduced.

This application relates to the field of metal processing technology, and more particularly to an intelligent monitoring method and system for the processing and manufacturing of parts. The method includes: acquiring sample data points of sample metal parts; determining a partitioning priority value for the dataset composed of multiple sample data points from different sample metal parts, using processing parameter combinations to jointly partition the dataset; recursively partitioning the dataset using the partitioning priority value of the processing parameter combinations, and constructing a decision tree based on the recursive partitioning results; the decision tree is used to output defect causes of defect information; acquiring defect information of the metal parts to be inspected and multiple processing parameters from the production stage, and inputting the defect information of the metal parts to be inspected and the multiple processing parameters into the decision tree to obtain the defect causes of the metal parts to be inspected, so as to output monitoring information using the defect causes. Through the above technical solution, the processing of metal parts can be monitored.

This application provides a three-dimensional extended global grid partitioning method and system based on a spatial octree, relating to the field of global grid partitioning. The method includes: constructing a basic spatial structure with consistent latitude and longitude spans for the Earth; recursively partitioning the Earth along the longitude and latitude dimensions to construct a multi-level grid structure; performing extended mapping processing on the altitude domain to construct a unified three-dimensional spatial representation system of longitude, latitude, and altitude; and simultaneously performing bisection recursive partitioning of the three-dimensional spatial units to construct a three-dimensional grid system adapted to the spatial octree structure; further, using a bitwise interpolation encoding method to fuse and encode the longitude, latitude, and altitude indices to obtain the grid code of the three-dimensional grid unit, and establishing parent-child mapping relationships between different levels of grids to obtain the global grid partitioning result. This application, used in the global grid partitioning process, solves the technical problem of existing technologies struggling to achieve spatially coordinated and unified expression in global grid partitioning.

A method of video processing includes, for a conversion between a current block in a video region of a video and a coded representation of the video, determining that a partitioning of the current block into a plurality of transform units is allowed based on a characteristic of the current block. Signaling of the partitioning is omitted in the coded representation. The method further includes performing the conversion based on the determining.

The application discloses an enterprise upstream and downstream cooperative risk control prediction method and system facing supply chain data, relates to the technical field of supply chain financial risk management, and comprises the following steps: extracting a payment time deviation value between enterprises to construct a payment behavior sequence, calculating supply chain cooperative credit rating based on fluctuation amplitude and historical default records, and identifying a risk source enterprise; a recursive partition time window is adopted to extract a periodic index and a mutation index of the payment behavior, and a payment behavior portrait is constructed; correlation enterprises are identified through phase overlap of the payment behavior track, and a risk transmission sequence is formed; based on the evolution law of the payment behavior portrait and the risk transmission sequence, a risk trigger node is predicted, and a risk prevention and control scheme containing a credit limit control instruction and a behavior monitoring strategy is generated. The application can effectively identify a supply chain risk source, predict a risk transmission path, and improve the supply chain financial risk control capability.

The present invention discloses a method, medium and electronic device for partitioning a distributed energy supply cluster. The method includes: determining regional distributed energy supply cluster partitioning indicators; constructing an objective function with the optimal comprehensive weighted indicator as the goal based on the determined regional distributed energy supply cluster partitioning indicators; and using a recursive partitioning clustering tree algorithm to solve the objective function to obtain an optimal distributed energy supply cluster partitioning result including the loads of each cluster. The present invention comprehensively considers factors such as the geographical distribution of sources and loads, the endowment of local clean energy resources in the region, and the matching degree of sources and loads, and can efficiently and stably achieve a reasonable partitioning of the regional distributed energy supply cluster.

This application discloses a video data processing method, apparatus, device, and storage medium, applicable to scenarios such as cloud gaming in cloud technology. The method includes: obtaining a target recursive partitioning strategy for a unit to be encoded in a target video frame, and encoding parameters for the unit to be encoded; partitioning the unit to be encoded according to the target recursive partitioning strategy to obtain N first sub-units to be encoded; obtaining the encoding parameters corresponding to each of the N first sub-units to be encoded, and determining the parameter relationship between the encoding parameters of the unit to be encoded and the encoding parameters corresponding to the N first sub-units to be encoded; if the parameter relationship indicates that the unit to be encoded does not meet the conditions for further partitioning, then pausing the further partitioning of the unit to be encoded, and determining the target partitioning result of the unit to be encoded based on the first sub-units to be encoded and the unit to be encoded. This application reduces the complexity of the video data encoding process and improves the encoding efficiency of video data.

The invention relates to the field of water supply system optimization, in particular to a water supply network intelligent grading and partitioning method based on multi-source data fusion. The invention provides a multi-source data fusion-based intelligent grading and partitioning method for a water supply pipe network, which comprises the following steps of: inputting pipe network topological data, flow pressure data and user water consumption historical data, and constructing a pipe network diagram G; according to the data input into the pipe network diagram G, calculating topological characteristics, hydraulic characteristics and user characteristics of each node in the pipe network diagram G, and calculating comprehensive similarity measurement between every two nodes; performing multi-constraint spectral clustering processing on all nodes, and outputting a first-level partition; and performing hierarchical recursive partitioning on the first-level partition, and outputting a second-level partition. Through multi-dimensional data fusion and constraint optimization, the partition reasonability is improved, the first-level partition and the second-level partition are finally output by preprocessing each group of data, the manual partition process is reduced, and the partition division accuracy is improved.

This application belongs to the field of computer-aided medical technology and discloses an adaptive hierarchical medical knowledge graph community construction method, device, and medium. The method first calculates the normalized evidence support of the medical knowledge graph triples; then, it constructs a collaborative initial edge weight matrix by combining the graph topology, entity semantic similarity, and evidence support; next, it adaptively calculates the initial resolution based on the graph size and density, and performs coarse partitioning to obtain a set of entity topic communities; for large communities, it removes outliers to obtain robust semantic centers, and performs secondary partitioning correction based on consistency to update edge weights; it calculates the maximum similarity between small communities and the centers of large communities and performs adsorption or containment processing; finally, it employs a strategy of increasing resolution with hierarchy to perform top-down recursive partitioning. This invention effectively suppresses the interference of clinical literature extraction noise and weak evidence on the community, ultimately generating a high-quality hierarchical community structure that can be used for medical consultation, clinical decision-making, and medical retrieval.