119 results about "Division algorithm" patented technology

The invention provides an urban functional area gridding identification and classification method based on multi-source spatio-temporal data, and relates to the technical field of area identification, and the method comprises the steps: carrying out the dynamic grid division of an urban area through employing a self-adaptive grid division algorithm, obtaining the multi-source spatio-temporal data of a gridding area, carrying out the space-time alignment, and generating a multi-dimensional time sequence feature set; extracting multi-dimensional feature vectors of the grid units through a multi-modal feature fusion algorithm, acquiring long-range time sequence features by using a bidirectional gating recursion unit, calculating spatial correlation features based on a multi-head attention mechanism, and inputting the two types of features into a feature fusion network with jump connection to generate enhanced spatial-temporal features; and an optimization strategy is adopted, a functional area clustering center point is determined through density peak value clustering in sequence to obtain an initial division result, the initial result is optimized by combining spectral clustering of spatial adjacency constraints, and the boundary of the functional area is optimized by using a simulated annealing algorithm, so that accurate identification and classification of the urban functional area are realized.

The invention discloses an adaptive task division and decision joint optimization-based cloud edge computing resource collaborative scheduling method for a complex manufacturing system. The method comprises the following steps of 1, constructing a hierarchical cloud-edge collaborative computing network model; constructing a multi-objective optimization model, and defining an objective function and constraint conditions; 2, dynamically predicting and calculating a resource state through a resource sensing module based on an LSTM neural network, and generating a node resource prediction matrix; 3, dividing a calculation task generated by the manufacturing system into fine-granularity, medium-granularity and coarse-granularity subtask sets by adopting a multi-granularity subtask division algorithm (MSPA), and mapping the subtasks to corresponding calculation nodes; 4, constructing a task unloading decision model based on the D3QN, and dynamically selecting unloading nodes and an execution sequence of the subtasks in combination with a multi-objective optimization reward function; and 5, iteratively optimizing parameters of the D3QN model through a target network updating mechanism and a self-adaptive exploration strategy to realize real-time dynamic adjustment of a task scheduling decision.

The invention relates to the technical field of data processing, in particular to a cloud backup platform data deduplication method based on distributed storage. Comprising the following steps of: performing self-adaptive blocking on data by utilizing a dynamic blocking algorithm, generating a unique identification code and extracting multi-dimensional feature information; establishing a global index through a distributed hash table, mapping a data position and maintaining index consistency; performing redundancy analysis and similarity matching based on a deep learning algorithm, and executing a duplicate removal strategy; according to the access frequency and the load condition, distributed storage of data blocks is realized by adopting a hierarchical graph division algorithm; and a partitioning strategy, index frequency and storage layout are dynamically optimized by using a reinforcement learning algorithm, and global optimization of performance and resources is realized. According to the invention, high efficiency, accuracy and dynamic optimization of data deduplication are realized, and the data storage and management efficiency of the cloud backup platform is improved.

The invention provides an electronic data evidence storage verification method and system based on hierarchical hash and an intelligent contract, and relates to the technical field of block chains and data security, and the method comprises the steps: obtaining to-be-stored electronic data; to-be-stored electronic data is divided into data blocks according to a preset rule, a unique identifier and a timestamp are added to each data block, and a standardized data unit is generated; performing hierarchical hash calculation on the standardized data unit, generating a leaf node hash value by each data block, aggregating the leaf node hash values layer by layer according to a Merkle tree structure, and generating root hash; calling a block chain smart contract, submitting the tree structure hash, the timestamp and the public key encrypted signature to a block chain network, after verification of a consensus node, writing the evidence information into the block chain, analyzing a hash path submitted by a user by the smart contract to perform data verification, if the hash path is consistent with the data verification, returning verification success, otherwise, triggering an exception handling contract; through the dynamic partitioning algorithm and the layered hash structure, the calculation overhead of large file processing is reduced.

The invention relates to the field of network security, and discloses a problem line determination method based on IP address classification and identification, which comprises the following steps of: collecting public network IP behavior data in a network flow log, extracting multi-dimensional characteristics such as a time regularity entropy value, a protocol diversity proportion, target port dispersity and a request rate change rate, and determining a problem line according to the extracted multi-dimensional characteristics; constructing an IP association graph fusing feature similarity, time synchronism and physical position constraints; an abnormal IP group is identified by adopting a community division algorithm, and the detection sensitivity is adaptively optimized according to a real-time network load and a historical false alarm rate in combination with a dynamic threshold adjustment mechanism based on a PID feedback control model; the attack type is judged through protocol-port mapping and Mahalanobis distance statistics, alarm information containing an abnormal IP list, a time window and a classification result is generated, and the alarm information is linked with safety equipment to execute a defense strategy. According to the method, the defects of a traditional method in the aspects of multi-dimensional attack recognition, dynamic environment adaptation and cooperative attack detection are overcome.

The invention discloses a scaffold steel plate net building method based on BIM, and relates to BIM modeling. The method comprises the steps that feature parameters of a scaffold steel plate net are obtained; according to the characteristic parameters, establishing a three-dimensional entity model of the scaffold steel screen by using a parametric modeling method; performing grid division on the three-dimensional solid model by adopting a self-adaptive grid division algorithm to obtain a finite element grid; identifying a stress concentration area in the finite element grid by adopting a self-adaptive grid encryption algorithm, and performing grid encryption on the stress concentration area to obtain an encrypted finite element grid; performing structure optimization on the encrypted finite element mesh by adopting a topological optimization algorithm to obtain an optimized three-dimensional entity model of the scaffold steel plate mesh; and performing component arrangement optimization on the three-dimensional entity model after structure optimization by adopting a discrete topological optimization algorithm to obtain a final scaffold steel plate net BIM model. In order to solve the problem that in the prior art, scaffold steel plate net erecting redundancy is large, the redundancy design is reduced.

The invention provides an intelligent business resource scheduling method and system based on big data, and relates to the technical field of data processing, and the method comprises the steps: 1, capturing equipment layer sensor data, business layer order data and logistics layer vehicle track data in real time through distributed data collection nodes, and carrying out the real-time acquisition of the data; a dynamic time window division algorithm is adopted to carry out time sequence alignment processing on the heterogeneous data flow, a ternary state vector matrix with a unified timestamp is generated, the row dimension of the matrix represents a collection time sequence, and the column dimension of the matrix corresponds to equipment layer operation parameters, a service layer task queue state and logistics layer transportation node coordinates respectively. According to the invention, through multi-source data fusion, three-dimensional space analysis and an intelligent algorithm, resource gap accurate identification and cross-level scheduling optimization are realized, and the business processing efficiency and the resource utilization rate are improved.

The invention discloses a model cavity detection method and system. The method comprises the following steps: acquiring target model data to be detected; based on a voxel grid division algorithm, dividing the target model data to obtain a plurality of model parts; based on a seed filling algorithm, determining cavity information corresponding to each model part; and according to the cavity information corresponding to each model part and preset area communication information, identifying isolated cavity data corresponding to the target model data. Therefore, accurate recognition and positioning of the isolated cavity in the complex model can be achieved, the accuracy and efficiency of subsequent geometric repair and structural analysis are improved, printing errors in subsequent 3D printing are reduced, and the printing quality is improved.

The invention provides a CNN-oriented batch matrix multiplication parallel optimization method and system on a SW architecture, and belongs to the technical field of artificial intelligence parallel optimization. Comprising the following steps: respectively converting an input feature map and a convolution kernel in a convolution layer into an input matrix and a weight matrix, and processing the input matrix and the weight matrix into a plurality of groups of independent matrix multiplication tasks in batches; the main core encapsulates a matrix multiplication task into a parameter structure array, the parameter structure array is transmitted to the slave core through single DMA, and the slave core divides rows of an input matrix into row block tasks by adopting a dynamic row block division algorithm according to the total number of threads and the height of the matrix; and executing sub-matrix multiplication calculation on the distributed independent row blocks, asynchronously prefetching matrix sub-blocks by adopting a double-buffer DMA (Direct Memory Access), and executing matrix multiply-accumulate calculation. The parallel processing efficiency of batch matrix multiplication between the master core and the slave core of the SW processor can be improved, and the algorithm performance is optimized.

The invention belongs to the field of micro-architecture design of an integrated circuit processor. The invention provides an H2P branch prediction chip circuit architecture based on BrPerceptron, a variable operation track of a program is constructed based on a context-sensitive variable tracking mechanism, a program execution path topology is constructed based on a basic block division algorithm according to the variable operation track, multi-dimensional correlation detection is performed according to the program execution path topology to judge whether an H2P branch exists or not, and if yes, the H2P branch is predicted. And when the H2P branch instruction is judged to be the H2P branch instruction, enabling the BrPerceptron predictor to output, performing multi-stage feature fusion and outputting a prediction result, and realizing high-precision prediction for the H2P branch instruction.

The invention discloses an automatic segmentation method and system based on a deep foundation pit point cloud model in the technical field of deep foundation pit modeling, and aims to solve the optimization problem of an existing deep foundation pit modeling technology. The method comprises the following steps: carrying out model simplification on an obtained deep foundation pit point cloud model by adopting a dynamic threshold voxel reduction algorithm to obtain a simplified point cloud model; dividing the underground diaphragm wall and the foundation pit ground for the simplified point cloud model by using a geometric constraint random optimization algorithm to obtain an underground diaphragm wall set and a foundation pit ground set; filtering the underground diaphragm wall and the foundation pit ground in the simplified point cloud model according to the underground diaphragm wall set and the foundation pit ground set to obtain a preliminarily filtered point cloud model; performing support layer division on the preliminarily filtered point cloud model by using a geometric constraint random optimization algorithm to obtain a support layer set; and carrying out independently supported point cloud division on the support layer set by adopting a dynamic radius domain division algorithm to obtain an independently supported point cloud set.

The invention discloses a power line collaborative inspection system based on an unmanned aerial vehicle cluster, and relates to the technical field of power correlation, and the system comprises a hypergraph topology modeling module which is used for constructing a power line hypergraph model, and extracting a hypergraph topology feature matrix; the fractal region division module is used for dividing the region to be inspected into a plurality of sub-regions by minimizing space coverage balance and boundary complexity under the condition that constraint conditions are met; the dynamic task allocation module is used for constructing an adaptation degree matrix, realizing optimal matching of the unmanned aerial vehicle based on the adaptation degree matrix, and carrying out power line collaborative inspection according to an optimal matching result; and the autonomous fault-tolerant recovery module is used for performing task redistribution when a fault occurs during cooperative inspection of the power line. Through hypergraph modeling, a region division algorithm and adaptive task matching, accurate modeling of a power system structure and risk factors, efficient division of an inspection region and intelligent scheduling of unmanned aerial vehicle resources are realized, and the efficiency of power line inspection and the resource utilization rate are improved.

The invention discloses an intelligent operation and maintenance management and control method and system based on a digital twinning wind power plant, and relates to the technical field of digital twinning, and the method comprises the steps: obtaining a wind power plant fan time sequence signal, constructing a feature vector, carrying out the Hash mapping, and storing the feature vector into a segmented Hash table; judging an operation mode through a Bloom filter, generating a label triple stream and summarizing abnormal hash keys; counting the Hash value frequency in the time window and marking a high-risk value, and generating a Hash frequency distribution vector; calculating mutual information between nodes, and constructing an adjacent forest through a minimum generation forest; carrying out sampling and low-frequency signal inflection point storage on the sample flow; constructing an exception decision-making tree, and judging exceptional nodes; extracting weighted sub-graphs and dividing the weighted sub-graphs into fault mode clusters; and calculating a fault urgency degree score and generating an operation and maintenance work order. Through a multi-source time sequence signal, Hash mapping, a decision tree and a spectrum division algorithm, wind power plant fault detection, mode recognition and operation and maintenance scheduling are optimized, and the operation efficiency and sustainability are improved.

The invention discloses a multi-source fusion point cloud data driven three-dimensional streetscape generation and optimization method, which relates to the technical field of three-dimensional streetscape generation, and comprises the following steps of: according to a space range of input large-range point cloud data, combining a three-dimensional coordinate system distribution characteristic of the input large-range point cloud data; dividing the whole registration area into a plurality of uniformly distributed subunit areas with the same area by adopting a structured space division algorithm; for each subunit region, geometric structure features of internal point clouds of the subunit region are obtained, and a geometric feature descriptor containing multiple dimensions is constructed; according to the method, subunit structure complexity identification is realized through a heterogeneity evaluation mechanism based on geometric features and a support vector machine model, the upper limit of the number of matching points is dynamically regulated and controlled, the registration precision of a high-heterogeneous region is improved, and the mismatching risk of a low-heterogeneous region is reduced, so that the structure restoration and continuity of a streetscape model are optimized, and the robustness of the streetscape model is improved. And the stability and intelligence of the three-dimensional modeling system are enhanced.

The invention belongs to the field of matrix operation, and particularly discloses a matrix preprocessing method, system and equipment, and the method comprises the steps: converting a finite element grid corresponding to a sparse matrix into a graph structure; simplifying the graph structure by adopting a repeated edge matching algorithm to obtain a coarsened graph structure; dividing the coarsened graph structure into a plurality of sub-graphs through a direct division algorithm; the divided sub-graphs are refined and amplified through a vertex splitting and partition refining algorithm, and a plurality of refined graphs are obtained; performing corresponding mapping on the sparse matrix by referring to a plurality of refined images to obtain a plurality of partitioned matrixes; performing reduced filling sorting on each partition matrix through a multi-layer nested decomposition normal form algorithm to obtain a rearranged matrix of a plurality of partitions; the rearrangement matrix of the multiple partitions is used for parallel decomposition solving to achieve solving of the sparse matrix. Through the method, the limitation that an existing preprocessing method is large in calculation amount and difficult to parallel is overcome, and support is provided for efficiently solving a large-scale matrix.

The invention relates to the technical field of equipment fault diagnosis, in particular to a generator set upper rack loosening fault diagnosis method and system based on a frequency division algorithm, and the method comprises the following steps: adjusting the sampling rate of vibration data according to the equipment operation state based on the real-time operation data of a generator, x-direction and Y-direction horizontal vibration data of a rack on the generator set are collected and recorded, and a real-time vibration data set is obtained. According to the method, the vibration data sampling rate is dynamically adjusted through the real-time operation data, storage of irrelevant data is reduced, it is ensured that fault signals are captured at the critical moment, the fault diagnosis efficiency is improved, a key frequency area is recognized through spectral analysis, the data collection frequency band is adjusted, the capacity of capturing fault related vibration characteristics is enhanced, and the fault diagnosis accuracy is improved. The relation between vibration data and the unit state is analyzed by combining the guide vane opening degree and the working water head data, the accuracy of fault source recognition is improved, and accurate data support is provided for equipment maintenance through abnormal vibration mode analysis and looseness degree quantification.

The invention discloses a dynamic fault analysis method and system for a pumped storage hydropower station, and the method comprises the steps: receiving multi-dimensional state parameters of the hydropower station, including the rotating speed of a water turbine, the opening degree of a guide vane, the water pressure and vibration data, and building a multi-dimensional state space model; determining segmentation points according to the change rate of the state parameters, performing initial segmentation on a state space by adopting an adaptive grid division algorithm, and establishing a linear subsystem model in each segmentation region to obtain a segmented affine (PWA) approximation model; and constructing a hierarchical learning framework, and carrying out online updating on the PWA approximation model and the hierarchical learning framework through an incremental learning algorithm, a sliding time window and a knowledge distillation technology.

The invention relates to the technical field of cutting simulation, in particular to a cutting simulation method based on a voxel division algorithm and notch sampling optimization, which comprises the following steps: acquiring topological information of a cut object, and dividing triangular patches; voxel division is carried out on the outer surface of the model through a voxelization algorithm of the outer surface, voxel division is carried out on the interior of the model through a scanning line algorithm, and three-dimensional voxel information of the cut object is obtained; constructing a BVH tree according to the three-dimensional voxel information, and establishing a drawing unit; acquiring a real-time position of a cutter, and performing collision detection with the BVH tree to obtain a cut voxel; recording the drawing units to which the cut voxels belong to an updated drawing list; a plurality of sampling optimizations are customized for the cut voxels; all the drawing units in the updated drawing list are traversed, and redrawing is carried out. The method has excellent performance and reliability in the simulation field, and comprehensive and accurate simulation service is provided for users.

The invention discloses a dynamic prediction-based three-dimensional space-time modeling method for a pipe network geographic information system, which comprises the following steps of: S1, constructing a multi-modal sensor array, and carrying out multi-modal data acquisition and preprocessing; s2, realizing asynchronous data stream space-time alignment by adopting a dynamic time warping algorithm; s3, constructing a dynamic prediction model based on a space-time causal graph convolutional network; s4, applying an adaptive grid division algorithm driven by reinforcement learning; and S5, a three-dimensional pipe network state matrix with a timestamp is generated, a photon mapping real-time rendering technology is supported, and the model refreshing delay is less than 0.5 second. According to the method, real-time simulation and sub-second three-dimensional rendering of the pipe network state are achieved through dynamic space-time modeling and multi-modal data fusion, accident response delay is shortened to 0.5 second from 30 minutes, the prediction error is reduced by more than 40%, reinforcement learning is adopted to optimize grid division and transfer learning is adopted to adapt to the pipe network material, the maintenance resource waste is reduced by 35%, and the maintenance efficiency is improved. And full-life-cycle dynamic optimization is achieved.

The invention discloses a wetland fine mapping method based on remote sensing images and a deep learning super-resolution algorithm, which comprises the following steps of: firstly, acquiring high-resolution and low-resolution remote sensing images of a research area, pre-processing the high-resolution and low-resolution remote sensing images, and pairing high-resolution wetland samples with low-resolution images to obtain a high-resolution wetland data set; secondly, acquiring land coverage data and extracting wetland information to obtain a low-resolution wetland data set; then, inputting the low-resolution wetland data set and the high-resolution wetland data set into a convolution transformation super-division network reconstruction feature based on a convolution block attention module, and performing segmentation by using a U-Net architecture based on Scharr convolution and fast Fourier convolution; thirdly, for a wetland mapping task, designing a weighted loss function of three loss functions, and improving the wetland mapping precision from image details, time sequence and image consistency; finally, an optimal model is selected through evaluation and used for wetland fine drawing, so that the precision of wetland fine drawing is improved, and powerful support is provided for wetland research in various fields.

The invention relates to the technical field of data classification processing, in particular to a government affair text data classification processing system and method based on a knowledge graph, and the method comprises the steps: a data collection processing unit collects and cleans government affair text data, and divides a key entity into a high key entity and a low key entity by using an entity importance dual weight division algorithm; the first-level knowledge graph unit constructs a first-level government affair knowledge graph by using a depth map convolution graph construction algorithm based on the high-critical entities and the high-critical entity relationship; the second-level knowledge graph unit constructs a plurality of second-level government affair knowledge graphs by using a cross-graph embedding alignment reasoning algorithm based on the low-key entities and the low-key entity relationship; and the horizontal and vertical classification query unit is used for data query. According to the government affair text data classification processing system and method based on the knowledge graph, dynamic storage processing and cross-graph transverse and longitudinal query are carried out on complex government affair text data by constructing a multi-level knowledge graph and applying a cross-graph embedding alignment reasoning algorithm.

The invention discloses an on-site historical evolution behavior analysis method for aerodynamic performance of a large wind turbine generator, which takes a power coefficient as a basic index for on-site analysis of the aerodynamic performance, and overcomes the influence of two adverse factors, namely deviation and randomness, of SCADA (Supervisory Control And Data Acquisition) wind speed data by adopting data compensation and data filtering. The relation between the wind speed and the wind wheel rotating speed is regarded as an inertial system, and a data driving time constant determination method is provided. A practical calculation working condition region division algorithm is designed, and a data binning method and a conservative data comparison method are adopted to solve the problems of scatter data singularization and interval critical value determination which need to be solved during working condition division respectively. Then, power coefficients based on the instantaneous power and the cumulative power are calculated, respectively. The five-year deterioration degree of the two units is respectively given by observing the effectiveness of the algorithm in a multi-angle manner through four indexes, the historical evolution of the on-site aerodynamic performance of the units is described, and the result shows that the method is effective and feasible.

The invention discloses a contour and filling direction adaptive path planning and SLM support-free forming method, which has the following beneficial effects that a low-angle overhanging region is separated based on a region division algorithm, a scanning vector is controlled by a contour on the basis of contour features of complex structure features and an overhanging direction recognition algorithm, and the accuracy of the contour and filling direction adaptive path planning and SLM support-free forming method is improved. The expansion direction of the lower surface area is a filling direction, and adaptively generating a scanning path and the filling direction; different from a conventional path filling strategy, the method can meet the complex structure characteristics of a curved surface, a round hole, rotation, a special-shaped runner, a curved surface and the like, and the scanning line vector and the filling direction of a lower surface area adapt to the structure contour characteristics, so that the influence of the placing position and the scraper direction on the forming thermal strain accumulation and the lower surface forming quality is reduced; according to the method, the high-quality and stable forming requirements of the low-angle feature of 30 degrees or below of existing mainstream metal materials can be met, and the data processing time cost, the powder material consumption and the post-processing time in the whole forming process are effectively reduced.

The invention discloses a hypergraph segmentation method considering logic replication under a multi-FPGA (Field Programmable Gate Array) system, which provides a staged layout strategy, and firstly segments an original circuit and then reduces cross-FPGA communication delay through logic replication on the premise of ensuring that the limitation of programmable resources of each FPGA is not exceeded. The method specifically comprises a pre-layout stage and a post-layout stage: in the pre-layout stage, a multi-stage division algorithm of a multi-resource weighting strategy is used to obtain a preliminary segmentation result, a multi-dimensional weight superposition strategy is applied to solve various resource constraint problems, and possible resource violations are repaired through a resource fine tuning algorithm; and in the post-layout stage, the logic units are selectively copied based on a simulated annealing algorithm, so that the inter-chip communication overhead is further reduced. By means of the method, the high-quality FPGA segmentation scheme copied through the inter-chip logic can be rapidly generated, and the operation frequency and verification efficiency of a multi-FPGA system are effectively improved.

The embodiment of the invention provides a mesh generation method for an additive manufacturing slice type multilayer structure. The mesh generation method can solve the technical problem that the mesh generation capacity is poor in the prior art. The method includes dividing a surface grid on a first surface and converting a second surface into a point cloud result. And matching the grid nodes and the surface nodes according to process constraints to obtain surface node pairs. In this way, not only the geometrical shape is considered, but also the additive manufacturing layering process is considered. And according to the surface node pairs, based on the quadrilateral grids, dividing layered finite element units mainly comprising hexahedrons, that is, generating volume grids through the surface grids. According to the method, the stability of various spatial curved surfaces is ensured while rapid grid division is realized, and a grid division algorithm which is high in automation degree, good in stability and good in grid quality is realized. The additive manufacturing slice type multilayer structure is subjected to grid division, the material direction and the layering direction are accurately appointed, and the efficiency and accuracy of simulation calculation of the additive manufacturing slice type multilayer structure are improved.

The invention relates to the technical field of big data processing and data integration, in particular to a Kettle-based database dynamic sub-table processing method and system, which comprises the following steps: receiving sub-table configuration parameters through a Kettle graphical interface, the sub-table configuration parameters comprising a fragmentation key, a sub-table algorithm and a sub-table number; table division rule metadata is loaded, a dynamic table division strategy engine is constructed, and the strategy engine supports Hash fragmentation, range fragmentation and time fragmentation algorithms; the construction of the dynamic table division strategy engine comprises the following steps: automatically recommending a table division algorithm based on data sampling analysis; providing a Grouping script interface to support a user-defined fragmentation rule; maintaining table division rule version history, and supporting rule rollback; the method has the beneficial effects that the large table is split into the plurality of sub-tables according to the specified rule, so that the data volume of a single table is remarkably reduced, query and update operations can focus on data in a smaller range, and the query response speed and the update efficiency are greatly improved.

The application provides a high-resolution reconstruction method for sheet microstructure modeling, and relates to the technical field of metal structure reconstruction. The application obtains sheet thickness and average grain size by performing morphology and EBSD characterization on a metal material, compiles a microstructure reconstruction program based on a space segmentation algorithm and a sheet division algorithm, and establishes a microstructure reconstruction model; the three-dimensional reconstruction model closest to the real microstructure is obtained by adjusting the control parameters in the reconstruction program, namely sheet orientation, grain distribution and iteration number; the application can quickly and accurately reconstruct the sheet microstructure, and the generated microstructure model has high compatibility and can be transplanted to various finite element software for simulation analysis.

The application discloses a method for dividing different cleanliness grade areas in a constant-temperature and constant-humidity dust-free workshop in the field of information technology, and comprises the following steps: preliminarily dividing different cleanliness grade areas according to production process requirements and functions of each area, obtaining a region division scheme by using a space division algorithm, including space position coordinates, area and cleanliness grade requirement parameters of each area; setting a buffer zone between different cleanliness grade areas, determining the spatial layout of the buffer zone, including position coordinates, area and shape parameters, by calculating the space position coordinates and area of each area, and determining the cleanliness grade transition requirements of the buffer zone according to the cleanliness grade difference between adjacent areas; analyzing airflow organization and pressure difference control conditions under the optimized region division scheme, obtaining airflow velocity, airflow direction and pressure difference parameters of each area, and obtaining an airflow organization and pressure difference control scheme meeting the cleanliness grade requirements of each area by using a gradient descent iterative optimization algorithm.

The invention discloses a case allocation method, device and equipment and a storage medium, and relates to the technical field of case allocation, and the method comprises the steps: obtaining a current case list, and determining a pre-calling case in the current case list; carrying out AI outbound calling on clients corresponding to the pre-calling cases, and determining effective contact cases according to outbound calling results; according to preset group configuration information, docking the effective arriving case to a shared case pool corresponding to each operation group; and based on a preset fusion case division strategy, determining a target processing case corresponding to each user in each shared case pool, the preset fusion case division strategy comprising at least one of a competition case grabbing strategy and an automatic case division strategy. According to the method, the effective communication efficiency with clients is improved through AI outbound, and a competition mechanism and an automatic case division algorithm are combined in a case division strategy, so that efficient allocation of case resources can be ensured, and improvement of the case processing efficiency and the completion effect is facilitated.

The invention discloses a power distribution network multi-point source storage cluster dynamic division and hybrid collaborative voltage treatment method, and belongs to the technical field of power system operation and control, and the method comprises the steps: constructing a cluster dynamic division comprehensive index based on a scale index and a sensitivity consistency index; proposing a cluster dynamic division algorithm based on division indexes, and ensuring that the sensitivity in the same cluster tends to be consistent after division; identifying a key bus according to the bus voltage offset, calculating the required voltage regulation quantity, determining a cluster hybrid collaborative algorithm control target, constructing a priority index, and establishing a reactive power and active power compensation sequence based on the priority index; the voltage violation of the key bus is eliminated with the purpose of minimizing the power compensation requirement, and the adjustment of the large-range bus voltage is realized. According to the method, voltage out-of-limit can be effectively suppressed in the high-photovoltaic-permeability power distribution network, the utilization rate of reactive compensation is remarkably increased, the demand of active compensation is reduced, and the service life of equipment is prolonged while control fairness is considered.