33 results about "Segment structure" patented technology

The invention relates to the technical field of transaction verification, in particular to a block chain financial transaction tracing method and system, and the method comprises the following steps: calculating a node interaction proportion, recognizing illegal chain segments and marking nodes, analyzing a field combination sequence matching path structure, judging numbering jump and behavior path pairing, and extracting a field corresponding relation and a matching difference. And identifying a node state offset and a structure jump trend, and generating a state chain segment backtracking section record. According to the method, the interaction frequency and the behavior type proportion are introduced to serve as the node state recognition basis, dynamic credible judgment of boundary nodes is achieved, the recognition precision of chain segment structure interruption is enhanced through the path field combination sequence and the serial number continuity, the serial number jumping behavior and node action offset are combined, the chain jumping path recognition capacity is optimized, and the reliability of the system is improved. The field consistency verification effect is improved by adopting field logic pairing and value domain offset comparison, and the backtracking positioning capability of the broken link node is enhanced by using a path state offset and backtracking pointer association strategy.

Embodiments of the present invention relate to the field of information security and disclose a method for constructing a DDoS attack event set and a DDoS attack defense method. The DDoS attack event set construction method includes: constructing an event set and setting a weight value for each event in the event set; updating the weight value corresponding to each event based on the occurrence frequency of each event and recording the update time; constructing a segment tree based on the updated weight value and update time of each event, and updating the information of each node in the segment tree; traversing each node in the segment tree, screening events that meet predetermined conditions, and obtaining a DDoS attack event set. The embodiment of the present invention uses an exponentially decaying event weight value calculation and update method to screen attack events to form a DDoS attack event set, facilitating the subsequent use of the DDoS attack event set to search for DDoS attack events from a large number of events, thereby improving the efficiency of attack event detection.

The invention discloses a method for preprocessing a nonlinear spring in a load structure method. The method comprises the following steps: S1, inputting a refined shield segment structure model; s2, extracting segment peripheral node information meeting constraint conditions, calculating spring end point coordinates and establishing a nonlinear spring set; s3, executing the following operations on each segment peripheral node obtained in the S2: traversing all units, and judging whether the traversed units contain the current peripheral node or not; if so, searching and recording all peripheral node coordinates of the unit; calculating the peripheral surface area of each unit based on the recorded unit node coordinates, and calculating the rigidity of a nonlinear spring at the current peripheral node according to the peripheral surface area of each unit; s4, updating the original calculation analysis file according to the nonlinear spring set established in S2 and the rigidity, calculated in S3, of the nonlinear springs at the peripheral nodes; according to the method, the rapid application preprocessing of the segment load is realized, and the calculation efficiency and the simulation precision of actual engineering geological conditions are improved.

The application discloses an integrated circuit manufacturing process vacuum transport simulation method, device, equipment and medium, relates to the field of integrated circuit manufacturing process simulation, and the integrated circuit manufacturing process vacuum transport simulation method takes the initial position of the transport particle, the motion direction vector of the simulation particle, and the sub-node region of the simulation region as the input of the finite state machine, simulates the motion of the transport particle by combining the method of the two-dimensional line segment tree, converts the process of moving the transport particle in the prior art and analyzing the motion of the transport particle into dividing the simulation region by the two-dimensional line segment tree, and simultaneously combines the process of updating the state of the transport particle by the finite state machine, so that the time complexity in the simulation process is reduced, and the running efficiency of the simulation is improved.

The present invention relates to the design technology of segment for tunneling machine, and discloses a three-dimensional parametric simulation calculation method for the segment structure of tunneling machine, which solves the problems of complex manual modeling, low efficiency and time-consuming calculation existing in the existing tunneling machine segment modeling and simulation scheme based on a simplified three-dimensional model. The method includes: calling a model initialization command set to initialize the modeling environment, and calling a basic parameter command set to establish a basic parameter system; calling a load analysis command set to calculate the formation pressure on the segment to obtain the load on the segment; calling a structure modeling command set to construct a three-dimensional segment calculation model of the tunneling machine including nodes, beam elements and spring elements; calling a calculation analysis command set to automatically simulate and solve the three-dimensional segment calculation model and automatically output the calculation results; obtaining the calculation results, calling a reinforcement analysis module and a structural calculation report module to automatically obtain the recommended segment reinforcement information and the structural calculation report.

The present invention discloses an online path planning method for a mobile robot based on deep reinforcement learning. The specific steps are as follows: Obtain obstacle information through a lidar, obtain the robot position information and target point information through ROS built-in plugins, and process and integrate the two into the robot state S. Input the state S into the DDPG network to obtain the optimal execution action, enabling the robot to quickly and safely reach the target point during navigation; Design a cascaded structure of LSTM and Actor networks; The structure of the improved experience replay pool adopts a segment tree, which can efficiently update the weight changes caused by the continuous increase in the number of episodes; For the optimization of the Critic network, a fractional gradient descent algorithm with a momentum term is adopted, which can improve both the accuracy and the convergence speed compared to the integer order; The present invention is used to solve problems such as low accuracy and slow convergence speed when conventional DDPG is used for robot path planning.

The invention relates to the technical field of pessimism elimination algorithms, in particular to a public path pessimism margin removal method, product and method and a storage medium, and the method comprises the steps: providing a clock tree, traversing the clock tree, and generating an index table, a sequence table and a hierarchical table based on information obtained through traversing; generating a line segment tree based on the sequence table and the hierarchical table; for a starting point and an ending point of any given time sequence path, obtaining a nearest common node through line segment tree query; and determining a sub-tree modified in the optimization process based on the nearest common node, correspondingly modifying an index table, a sequence table and / or a hierarchical table, and then carrying out common path pessimistic margin removal analysis to remove pessimistic margin. The method provided by the invention can solve the technical problem that the current public path pessimistic margin removal method consumes much time.

The present invention discloses a large-scale measured point set generated for large hull segment components. It provides fast and efficient coarse and fine registration methods based on the RANSAC principle and a coarse registration method of salient structures and a point cloud-model fine registration method that considers multiple information. The point cloud registration based on deep learning methods can fully consider a wide range of information such as process constraints, inherent information of the measured point cloud, and local semantic information of the point cloud, greatly improving the intelligence and rationality of the registration process, while enhancing the engineering guidance of the registration results, realizing rapid error evaluation of processed components, and providing a basis for the formulation of assembly parameters.

The invention discloses an underground pipeline construction path planning optimization analysis method, and particularly relates to the field of pipeline construction planning, and the method comprises the steps: collecting elevation grid data covering a construction region, and generating a node-level continuous slope direction sequence in combination with a replied path initial line; recognizing a continuous one-way slope section in the path, and delimiting a one-way high-voltage path section with continuously accumulated height difference as a structure optimization target; aiming at the path segments, constructing a feasible structure compression candidate segment set in a transverse adjacent space of the path segments; slope fluctuation and break angle distribution conjoint analysis is carried out on the candidate segments, structural complexity features are extracted, and a two-dimensional compression matrix is constructed; constructing a structure empowerment connection graph based on the matrix, executing minimum empowerment path search in combination with structure continuity constraint, and screening out an optimal alternative path segment structure; and finally, the head and tail nodes of the original path section are used as anchor points, the alternative path section is inserted into the original path initial line, and construction path planning after structure optimization is formed.

The embodiment of the invention discloses a block chain data processing method and device, equipment, a storage medium and a product. The method comprises the steps that an updating request for a line segment tree is obtained, the line segment tree comprises state data of a block with the height within a first interval, the updating request comprises a second interval, updating data and verification information, in response to verification of the verification information, uplink processing is carried out on the updating request, and in response to successful uplink of the updating request, uplink processing is carried out on the updating request. And establishing an association relationship between at least one node in the line segment tree and the update data, the update data associated with any node being used for updating the state data carried by the node, the at least one node including a target node, the target node being a non-leaf node, and the third interval corresponding to the target node being included in the second interval. Therefore, the state data in the third interval is updated in batches by establishing the incidence relation between the non-leaf nodes and the update data, and the data update efficiency of the block data in a certain height interval can be improved.

This application provides a data backup and recovery method, apparatus, electronic device, and storage medium. The method constructs BIO metadata containing segment information to be backed up, enabling the BIO data to be backed up to be backed up at the sector granularity based on this BIO metadata. Since the segment length of each segment to be backed up under different file systems is an integer multiple of the sector length, this backup method is compatible with file systems where the segment length is not block aligned. Each segment to be backed up can be stored in a corresponding set of target sectors without truncation issues. Furthermore, during recovery, the segment structure of the backed-up BIO data can be determined first based on this segment information, and then the backed-up BIO data can be recovered based on this segment structure. Since this segment structure is the same as the segment structure of the original BIO data, the recovered target BIO data can maintain consistency with the original BIO data.

The invention provides a data backup and recovery method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing to-be-backed-up BIO metadata containing segment information, enabling the to-be-backed-up BIO data to be backed up in a sector granularity manner based on the to-be-backed-up BIO metadata, and enabling the to-be-backed-up BIO metadata to be backed up in a sector granularity manner. The segment length of each to-be-backed-up segment under different file systems is the integral multiple of the sector length, so that the backup mode can be compatible with file systems of which the segment length is not in block alignment, each to-be-backed-up segment can be stored in a corresponding group of target sectors, the problem of truncation does not exist, and in addition, the backup efficiency of the to-be-backed-up segment is improved. In the recovery process, the segment structure of the backed-up BIO data can be determined on the basis of the segment information, then data recovery is conducted on the backed-up BIO data on the basis of the segment structure, and due to the fact that the segment structure is the same as the segment structure of the original BIO data, the recovered target BIO data can be kept consistent with the original BIO data.

A computer program product, system, and computer implemented method for automatic, repetitive, and gradual shrink of very large database object structures. The approach disclosed herein generally comprises an iterative best efforts approach to automatically shrink a large database object structure(s). The process may comprise identifying a large object (LOB) structure shrink event and LOB structure or LOB structure segment to be shrunk. And executing a shrink operation on the LOB structure or LOB structure segment without blocking access to other extents in the LOB structure or LOB segment structure. This process allows an extent to be shrunk and deallocated. As a result, the process can operate to continually clean a LOB structure or LOB structure segment to decrease fragmentation within the extents of the LOB structure or LOB segment structure.

Embodiments of the present application provide a data processing method and device based on a line segment tree structure, equipment and medium, the method comprises: after the oracle contract receives the first transaction request sent by the business contract, the first request condition corresponding to the first condition hash value is obtained based on the first transaction request, and the condition hash value matched with the first condition hash value is found in the line segment tree structure of the on-chain storage space. If not found, the oracle contract is called to send the first off-chain query request for the first request condition to the off-chain oracle, so that the oracle returns the first off-chain business data to the oracle contract. The received first off-chain business data is taken as the first business data by calling the oracle contract, and the first business data and the first condition hash value are updated to the tree node associated with the first request condition in the on-chain storage space. By using the embodiments of the present application, the utilization rate of off-chain data can be improved, and the waste of storage resources can be avoided.

The embodiment of the invention relates to the technical field of data processing, and discloses an efficient management method and device for congestion degree data, a medium and a product. The method comprises the following steps: acquiring congestion degree data of at least two monitored target road sections; establishing a line segment tree, and initializing the line segment tree; determining congestion degree superposition data of the intermediate node and the root node according to the congestion degree data recorded in each leaf node and a data superposition rule of the line segment tree; a maximum heap is established, and the maximum heap is initialized; adjusting the position of each node in the maximum heap to obtain the maximum heap conforming to a data incremental rule; and in response to a query request of congestion degree data, feeding back a query result according to the line segment tree and / or the maximum heap. By the adoption of the scheme, real-time updating of the data can be achieved, after the query request is received, a quick response can be obtained, the query result can be accurately returned, and the processing efficiency of data query is improved.

An access object (AOB) service receives a restore stream from a client for a file managed by a deduplication filesystem. The file is represented by a segment tree including segments in an upper level referencing chunks of the file. The upper level segments are grouped into similarity groups and the similarity groups are assigned to deduplication object (DOB) services. The upper level segments are iterated over to open multiple internal read-ahead streams from the AOB service to the DOB service to populate a read-ahead cache maintained at the AOB service. The restore stream is serviced using the read-ahead cache.

This invention provides a method for constructing relationships in a land spatial information model (TIM), relating to the field of data processing. Specifically, it includes the following steps: generating a three-segment structured code for geographic entities; constructing a "topological potential field emergence engine for geographic entity relationships" based on the generated three-segment structured code, automatically deriving multi-dimensional relationships between geographic entities, and outputting a standardized and evolvable geographic entity relationship map; and achieving integrated coding fusion and application across provinces, cities, and counties based on a hierarchical mapping knowledge base and a resource code fusion model, supporting smooth migration of existing data and end-to-end business collaboration. The technical solution of this invention overcomes the problem in existing technologies where the association logic between various domains and TIM geographic entity relationships is not yet clear, making it difficult to support the integration of multi-dimensional geographic entity relationships based on time, attributes, and business.

The application provides a graph stream time query method based on a FlatMap line segment tree, and belongs to the technical field of data processing, and comprises the following steps: finding a corresponding storage location in a graph stream summary storage structure according to a target query edge; the graph stream summary storage structure comprises a hash compression matrix and a buffer area, each cell in the hash compression matrix comprises a fingerprint pair of a stream edge and a group of FlatMap line segment trees; decomposing a target query range to corresponding intervals in the pre-updated FlatMap line segment trees to obtain a queryable subinterval set; finding all nodes corresponding to the queryable subintervals in the pre-updated FlatMap line segment trees according to the corresponding storage location found in the graph stream summary storage structure and the queryable subinterval set, and taking the sum of the weights saved in the found nodes as a graph stream time query result. The method provided by the application reduces hash collision, improves query accuracy, and effectively reduces query delay.

The invention belongs to the technical field of modeling and analysis of a shield tunnel segment lining structure, particularly relates to an intelligent modeling and analysis method for a shield tunnel distributed tenon type segment lining structure, and aims to solve the problems of high manual dependence, difficulty in batch operation, low result processing efficiency and the like in the modeling process of the shield tunnel segment structure. A whole set of shield segment modeling and simulation full-process automation method which takes a Python script as a core and is based on an Abaqus secondary development platform is provided. According to the method, a traditional modeling process of gradual interaction of a graphical user interface depending on Abaqus is converted into an automatic modeling mode driven by a full-process script, and a complete closed loop of automatic input of structural parameters, automatic construction of a geometric structure, automatic execution of Boolean cutting and automatic deployment of grid division is achieved. Meanwhile, the technology of the patent supports parameterized modification, can easily adapt to various structures with different radiuses, concave-convex tenon arrangements and bolt angles, and greatly enhances the reusability and engineering universality of the model.

Cloud units are maintained in cloud storage. Each cloud unit includes containers including data containers storing segments of files, segment tree containers storing upper-level segments of segment trees representing the files, and cloud containers storing headers from the data and segment tree containers. A header for a data container includes fingerprints identifying the segments of files. A header for a segment tree container includes fingerprints identifying the upper-level segments. A cloud unit is attached to a storage appliance in one of a read-write state or a read-only state by recovering, from cloud storage, to a local storage of the appliance, at least the cloud containers associated with the first cloud unit. While the cloud containers are being recovered, a request is received to access a segment tree of the cloud unit. The request is redirected to cloud storage.

The present invention relates to a method, storage medium and electronic device for identifying drawing frames and labels in architectural drawings, comprising the following steps: obtaining coordinate information of endpoints of all straight line segments detected from the architectural drawings; constructing a two-dimensional segment tree using the coordinate information of the endpoints of all straight line segments; traversing and searching the two-dimensional segment tree using a depth-first search algorithm to obtain a plurality of target straight line segments related to an outer frame of a drawing frame area in the architectural drawings from among all the straight line segments, and determining the position of the drawing frame area based on the plurality of target straight line segments; and identifying a drawing label area in the architectural drawing from within the drawing frame area based on the position of the drawing frame area. Compared with the existing method of manually marking drawing frames, the method can not only reduce workload but also improve recognition efficiency.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a dispatching optimization method, device and equipment based on a line segment tree and a dichotomy and a medium. Performing priority ranking on the initial workers according to the historical work behaviors to obtain to-be-allocated workers, obtaining the number of allocated tasks of each to-be-allocated worker, constructing a line segment tree according to the to-be-allocated workers and the number of allocated tasks, obtaining to-be-allocated work tasks, and allocating the to-be-allocated work tasks to the to-be-allocated work tasks according to the to-be-allocated work tasks. And performing binary search matching on the to-be-allocated work task and the to-be-allocated workers according to the line segment tree, if the matching succeeds, obtaining a target dispatching result, and if the matching fails, performing dispatchable screening on each to-be-allocated worker to obtain a dispatchable worker, and allocating the to-be-allocated work task to the dispatchable worker to obtain a target dispatching result. According to the invention, the work task allocation efficiency can be effectively improved.

The embodiment of the application belongs to the technical field of research and development design, is applied to the data management and statistics scene under the big data scale, relates to a data management and statistics method based on a dynamic open point line segment tree and related equipment thereof, all task index data in the source database is arranged and analyzed by the dynamic open point line segment tree data structure, when secondary analysis is performed on all task index data in the source database subsequently, the arrangement and summary results of the dynamic open point line segment tree data structure can be directly used for secondary analysis, compared with direct analysis from the source database, the time complexity of query and update is reduced from O(N) to O(logN), and the analysis efficiency of subsequent secondary analysis is improved. The application can be applied to the management and statistics between insurance sales prices and sales, the insurance sales price is taken as task index data, and the sales is taken as the statistical result at different nodes, so that the statistical analysis efficiency between the insurance sales price and the sales is improved.

Cloud units in cloud storage include containers including data containers storing file segments, segment tree containers storing upper-level segments of segment trees representing the files, and cloud containers storing headers from the data and segment tree containers. A header for a data container includes fingerprints identifying the file segments. A header for a segment tree container includes fingerprints identifying the upper-level segments. A storage appliance stores a first level of metadata for each cloud unit in a read-write state, a second level of metadata for each cloud unit in a read-only state, and a third level of metadata for each cloud unit in an offline state. The third level requires less storage than the first and second levels. The second level requires less storage than the first level. The limited metadata space of the appliance is managed by maintaining at least a subset of cloud units in the read-only state.

A deep learning method for single-tree crown segmentation based on multimodal data belongs to the field of artificial intelligence technology in forestry research. Based on high-resolution GF-2 imagery, LiDAR point cloud features, and ultra-high-resolution RGB multimodal data, and integrated with a vectorized crown post-processing strategy, a deep learning model, the Multimodal Crown Segmentation Fusion Network (MTCDFNet), is proposed. By integrating forest structure, spectral, and texture features from multimodal remote sensing data, this method accurately segments tree crowns in multi-canopy, multi-species subtropical forests.

The application discloses a kind of secondary design methods of topologically optimized bridge, comprising: the shape profile and size of bridge are preliminarily designed;The grid structure of bridge is generated to determine the material distribution and geometry of bridge;The grid structure is generated 3D structure with cavity by scaling and stretching;3D structure is divided into multiple segmented structures to facilitate the 3D printing of segmented structure;Determine the reinforcement of bridge and its arrangement to improve the tensile and crack resistance of bridge;Finite element analysis is carried out on the bridge after secondary design;Judge the strength, deformation and whether reasonable of bridge, if reasonable, then proceed to the next step, if not reasonable, then repeat the foregoing steps;Based on 3D structure, multiple segmented structures and the reinforcement of bridge and its arrangement, generate secondary design structure and generate the 3D printing file of bridge.The application solves the problem of insufficient structural performance, high manufacturing complexity and high cost of topologically optimized design bridge.

The invention discloses a cooperative tracking and surrounding method and system based on an improved MADDPG, and relates to the technical field of multi-agent cooperative decision making, and the method mainly comprises the steps: constructing a reward function, setting the priority of each experience sample in an experience library, constructing a line segment tree, carrying out the sampling of the experience library through the line segment tree, and carrying out the calculation of the priority of each experience sample in the experience library. And independently updating each agent, updating the priority of the experience samples in the experience library, carrying out circular sampling until training convergence is carried out to obtain the trained agents, and carrying out cooperative tracking and hunting according to the trained agents. By implementing the cooperative tracking hunting method and system based on the improved MADDPG provided by the invention, the high cooperative tracking hunting efficiency of the unmanned aerial vehicle and the unmanned ship can be improved.

Data migrated from a deduplicated storage appliance are stored as cloud units at cloud storage. Each cloud unit includes containers including data containers storing segments of files, segment tree containers storing upper-level segments of segment trees representing the files, and cloud containers storing headers from the data and segment tree containers. A header for a data container includes fingerprints identifying the segments of files. A header for a segment tree container includes fingerprints identifying the upper-level segments. The cloud units are maintained in different states of accessibility including read-write, read-only, and offline. Upon detecting that local storage of the appliance does not have space to support a cloud unit in a read-write or read-only state, another cloud unit that is in a first state is selected, the first state being the read-write or read-only state. The selected cloud unit is placed in a second state, different from the first state.

The invention relates to the technical field of model training, in particular to a large-scale GPU (Graphics Processing Unit) cluster hybrid parallel model training method, which comprises the following steps of: acquiring a model parameter updating record of each node of a GPU cluster, counting parameter occurrence density and generating density characteristics; extracting a maximum density parameter, comparing the maximum density parameter with the minimum node offset, and obtaining a task parameter segment structure offset block; constructing a path complexity score, and generating a path complexity result after sorting; the uploading delay proportion is compared with the maximum value, and a delay difference analysis result is generated; and generating a node training rhythm regulation and control result according to an analysis adjustment task segment state. According to the method, the task segment structure in the GPU cluster is optimized and adjusted, the occurrence frequency of each model parameter in continuous training rounds is counted, the parameter density feature of each node is analyzed, and then the parameter with the highest density is extracted, so that the problem of uneven load in model training is solved.

The embodiment of the invention provides a ranking determination method and device based on a line segment tree, equipment and a storage medium. According to the technical scheme provided by the embodiment of the invention, when the ranking query request is received, one or more target score intervals are determined in the preset line segment tree according to the target user score indicated by the ranking query request, and the ranking query request is obtained according to the node marks corresponding to the one or more target score intervals. According to the method, the target user number corresponding to the one or more target score intervals is determined in the preset hash table, the target ranking information can be determined according to the one or more target user numbers, query of user ranking can be achieved without large key splitting, the data processing amount of ranking query can be effectively reduced, and ranking query efficiency is improved.